Variability of mortality rates for<i>Calanus finmarchicus</i>early life stages in the Labrador Sea and the significance of egg viability
Bibliographic record
Abstract
Mortality rates of eggs and nauplii are essential for understanding and modelling dynamics of copepod populations. Abundances of Calanus finmarchicus females, eggs and nauplii were determined at 88 stations in the Labrador Sea. Egg production rates (EPRs) and egg and naupliar stage durations were calculated using published relationships with in situ chlorophyll concentration and temperature. The data were used to estimate mortality rates for eggs (ME), eggs and early naupliar stages (ME-NIII) and naupliar stages (MNI–NVI). Estimated mortality rates in the central basin were higher than those on the shelves and within regions generally ME > ME-NIII > MNI–NVI. The “Basic Method” for eggs, arguably the most reliable method, gave a high proportion of seemingly erroneous (negative) values. These became positive when a modified estimation formula was used, which assumes that some eggs being laid could not hatch. Egg hatching success is often <100%, but this is rarely considered when calculating mortality rates, although it affects all estimates that include egg abundances and/or EPRs as variables, mostly at low mortality rates. Egg and early life stage mortality rates were correlated with female abundance, but cannibalism may not be the appropriate interpretation. The issues of egg viability and cannibalism require more careful consideration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".