The influence of thermal cues on the reproductive phenology of<scp>S</scp>ilver<scp>S</scp>hiner,<i>Notropis photogenis</i>
Bibliographic record
Abstract
Abstract Reproductive phenology and the length of the growing season vary in response to interannual environmental variability, with implications for population dynamics of freshwater fishes. Understanding the reproductive phenology of imperilled species in relation to environmental conditions is needed to better evaluate potential responses to changing environmental conditions, estimate future population dynamics and develop comprehensive recovery strategies. We examined Silver Shiner, a species listed as “Threatened” under Canada'sSpecies at Risk Act, during spring 2018 and 2019 to better understand the reproductive phenology of the species at the northern edge of its range in Canada. The initiation of Silver Shiner spawning occurred on the descending limb of the hydrograph and was completed before the onset of the extended period of low summer flow. In addition, both the initiation and cessation of spawning occurred in response to a cumulative growing degree day base 5 (GDD5) cue, with logistic regression models indicating a 50% probability the population initiated and ceased spawning when GDD5reached 68°C•days and 368°C•days, respectively. Logistic regression incorporating GDD5effectively predicted spawning initiation and cessation, providing useful models for examining the impacts of alterations to the thermal regime on reproductive phenology and improving the ability to evaluate changes in the larval growth period. Furthermore, the models can facilitate the development of real‐time estimates of spawning activity, and therefore ensure that disturbance to the species is minimized during the sensitive reproductive period.
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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 | 0.000 |
| 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".