Covariance Measurements Research Articles

Estimating the Effect of Adhering to the Recommendations of the 2019 Canada's Food Guide on Health Outcomes in Older Adults: Protocol for a Target Trial Emulation.

The 2019 Canada's Food Guide provides universal recommendations to individuals aged ≥2 years. However, the extent to which these recommendations are appropriate for older adults is unknown. Although ideal, conducting a large randomized controlled trial is unrealistic in the short term. An alternative is the target trial emulation framework for causal inference, a novel approach to improve the analysis of observational data. This study aims to describe the protocol for a target trial emulation in older adults, with an emphasis on key aspects of a hypothetical sustained diet and physical activity intervention. To emulate the target trial, nonexperimental data from the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge; N=1753 adults aged ≥67 years) will be used. NuAge includes 4 yearly measurements of dietary intakes, covariates, and outcomes. The per-protocol causal contrast will be the primary causal contrast of interest to account for nonadherence. The sustained intervention strategy will be modeled using the parametric g-formula. In the hypothetical trial, participants will be instructed to meet sex-specific minimal intakes for vegetables and fruits, whole grains, animal- and plant-based protein foods, milk and plant-based beverages, and unsaturated fats. The eligibility criteria, follow-up, intervention, outcomes, and causal contrast in the emulation will closely align with those of the target trial, with only minor modifications. We will attempt to emulate the randomization of treatment by adjusting for baseline covariates and prebaseline dietary habits. Data collection for NuAge was completed in June 2008. For this study, the main analysis was started in May 2024. Submission of the manuscript is expected by February 2025. Emulating a target trial will provide the first evidence of the adequacy of the 2019 Canada's Food Guide recommendations for older adults in relation to health outcomes. DERR1-10.2196/65182.

Characterization of gene-environment interactions for vitamin D through variance quantitative trait loci: a UK Biobank-based genetic epidemiology study.

Understanding gene-environment interactions associated with vitamin D status may refine nutrition and public health strategies for vitamin D deficiency. Recent methodological advances have enabled the identification of variance quantitative trait loci (vQTLs) where gene-environment interactions are enriched. To identify vQTLs for serum 25-hydroxy vitamin D (25OHD) concentrations and characterize potential gene-environment interactions of vQTLs. We conducted vQTL discovery for 25OHD using a newly developed quantile integral linear model in the UK Biobank individuals of European (N = 313,514), African (N = 7,800), East Asian (N = 2,146), and South Asian (N = 8,771) ancestries, respectively. We tested for interactions between the identified vQTL lead variants and 18 environmental, biological, or lifestyle factors, followed by multiple sensitivity analyses. We identified 19 independent vQTL lead variants (p-value <5x10-8) in the European ancestry population. No vQTLs were identified in the non-European ancestry populations, likely due to limited sample sizes. A total of 32 interactions were detected with a false discovery rate <0.05. While known gene-season of measurement interactions were confirmed, additional interactions were identified involving modifiable risk factors, including time spent outdoors and body mass index. The magnitudes of these interactions were consistent within each locus upon adjusting for season of measurement and other covariates. We also identified a gene-sex interaction at a vQTL that implicates DHCR7. Integrating transcript- and protein-level evidence, we found that the sex-differentiated genetic associations may act through sex-biased expression of DHCR7 isoforms in skin tissues due to alternative splicing. Through the lens of vQTLs, we identified additional gene-environment interactions affecting vitamin D status in addition to season of measurement. These findings may provide new insights into the etiology of vitamin D deficiency and encourage personalized prevention and management of associated diseases for at-risk individuals.

Enhancing evapotranspiration estimates in composite terrain through the integration of satellite remote sensing and eddy covariance measurements.

