MétaCan
Menu
← Back to cohort
Record W2605899719 · doi:10.1111/gcb.13714

A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes

2017· review· en· W2605899719 on OpenAlexaff
Elinor M. Lichtenberg, Christina M. Kennedy, Claire Kremen, Péter Batáry, Frank Berendse, Riccardo Bommarco, Nilsa A. Bosque‐Pérez, Luísa G. Carvalheiro, William E. Snyder, Neal M. Williams, Rachael Winfree, Björn K. Klatt, Sandra Åström, Faye Benjamin, Claire Brittain, Rebecca Chaplin‐Kramer, Yann Clough, Bryan N. Danforth, Tim Diekötter, Sanford D. Eigenbrode, Johan Ekroos, Elizabeth Elle, Breno Magalhães Freitas, Yuki Fukuda, Hannah R. Gaines‐Day, Heather Grab, Claudio Gratton, Andrea Holzschuh, Rufus Isaacs, Marco Isaia, Shalene Jha, Dennis Jonason, Vincent P. Jones, Alexandra‐Maria Klein, Jochen Krauß, Deborah K. Letourneau, Sarina Macfadyen, Rachel E. Mallinger, Emily A. Martin, Eliana Martínez Pachón, Jane Memmott, Lora A. Morandin, Lisa A. Neame, Mark Otieno, Mia Park, Lukas Pfiffner, Michael J. O. Pocock, Carlos Ponce, Simon G. Potts, Katja Poveda, Mariangie Ramos, Jay A. Rosenheim, Maj Rundlöf, Hillary Sardiñas, Manu E. Saunders, Nicole L. Schon, Amber R. Sciligo, C. Sheena Sidhu, Ingolf Steffan‐Dewenter, Teja Tscharntke, Milan Veselý, Wolfgang W. Weisser, Julianna K. Wilson, David W. Crowder

Bibliographic record

VenueGlobal Change Biology · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsAlberta Environment and Protected AreasLearning PartnershipSimon Fraser University
FundersNational Institute of Food and AgricultureConselho Nacional de Desenvolvimento Científico e TecnológicoU.S. Department of Agriculture
KeywordsAgricultureArthropodDiversity (politics)EcologyGeographyAgroforestryGlobal changeEnvironmental scienceBiologyClimate change

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.312
Teacher spread0.263 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations434
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Change Biology→Same topicEcology and Vegetation Dynamics Studies→French-language works237,207→