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Record W2317416043 · doi:10.2166/wqrjc.2013.026

Regulatory ecotoxicology testing in Canada – activities and influence of the Inter-Governmental Ecotoxicological Testing Group

2013· article· en· W2317416043 on OpenAlexaffabout
Lisa N. Taylor, Kenneth G. Doe, Richard P. Scroggins, Peter G. Wells

Bibliographic record

VenueWater Quality Research Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsDalhousie UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsContext (archaeology)EcotoxicityGovernment (linguistics)Test (biology)Good laboratory practiceQuality assuranceEngineeringEnvironmental planningEnvironmental scienceMedicineBiologyEcologyToxicityOperations management

Abstract

fetched live from OpenAlex

The Inter-Governmental Ecotoxicological Testing Group (IGETG) is an ad hoc group of government scientists, technologists, data users, and scientific advisors that has been active in the development and application of ecotoxicological testing in Canada. Membership includes representatives from government laboratories that conduct toxicity testing for research and development purposes, monitor effluent discharge for compliance with regulations, and/or perform exploratory monitoring of non-regulated sectors. The original focus of the group was to support the development and application of standardized toxicity test methods under the Fisheries Act but as the group matured it broadened its focus to five goals: (1) to promote the use of ecotoxicity testing; (2) to disseminate and harmonize new knowledge and understanding of issues related to ecotoxicity testing; (3) to provide scientific support to environmental programs; (4) to develop, validate and publish toxicological test methods; and (5) to establish and implement quality assurance practices in toxicology laboratories. Since 1990, IGETG has assisted Environment Canada in standardizing 22 toxicity test methods and in developing eight guidance documents. In this context, we briefly outline the history and future of applied ecotoxicological testing in Canada illustrated by specific examples wherein standard toxicity tests are useful. This paper commemorates IGETG's 35th anniversary.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.297
Teacher spread0.233 · 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 teacher head, not a consensus.

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

Citations6
Published2013
Admission routes2
Has abstractyes

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