Accurate evaluation of water resource systems is essential for informed planning and decision-making. Evapotranspiration (ET), a key component of water resource management, is often estimated using remote sensing techniques; however, such estimates can be subject to significant uncertainties under certain conditions. In this study, we present a novel approach to improving the accuracy of ET estimates in composite terrains. The methodology involves optimizing the Surface Energy Balance Algorithm for Land (SEBAL-OPT) by integrating ground-based eddy covariance (EC) flux tower data into the satellite-based ET retrieval process. The approach was evaluated at four sites in California, each representing different land uses. Parameter optimization was achieved through Bayesian inference using the Differential Evolution Adaptive Metropolis (DREAM) algorithm, which minimized discrepancies between ET estimates derived from Landsat 8 and 9 imagery and the observed ET from EC measurements. Results from the global sensitivity analysis identified solar radiation and hot/cold pixel selection as the most sensitive parameters in the SEBAL algorithm, highlighting their critical role in reducing uncertainty in ET estimates. SEBAL-OPT demonstrated significantly improved accuracy, with root mean square error (RMSE) values ranging from 0.72mm to 1.33mm, compared to the original SEBAL parameterization (SEBAL-ORG), which produced RMSE values between 1.03mm and 2.14mm. This approach highlights that, when properly calibrated, the model can be effectively applied across diverse agricultural landscapes, regardless of the specific land use at individual sites. These findings have significant implications for water resource planning, agricultural water management, and water rights adjudication and could be applied to other remote sensing of ET models.

Real time SOC estimation for Li-ion batteries in Electric vehicles using UKBF with online parameter identification

In the recent era, Lithium ion batteries plays a significant role in EV industry due to their high specific energy density, power density, low self-discharge rate, and prolonged lifespan. Modeling the battery precisely and estimating its State of Charge with great precision is essential to improve the performance of the lithium-ion batteries. Though numerous methods has been proposed for estimating the SOC, accurate estimation approach is not proposed yet since all these approaches consider the discrete-time dynamics of the battery. Hence in this proposed approach, the implementation of Thevenin 2RC battery model in conjunction with the Unscented Kalman Bucy Filter (UKBF) for SOC estimation is suggested. Thevenin 2RC battery model is used to captures the nonlinear relationship between the battery’s voltage, current, and SOC. The UKBF is then used to estimate the SOC by fusing the battery model with noisy measurements of the battery’s voltage and current. The UKBF is able to handle the nonlinearity of the battery model and the noise in the measurements, resulting in a more accurate estimate of the SOC by capturing the continuous-time dynamics of the battery. The model is simulated in Matlab Simulink. With similar covariance noise and measurement noise taken into consideration, the battery’s SOC is estimated using the EKF, UKF, and UKBF. The performance comparison indicate that the UKBF approach provides an accurate estimation of the SOC, with a significantly lower RMSE of 0.003276.

Optimization of Covariance Matrices of Kalman Filter with Unknown Input Using Modified Directional Bat Algorithm

The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values.

Development and Clinical Validation of Model-Informed Precision Dosing for Everolimus in Liver Transplant Recipients.

Everolimus presents significant dosing challenges due to between- and within-patient pharmacokinetic variabilities. This study aimed to develop and validate a model-informed precision dosing strategy for everolimus in liver transplant recipients. The dosing strategy was initially developed using retrospective data, employing nonlinear mixed-effects modeling. The model included readily measurable covariates, body surface area, albumin, and tacrolimus trough concentration. The dosing strategy was subsequently validated in a prospective trial, recommending 1 to 1.75 mg dosages every 12 h, depending on covariates. Lower dosages were recommended for patients with lower body surface area and albumin with adjustments based on tacrolimus trough concentration. The estimated pharmacokinetic parameters (typical value ± standard error), apparent clearance (CL/F: 15.0 ± 0.5 L/h), and apparent volume of distribution (Vd/F: 862 ± 79.3 L) were refined using prospective clinical data from 20 patients, reducing interindividual variations. This research successfully developed and validated a population pharmacokinetic model for everolimus. The developed "dosE" web-based platform translates our pharmacokinetic model into a practical tool for healthcare providers, exemplifying the application of pharmaceutical research in clinical practice and potentially improving therapeutic outcomes in liver transplantation.

Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study.

