Don't be fooled—A no-observed-effect concentration is no substitute for a poor concentration–response experiment
Bibliographic record
Abstract
Renowned mathematician and science historian Jacob Bronowski once defined science as "the acceptance of what works and the rejection of what does not" and noted "that needs more courage than we might think." Such would also seem to be the case with no-observed-effect concentrations (NOECs) and no-observed-effect levels in ecotoxicology. Compelling arguments were advanced more than a quarter of a century ago as to why the use of a model to describe the concentration-response relationship was preferable to an isolated metric, with the NOEC singled out as a particularly poor toxicity measure. In the ensuing years numerous articles critical of the NOEC have been written, with some calling for an outright ban on its use. More recently, arguments have been made for the retention of NOECs, with supporters suggesting that this metric is particularly useful in situations where the concentration-response relationship is weak or nonexistent. In addition, it has been claimed that there are situations in ecotoxicology where suitable models are simply not available. These arguments are not correct, and they also have impeded the decades-overdue incorporation of numerous recommendations based on research that NOECs should no longer be used. In the present study the authors counter some of the most recent claims in support of NOECs and provide new insights for 1 class of problem claimed not to be amenable to such modeling. They are confident that similar insights will be developed as further original research in this area is undertaken. Environ Toxicol Chem 2016;35:2141-2148. © 2016 SETAC.
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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 | 0.062 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".