Application of sediment toxicity identification evaluation techniques to a site with multiple contaminants
Bibliographic record
Abstract
Sediment toxicity identification evaluations (TIEs) are conducted to determine causes of adverse effects observed in whole-sediment toxicity tests. However, in multiple contaminant scenarios, it is problematic to partition contributions of individual contaminants to overall toxicity. Using data from a site with multiple inputs and contaminants of concern, the authors describe a quantitative approach for the TIE process by tracking toxicity units to determine whether all toxicity is accounted for. The initial step established the level of toxicity associated with the whole sediment and then partitioned sources of toxicity into general contaminant classes (e.g., ammonia, metals, nonpolar organic compounds). In this case, toxicity was largely the result of nonpolar organics, so the sediments were extracted and the extracts added back into dilution water and tested to confirm recovery of toxicity. Individual fractions were then generated using a solvent gradient and tested for toxicity. Fractions of interest were evaluated with gas chromatography/mass spectrometry to identify specific constituents associated with toxicity. Toxicity units associated with these constituents were then evaluated to determine probable associations with cause and whether all toxicity was accounted for. The data indicated that toxicity was associated with 2 contaminant classes, representing legacy compounds and contaminants of emerging concern, with the contribution of each varying across the site. Environ Toxicol Chem 2016;35:2456-2465. © 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".