FINANCIAL AND HEALTH COSTS OF PESTICIDE USE IN GROWING CONVENTIONAL AND GENETICALLY MODIFIED POTATOES IN PRINCE EDWARD ISLAND
Bibliographic record
Abstract
The majority of potato farming in Canada occurs in tightly clustered geographic locations and requires substantial chemical inputs. The possibility of pesticide drift, pesticide residues on food and the effect of pesticides on the environment, leads to interest in quantifying the different effects that pesticides may have on human health and the environment. This study focuses on the potential use of genetically modified potatoes, the associated issue of pesticide residues in the air, and the potential impact of this on the health of farmers, their families, and others in the context of Prince Edward Island. Reductions in costs of potato farming and reduced health costs that may be associated with lower pesticide applications in growing genetically engineered potatoes (NewLeaf, NewLeaf Plus and NewLeaf Pro potatoes, each genetically modified for particular traits), relative to conventional potato growing practices in Prince Edward Island are identified and quantified. It is concluded that the financial benefits from the use of fewer inputs with the modified potatoes are significant while the health benefits associated with reduced exposure to pesticides are relatively small.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".