Latitudinal variation in the growth and maturation of masu salmon (Oncorhynchus masou) parr
Bibliographic record
Abstract
We examined latitudinal variation in riverine growth and parr maturation of an endemic Asian salmonid, masu salmon ( Oncorhynchus masou ), in 12 rivers located between 36.6°N and 45.4°N. Masu salmon parr showed considerable variation in growth and maturation patterns among populations. Body sizes were generally larger, and parr maturation was common at southern latitudes. Male parr matured at smaller sizes at more southern latitudes. Latitudinal variation in riverine growth and maturation of masu salmon parr was largely attributed to latitudinal changes in temperature and population density. Parr size at age increased with increasing temperature and decreased with population density. Riverine growth conditions were an important environmental factor determining parr maturation for both males and females; however, the occurrence of mature female parr required extremely favorable growth conditions. Water temperature in May, approximately four months before maturation, was the most important environmental factor affecting the maturation of male parr. Our study supports the hypothesis that freshwater residency was promoted by favorable growth conditions at southern latitudes.
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 | 0.000 | 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".