Observer Bias Research Articles

Revealing biases in insect observations: A comparative analysis between academic and citizen science data.

Citizen Science is a powerful tool for biodiversity research, as it facilitates data recording at large scales that would otherwise be impossible to cover by standard academic research. Despite its benefits, the accuracy of citizen science data remains a subject of concern among scientists, with varying results reported so far. Neither citizen science data nor academic records are immune to biases, which can significantly impact the quality and reliability of observations. Here, using insects in the Iberian Peninsula as a case study, we compare data collected by participatory platforms to those obtained through academic research projects, and assess their taxonomic, spatial, temporal, and environmental biases. Results show a prominent taxonomic bias in both academic and citizen science data, with certain insect orders receiving more attention than others. These taxonomic biases are conserved between different participatory platforms, as well as between groups of users with different levels of contribution performance. The biases captured by leading contributors in participatory platforms mirrored those of sporadic users and academic data. Citizen science data had higher spatial coverage and less spatial clustering than academic data, showing also clearer trends in temporal seasonality. Environmental coverage over time was more stable in citizen science than in academic records. User behaviour, preference, taxonomical expertise, data collection methodologies and external factors may contribute to these biases. This study shows the multifaceted nature of biases present in academic records and citizen science platforms. The insights gained from this analysis emphasize the need for careful consideration of these biases when making use of biodiversity data from different sources. Combining academic and citizen science data enhances our understanding of biodiversity, as their integration offers a more comprehensive perspective than relying solely on either dataset alone, especially since biases in these two types of data are not always the same.

Depression increases the risk of gallstone: A cross-sectional study and Mendelian randomization analysis

BackgroundGallstone, a common digestive disorder, poses a significant public health burden. Concurrently, depression is acknowledged as a health risk. However, limited information exists on depression's impact on gallstone formation. This study investigates depression's causal effect on gallstone risk. MethodsUsing National Health and Nutrition Examination Survey (NHANES) data, we conducted an observational study. The severity of depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression and subgroup analyses explored the correlation between depression and gallstone risk. Mendelian Randomization (MR) analysis, leveraging Genome-Wide Association Studies (GWAS) data, reduced observational bias and elucidated causality. Inverse Variance Weighting (IVW) was the primary method, with sensitivity analyses validating results. ResultsIn the observational study (7707 participants), gallstone risk was elevated in mild (OR: 1.58, 95 % CI 1.31–1.90, P < 0.001), moderate (OR: 2.07, 95 % CI 1.59–2.67, P < 0.001), and severe (OR: 2.41, 95 % CI 1.70–3.34, P < 0.001) depression groups (P for trend <0.001). Subgroup analyses revealed a stronger association in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Mendelian Randomization indicated a causal link between genetically predicted depression and higher cholelithiasis risk (OR: 2.06, 95 % CI 1.34–3.17, P = 0.001), validated through sensitivity analyses and multi-cohort verification. ConclusionDepression independently increases gallstone risk, particularly in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Further validation is needed through multi-center, prospective cohort studies.

Do outgrower schemes enhance technology adoption and productivity? Evidence from maize farmers in Northern Ghana

AbstractNucleus outgrower schemes are contractual arrangements where well‐resourced large‐scale farmers (nucleus farmers) are empowered by development support agencies to take charge of smallholder farmers, by providing them with market access and the necessary training on agronomic practices and farm inputs for production. In this study, we examine the impact of participation in outgrower scheme on technology adoption and farm productivity, using data from 634 smallholder maize farmers in Northern Ghana. We use the linear regression with endogenous treatment effects model to account for both observable and unobservable selection bias. The results reveal positive and significant marginal effects of outgrower scheme participation on technology adoption and maize yields. Outgrower scheme participants applied 6% and 82% more inorganic fertilizer and weedicides, respectively, than nonparticipants. Maize yield increased by about 3% for the participants more than nonparticipants. The findings also show that factors such as distance to market, distance to nucleus farmers' satellite office, nucleus outgrower scheme awareness, input price, and farmer‐based organization significantly influence outgrower scheme participation decisions, as well as technology adoption and maize yields.

Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus)

Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus)

Performance pressure and annual report text manipulation: Evidence from China

AbstractBuilding on attribution theory, this study investigates the antecedents of corporate annual report text manipulation from the perspective of managerial performance pressure. Using a data set comprising 15,076 samples from companies listed on China's Shanghai and Shenzhen A‐share markets between 2009 and 2021, we reveal that as management faces performance pressure, they tend to increase content about external environmental risk and policy uncertainty in annual reports due to actor–observer bias. When incorporating Confucian cultural factors into the research framework, the study found that the strength of Confucian culture mitigates the correlation between the pressure of management performance and annual report manipulation. However, the level of marketization where a corporation is located can undermine the ethical norms upheld by Confucian culture. In additional analysis, we also discovered that the longer the duration of performance pressure, the greater the extent of manipulation within the annual reports initiated by the management. Interestingly, the social performance pressure generated from ESG performance also positively influences annual report textual manipulation, compared to operational performance pressure. This study explores the impact of management performance pressure on information disclosure decisions from the perspective of performance attribution, not only extending the boundaries of attribution theory but also providing guidance for policymakers to enhance the supervision of annual report disclosures and for capital market investors to optimize investment decisions.

The experimental implications of the rate of temperature change and timing of nutrient availability on growth and stoichiometry of a natural marine phytoplankton community

AbstractClimate change increases the need to understand the effect of predicted future temperature and nutrient scenarios on marine phytoplankton. However, experimental studies addressing the effects of both drivers use a variety of design approaches regarding their temperature change rate and nutrient supply regimes. This study combines a systematic literature map to identify the existing bias in the experimental design of studies evaluating the phytoplankton response to temperature change, with a laboratory experiment. The experiment was designed to quantify how different temperature levels (6°C, 12°C, and 18°C), temperature regimes (abrupt vs. gradual increase), timings of nutrient addition (before or after the temperature change) and nutrient regimes (limiting vs. balanced) alter the growth and stoichiometry of a natural marine phytoplankton community. The systematic map revealed three key biases in marine global change experiments: (1) 66% of the studies do not explicitly describe the experimental temperature change or nutrient regime, (2) 84% applied an abrupt temperature exposure, and (3) only 15% experimentally manipulated the nutrient regime. Our experiment demonstrated that the identified biases in experimental design toward abrupt temperature exposure induced a short‐term growth overshoot compared to gradually increasing temperatures. Additionally, the timing of nutrient availability strongly modulated the direction of the temperature effect and strength of growth enhancement along balanced N : P supply ratios. Our study stresses that the rate of temperature change, the timing of nutrient addition and the N : P supply ratio should be considered in experimental planning to produce ecologically relevant results as different setups lead to contrasting directions of outcome.

Bias and variation in salmonid redd counting using remotely piloted vehicles

AbstractRedd surveys estimate spawning population size for many salmonid species. Studies of field‐based redd counting methods highlight observer bias caused by redd density, observer experience, and environmental factors. Researchers have begun using remotely piloted vehicles (RPVs, drones) to count redds; yet, no studies have quantified bias and variability in counts. This study aimed to quantify the influence of redd density, observer experience, and environmental factors (namely, water clarity) on redd counting bias and variability when using RPVs. We found that technological and procedural improvements from our previous study increased precision and reduced variability among observers (coefficient of variation, сυ = 11% compared to сυ = 42%). Redd density was the leading covariate causing differences between RPV and both “best counts” (p &lt; 0.05) and field counts (p &lt; 0.05). We found a reduction in variability with experience level (no experience сυ = 78%; semi‐experienced сυ = 33%; experienced сυ = 20%), with no directional bias in counting. Our paper is the first to quantify observer bias in RPV‐based redd counts. This study describes RPV methods and can help agencies decide how to use RPVs in redd counting and incorporate RPV methods into long‐term datasets.

P-448 Placental histopathology in frozen embryo transfer HRT induced IVF pregnancies and non-IVF pregnancies and its correlation with maternal and fetal outcomes: a prospective case-control study

