Spawn Timing of Winter-Run Chinook Salmon in the Upper Sacramento River
Bibliographic record
Abstract
Spawn timing in anadromous Pacific salmon may be especially sensitive to environmental cues such as river temperature and flow regimes. In this study, we explored correlations between peak spawn timing and water temperature in endangered Sacramento River winter-run Chinook Salmon. In recent drought years, rising water temperatures during egg incubation have negatively affected the winter-run Chinook Salmon population. This paper seeks to understand how winter-run spawn timing may be affected by temperatures during the staging period prior to spawning, and how water releases from Shasta Dam might affect these dynamics. We fit a proportional-odds logistic regression model to evaluate annual spawn timing as a function of average temperatures in April and May below Keswick Dam. While the start date of spawning remains relatively constant from year to year, the timing of peak spawning varies annually. Cool springtime temperatures trigger winter-run Chinook Salmon to spawn earlier, whereas warm springtime temperatures trigger fish to spawn later. Before dam construction, winter-run Chinook Salmon spawned in cool, spring-fed streams that are now inaccessible to migrating salmonids. In their natal spawning grounds, temperature-driven spawn timing would have primarily ensured sufficient time for egg maturation in cool years, while secondarily preventing mortality in warm years. In the current winter-run spawning grounds, the relationship between temperature and spawn timing may have important applications to management of Shasta Dam water releases, especially during conditions when thermal mortality can affect developing winter-run Chinook Salmon eggs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| 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 teacher head, 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".