In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured. To incorporate external information while accounting for the different data forms, we posit working models and obtain informative weights by empirical likelihood, which will be used to construct a weighted estimator in the main analysis. We have established the theory demonstrating that the new estimator has higher estimation efficiency compared to the conventional ones, and this advantage is robust to working model misspecification, as confirmed in our simulation studies. To assess its utility, we apply our method to accommodate data from the National Alzheimer's Coordinating Center to improve the analysis of the Alzheimer's Disease Neuroimaging Initiative Phase 1 study.

Early life stress and pubertal predictors of subsequent substance use in a national diverse sample of adolescents: Sex and substance type matter.

Early life stress (ELS) increases the risk of substance use disorder (SUD) in adulthood. The pathway from ELS to SUD is hypothesized to be influenced by sex. We examine the impact of ELS on adolescent first substance use, a common precursor to adult SUDs, and test for sex differences in the relationship between ELS and risk of first use of alcohol, nicotine, and marijuana. Individuals from the Adolescent Brain Cognitive Development study (ABCD; N = 8608 US children aged 9-10 at baseline) were assessed for multiple measures of ELS (Environmental, Family, Trauma), covariates (race/ethnicity, age in months at baseline, pubertal development score), and first substance use. Cox proportional hazards regression was performed for total population and stratified by sex, generating adjusted hazard ratios (aHRs) of first substance use. One unit increase in ELS Total scores significantly increased aHRs of first use of all substances. Dimensional ELS analysis revealed associations with risk that differed across sex, type of ELS, and substance. Females with higher ELS Environmental scores display a significant increase in risk of first use across all substances, while males with higher ELS Trauma scores showed a significant increase in risk of first nicotine and marijuana use. Findings highlight sex differences in the association of ELS Environmental and Trauma scores with the risk of first substance use, while illustrating the consistency of the association of ELS Family scores with risk. Existing family-based prevention and intervention strategies should consider addressing sex differences in adolescent substance use risk and protection.

Increased structural covariance of cortical measures in individuals with an at-risk mental state.

An anomalous pattern of structural covariance has been reported in schizophrenia, which has been suggested to represent connectome changes during brain maturation and neuroprogressive processes. It remains unclear whether similar differences exist in a clinical high-risk state for psychosis, and if they are associated with a prodromal phenotype and/or later psychosis onset. This multicenter magnetic resonance imaging study cross-sectionally examined structural covariance in a large at-risk mental state (ARMS) sample with different outcomes. The whole-brain structural covariance of four cortical measures (thickness, area, volume, and gyrification) was assessed in 155 individuals with ARMS, who were subclassified into 26 (16.8%) with a later psychosis onset (ARMS-P), 44 with persistent subthreshold psychotic symptoms, and 53 with the remission of psychotic symptoms (ARMS-R) during the clinical follow-up, and 191 healthy controls. The relationships of changes in structural covariance with clinical symptoms and cognitive impairments were also investigated in the ARMS subsample. Structural covariance was significantly higher in widespread cortical regions in the ARMS group than in the controls, with each cortical measure having a different pattern in affected cortical regions. The higher structural covariance of the cortical area was partly related to severe suspiciousness-persecutory ideation. Structural covariance was significantly higher, mainly in fronto-parietal gyrification, in the ARMS-P group than in the ARMS-R group. The present results suggest that changes in structural covariance result in psychosis vulnerability and the excessive structural covariance of brain gyrification in ARMS subjects may contribute to their later clinical course.

Vegetation Types Shift Physiological and Phenological Controls on Carbon Sink Strength in a Coastal Zone.

The carbon sink function performed by the different vegetation types along the environmental gradient in coastal zones plays a vital role in mitigating climate change. However, inadequate understanding of its spatiotemporal variations across different vegetation types and associated regulatory mechanisms hampers determining its potential shifts in a changing climate. Here, we present long-term (2011-2022) eddy covariance measurements of the net ecosystem exchange (NEE) of CO2 at three sites with different vegetation types (tidal wetland, nontidal wetland, and cropland) in a coastal zone to examine the role of vegetation type on annual carbon sink strength. We found that the three study sites are stable carbon sinks and are influenced by their distinct physiological and phenological factors. The annual NEE of the tidal wetland, nontidal wetland, and cropland were determined predominantly by the seasonal peaks of net CO2 uptake, release, and duration of CO2 uptake period. Furthermore, the changes in annual NEE were sensitive to climatic variables, as spring mean air temperature reduced the carbon sink strength in the tidal wetland, maximum daily precipitation in summer reduced it in the nontidal wetland, and summer mean global radiation elicited the same effect in the cropland. Finally, a worldwide database of the three vegetation types was compiled, using which we further validated the global consistency of the biological controls. Overall, these results emphasize the importance of considering the underlying mechanisms by which vegetation types influence NEE for the accurate forecasting of carbon sink dynamics across different coastal vegetation types under climate change.

