Bibliographic record
Abstract
Introduction In Chapter 2, attention was drawn to some of the distinctions between classical toxicology and environmental or ecotoxicology, as well as to the fact that the two types of toxicology share considerable common ground. For example, concepts such as acute and chronic toxicity and thresholds, which were developed for classical toxicology, have been applied to environmental toxicology (see Sections 2.2.2, 2.2.3, and 2.2.4). Classical toxicology has relied mainly on evidence from controlled exposure of individual organisms or from epidemiological approaches including retrospective case studies, and more recently it has seen the development of more generic tests on cell lines and microorganisms. Environmental toxicology includes comparable types of testing but by its very nature has to go beyond the responses of individual organisms or populations. The development of methods for determining the impact of man on the environment has advanced on several different fronts. The term ecotoxicology , first used by Truhaut as recently as 1969 (see Chapter 1), encompasses the study of all levels of biological organisation described in Figure 2.1. At lower levels of biological organisation (subcellular to individual), the approaches used have much in common with human toxicology. Indeed, the distinction between the two disciplines may be somewhat arbitrary, particularly because so many aspects of environmental toxicology have implications for human health. A clearer distinction between the two disciplines is seen in studies at the population and community levels where methods for determining the impact of man on ecosystems have matured in parallel with a better understanding of ecology.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".