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Record W2799293699 · doi:10.1093/jee/toy125

Pollen Collection, Honey Production, and Pollination Services: Managing Honey Bees in an Agricultural Setting

2018· article· en· W2799293699 on OpenAlexafffundabout
Shelley E. Hoover, Lynae P Ovinge

Bibliographic record

VenueJournal of Economic Entomology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersAlberta Crop Industry Development Fund
KeywordsBiologyPollinationPollenBroodHand-pollinationHoney beePollination managementBotanyAgronomyPollinator

Abstract

fetched live from OpenAlex

Hybrid canola seed production is an important pollination market in Canada; typically both honey bees (Apis mellifera L. (Hymenoptera: Apidae)) and Alfalfa Leafcutting bees (Megachile rotundata Fab. (Hymenoptera: Megachilidae)) are concurrently managed to ensure pollination in this high-value crop. Beekeepers are paid to provide pollination services, and the colonies also produce a honey crop from the canola. Pollen availability from male-fertile plants is carefully managed in this crop to provide an abundance of pollen to fertilize male-sterile ('female') plants. This abundance of pollen represents an underutilized resource for beekeepers, and an opportunity to diversify the hive-products produced for market in this management system. We used a commercial-style pollen trap to collect pollen from colonies twice weekly for the duration of canola pollination, and compared the honey production and amount of sealed brood in colonies with pollen traps to those without pollen traps. We found that while pollen trapping reduced honey production, there was no negative impact on brood production, and at current market prices, the per-hive revenue was higher in colonies from which pollen was trapped. Pollen trapping honey bee colonies in the context of hybrid canola pollination, therefore, offers beekeepers an opportunity to diversify their products and increase their revenue.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations79
Published2018
Admission routes3
Has abstractyes

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