Sea Ice Modulates Air–Sea Methane Flux in the Southern Ocean

AbstractThe Southern Ocean (SO) is predicted to be a weak sink for atmospheric CH4, although the magnitude is uncertain due to a lack of observations of the marginal ice zone (MIZ). Using both eddy covariance and bulk formula flux measurements from the icebreaker R/V Xuelong2, we found that the eastern SO during an austral summer was a sink for CH4. The strongest downward CH4 fluxes occurred in areas of low sea ice concentration (10%–40%), where sea‐ice melting resulted in low temperature and salinity, increasing CH4 solubility. The CH4 fluxes are weak in regions of high sea ice concentration (&gt;50%) due to the blocking effect of sea ice. We estimate that the uptake of CH4 during one summer month in the study region offsets 1.2%–2.6% of annual global oceanic CH4 emissions. Suggesting that the Antarctic MIZ is more important in the global CH4 budget than previously thought.

Characteristics of Energy Fluxes and Cold Frontal Effects on Energy Exchange over a Boreal Lake

Understanding the characteristics and variations of heat exchange and evaporation of lakes is important for regional water resource management and sustainable development. Based on eddy covariance measurements over Lake Vanajavesi in southern Finland, characteristics of energy fluxes and cold frontal effects on energy exchange were investigated. The lake acted as a heat sink in spring and summer and a heat source in winter. The latent heat flux reached its minimum value in the morning and peaked in the afternoon. The diurnal variation of sensible heat flux was opposite to that of latent heat flux. Impact factors for the sensible heat flux were mainly the lake-air temperature difference and the product of lake-air temperature difference and wind speed. The latent heat flux was mainly affected by the vapor pressure deficit and the product of vapor pressure deficit and wind speed. The annual mean values of bulk transfer coefficients for momentum, heat, and water vapor were 1.98 × 10−3, 1.62 × 10−3, and 1.31 × 10−3, respectively. Bulk transfer coefficients for heat and water vapor were not equal, indicating that the parameterization of energy exchange in numerical models, where the assumption that the heat coefficient equals the water vapor coefficient needs improvement. During the ice-free season, cold fronts resulted in 28 sensible heat pulses and 17 latent heat pulses, contributing to 50.59% and 34.89% of sensible and latent heat exchange in Lake Vanajavesi. These results indicate that cold fronts significantly impact the surface energy budget and evaporation over lakes.

The influence of internal gravity waves in the atmospheric boundary layer on turbulence characteristics measured by the eddy covariance technique

The paper presents the results of an analysis of the influence of submesoscale internal gravity waves (IGWs), registered with the help of a sodar in the atmospheric boundary layer, on turbulence characteristics measured in the surface layer. Data obtained in rural areas in the Moscow region were used.Two types of IGW were identified visually on sodar echograms: internal gravity-shear waves (IGSW) of the Kelvin-Helmholtz wave type and buoyancy waves (BW). For 28 episodes of IGSW and 10 episodes of BW, based on the data of eddy covariance measurements carried out with an ultrasonic thermometer-anemometer located on a mast 56 m high, turbulent kinetic energy, as well as heat and momentum fluxes were calculated. Alterations of these characteristics accompanying the passage of wave trains were investigated, particularly, quantitative estimates were made, and degree of influence of IGSW and BW on the turbulence was compared.

Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations

Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations

Serum concentrations of different or multiple vitamins and Sarcopenia risk among US adults: insights from NHANES

BackgroundThe relationship between serum concentrations of different or multiple vitamins and sarcopenia remains underexplored. This investigation evaluates potential links between serum concentrations of different or multiple vitamins and sarcopenia prevalence among adults in the United States.MethodsUtilizing a cross-sectional design, this research draws from the National Health and Nutrition Examination Survey (NHANES) dataset of 2003–2006, encompassing 5,060 participants with comprehensive serum vitamin A, E, B9, B12, C, and D concentrations, alongside sarcopenia and covariate measurements. Participant stratification into distinct vitamin co-exposure clusters was achieved through K-means clustering. Analytical models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), quantile g-computation (Q-gcomp), and Bayesian kernel machine regression (BKMR), were employed to evaluate the association between serum concentrations of different or multiple vitamins and sarcopenia risk, with an emphasis on nonlinearity.ResultsIn this study, sarcopenia was detected in 681 individuals (13.46%). Logistic regression results did not demonstrate any linear association between individual vitamin levels and sarcopenia risk (PFDR > 0.05). Contrastingly, the RCS model unveiled significant non-linear relationships for vitamins A and D (P_non-linear < 0.05). The K-means clustering results showed that participants in high-level vitamin exposure group had lower sarcopenia risk compared with those in low-level vitamin exposure group (OR (95% CI): 0.582 (0.397, 0.852)). Additionally, higher serum concentrations of different or multiple vitamins correlated inversely with sarcopenia risk (P_trend = 0.002). This inverse association was corroborated by WQS, Q-gcomp, and theBKMR models and remained consistent upon sensitivity analysis.ConclusionsThis study elucidates an inverse correlation between serum concentrations of different or multiple vitamins and sarcopenia risk, emphasizing a non-linear association, particularly with suboptimal vitamin D concentrations. Given the limitations of the NHANES study, further researches are required to clarify the existence of these relationships.

Meat yields and primal cut weights from beef carcasses can be predicted with similar accuracies using in-abattoir 3D measurements or EUROP classification grade.

Three-dimensional (3D) measurements extracted from beef carcass images were used to predict the weight of four saleable meat yield (SMY) traits (total SMY and the SMY of the forequarter, flank, and hindquarter) and four primal cuts (sirloin, ribeye, topside and rump). Data were collected at two UK abattoirs using time-of-flight cameras and manual bone out methods. Predictions were made for 484 carcasses, using multiple linear regression (MLR) or machine learning (ML) techniques. Model inputs included breed type, sex, and abattoir as fixed effects, and cold carcass weight, visually assessed EUROP fat and conformation classes, and 3D measurements as covariates. Machine learning techniques were only used for models including 3D measurements. The CCW and fixed effects resulted in high accuracy (SMY R2=0.72-0.90, RMSE=2.12-3.96kg, primal R2=0.56-0.67, RMSE=0.36-0.91kg), and including the EUROP covariates increased accuracies (SMY R2=0.75-0.96, RMSE=2.00-3.11kg, primal R2=0.58-0.79, RMSE=0.36-0.79kg). The 3D measurement covariates and abattoir resulted in moderate accuracy (SMY MLR R2=0.39-0.58, RMSE=3.26-10.31kg, primal MLR R2=0.33-0.52, RMSE=0.44-1.14kg) and high accuracy when combined with CCW and all fixed effects (SMY MLR R2=0.72-0.95, RMSE=1.81-3.42kg, primal MLR R2=0.52-0.74, RMSE=0.40-0.81kg). The best ML models resulted in similar accuracies to the MLR models. Models including 3D measurements produced similar accuracies to models built using conventional data recorded at the abattoir, indicting the potential for automated prediction.

Advances in Miscanthus × Giganteus Planting Techniques May Increase Carbon Uptake in the Establishment Year.

