Alternative approaches to vertebrate ecotoxicity tests in the 21st century: A review of developments over the last 2 decades and current status
Bibliographic record
Abstract
The need for alternative approaches to the use of vertebrate animals for hazard assessment of chemicals and pollutants has become of increasing importance. It is now the first consideration when initiating a vertebrate ecotoxicity test, to ensure that unnecessary use of vertebrate organisms is minimized wherever possible. For some regulatory purposes, the use of vertebrate organisms for environmental risk assessments has been banned; in other situations, the number of organisms tested has been dramatically reduced or the severity of the procedure refined. However, there is still a long way to go to achieve a complete replacement of vertebrate organisms to generate environmental hazard data. The development of animal alternatives is based not just on ethical considerations but also on reducing the cost of performing vertebrate ecotoxicity tests and in some cases on providing better information aimed at improving environmental risk assessments. The present Focus article provides an overview of the considerable advances that have been made toward alternative approaches for ecotoxicity assessments over the last few decades. Environ Toxicol Chem 2016;35:2637-2646. © 2016 SETAC.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".