Modelling Exercise Research Articles

Global climate change and its impact on the distribution and efficacy of Bacillus thuringiensis as a biopesticide.

This study is the first modeling exercise to assess the impacts of climate change on the current and future global distribution of Bacillus thuringiensis (Bt). Bt is a common Gram-positive, rod-shaped bacterium widely distributed in various environments, including soil and water. It is widely recognized as a source of effective and safe agricultural biopesticides for pest management in various climatic regions globally. In the present work, ensemble species distribution models were developed for Bt based on the generalized linear model (GLM), generalized boosting model (GBM), random forest (RF), and maximum entropy (MaxEnt) under two distinct scenarios, SSP2-4.5 (optimistic) and SSP5-8.5 (pessimistic) for the year of 2050, 2070, and 2090. The performance of our models was evaluated based on true skill statistics (TSS) and the area under the receiver-operator curve (AUC) indices. Both AUC and TSS values were observed in an acceptable range, with AUC at 0.84 and TSS at 0.512, respectively. Results indicate that most of the areas currently suitable for Bt will likely remain stable in the future, particularly Central America, Central and South Africa, South Asia, and parts of Oceania. Norway, Peru, and the UK will have notable habitat gains by 2090 based on SSP2-4.5 and SSP5-8.5 scenarios. On the contrary, Serbia, Guinea, Poland, Croatia, Spain and Romania showed notable losses under both scenarios. Our results underscore Bt potential to improve pest control, crop yields, and environmental sustainability, especially in regions where agriculture is predominant. Our research highlights the need to understand ecological dynamics for future conservation and agricultural planning in the face of climate change.

Protocol for extracting flow hydrograph shape metrics for use in time-series flood hydrology analysis.

The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flow and flood hydrology at reach and catchment scales. However, the analysis of individual hydrographs is time consuming, making the analysis of hundreds or thousands of them unachievable. A method or protocol is needed to ensure that the datasets being generated, and the metrics produced, have been consistently derived and validated. In this lab protocol, we present workflows in Python for extracting flow hydrographs with any available temporal resolution from any Open Access or publicly available gauging station records. The workflow identifies morphologically-defined flow and flood types (i.e. in-channel fresh, high flow and overbank flood) and uses them to classify hydrographs. It then calculates several at-a-station and upstream-to-downstream hydrograph shape metrics including kurtosis, skewness, peak hydrograph stage, peak arrival time, rate-of-rise, peak-to-peak travel time, flood wave celerity, flood peak attenuation, and flood wave attenuation index. Some metrics require GIS-derived data, such as catchment area and upstream-to-downstream channel distance between gauges. The output dataset provides quantified hydrograph shape metrics which can be used to track changes in flow and flood hydrographs over time, or to characterise the flow and flood hydrology of catchments and regions. The workflows are flexible enough to allow for additional hydrograph shape indicators to be added or swapped out, or to use a different hydrograph classification method that suits local conditions. The protocol could be considered a change detection tool to identify where changes in hydrology are occurring and where to target more sophisticated modelling exercises to explain the changes detected. We demonstrate the workflow using 117 Open Access gauging station records that are available for coastal rivers of New South Wales (NSW), Australia.

Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study.

Online wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic. This study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination. Following a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer's pro- and antivaccine messages. Wellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20%, 614/3045) compared with controls (n=242 accounts, with 42% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant (β1=-2.2668, SE=0.2940; P<.001). Covariate analysis suggests an association between antivaccination tweets and accounts representing individuals (β=-0.9591, SE=0.2917; P=.001) but not social network position. A complementary modeling exercise of stance within user accounts showed a significant difference in the proportion of antivaccination users by group (χ21[N=321]=36.1, P<.001). While nearly half of the influencer accounts were labeled by a K-nearest neighbor classifier as predominantly antivaccination (48%, 58/120), only 16% of control accounts were labeled this way (33/201). Topic modeling of influencer tweets showed that the most prevalent antivaccination themes were protecting children, guarding against government overreach, and the corruption of the pharmaceutical industry. Provaccination messaging tended to encourage followers to take action or emphasize the efficacy of the vaccine. Wellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies.

