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Record W4244407814 · doi:10.1002/evl3.82

Issue information

2018· paratext· en· W4244407814 on OpenAlexaff
Guy Alexander Cooper, Samuel R. Levin, Geoff Wild, Stuart A. West, Veronica Chong, Hannah F. Fung, John R. Stinchcombe, Richard Wang, Matthew W. Hahn, Anja M. Westram, Marina Rafajlovi, Pragya Chaube, Rui Faria, Tomas Larsson, Marina Panova, Mark Ravinet, Anders Blomberg, B. Mehlig, Kerstin Johannesson, Roger K. Butlin, Joel W. McGlothlin, Megan E. Kobiela, H. P. Wright, D. Luke Mahler, Jason J. Kolbe, Jonathan B. Losos, Edmund D. Brodie, Andreas F. Kautt, Gonzalo Machado‐Schiaffino, Axel Meyer, Andreas Härer, Julián Torres‐Dowdall, C. H. Turner, Christopher H. Marshall, Vaughn S. Cooper, George Sandler, Felix E.G. Beaudry, Spencer C. H. Barrett, Stephen Wright, Fl Uctuations, Romain Pigeault, Quentin Caudron, Antoine Nicot, Ana Rivero, Sylvain Gandon, Eva Lievens, Julie Perreau, Philip Agnew, Yannis Michalakis, Thomas Lenormand, Michael Hague, Gabriela Toledo, Shana L. Geffeney, Charles T. Hanifin, Maria Goller, Daizaburo Shizuka, Gavin Woodruff, JOHN WILLIS, Patrick Phillips, E. Chang, Maria E. Orive, Paulyn Cartwright, Shayna Holmes, Nicola Hemmings, Associate Lindell, Anne Charmantier, Andy Gardner, Zach Gompert, Anjali Goswami, Katrina Lythgoe, Rhonda R. Snook

Bibliographic record

VenueEvolution Letters · 2018
Typeparatext
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCitationComputer scienceInformation retrievalWorld Wide Web

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.086
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9140.871

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 head0.018
GPT teacher head0.198
Teacher spread0.180 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractno

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