Abstract Study question Is Placental histopathology altered in Frozen Embryo transfer, HRT induced IVF pregnancies when compared to non-IVF pregnancies; if yes,any implications on maternal and fetal outcome? Summary answer Pathological placental lesions and adverse maternal outcomes were found to be significantly higher in IVF pregnancies compared to non-IVF pregnancies. What is known already Studies on placental histopathology in IVF pregnancies suggest that there may be differences compared to naturally conceived pregnancies. Some research indicates an increased risk of certain placental abnormalities such as changes in vascularization and structure (Uroka Belak, et al., 2023). Additionally, alterations in gene expressions and epigenetic modifications in placenta have been observed in IVF pregnancies. However, it is essential to note that findings can vary, and more research is needed to fully understand the implications and potential long-term effects. Study design, size, duration Prospective, single centre, comparative clinical study, including 100 pregnancies between November, 2022 and November, 2023; 50 FET HRT induced IVF pregnancies, 50 non-IVF pregnancies. Participants/materials, setting, methods Patients aged 32-45 years, &amp;gt;34 weeks gestation, with no associated maternal and obstetrical complications were recruited after proper screening and informed consent. Among 100 pregnancies, we compared placental HPE using the Amsterdam Placental Workshop Group Consensus and feto-maternal outcomes between FET HRT induced IVF pregnancies and non-IVF pregnancies. We used Chi-square test to compare groups(p &amp;lt; 0.05). Main results and the role of chance Out of 100 participants, 50 were frozen embryo transfer, HRT induced IVF pregnancies and 50 were non-IVF pregnancies. Out of 50 IVF pregnancies; 27(54%) had pathological placental lesions, of which 17(63%) were noted to have adverse maternal outcomes and 6(22.2%) had adverse fetal outcomes. Out of 50 non-IVF pregnancies, 4(8%) had pathological placental lesions; of which 1(25%) were noted to have adverse maternal outcomes and 1(25%) had adverse fetal outcomes. On comparing these 2 groups, our result was found to be statistically significant(p &amp;lt; 0.05). Limitations, reasons for caution This study has some limitations owing to it being a single centered comparative trial, possibility of observer bias in histopathological examination and that the study is restricted to HRT induced IVF pregnancies. Wider implications of the findings This study reveals valuable insights into potential differences in placental and fetal development. Understanding these variances could influence clinical practices, patient counseling, and contribute to advancements in assisted reproductive technologies. Additionally, insights into long-term health outcomes for both mothers and babies may emerge, guiding future research and healthcare strategies. Trial registration number Not applicable

Kinematic origin of white dwarfs in the solar neighborhood

Context. White dwarfs are considered to be efficient cosmochronometers. Thanks to the recent space-borne mission Gaia, a nearly complete sample up to about 100 parsecs from the Sun has been compiled. However, the Galaxy, as a dynamic system, implies that these objects may have very diverse origins. It is therefore of paramount importance to characterize the origins of white dwarfs from the different Galactic structure components found in our solar neighborhood. Aims. We aim to compute the Galactic orbits for white dwarfs of the thin and thick disk, as well as the halo components observed in our solar neighborhood. On the basis of these determinations, we analyze the most probable regions of the Galaxy where they could have formed, along with the distribution of their orbital parameters and the observational biases introduced when constructing the local sample. Methods. We used a detailed Galactic orbit integration package, in conjunction with a detailed population synthesis code specifically designed to replicate the different Galactic components of the white dwarf population. Synthetic stars were generated based on the current observational sample and their orbital integration allowed for the reconstruction of the population’s history. Results. Our kinematic analysis of the white dwarf population reveals the ephemeral nature of the concept of the solar neighborhood, as the majority of thin-disk, thick-disk, and halo white dwarfs will have left our 100 pc neighborhood in approximately 3.30 Myr, 1.05 Myr, and 0.6 Myr, respectively. Moreover, the spatial distribution of the integrated thin-disk orbits suggests that 68% of these stars were formed at less than 1 kpc from the Sun, while most of the thick-disk members have undergone radial disk migration. Halo members are those stars that typically belong to the “inner halo”, given that their orbits mostly planar and do not extend beyond R = 20 − 25 kpc. Despite the observational bias, which mostly affects the oldest stars in the thick disk and halo, we show that the wider distribution of orbital parameters is well represented by the sample. Conclusions. The solar neighborhood is a transitory concept, whereby its current population of white dwarfs originates from larger regions of the Galaxy. This fact must be taken into account when analyzing the overall properties of such a population, such as its age distribution, metallicities, and formation history. Even so, the kinematic properties observed by recent missions such as Gaia are representative of the total population up to a distance of approximately 500 pc.