Benefits of integrating seismic and gravity data in a subsalt environment: A case study at block BM-C-33, deepwater Campos Basin, offshore Brazil

Imaging subsalt layers is challenging in the BM-C-33 block in the Campos Basin’s ultradeep waters. This block is important because it has three significant gas and condensate discoveries in the presalt carbonate and volcanic reservoirs—Seat, Gávea, and Pão de Açucar. Understanding basement morphology is essential to better define the petroleum system in the area. To that end, we performed a 2D gravity modeling exercise along a northwest–southeast section using seismic interpretation as input to reduce the ambiguity of the modeling exercise. To achieve this, we fixed the main sedimentary horizons and their densities (calculated via well-log information) to better estimate the top of the basement morphology. This interpretation strategy better delineated the presalt sedimentary section and the complex structural framework with two asymmetric grabens bounded by a prominent horst, interpreted as a migration focusing high in the BM-C-33 area. In addition, our findings show that obtaining clear images remains challenging even after extensive processing of the seismic data set. We suggest that seismic node acquisition with ultralong offsets and multiazimuthal illumination and then carefully processing the data is required to accurately reveal the area’s prerift section and basement morphology under study.

The effects of fair allocation principles on energy system model designs

Abstract What constitutes socially just or unjust energy systems or transitions can be derived from the philosophy and theories of justice. Assessments of distributive justice and utilising them in modelling lead to great differences based on which justice principles are applied. From the limited research so far published in the intersection between energy systems modelling and justice, we find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a 'just energy system'. The lack of recognition of alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, leads to a narrow understanding of justice that fails to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. &amp;#xD; &amp;#xD;In this work, we contribute to the growing amount of research on distributive justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe (highRES), we explore different designs of a future (2050) net-zero European electricity system, and its distributional implications based on the application of different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of 'polluters pay' and 'ability-to-pay', which take historical contributions of greenhouse gas emissions and the socio-economic conditions of a region into account. &amp;#xD; &amp;#xD;We find that fair distributions of electricity generating infrastructure look significantly different depending on the justice principles applied. The results may stimulate a greater discussion among researchers and policymakers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results.

Using Peer Assessment Leveraging Large Language Models in Software Engineering Education

This paper explores the integration of generative AI and large language models into the realm of software engineering education and training, with a specific focus on the transformation of traditional peer assessment methodologies. The motivation stems from the growing demand for innovative educational techniques that can effectively engage and empower learners in mastering Software Engineering principles. The proposed approach involves presenting students with modeling exercises solved by ChatGPT, prompting them to critically evaluate and provide constructive feedback on the generated solutions. By engaging students in a dialogue with the AI model, we aim to foster a dynamic learning environment where learners can articulate their considerations and insights, thereby enhancing their comprehension of software engineering principles, critical thinking and self evaluation skills. Preliminary results from pilot implementations indicate promising outcomes, suggesting that this approach not only enhances the quality of peer feedback but also contributes to a more interactive and engaging educational experience.

Ramadan fasting and exercise combination therapy: A novel approach for osteoporosis prevention in ovariectomized rats.

Osteoporosis is a chronic bone metabolic disease that affects millions of people worldwide, particularly the elderly and postmenopausal women. It is characterized by weakened bones, increasing the risk of fractures and leading to significant morbidity and mortality. The goal of the current study is to examine the reported osteo-preservative effects of exercise and/or fasting in the Ramadan fasting model (RFM) in ovariectomized (OVX) rats. The experimental intervention started 1month following the ovariectomy procedure and consisted of five 15-min exercise sessions per week at 18-25m/min and/or an approximately 13-h fast from sunrise to sunset (6:00 AM-19:00 PM). Serum bone metabolism biomarker levels were measured, and mineral concentrations in femoral ashed bones and digested serum were determined. Additionally, serum bone alkaline phosphatase (b-ALP), parathyroid hormone, osteocalcin, calcitonin, and vitamin D3 concentrations were measured using the competitive enzyme immunoassay technique. Calcium, magnesium, and phosphorus showed a notable decrease in mineral concentration among OVX rat femurs compared with the combination group (OVX + RFM + E) and control groups. In addition, homeostasis of serum concentrations of calcium, magnesium, and phosphorus was observed to increase in the OVX + RFM + E group rather than in the OVX group without intervention when compared with a control group. Furthermore, fasting and exercise, either alone or concurrently with ovariectomy, induced a non-significant elevation in osteocalcin, parathyroid hormone, and vitamin D3, whereas b-ALP and calcitonin increased significantly compared with those in control rats. The combination of the Ramadan fasting model and moderate intensity exercises among OVX rats manifested advantageous effects in bone biomarkers compared with OVX rats without intervention. This could be recommended as a lifestyle modification that is protective against osteoporosis, especially in the context of depleted estrogen hormone after menopause.

