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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".