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Record W4288436201 · doi:10.26786/1920-7603(2022)709

Response to Pyke and Ren: How to study interactions

2022· article· en· W4288436201 on OpenAlexvenueno aff
Carrie Finkelstein, Paul J. CaraDonna, Andrea Gruver, Ellen A. R. Welti, Michael Kaspari, Nathan J. Sanders

Bibliographic record

VenueJournal of Pollination Ecology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsFinkelstein's testBiologyNectarMeasure (data warehouse)EcologyPsychologyEpistemologyComputer sciencePhilosophyPollenData mining

Abstract

fetched live from OpenAlex

We published a paper in Biology Letters earlier this year that asks a straightforward question: might flowers with sodium-enriched nectar receive higher visitation rates from a more diverse suite of pollinators? The answer was unequivocally yes (Finkelstein et al. 2022). Pyke and Ren wrote an opinion piece (Pyke and Ren 2022) taking issue with our experiment, calling it ‘irrelevant.’ Here, we briefly respond to their criticisms.

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 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)0.025
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.975
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.116
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0060.013
Open science0.0050.006
Research integrity0.0390.095
Insufficient payload (model declined to judge)0.0100.015

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 head0.045
GPT teacher head0.271
Teacher spread0.227 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations2
Published2022
Admission routes1
Has abstractyes

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