Effect of spawning day and temperature on salmon emergence: interpretations of a growth model for Methow River chinook
Bibliographic record
Abstract
A mechanistic temperature-dependent model for preemergent growth coupled with spawning and river temperature data is used to evaluate early life history strategies for chinook salmon (Oncorhynchus tshawytscha) in the Methow River, Washington, U.S.A. Since the model provides a predictable coupling between time of spawning and fry emergence, it helps explain the spatial and temporal patterns observed for the sympatric stocks. The model suggests that progeny of August-spawning chinook in upper river habitats emerge at button-up (no visible yolk sac) over a wide range of days in the early spring. The eggs grow efficiently, which maximizes fry mass. The progeny of October-spawning downstream chinook can exploit a wide range of habitats in the river because their emergence mass is not sensitive to location in the river, but the adults must spawn later in the season to avoid summer high temperatures. Late spawning forces fry to emerge prior to button-up to avoid scouring flows but synchronizes their emergence times. The trade-offs between the spawning strategies of these two salmon runs are complex and the seasonal and spatial distribution of temperatures plays a critical role in these trade-offs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".