Starspots and Undetected Binary Stars Have Distinct Signatures in Young Stellar Associations

Young stars form in associations, meaning that young stellar associations provide an ideal environment to measure the age of a nominally coeval population. Isochrone fitting, which is the typical method for measuring the age of a coeval population, can be impacted by observational biases that obscure the physical properties of a population. One feature in isochrone fits of star-forming regions is an apparent mass-dependent age gradient, where lower-mass stars appear systematically younger than higher-mass stars. Starspots and stellar multiplicity are proposed mechanisms for producing the mass-dependent age gradient, but the relative importance of starspots versus multiplicity remains unclear. We performed a synthetic red optical low-resolution spectroscopic survey of a simulated analog to a 10 Myr stellar association including mass-dependent multiplicity statistics and age-dependent starspot coverage fractions. We found that undetected starspots alone do not produce an apparent mass-dependent age gradient, but instead uniformly reduce the average measured age of the population. We also found that binaries continue to produce an apparent mass-dependent age gradient and introduce more scatter in the age measurement than spots, but are easily removed from the population as long as there are good distance measurements to each target. We conclude that it is crucial to incorporate treatments of both starspots and undetected stellar multiplicity into isochrone fits of young stellar associations to attain reliable ages.

Relationship between Hand Hygiene and MDRO Acquisition after Implementation of an Electronic Hand Hygiene Monitoring System

Background: Hand hygiene (HH) is fundamental to preventing the transmission of pathogens between patients. Unfortunately, adherence to HH is suboptimal and monitoring adherence is challenging. Electronic HH monitoring systems (EHHMS) are emerging potential solutions to increase the number of HH observations and eliminate the potential for observation bias. This aim of this study is to assess the impact of improved HH adherence after the introduction of an EHHMS on the rates of healthcare facility-onset multidrug-resistant organisms and Clostridioides difficile (HO-MDRO). Methods: We performed a retrospective, quasi-experimental study to evaluate the impact of HH on HO-MDROs across 4 acute care facilities (59 hospital units, 14 of which were ICUs) from January 2021 - September 2022 after implementing an EHHMS in a large academic healthcare system. Clinical cultures from all sources were included; routine surveillance cultures were not collected during this period. HO determination was made using National Healthcare Safety Network (NHSN) definitions. The association between monthly unit-level HH adherence (%) and HO-MDRO rate per 1000 patient-days was assessed using mixed-effects Poisson regression using rate ratios (RR), which accounts for unobserved heterogeneity between units while controlling for number of tests ordered per month per hospital unit. HH adherence was stratified in quartiles (Q1: 24-43%, Q2: 43-51%, Q3: 52-61%, Q4: 61-84%). Results: During the study period, there were 23 million HH opportunities and 1875 MDROs in 772,930 patient-days. HH adherence increased from 41% January 2021 to 57% September 2022. ESBL, MSSA, and CDIFF accounted for most MDROs (Figure 1). The mean monthly HH adherence rate was 52% per unit, with a median of 1.66 (IQR: 0-3.5) MDROs/1000 patient-days. Mixed-effects Poisson regression suggested no significant overall relationship between HH adherence and MDRO rate (Figure 2). A close to null association was observed when comparing quartile two to quartile one (RR: 0.97, 95% CI: 0.82, 1.15), quartile three to quartile one (RR: 0.96, 95% CI: 0.79, 1.17), and quartile four to quartile one (RR: 1.05, 95% CI: 0.86, 1.28). Results were similar across hospitals (Figure 3). Conclusions: Although implementing an EHHMS led to an improvement in HH adherence, we were not able to demonstrate a resultant decrease in HO-MDROs. Potential explanations include the relatively rare outcomes of interest, unrecognized confounders, and the complex interaction between HH and HO-MDROs, since poor HH adherence on a unit may lead to increased attention from infection prevention and therefore increased focus on other MDRO prevention measures.

The niche through time: Considering phenology and demographic stages in plant distribution models

Abstract Species distribution models (SDMs) are widely used to infer species–environment relationships, predict spatial distributions and characterise species' environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors and the calibration of stage‐specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change. The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals.

Accounting for observation biases associated with counts of young: you may count too many or too few...

Accounting for observation biases associated with counts of young: you may count too many or too few...

