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Record W2604439031 · doi:10.2166/wqrj.2001.018

Endocrine Disrupting Substances and Ecological Risk Assessment of Commercial Chemicals in Canada

2001· article· en· W2604439031 on OpenAlexaffabout
Roger Sutcliffe

Bibliographic record

VenueWater Quality Research Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsRisk assessmentEndocrine systemIdentification (biology)Environmental risk assessmentRisk analysis (engineering)Environmental planningEnvironmental scienceEnvironmental resource managementBusinessEcologyEnvironmental protectionBiologyComputer scienceHormone

Abstract

fetched live from OpenAlex

Abstract The ecological risk assessment of commercial chemicals in Canada by the regulatory programs of the Commercial Chemicals Evaluation Branch, Environment Canada, are based on results from traditional toxicity data (e.g., lethality, effects to growth or reproduction). Some of the chemicals under consideration are known to alter endocrine systems in exposed organisms; however, effects to the endocrine system are used only as additional supporting information. Presently, there are no internationally accepted methodologies or tests for endocrine disrupting substances that can be used by these regulatory programs. The need for research with respect to hormone disrupting substances has been recognized in the revised Canadian Environmental Protection Act, 1999 (CEPA 1999). This paper describes the framework for the ecological risk assessment of new and existing substances and identifies issues and research needs in both screening level and in-depth ecological risk assessments with respect to the identification and assessment of potentially endocrine disrupting substances.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.460
Teacher spread0.402 · 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 designNot applicable
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
Published2001
Admission routes2
Has abstractyes

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