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Record W4317905340 · doi:10.26786/1920-7603(2023)721

An evidence-based rationale for a North American commercial bumble bee clean stock certification program

2023· article· en· W4317905340 on OpenAlexaffvenue
James P. Strange, Sheila R. Colla, Laurie Davies Adams, Michelle A. Duennes, Elaine Evans, Laura L. Figueroa, David M. Lehmann, Heather Moylett, Leif L. Richardson, Ben M. Sadd, James Smith, Tamara Smith, Amber D. Tripodi, David W. Inouye

Bibliographic record

VenueJournal of Pollination Ecology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsYork University
FundersU.S. Department of AgricultureNational Institute of Food and AgricultureU.S. Environmental Protection AgencyNational Science Foundation
KeywordsPollinatorBiologyPollinationStock (firearms)AgricultureGreenhousePollenEcologyAgroforestryGeographyAgronomy

Abstract

fetched live from OpenAlex

The commercial production and subsequent movement of bumble bees for pollination of agricultural field and greenhouse crops is a growing industry in North America and globally. Concerns have been raised about the impacts of pathogen spillover from managed bees to wild pollinators, including from commercial bumble bees. We recommend development of a program to mitigate disease risk in commercial bumble bee production, which will in turn reduce disease stressors on wild pollinators and other insects. We provide recommendations for the components of a clean stock program with specific best management practices for rearing commercial bumble bees including related products such as wax, pollen, and nesting material.

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)0.133
metaresearch head score (Gemma)0.298
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1330.298
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.003
Science and technology studies0.0040.005
Scholarly communication0.0050.004
Open science0.0050.004
Research integrity0.0180.011
Insufficient payload (model declined to judge)0.0180.001

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 head0.142
GPT teacher head0.381
Teacher spread0.239 · 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 designTheoretical or conceptual
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

Citations11
Published2023
Admission routes2
Has abstractyes

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