Influence of natural organic matter source on acute copper, lead, and cadmium toxicity to rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
Natural organic matter (NOM) was concentrated from various sites across Canada using a portable reverse-osmosis unit to obtain a range of NOM types, from mainly allochthonous (terrestrially derived) to mainly autochthonous (aquatically derived) NOM. The addition of NOM to Cu exposures in ion-poor water always decreased Cu toxicity to rainbow trout (Oncorhynchus mykiss, approximately 1 g) over a 96-h period, and the degree of protection varied with respect to NOM source. A good correlation was found between the specific absorbance coefficient (SAC) and time to reach 50% mortality (LT50; p < 0.001), indicating that more optically dark, allochthonous-like NOM decreases Cu toxicity better than does optically light, more autochthonous-like NOM. A similar, good relationship between NOM source and Pb toxicity was seen (p < 0.001), once confounding effects of Ca binding to NOM were accounted for. No significant relationship between Cd toxicity and NOM optical quality was seen (p = 0.082), and in toxicity tests with Cd the presence of some of the NOM sources increased Cd toxicity compared to Cd-only controls. Specific absorbance coefficients were used as a proxy measurement of NOM aromaticity in our study, and fluorescence indices were run on some NOM samples to obtain percent aromaticity for each sample. A good correlation was found between SAC and percent aromaticity, indicating that the simple SAC measurement is a reasonable indication of NOM aromaticity and of metal binding by NOM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".