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Record W3196689654 · doi:10.1038/s41586-021-03740-8

The contribution of insects to global forest deadwood decomposition

2021· article· en· W3196689654 on OpenAlexaff
Sebastian Seibold, Werner Rammer, Torsten Hothorn, Rupert Seidl, Michael D. Ulyshen, Janina Lorz, Marc W. Cadotte, David B. Lindenmayer, Yagya P. Adhikari, Roxana Aragón, Soyeon Bae, Petr Baldrián, Hassan Barimani Varandi, Jos Barlow, Claus Bässler, Jacques Beauchêne, Érika Berenguer, Rodrigo Scarton Bergamin, Tone Birkemoe, Gergely Boros, Roland Brandl, Hervé Brustel, Philip J. Burton, Yvonne Tété Cakpo, Jorge Castro, Estelle Cateau, Tyler Cobb, Nina Farwig, Romina Fernández, Jennifer Firn, Kee Seng Gan, Grizelle González, Martin M. Goßner, Jan Christian Habel, Christian Hébert, Christoph Heibl, Osmo Heikkala, Andreas Hemp, Claudia Hemp, Joakim Hjältén, Stefan Hotes, Jari Kouki, Thibault Lachat, Jie Liu, Yu Liu, Ya‐Huang Luo, Damasa B. Magcale-Macandog, Pablo E. Martina, Sharif A. Mukul, Baatarbileg Nachin, Kurtis Nisbet, John O’Halloran, Anne Oxbrough, Jeev Nath Pandey, Tomáš Pavlı́ček, Stephen M. Pawson, Jacques Rakotondranary, Jean‐Baptiste Ramanamanjato, Liana Chesini Rossi, Jürgen Schmidl, Mark Schulze, Stephen Seaton, Marisa J. Stone, Nigel E. Stork, Byambagerel Suran, Anne Sverdrup‐Thygeson, Simon Thorn, T. Ganesh, Tim Wardlaw, Wolfgang W. Weisser, Sungsoo Yoon, Naili Zhang, Jörg Müller

Bibliographic record

VenueNature · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsNatural Resources CanadaUniversity of TorontoCanadian Forest ServiceUniversity of Northern British ColumbiaRoyal Alberta MuseumThe Scarborough Hospital
FundersUniversity of the PhilippinesBundesministerium für Bildung und ForschungDeutscher Akademischer AustauschdienstMinisterio del Ambiente, Agua y Transición EcológicaEuropean CommissionDeutsche Forschungsgemeinschaft
KeywordsDecompositionDecomposerCarbon fibersEnvironmental scienceEcosystemCarbon cyclePrecipitationTaigaBorealTemperate rainforestEcologyBiologyGeographyMathematics

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)0.000
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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

Citations281
Published2021
Admission routes1
Has abstractno

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