A primer of community ecology using the R language

AbstractCommunity ecology beginners often struggle to understand theories expressed in complex mathematical formulas and to master computer programming. To remedy this situation, this article provides a practical, R‐based introduction to community ecology by illustrating core concepts (vital rates, carrying capacity, density dependence) and models that can be used to explore the patterns of species abundance and diversity. The structure of this article consists of three modeling exercises, each asking a general question that can be answered by a combination of theory and R programming: (1) what determines the abundance of species, and what makes a population persist and go extinct?; (2) what determines the distribution of species and species diversity?; (3) what determines the relative abundance of species and what allows species to coexist? Through the exercises, I discuss the following five concepts and ideas that provide valuable insights into the questions: (i) the tragedy of the commons, (ii) the theory of island biogeography, (iii) competitive exclusion, (iv) the neutral theory of biodiversity, and (v) frequency dependence. These materials are thus designed to guide the reader in developing an intuition for ecological thinking that will help capture the essence of the global environmental and biodiversity crisis. Although this article does not delineate the scope and depth of the vast field of community ecology, I hope that it will motivate the reader to step up to a more formal introduction to community ecology.

Modeling chemical bioaccumulation in snakes, part 2: Model testing and high-throughput simulation

In part 2 of the physiologically based kinetic (PBK) model for snakes, using default and generic input values, the simulation outcomes showed that chemicals with moderate lipophilicity, low volatility, and low biotransformability exhibited a high potential for biotransfer in the snake’s skin. Furthermore, the inclusion or exclusion of the skin compartment in the PBK model had a substantial impact on the fate, transport, and distribution of these chemicals within the snake’s body. In comparison to the elimination routes via blood transport and volatilization, the shedding of skin and growth processes did not contribute substantially to the overall kinetics of chemical elimination from the skin for most chemicals. The proposed model has demonstrated a consistent alignment with the observed patterns of chemical distribution, as supported by certain experimental data. Furthermore, it has the potential to provide an explanation for and enhance the understanding of the discrepancies found in other field observations. The modeling exercise clearly illustrated the significant role of the skin compartment in the biotransfer of chemicals within the snake’s body and highlighted the importance of including the snake’s physiological features into the PBK model. To further enhance the model’s performance and accuracy, additional research focused on obtaining specific physiological data pertaining to snakes would be beneficial.

Consequences of acute and long-term excessive iodine intake: A literature review focusing on seaweed as a potential dietary iodine source.

Macroalgae, also called seaweed, are becoming more widespread as food in Western diets. Seaweed can accumulate iodine, an essential nutrient for humans. However, some species of seaweed may contain very high amounts of iodine, and therefore, iodine has been identified as one of the major hazards in the seaweed food chain. Macroalgae may be consumed regularly, though many consumers report eating macroalgae only occasionally. The aim of this paper is to explore possible health consequences of excessive iodine intake according to long-term (chronic) or occasional (acute) excessive exposure to iodine, relating to a regular (chronic) or occasional (acute) seaweed intake, respectively. Furthermore, through a modeling exercise, we add different amounts of seaweed to the diet in a population group to explore the possible safe amounts that can be added without exceeding excessive iodine intakes and risking detrimental health effects. Chronic excessive iodine intakes were associated with several negative health outcomes at variable doses in various studies. For acute excessive iodine exposure, negative health effects seemed to be associated with higher iodine exposures. However, the research on this topic was limited. The chronic and acute iodine exposures needed to result in negative health outcomes may easily be ingested by macroalgae consumption. Adding seaweed to the diet must be done thoughtfully to avoid the risk of exceeding thresholds for excessive iodine intake.

Improving Health Outcomes Through Treatment Sequencing Optimization in Multiple Myeloma: A Simulation Model in Transplant-Ineligible Patients.

Patients with multiple myeloma often require multiple treatment lines. The order in which treatments are sequenced has impact on clinical outcomes. This study aimed to estimate progression-free survival (PFS) and overall survival (OS) with common treatment sequences used in Portugal and the incremental benefit of an optimal sequence in transplant-ineligible patients with multiple myeloma. A state-transition sequential model with a five-health state conceptual structure was developed to simulate and compare survival outcomes between treatment sequences up to four lines of treatments. Data sources included randomized clinical trials and indirect treatment comparisons. A panel of Portuguese hematologists listed four most common treatment sequences and optimal sequence of choice in transplant-ineligible patients. Our simulation estimated an OS between 6.1 and 7.8 years using the most common sequences, with VMP + DRd + Pd + Kd as the most effective (7.8 years). Optimal sequence of choice (DRd + PVd + Kd + Vd) achieved OS of 9.8 years and may extend OS in 2.0-3.7 years vs. most common sequences (26%-61% increase). This benefit was mostly explained by extended PFS in the first line of treatment. Model results demonstrate that choosing the most effective treatment upfront is crucial in delaying disease progression thus yielding better survival outcomes in transplant-ineligible patients. There was a clear survival benefit in using daratumumab-based regimens in first line. This modelling exercise highlights the need to raise awareness around the impact of sequencing strategies to improve patient's outcomes.

