Seasonal variation of growth and reproduction of the subarctic krill species, <i>Thysanoessa raschii,</i> driven by environmental conditions in the Estuary and Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract The aim of this study was to quantify somatic growth and reproduction of Thysanoessa raschii in response to environmental conditions in the St. Lawrence Estuary and Gulf of St. Lawrence, Canada. We sampled between 2010 and 2016 from spring to late summer and incubated individuals. Fresh molts were collected daily and measured to calculate the growth increment following the instantaneous growth rate method while eggs were counted daily. Our results showed a seasonal pattern of somatic growth and reproduction driven by temperature and chl. a concentration with a decrease in somatic growth in August when egg production was maximal, suggesting a trade-off. Functional relationship analyses revealed a narrow optimal temperature window for somatic growth with maximum temperatures observed between 1.2 and 2.0°C in the cold intermediate layer (50–150 m). Maximum egg production was observed at temperatures between 3.8 and 5.7°C in the surface layer (0–50 m). A required minimum concentration of chl. a of 9 mg.m−3 for somatic growth was observed. For egg production, the minimum observed was integrated chl. a (0–50 m) of 80 mg.m−2. We also observed the importance of optimal conditions lasting for one to 3 weeks to support biological processes in T. raschii.
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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".