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Record W4244354049 · doi:10.4324/9781315224039-4

Pesticide Reduction: A Case Study From Canada

2017· book-chapter· en· W4244354049 on OpenAlexaboutno aff
David Bennett

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReduction (mathematics)PesticideEnvironmental scienceEcologyBiologyMathematics

Abstract

fetched live from OpenAlex

The Canadian federal government completed a review in 1991 of the pesticide registration process, which is the procedure by which pesticides are licensed and re-licensed for use in Canada. The Canadian Labour Congress&s;s (CLC) position throughout the review was that there should be a phasing out by 1998 of chemical pesticides for which there is significant evidence of chronic human health or persistent harmful effects on the environment. More generally, the CLC claimed that without such legislated criteria, a pesticide control program cannot be effective. "Targets for reduction" permit too much flexibility, too many loopholes. For those who see information as a tool by which people can influence public policy, the new rights proposed in the Pesticide Registration Review are unlikely to be of much practical use. The effects of pesticides on the environment would not be so bad if wildlife were destroyed on immediate contact with pesticides. But pesticides also persist, doing long-term damage to ecosystems and water supplies.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0090.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.023
GPT teacher head0.227
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractno

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