Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
Investigating the relationship between biodiversity and ecosystem multifunctionality: Challenges and solutions
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Extensive research shows that more species-rich assemblages are generally more productive and efficient in resource use than comparable assemblages with fewer species. But the question of how diversity simultaneously affects the wide variety of ecological functions that ecosystems perform remains relatively understudied, and it presents several analytical and empirical challenges that remain unresolved. In particular, researchers have developed several disparate metrics to quantify multifunctionality, each characterizing different aspects of the concept, and each with pros and cons. We compare four approaches to characterizing multifunctionality and its dependence on biodiversity, quantifying 1) magnitudes of multiple individual functions separately, 2) the extent to which different species promote different functions, 3) the average level of a suite of functions, and 4) the number of functions that simultaneously exceed a critical threshold. We illustrate each approach using data from the pan-European BIODEPTH experiment and the R multifunc package developed for this purpose, evaluate the strengths and weaknesses of each approach, and implement several methodological improvements. We conclude that a extension of the fourth approach that systematically explores all possible threshold values provides the most comprehensive description of multifunctionality to date. We outline this method and recommend its use in future research.
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
Causal study of 151 Java repos shows agentic AI adoption raises LOC 12.8% with unchanged total smells, producing 6.7% lower ASD as a size-driven denominator effect.
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
Alternative restitution model produces earlier onset of complex oscillations and different attractors than classical model in rocking blocks, with both converging at high slenderness ratios.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
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Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories
Causal study of 151 Java repos shows agentic AI adoption raises LOC 12.8% with unchanged total smells, producing 6.7% lower ASD as a size-driven denominator effect.
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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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Characterization of Rocking Block Behaviors with Classical and Alternative Restitution Models
Alternative restitution model produces earlier onset of complex oscillations and different attractors than classical model in rocking blocks, with both converging at high slenderness ratios.