Pesticide impacts on bumblebee decline: a missing piece
Bibliographic record
Abstract
Szabo et al. (2012) have addressed an important issue by examining potential causes of widespread declines in bumblebees across North America. While we commend their investigation of the role pesticides may play in pollinator declines, the data set they relied on is out of date and missing a key class of pesticides. The 2002 USDA Agriculture Census tracked pesticide application in an “acres treated” metric that captured information about how fields were treated via traditional methods like spraying (USDA NASS 2002). In recent years, the use of seed treatments to protect newly emerged plants has greatly increased; almost all of the corn seed planted in the United States is now treated with neonicotinoid pesticides (Krupke et al. 2012). However, these insecticidal seed treatments are not included in the “acres treated” data from the 2002 USDA Agriculture Census (Theresa Varner, USDA NASS Specialist, personal communication, July 11, 2012) and are not annually tracked in any public data collection. Neonicotinoids, the active ingredients in seed treatments, are systemic insecticides expressed throughout the plant tissue. They can negatively impact bumblebees with a range of sublethal to lethal impacts depending on the dose and exposure (Scott-Dupree et al. 2009; Whitehorn et al. 2012). New routes of exposure to pollinators from treated seed have been identified, including dust from planting machinery (Krupke et al. 2012). Several countries have restricted neonicotinoids (e.g., France, Germany, and Italy) or initiated reevaluations of their registration (e.g., Canada) based on recent evidence of harm to bees (US EPA 2012; Health Canada Pest Management Regulatory Agency 2012). Without data that include these widely used systemic insecticides, the authors cannot reliably assert that pesticides have not played a role in bumblebee declines across North America.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".