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Record W7099541963

SUMMARY

2004· article· en· W7099541963 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeurology and Historical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLawnRecreationPesticideHealth careAdministration (probate law)
DOInot available

Abstract

fetched live from OpenAlex

came into effect on April 1, 2004. Toronto Public Health led the implementation of the bylaw, frequently collaborating with Parks, Forestry and Recreation and Toronto Water. Toronto’s bylaw succeeded in reducing pesticide use and encouraging residents and companies to adopt more sustainable lawn and garden care approaches. In 2007, almost 60 per cent fewer Toronto households reported any use of pesticides on their lawns, as compared with 2003. Furthermore, about two-thirds of homeowners report that they or their lawn care companies are using lower-risk pesticides and/or more natural alternatives, which is a higher proportion than before the bylaw was in place. An estimated 154 municipalities in seven provinces – including 35 in Ontario – have passed bylaws to restrict pesticide use to protect health and the environment. This municipal leadership has prompted province-wide restrictions on the use and sale of pesticides, first in Quebec and now in Ontario. On April 22, 2009, Ontario Regulation 63/09 came into effect, restricting the use and sale of cosmetic pesticides across the province. The new regulation replaces municipal bylaws in Ontario, therefore Toronto’s Pesticide Bylaw is no longer in effect.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.603
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3970.194

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 head0.038
GPT teacher head0.250
Teacher spread0.212 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2004
Admission routes1
Has abstractyes

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Same topicNeurology and Historical StudiesFrench-language works237,207