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Record W7134985914 · doi:10.5376/me.2025.16.0001

The Impact of Bee Feed Optimization on Colony Health

2025· article· W7134985914 on OpenAlex
Yaoqiang Shan, Haoyi Shan, Fangfang Zhu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMolecular Entomology · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
Fundersnot available
KeywordsBee pollenProduction (economics)Animal health

Abstract

fetched live from OpenAlex

This study examines the impact of optimized bee feed, focusing on supplemental diets rich in proteins, carbohydrates, and essential micronutrients. Key findings demonstrate that supplemental feeding with balanced protein sources enhances brood production, colony strength, and honey yield. Additionally, supplemental diets can improve bee immunity and resilience to stressors such as malnutrition and pesticide exposure. Seasonal feeding strategies and nutrient-rich artificial diets are highlighted as effective methods to support colony health during forage scarcity. This study underscores the importance of integrating optimized feeding practices with environmental conservation to ensure sustainable beekeeping practices and ecosystem health. The insights provide a valuable framework for beekeepers, researchers, and policymakers aiming to enhance bee colony resilience and productivity.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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