Évaluation des risques pour l’environnement et des risques indirects pour la santé humaine posés par les barbus puntigrus tetrazona glofishMD, starfire redMD, electric greenMD, sunburst orangeMD et galactic purpleMD : des poissons d’ornement transgéniques
Bibliographic record
Abstract
Conformément à la Loi canadienne sur la protection de l’environnement (LCPE), Spectrum Brands a présenté à Environnement et Changement climatique Canada (ECCC), au titre du Règlement sur les renseignements concernant les substances nouvelles (organismes) [RRSN(O)], quatre avis concernant des barbus Puntigrus tetrazona génétiquement modifiés : GloFishMD Electric GreenMD (GB2011), GloFishMD Starfire RedMD (RB2015), GloFishMD Sunburst OrangeMD (OB2019) et GloFishMD Galactic PurpleMD (PB2019). Des évaluations des risques pour l’environnement et des risques indirects pour la santé humaine ont été menées et comprenaient une analyse des dangers potentiels, des probabilités d’exposition et des incertitudes connexes afin de tirer des conclusions sur les risques et fournir un Avis scientifique à ECCC et à Santé Canada (SC) de manière à éclairer leur évaluation des risques aux termes de la LCPE. Les évaluations ont été comparées à celles des lignées de tétras, de poissons-zèbres et de combattants GloFishMD précédemment notifiées.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".