Regulatory ecotoxicology testing in Canada – activities and influence of the Inter-Governmental Ecotoxicological Testing Group
Bibliographic record
Abstract
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.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".