Blood chemistry correlates of nutritional condition, tissue damage, and stress in migrating juvenile chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
We used factor analysis to examine the correlation structure of six multivariate blood chemistry data sets for migrating hatchery and wild juvenile chinook salmon (Oncorhynchus tshawytscha). Fish were sampled (19982002) from juvenile fish bypass systems at dams or (one data set) from fish transport barges on the Snake and Columbia rivers. Analyses were performed to determine which blood chemistry analytes covaried, to facilitate interpretation of the data sets, and to provide insight into controlling physiological mechanisms. Four underlying factors were derived from the analyses: (i) a nutritional factor composed of total protein, cholesterol, calcium, and alkaline phosphatase, (ii) a tissue damage factor composed of the enzymes alanine aminotransferase, aspartate aminotransferase, and creatine kinase, (iii) a lipid metabolism factor composed of triacylglycerol lipase and triglycerides, and (iv) a stress factor composed of cortisol, glucose, Na+, and Cl. Although causal mechanisms cannot be directly inferred from our analyses, findings of published research provide tenable causal mechanisms for the observed structure. The consistency of the correlation structure among data sets suggests that composite (latent) variables may be more reliable indicators of some physiological responses than changes in individual variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".