Influence of temperature on silver accumulation and depuration in rainbow trout
Bibliographic record
Abstract
To assess the influence of water temperature on silver uptake, rainbow trout Oncorhynchus mykiss(c. 50 g; held at 13° C) were exposed to 0·1 μM AgNO3 in ion‐poor water for 1 week at 4 and 16° C without previous temperature acclimation. To assess the influence of temperature on elimination of previously accumulated Ag, rainbow trout were exposed to 0·1 μM AgNO3 in ion‐poor water for 1 week at 12° C, then were randomly divided amongst two Ag‐free water containers, differing only in temperature (3 and 16° C), for 2 months. In the uptake study greater accumulation of Ag was seen in the gills, plasma and especially the livers and bile of ‘warm’ rainbow trout (16° C) compared to ‘cold’ rainbow trout (4° C), which can be explained by the higher metabolic rates of the warmer fish. In the depuration study there was no net elimination of Ag from the livers and bile but there was biphasic elimination of Ag from the gills and plasma of ‘warm’ and ‘cold’ fish, but with few differences between them. This indicated that temperature‐dependent processes were less important in Ag elimination than in Ag uptake. Toxicokinetic modelling of Ag uptake by livers indicated four‐fold greater uptake of Ag by ‘warm’ rainbow trout compared to ‘cold’ rainbow trout (one compartment uptake model). Elimination of previously accumulated Ag from the plasma was best fitted by a two compartment rate‐constant based model, with approximately half the plasma Ag load eliminated within 24 h, followed by slower elimination of Ag over 2 months.
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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 | 0.001 |
| 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".