Agricultural lands hold significant potential for CO2 sequestration, particularly when utilizing biomass crops and agricultural residues. Among these, Miscanthus × giganteus (mxg) stands out due to its high productivity and carbon sequestration capabilities. Recognizing the importance of such biomass crops, the Intergovernmental Panel on Climate Change (IPCC) has identified Bioenergy with Carbon Capture and Storage (BECCS) as a crucial strategy for achieving net-zero CO2 emissions by 2050. This study examines the carbon uptake potential of mxg during its establishment year at the Sustainable Advanced Bioeconomy Research (SABR) farm in Iowa, USA, where mxg was planted at a density exceeding previous studies. Using eddy covariance (EC) measurements, we quantified the net ecosystem carbon exchange (NEE), and derived gross primary productivity (GPP), and ecosystem respiration (R eco). Our findings reveal that SABR's mxg exhibited a significant carbon uptake of -621 g C m-2, a threefold increase compared to a similar EC site in the "corn-belt" (University of Illinois Energy Research Farm; UIEF), which was established with lower planting density and pre-commercial planting equipment. Favorable growing conditions and advanced planting technologies at SABR likely contributed to this high carbon uptake. Comparisons with other global EC studies indicated a strong correlation between higher planting densities and greater carbon uptake. These results suggest that increasing mxg planting density can enhance carbon uptake, but further studies are necessary to evaluate the impacts under varying environmental conditions and management practices. Additionally, economic analyses are essential to determine the viability of higher planting densities. Our study underscores the potential of optimized mxg management practices to contribute significantly to CO2 uptake and supports the development of BECCS as a viable climate change mitigation strategy.

High-frequency attenuation in eddy covariance measurements from the LI-7200 IRGA with various heating and filter configurations – a spectral correction approach

The use of (en)closed-path Infrared Gas Analysers (IRGA) in the measurement of Eddy Covariance (EC) fluxes results in inadvertent high-frequency tube attenuation due to diffusion and mixing of sampled gas inside the tube. The application of tube heating and installation of particulate filters along the tube length also contributes to high-frequency attenuation. The goal of this research is first, to quantify the attenuation effects of different tube heating and filter configurations on CO2 and H2O fluxes. And second, to present a modified power spectral approach (PSA) based on theoretical power spectra to calculate the effective cut-off frequency fc. Measurements for each experimental configuration were performed at an Integrated Carbon Observation System (ICOS) station equipped with the standard LI-7200 enclosed-path IRGA and Gill HS-50 3D sonic anemometer. Correction factors for each dataset were determined and implemented in post-processing. We found only very small attenuation effects of CO2 fluxes between the examined configurations. In agreement with previous studies, we found attenuation worsens with increasing relative humidity rH, in the fluxes of H2O. As expected, the highest (best) fc for H2O was found in the lowest examined rH class of 45-50 % with the configuration of heating on, no filter. The lowest (worst) fc for H2O was in the highest rH class of 90-95 % with the configuration of heating off with the 7 μm filter. Our results confirm that tube attenuation effects for the standard ICOS setup are negligible for CO2 and small for H2O, depending on tube heating settings and use of particulate filters. We also show that the post-processing of attenuation effects, especially for H2O, could improve the accuracy of long-term EC measurements. We recommend that this novel approach be considered by users of datasets collected with the LI-7200 enclosed-path IRGA.

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Abstract. Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr−1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr−1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×103 ± 0.9×103 km3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Biodiversity skepticism and measurement practices

This paper challenges “biodiversity skepticism:” an inferential move that acknowledges the proliferation, heterogeneity, and lack of covariance of biodiversity measurements, and concludes that we should doubt the scientific validity of the biodiversity concept. As a way out of skepticism, philosophers have advocated for eliminating “biodiversity” from scientific inquiry, revising it, or deflating its meaning into a single measurable dimension. I present a counterargument to the inferential move of the skeptic by revealing how it stands on two unstated premises, namely a reflective view of measurements and the unidirectional dynamics between definitional and measurement practices, and corollary assumptions. These premises and assumptions are misaligned with a richer theoretical understanding of measurement and are sometimes inconsistent with how science operates. A more nuanced view of measurement could better explain measurement proliferation while being consistent with new ways in which the general biodiversity concept could be useful. To conclude, I urge philosophers of measurement and conceptual engineers to collaborate in tackling the interplay between conceptual change and measurement practices.