Computed tomography-based structural rigidity analysis can assess tumor- and treatment-induced changes in rat bones with metastatic lesions

BackgroundBreast cancer (BrCa) is a predominant malignancy, with metastasis occurring in one in eight patients, nearly half of which target the bone, leading to serious complications such as pain, fractures, and compromised mobility. Structural rigidity, crucial for bone strength, becomes compromised with osteolytic lesions, highlighting the vulnerability and increased fracture risk in affected areas. Historically, two-dimensional radiographs have been employed to predict these fracture risks; however, their limitations in capturing the three-dimensional structural and material changes in bone have raised concerns. Recent advances in CT-based Structural Rigidity Analysis (CTRA), offer a promising, more accurate non-invasive 3D approach. This study aims to assess the efficacy of CTRA in monitoring osteolytic lesions’ progression and response to therapy, suggesting its potential superiority over existing methodologies in guiding treatment strategies.MethodsTwenty-seven female nude rats underwent femoral intra-medullary inoculation with MDA-MB-231 human breast cancer cells or saline control. They were divided into Control, Cancer Control, Ibandronate, and Paclitaxel groups. Osteolytic progression was monitored weekly using biplanar radiography, quantitative computed tomography (QCT), and dual-energy X-ray absorptiometry (DEXA). CTRA was employed to predict fracture risk, normalized using the contralateral femur. Statistical analyses, including Kruskal-Wallis and ANOVA, assessed differences in outcomes among groups and over time.ResultsBiplanar radiographs showed treatment benefits over time; however, only certain time-specific differences between the Control and other treatment groups were discernible. Notably, observer subjectivity in X-ray scoring became evident, with significant inter-operator variations. DEXA measurements for metaphyseal Bone Mineral Content (BMC) did not exhibit notable differences between groups. Although diaphyseal BMC highlighted some variance, it did not reveal significant differences between treatments at specific time points, suggesting a limited ability for DEXA to differentiate between treatment effects. In contrast, the CTRA consistently demonstrated variations across different treatments, effectively capturing bone rigidity changes over time, and the axial- (EA), bending- (EI), and torsional rigidity (GJ) outcomes from the CTRA method successfully distinguished differences among treatments at specific time points.ConclusionTraditional approaches, such as biplanar radiographs and DEXA, have exhibited inherent limitations, notably observer bias and time-specific inefficacies. Our study accentuates the capability of CTRA in capturing real-time, progressive changes in bone structure, with the potential to predict fractures more accurately and provide a more objective analysis. Ultimately, this innovative approach may bridge the existing gap in clinical guidelines, ushering in enhanced Clinical Decision Support Tool (CDST) for both surgical and non-surgical treatments.

Female‐biased population sex ratios caused by genetic rather than ecological mechanisms in dwarf willow (Salix herbacea L.)

Abstract Biased sex ratios among reproductive individuals are common in plants, but the underlying mechanisms, as well as the evolutionary consequences, are not well understood. The classical theory of Düsing and Fisher predicts an equal primary sex ratio at seed production, based on the selective advantage of the rare sex. Biased sex ratios among reproductive plants can arise from sexual dimorphism in survival and flowering. Sex ratio biases can also be present from the seed stage; in these cases, assumptions of Düsing's and Fisher's theory, for example, random mating or demographic equilibrium, are thought to be violated. We investigated mechanisms leading to female‐biased sex ratios in the arctic‐alpine dwarf willow Salix herbacea L. We studied sex ratios in three natural populations over 3 years as well as in 29 crosses (full‐sib families) under controlled conditions over four growth periods. We tested whether sex ratio was associated with habitat parameters (elevation and snowmelt time), or with germination, survival or flowering, and whether females and males differed in size or flowering that may cause observation bias. We detected a strong and consistent female bias, both in natural populations (sex ratio [proportion of females]: 0.71–0.82) and in our controlled experiment (overall sex ratio: 0.70–0‐72). Female bias became more pronounced with increasing elevation. Our data did not support sexual dimorphism in size or flowering. Family sex ratios varied largely (from 0.25 to 1), including many female‐biased families, unbiased families and two male‐biased families. Families with lower germination, seedling establishment, survival or flowering did not have stronger female bias, indicating that intrinsically higher survival or flowering in females does not explain overall female bias. Synthesis. Our results suggest that sex ratio bias in S. herbacea is already present in seeds and does not arise through intrinsic differences between sexes. Candidate mechanisms that can lead to both overall female bias and variation in sex ratio among families are meiotic drive or cyto‐nuclear interactions. The pioneer habit of Salix may lead to non‐equilibrium population dynamics that allow for the long‐term persistence of variable genetic sex ratio distortion systems that arise from genetic conflict.

