Joint effects of elevated copper and temperature in juvenile Tambaqui exposed in black and white waters of the Amazon
Bibliographic record
Abstract
Abstract This study aimed to investigate how exposure to elevated water temperature and metal concentration jointly affect the physiology of Amazonian fish. Aboard a research vessel in the Amazon, we evaluated the effects of water temperature (river T°C at 31.5°C and a + 4°C increase to 35.5°C) and of 3‐h copper (Cu) exposure (up to 600 μg/L) in juvenile Tambaqui ( Colossoma macropomum ) exposed in freshly collected Rio Negro (‘black water’) and Rio Solimões (‘white water’) waters. In Cu‐free water, the +4°C raise accelerated physiological Na + influx and efflux rates, but only in Rio Negro water. Temperature had no effects on the other physiological fluxes (Cl − , K + and ammonia fluxes). Cu exposure led to net losses of Na + (via increased efflux), Cl − and K + and decrease in acute upper thermal tolerance (CT max ). These Cu effects were more prominent in Rio Negro water, where Cu bioavailability was the greatest. The +4°C change had no effect on gill Cu accumulation and, overall, there was limited evidence that warming worsened Cu‐induced ionoregulatory disturbances. However, in Rio Negro, as Cu and heat both separately promoted Na + net losses, fish Na + balance was the most compromised in the presence of the two stressors. Altogether, the impaired thermotolerance and ionoregulation under combined Cu and heat exposures suggest a cumulative physiological interaction between two stressors that are increasing threats to the Amazon basin.
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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".