Hardness does not affect the physiological responses of wild and domestic strains of diploid and triploid rainbow trout <i>Oncorhynchus mykiss</i> to short‐term exposure to <scp>pH</scp> 9.5
Bibliographic record
Abstract
This study examined the effects of water hardness on the physiological responses associated with high pH exposure in multiple strains of diploid and triploid rainbow trout Oncorhynchus mykiss. To accomplish this, three wild strains and one domesticated strain of diploid and triploid O. mykiss were abruptly transferred from control soft water (City of Vancouver dechlorinated tap water; pH 6·7; [CaCO3 ] < 17·9 mg l(-1) ) to control soft water (handling control), high pH soft water (pH 9·5; [CaCO3 ] < 17·9 mg l(-1) ), or high pH hard water (pH 9·5; [CaCO3 ] = 320 mg l(-1) ) followed by sampling at 24 h for physiological measurements. There was a significant effect of ploidy on loss of equilibrium (LOE) over the 24 h exposure, with only triploid O. mykiss losing equilibrium at high pH in both soft and hard water. Furthermore, exposure to pH 9·5 resulted in significant decreases in plasma sodium and chloride, and increases in plasma and brain ammonia with no differences between soft and hard water. There was no significant effect of strain on LOE, but there were significant differences between strains in brain ammonia and plasma cortisol. Overall, there were no clear protective effects of hardness on high pH exposure in these strains of O. mykiss.
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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 | 0.001 |
| Research integrity | 0.000 | 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".