Establishment of apiary-level risk of American foulbrood through the detection of Paenibacillus larvae spores in pooled, extracted honey in Saskatchewan
Bibliographic record
Abstract
Paenibacillus larvae, the causative agent of American foulbrood (AFB), produces spores that may be detectable within honey. We analyzed the spore content of pooled, extracted honey from 52 large-scale (L) and 64 small-scale (S) Saskatchewan beekeepers over a two-year period (2019-2020). Our objectives were: (i) establish reliable prognostic reference ranges for spore concentrations in extracted honey to determine future AFB risk at the apiary level; (ii) identify management practices as targets for mitigation of risk. P. larvae spores were detected in 753 of 1476 samples (51%). Beekeepers were stratified into low (< 2 spores/gram), moderate (2- < 100 spores/gram), and high (≥ 100 spores/gram) risk categories. Of forty-nine L beekeepers sampled in 2019, those that reported AFB in 2020 included 0/26 low, 3/18 moderate, and 3/5 high risk. Of twenty-seven L beekeepers sampled in 2020, those that reported AFB in 2021 included 0/11 low, 2/14 moderate, and 1/2 high risk. Predictive modelling included indoor overwintering of hives, purchase of used equipment, movement of honey-producing colonies between apiaries, beekeeper demographic, and antimicrobial use as risk category predictors. Saskatchewan beekeepers with fewer than 2 spores/gram in extracted honey that avoid high risk activities may be considered at low risk of AFB the following year.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".