A new method for ranking mode-specific sensitivity of freshwater arthropods to insecticides and its relationship to biological traits
Bibliographic record
Abstract
The problem of how to deal with species sensitivity differences to toxic substances has been addressed successfully with the species sensitivity distribution (SSD), yet this has not increased understanding about the underlying mechanisms of sensitivity. Other researchers have identified the mode of action of chemicals and also biological traits of species as determinants for sensitivity, yet no systematic approach combines these factors. To achieve this, first existing data on organophosphate, carbamate, and pyrethroid toxicity and mode of action and also species trait information were mined. Second, we linked taxon sensitivity to their traits at the family level to generate empirical and mechanistic hypotheses about sensitivity-trait relationships. In this way, a mode-specific sensitivity (MSS) ranking method was developed, and tested at the taxonomic level of family and genus. The application of several quality criteria indicated overall confidence in rankings, but confidence in exact taxon rank was less certain, due to data insufficiency for certain groups. The MSS rankings were found to be applicable for trait-based approaches and were successfully linked to existing trait data to identify traits with predictive potential. Although this empirical analysis cannot test causality relationships between traits and sensitivity, testable hypotheses were generated, for further experimental investigation. Single traits as well as combinations of traits can be used to predict laboratory sensitivity to the substances tested, although associations were not as strong as in previous studies. We conclude that existing trait data are not suitable for every trait-based research question and that important traits remain to be identified and quantified in relation to the processes of toxicity, i.e., the toxicokinetics and toxicodynamics.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".