A new quality assurance system for the evaluation of ecotoxicity studies submitted under the new substances notification regulations in Canada
Bibliographic record
Abstract
New substances destined for import into, or manufacture in, Canada must be reported to Environment Canada and Health Canada under the New Substances Notification Regulations (Chemicals and Polymers) (NSNR). With the use of information provided by the notifier, and other complementary information available to the 2 departments, the New Substances Program conducts ecological and human health risk assessments. Over the past 10 y, more than 750 ecotoxicity studies have been submitted to the New Substances Program of Environment Canada under the NSNR. Most of these experimental studies are not publicly available but are useful in the ecological risk assessment of new substances and for the development of Quantitative Structure-Activity Relationships (QSARs). In this paper, we describe the development and validation of a computer-based scoring system and our approach in the development of scoring methods used to assess the quality and usability of ecotoxicity studies with fish, Daphnia spp., and green algae. Results of ranking exercises conducted with these methods are described and discussed, together with the potential use of these results in a regulatory context. In addition, the methods are discussed in comparison with other similar evaluation schemes described in the literature.
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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 | 0.030 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".