South African ecotoxicology — present status and future prognosis
Bibliographic record
Abstract
Toxicology studies the interactions of a chemical substance with individual organisms, whereas ecotoxicology is a multidisciplinary approach incorporating ecology and other disciplines, e.g. chemistry, microbiology, etc., to determine responses of individuals, populations and whole ecosystems to stressors such as chemicals. We present here the current status of toxicity testing in South Africa and propose a future prognosis for such tests. We propose a path forward for the development of ecotoxicology in South Africa and also globally. Toxicity testing issues dealt with are the use of surrogate species as opposed to indigenous species, their comparative tolerances, and the selection of relevant endpoints as measures of toxicity. Ecotoxicological considerations need to address the following key ecological realities: tolerance (both physiological acclimation and genetic adaptation), trophic redundancies, resilience, compensation (e.g. density dependence), evolution, and recovery. We believe that predictive ecotoxicology will play a major role in the future management of ecosystems that are constantly changing. We also believe that such management must be proactive to the point of intervention to create desired change, specifically the maintenance of ecosystem services.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".