A comparative review of de- and post-growth modeling studies

In recent years, a small but rapidly growing field of modeling alternatives to growth as represented by the de- (DG) and post-growth (PG) discourses has emerged. We compare selected model characteristics of 75 DG and PG related modeling studies, compiled through a systematic literature review (2000−2023), and link model structures and results to different theoretically contested debates surrounding DG/PG. The reviewed studies cover different geographical and temporal scopes, economic theories, modeling techniques and operationalizations of DG/PG. The majority of studies models DG/PG as intentional transition and does not question its compatibility with a capitalist system, while more radical strands of the DG/PG discourse are excluded. Although DG/PG modeling exercises frequently explore the effects of sustainability policies, they represent only a fraction of theoretical DG policy proposals, with the most frequent being: working time reduction, maximum income caps, carbon taxes and a universal basic income. DG/PG modeling studies have demonstrated the importance of integrating biophysical constraints in economic modeling but also have quantitatively assessed the feasibility of environmental integrity and social well-being without growth. Nonetheless, future modeling could be rendered more realistic by paying more attention to the Global South, introducing heterogeneous agents driving sustainability transitions and including multiple planetary boundaries.

Contextual Intelligence: An AI Approach to Manufacturing Skills’ Forecasting

The manufacturing industry is skill-intensive and plays a pivotal role in South Africa’s economy, reflecting the nation’s progress and development. The advent of technology has initiated a transformative era within the manufacturing sector. Workforce skills are at the heart of ensuring the sustained growth of the industry. This study delves into the skill-related aspects of the occupational landscape of the South African manufacturing sector, with a particular focus on two important manufacturing sectors: the food and beverage manufacturing (FoodBev) sector and the chemical manufacturing (CHIETA) sector. Leveraging the forecasting prowess of Autoregressive Integrated Moving Average (ARIMA), this paper outlines a sectorial occupational forecasting modeling exercise to reveal which job roles are poised for expansion and which are expected to decline. The approach predicted future skills’ demand 80% accuracy for 473 out of 713 (66%) occupations for FoodBev and 474 out of 522 (91%) for CHIETA. These insights are invaluable for industry stakeholders and educational institutions, providing guidance to support the sector’s growth in an era marked by technological advancement.

Situating spatial determinism in urban design and planning for sustainable walkability: a simulation of street morphology and pedestrian behaviour

Streets and roads have been constantly identified as key spaces to improve walkability and—by extension—the entire urban environment. Such improvements lie on planning and design theories that are often supported by qualitative evidence and as such, tend to remain highly speculative on how street design influences walkability in more deterministic ways. Drawbacks in further exploring this street-walkability correlation are partly defined by an explicit rejection to ‘spatial determinism’ in urban morphology and social sciences, but also because combining differing epistemological approaches for understanding social processes linked to the physical aspects of the space is complex. In this paper, it is proposed that qualitative (social-based) theories of the space and quantitative (positivist) understandings of people’s behaviour can complement each other to elaborate upon ‘spatial determinism’ with focus on streetscapes and pedestrian behaviour. By using Agent-Based Modelling (ABMs), morphological components of streets linked to population characteristics are analysed to understand walkability from the perspective of spatial determinism. The findings suggest that the street settings directly affect pedestrian behaviour, and that changes in the built environment result in changes on walkability patterns. Ultimately, the modelling exercise shed light on street design and walkability, but also on wider debates on how urban design and social processes correlate in more deterministic and, therefore, predictable ways.

