Natural growth rates in Antarctic krill (Euphausia superba)— II. Predictive models based on food, temperature, body length, sex and maturity
Bibliographic record
Abstract
The growth rates of postlarval krill (Euphausia superba) were measured across a wide range of environments in the Scotia Sea and around South Georgia using the Instantaneous Growth Rate (IGR) method. Each IGR experiment determined the intermolt period (IMP) and growth increment at molt (GI) of an average of 120 individuals incubated for 5 d in through-flowing ambient, filtered seawater. We examined the results from 51 IGR experiments involving 5,927 animals ranging between 25 mm and 62 mm. Animals were collected from an area that covered a latitudinal range of 10 � and surface temperatures of between �0.85�C and 4.75�C. The measurement of IMP has rarely been achieved in IGR experiments because synchronous molting biases estimates. We overcame this by applying a binary logistic regression model to our data. This related IMP to temperature, body length, and maturity stage. Food did not influence IMP. Our model estimated that krill within our experiments had IMPs ranging from 9 d to 57 d. Temperature affected the IMP of females more than that of males. The IMPs of females were shortest around 2�C and increased at lower and higher temperatures. IMP increased with body size and altered according to gender, with male IMPs being 50 % longer than those of equivalently sized females. One of the main assumptions of the IGR method is that the GI measured in the first few days reflects the in situ conditions experienced by krill in the previous intermolt period. However, we found that the GIs declined immediately and rapidly after capture, particularly
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".