Honey bees, neonicotinoids and bee incident reports: the Canadian situation
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.
Bibliographic record
Abstract
BACKGROUND: Neonicotinoid insecticides have been the target of much scrutiny as possible causes of recent declines observed in pollinator populations. Although neonicotinoids have been implicated in honey bee pesticide incidents, there has been little examination of incident report data. Here we summarize honey bee incident report data obtained from the Canadian Pest Management Regulatory Agency (PMRA). RESULTS: In Canada, there were very few honey bee incidents reported in 2007-2011 and data were not collected prior to 2007. In 2012, a significant number of incidents were reported in the province of Ontario, where exposure to neonicotinoid dust during planting of corn was suspected to have caused the incident in up to 70% of cases. Most of these incidents were classified as 'minor' by the PMRA, and only six cases were considered 'moderate' or 'major'. In that same year, there were over three times as many moderate or major incidents due to older non-neonicotinoid pesticides, involving numbers of hives or bees far greater than the number of moderate or major incidents suspected to be due to neonicotinoid poisoning. CONCLUSIONS: These data emphasize that, while exposure of honey bees to neonicotinoid-contaminated dust during corn planting needs to be mitigated, other pesticides also pose a risk.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it