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Record W3168507494 · doi:10.1038/s41559-021-01471-7

Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs

2021· article· en· W3168507494 on OpenAlexaff
Daniel C. Laughlin, Liesje Mommer, Francesco María Sabatini, Helge Bruelheide, Thomas W. Kuyper, Michael McCormack, Joana Bergmann, Grégoire T. Freschet, Nathaly R. Guerrero‐Ramírez, Colleen M. Iversen, Jens Kattge, Ina C. Meier, Hendrik Poorter, Catherine Roumet, Marina Semchenko, Christopher J. Sweeney, Oscar J. Valverde‐Barrantes, Fons van der Plas, Jasper van Ruijven, Larry M. York, Isabelle Aubin, Olivia R. Burge, Chaeho Byun, Renata Ćušterevska, Jürgen Dengler, Estelle Forey, Greg R. Guerin, Bruno Hérault, Robert B. Jackson, Dirk Nikolaus Karger, Jonathan Lenoir, Tatiana Lysenko, Patrick Meir, Ülo Niinemets, W.A. Ozinga, Josep Peñuelas, Peter B. Reich, Marco Schmidt, Franziska Schrodt, Eduardo Velázquez, Alexandra Weigelt

Bibliographic record

VenueNature Ecology & Evolution · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersBiological and Environmental ResearchOffice of ScienceDeutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-LeipzigNederlandse Organisatie voor Wetenschappelijk OnderzoekDeutsche ForschungsgemeinschaftUniversity of WyomingU.S. Department of Energy
KeywordsTraitEcologyBiologyTrade-offBiodiversityRoot (linguistics)Range (aeronautics)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations188
Published2021
Admission routes1
Has abstractno

Explore more

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