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Record W2973623848 · doi:10.1101/774356

Blind spots in global soil biodiversity and ecosystem function research

2019· preprint· en· W2973623848 on OpenAlexaff
Carlos A. Guerra, Anna Heintz‐Buschart, Johannes Sikorski, Antonis Chatzinotas, Nathaly R. Guerrero‐Ramírez, Simone Cesarz, Léa Beaumelle, Matthias C. Rillig, Fernando T. Maestre, Manuel Delgado‐Baquerizo, François Buscot, Jörg Overmann, Guillaume Patoine, Helen R. P. Phillips, Marten Winter, Tesfaye Wubet, Kirsten Küsel, Richard D. Bardgett, Erin K. Cameron, Don A. Cowan, Tine Grebenc, César Marín, Alberto Orgiazzi, Brajesh K. Singh, Diana H. Wall, Nico Eisenhauer

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsSaint Mary's University
FundersDeutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-LeipzigDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsBiodiversityEcosystemEcosystem servicesEcologyEnvironmental scienceMacroecologyGeographyEnvironmental resource managementBiology

Abstract

fetched live from OpenAlex

Abstract Soils harbor a substantial fraction of the world’s biodiversity, contributing to many crucial ecosystem functions. It is thus essential to identify general macroecological patterns related to the distribution and functioning of soil organisms to support their conservation and governance. Here we identify and characterize the existing gaps in soil biodiversity and ecosystem function data across soil macroecological studies and >11,000 sampling sites. These include significant spatial, environmental, taxonomic, and functional gaps, and an almost complete absence of temporally explicit data. We also identify the limitations of soil macroecological studies to explore general patterns in soil biodiversity-ecosystem functioning relationships, with only 0.6% of all sampling sites having a non-systematic coverage of both biodiversity and function datasets. Based on this information, we provide clear priorities to support and expand soil macroecological research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.235
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2019
Admission routes1
Has abstractyes

Explore more

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