Temperature-dependent temporal variation in the size and growth of Thames estuary smelt Osmerus eperlanus
Bibliographic record
Abstract
Long-term monitoring data collected in the Thames estuary, UK, were used to investigate inter-annual trends in year-end size and growth rate of smelt Osmerus eperlanus (L.).Patterns were related to variations in the thermal opportunity for growth, defined in terms of temperaturedependent cumulative degree-days at estuarine temperatures ≥5°C.Year-end size was positively correlated to the thermal opportunity for growth, whereas growth rate was negatively correlated.Cohort-specific comparisons of estuarine growth patterns indicated significant differences in starting sizes, possibly related to the influences of temperature on early larval development.Interannual differences in within-estuary growth rate as a function of cumulative degree-days were found and suggested thermally dependent growth.Smelt in normal and warm years showed similar rates of growth that differed significantly from rates observed in cold years.Reductions in the proportions of older fish in warm years were also found and interpreted in the context of spatial segregation patterns found in freshwater environments.Overall results suggest that use of mid-estuarine environments during larval development and migration to colder outer-estuarine areas are growth/maturation optimization strategies that enhance individual fitness.Thus, smelt migration phenologies in the Thames estuary appear to be driven by short-term variations in the suitability of thermal habitat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".