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Record W4281633278 · doi:10.1111/csp2.12734

Pursuing best practices for minimizing wild bee captures to support biological research

2022· article· en· W4281633278 on OpenAlexaff
Ana Montero‐Castaño, Jonathan B. Koch, Thuy‐Tien Thai Lindsay, Byron Love, John M. Mola, Kiera Newman, Janean K. Sharkey

Bibliographic record

VenueConservation Science and Practice · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdentification (biology)BiologyPollinatorDomesticationEcologyHoney BeesUrbanizationPollinationPollen

Abstract

fetched live from OpenAlex

Abstract Bees are important pollinators of wild and domesticated flowering plant species. Over the last 30 years, an increasing number of scientific articles have been published on the ecology and conservation of wild bees. To achieve research goals, many studies have pursued the lethal take of wild bees. Although the impact of lethal take for scientific pursuits is likely negligible compared to the negative impacts of human‐mediated phenomena such as climate change, urbanization, and agricultural intensification, it is important to evaluate the history of lethal take on scientific endeavors. In our study, we evaluated a random sample of 30 years of scientific publications on wild bees. Across 1426 surveyed publications, 536 reported the lethal take of wild bees. We found that 61% of these studies lethally captured wild bees primarily for species identification. Furthermore, we determined passive sampling of wild bees resulted in substantially more lethal collections than active methods per study. However, combined approaches of passive and active collection resulted in the greatest lethal take of wild bees per study. Finally, we determined that 64% of the studies did not provide deposition information for their samples, hindering additional research that could be done with them. The increasing availability of video and photographic devices and artificial intelligence approaches to identification, the development of low and noninvasive molecular methods, and the ease of sharing information, allow for a timely discussion on alternative routes and potentially new best practices in bee research. We focus our discussion on alternative methods for minimizing lethal captures for identification purposes and through passive methods, and for maximizing the utility of the data collected. Finally, we provide a framework for continued engagement among researchers and managers to develop strategies that can contribute to reducing our impact on wild bee communities and making the most of collected specimens.

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)0.099
metaresearch head score (Gemma)0.160
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 score0.099
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.160
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0020.003
Scholarly communication0.0080.008
Open science0.0050.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.600
GPT teacher head0.447
Teacher spread0.153 · 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

Citations40
Published2022
Admission routes1
Has abstractyes

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