Bibliographic record
Abstract
Environmental Toxicology is a welcome addition to the Cambridge University Press Environmental Chemistry Series. The inclusion of a textbook on toxicology in a series devoted to environmental chemistry might, at first glance, appear surprising. However, as will become evident to the reader, the authors have approached their topic in a truly interdisciplinary manner, with environmental chemistry playing a prominent role in their analysis. Environmental toxicology is a young and dynamic science, as pointed out by the authors in their welcome historical perspective of its development over the past 30+ years. One of the inevitable consequences of the rapid evolution of this area of science has been the scarcity of useful textbooks. Several multiauthored books have appeared in recent years, usually consisting of specialised chapters written by researchers familiar with a specific area of environmental toxicology. The individual chapters in such volumes are often very useful as state-of-the-art reviews, but links between and among chapters are difficult to establish. Environmental Toxicology breaks with this trend and offers a broad and coherent vision of the field, as developed by two senior researchers who have been active in this area of research since its inception in the 1970s. In keeping with the aims of the Environmental Chemistry Series, this book is designed for use in courses offered to senior undergraduates and to graduate students. As university professors, the authors have used much of the material in their own courses, and thus, in a certain sense, the overall approach has already been tested and refined in the classroom.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.589 | 0.549 |
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".