Effects of climate and anthropogenic pressures on chemical warfare agent transfer in the Baltic Sea food web

The Baltic Sea is a severely disturbed marine ecosystem previously used as a dumping ground for chemical warfare agents (CWA), which are now known to enter its food web. We have performed a modelling exercise using a calibrated and validated Central Baltic Ecopath with Ecosim (EwE) model to recreate the potential environmental pathways of the infamous Clark I (diphenylchlorarsine). Observations from modelling timestamps covering recent times correspond with in situ detections in sediments and Atlantic cod (Gadus morhua). Under applied modelling conditions and scenarios, there is an active transfer of Clark I from sediments through the Baltic Sea food-web. According to our results, Clark I bioaccumulates within the Baltic Sea food web exclusively throughout the detritus-based food chain. The EwE model for the Central Baltic Sea also allows the simulation of changes in the food web under multiple anthropogenic stressors and management efforts, including recommendations from the Helsinki Commission Baltic Sea Action Plan (HELCOM BSAP). Among all investigated scentarios and factors, the commercial fishing is the most impactful on Clark I accumulation rate and contamination transfer within the Baltic Sea food web. The study indicates the need to extend the existing monitoring approach by adding additional species representing a broader range of ecological niches and tiers within the food chains. From the environmental perspective, the remediation of Chemical Weapons by removal should be considered as part of the integrated management of the Baltic Sea.

Towards an ecosystem capacity to stabilise organic carbon in soils.

Soil organic carbon (SOC) accrual, and particularly the formation of fine fraction carbon (OCfine), has a large potential to act as sink for atmospheric CO2. For reliable estimates of this potential and efficient policy advice, the major limiting factors for OCfine accrual need to be understood. The upper boundary of the correlation between fine mineral particles (silt + clay) and OCfine is widely used to estimate the maximum mineralogical capacity of soils to store OCfine, suggesting that mineral surfaces get C saturated. Using a dataset covering the temperate zone and partly other climates on OCfine contents and a SOC turnover model, we provide two independent lines of evidence, that this empirical upper boundary does not indicate C saturation. Firstly, the C loading of the silt + clay fraction was found to strongly exceed previous saturation estimates in coarse-textured soils, which raises the question of why this is not observed in fine-textured soils. Secondly, a subsequent modelling exercise revealed, that for 74% of all investigated soils, local net primary production (NPP) would not be sufficient to reach a C loading of 80 g C kg-1 silt + clay, which was previously assumed to be a general C saturation point. The proportion of soils with potentially enough NPP to reach that point decreased strongly with increasing silt + clay content. High C loadings can thus hardly be reached in more fine-textured soils, even if all NPP would be available as C input. As a pragmatic approach, we introduced texture-dependent, empirical maximum C loadings of the fine fraction, that decreased from 160 g kg-1 in coarse to 75 g kg-1 in most fine-textured soils. We conclude that OCfine accrual in soils is mainly limited by C inputs and is strongly modulated by texture, mineralogy, climate and other site properties, which could be formulated as an ecosystem capacity to stabilise SOC.

A case-history tutorial describing the incorporation of geophysical, petrophysical and geological constraints to generate realistic geological models of the Matheson Study Area, Ontario

The model that is used to explain potential-field data is highly dependent on the constraints applied in the modelling process. Many studies demonstrate the necessity of constraining gravity and magnetic models. However, typically they do not demonstrate the individual enhancements that come as a consequence of integrating each constraint into the geophysical model. In this paper, we show that when there are no constraints, it is possible to find an inverse model that is consistent with gravity data, but the model is unrealistic, as one sedimentary basin is too deep. Adding a depth weighting constraint can ensure the depth is correct, but all other features have the same depth, which is unrealistic. Including densities from a density compilation makes the densities at surface realistic, but the dips are all close to vertical and the thicknesses are similar, which is unrealistic. In this case, the inversion is believed to have found a local minimum close to the starting model. Reflection seismic data was used to constrain a two-dimensional (2D) modeling exercise (on multiple profiles) to determine the geometry of one sedimentary sub-basin. These 2D models were then combined to build a realistic three-dimensional (3D) starting model. An inversion from this model fixed the densities of each lithology, but allowed the thicknesses of the layers to vary. The resulting model was realistic, with the dips and thicknesses away from the seismic constraints being consistent with geological expectations. Although the fit to the data was much better than the previous model, it was poorer than hoped. If the densities were then allowed to vary within a realistic range of values, the fit could be improved so that both the fit to the data and the geologic model are realistic.

Estimating the strength of soil stabilized with cement and lime at optimal compaction using ensemble-based multiple machine learning

It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74–26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65–80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

HubEnsembles: Ensembling Methods in R.

Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. The R package hubEnsembles provides a flexible framework for ensembling various types of predictions, including point estimates and probabilistic predictions. A range of common methods for generating ensembles are supported, including weighted averages, quantile averages, and linear pools. The hubEnsembles package fits within a broader framework of open-source software and data tools called the "hubverse", which facilitates the development and management of collaborative modelling exercises.

An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years’ monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.