Observations and detectability of young Suns’ flaring and CME activity in optical spectra

ABSTRACT The Sun’s history is still a subject of interest to modern astrophysics. Observationally constrained rates of Coronal Mass Ejections (CMEs) of young solar analogues are still lacking, as those require dedicated monitoring. We present medium-resolution optical spectroscopic monitoring of a small sample of bright and prominent solar analogues over a period of 3 yr using the 0.5-m telescope at observatory Lustbühel Graz (OLG) of the University of Graz, Austria. The aim is the detection of flares and CMEs from those spectra. In more than 1700 h of spectroscopic monitoring, we found signatures of four flares and one filament eruption on EK Dra which has been reported in previous literature, but we complementarily extended the data to cover the latter phase. The other stars did not reveal detectable signatures of activity. For these non-detections, we derive upper limits of occurrence rates of very massive CMEs, which are detectable with our observational setup, ranging from 0.1 to 2.2 d−1, but these may be even smaller than the given rates considering observational biases. Furthermore, we investigate the detectability of flares/CMEs in OLG spectra by utilizing solar 2D Hα spectra from Mees Solar Observatory. We find that solar-sized events are not detectable within our observations. By scaling up the size of the solar event, we show that with a fractional active region area of 18 per cent in residual spectra and 24 per cent in equivalent width time series derived from the same residuals that solar events are detectable if they had hypothetically occurred on HN Peg.

The Fundamental Role of Character Coding in Bayesian Morphological Phylogenetics.

Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are 4 nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many characters states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small.

PATNet: Propensity-Adjusted Temporal Network for Joint Imputation and Prediction Using Binary EHRs With Observation Bias

PATNet: Propensity-Adjusted Temporal Network for Joint Imputation and Prediction Using Binary EHRs With Observation Bias

Isolated Pulsar Population Synthesis with Simulation-based Inference

We combine pulsar population synthesis with simulation-based inference (SBI) to constrain the magnetorotational properties of isolated Galactic radio pulsars. We first develop a framework to model neutron star birth properties and their dynamical and magnetorotational evolution. We specifically sample initial magnetic field strengths, B, and spin periods, P, from lognormal distributions and capture the late-time magnetic field decay with a power law. Each lognormal is described by a mean, μlogB,μlogP , and standard deviation, σlogB,σlogP , while the power law is characterized by the index, a late. We subsequently model the stars’ radio emission and observational biases to mimic detections with three radio surveys, and we produce a large database of synthetic P– Ṗ diagrams by varying our five magnetorotational input parameters. We then follow an SBI approach that focuses on neural posterior estimation and train deep neural networks to infer the parameters’ posterior distributions. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain μlogB=13.10−0.10+0.08 , σlogB=0.45−0.05+0.05 and μlogP=−1.00−0.21+0.26 , σlogP=0.38−0.18+0.33 for the lognormal distributions and alate=−1.80−0.61+0.65 for the power law at the 95% credible interval. We contrast our results with previous studies and highlight uncertainties of the inferred a late value. Our approach represents a crucial step toward robust statistical inference for complex population synthesis frameworks and forms the basis for future multiwavelength analyses of Galactic pulsars.

Accurately predicting rare and poorly detectable species habitat for spatial protection

Abstract With the loss of biodiversity worldwide, understanding species distribution is essential for species management, but modelling the distribution of rare and poorly detectable species can be challenging because of data gaps and observer biases. Over‐ or under‐predictions are frequent, leading to uncertainty in spatial management measures, particularly for highly mobile and data‐poor species. Here we developed a ‘Combined Model for Accurate Prediction’ to accurately predict the distribution of data‐poor and rare species. This modelling framework aims to improve the accuracy of both predicted ‘core’ and ‘unsuitable’ habitats, to help support managers with spatial protection measures. We tested the combined modelling approach on 11 data‐poor and rare diadromous fish during their at‐sea life history phase and used the combined model to analyse the adequacy of existing marine protected areas (MPAs) for these fish. The combined modelling approach modelled both ‘core’ and ‘unsuitable’ habitats with high accuracy. Of the seven diadromous species modelled, most MPAs designated to protect diadromous fish are outside their core habitats. Furthermore, when their core habitat was within an MPA, only 50% of this area was designated to protect them. These results highlight inadequate protection of the existing networks of MPAs for protected and threatened species. Synthesis and applications. Being able to accurately model species distribution is critical to reliable and transparent biodiversity and conservation assessments. By modelling accurate ‘core’ and ‘unsuitable’ habitats with models that minimise omission and commission rates respectively, conservation measures could be targeted in specific spatial areas that maximise the protection of rare and poorly detected species. This method therefore helps minimise impacts on stakeholders, while providing managers with increased confidence in the model predictions.