Supersedure, mites, and visible disease in <i>Apis mellifera</i> (Hymenoptera: Apidae) colonies explain differences in productivity and survival, but the effects may be difficult to see
Bibliographic record
Abstract
We investigated whether field assessments of honey bee (Apis mellifera L.) colony health explain subsequent colony size, honey production, and survival. Field detections of visible diseases, Varroa destructor (Anderson and Trueman) and queen replacement events were recorded during a multisite cohort study, which also incorporated fumagillin and protein supplementation as colony-level treatments. Together, treatment groups and field observations explained between 5% of the variability in adult bee counts and 28% of the variability in honey production among colonies, after accounting for the effects of region and date. In particular, detections of minor disease symptoms, mainly chalkbrood, were associated with large reductions in honey production and approximately doubled the short-term probability of colony death. Although the effects of treatments and field-observed events were significant, unexplained variability among similarly managed colonies was much greater. Consequently, beekeepers may be unable to detect the effects of these field-observable factors, or distinguish effective treatments from ineffective ones. Despite this, interventions to reduce the prevalence of varroa and visible diseases, and to prevent queen loss, are likely to improve honey bee health and productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".