Osmotic condition, buoyancy change and mortality in larval cod Gadus morhua. A bioassay for assessing near-term mortality
Bibliographic record
Abstract
In larvae ot marine fish, density differences are known to be associated with nutritional state and osmotic failure We hypothesized that these density measures can be used to predict the nearterm mortal~ty of larvae.Specifically, we predicted increased larval mortality to be positively and continuously associated with increased larval density.Under controlled conditions, we measured (1) the average density (g cm'3) and daily percent mortality of fed and starved larval cod Cadus morhua L, in culture tanks and (2) the relationship between larval density and percent mortality of both fed and starved larvae whose survival was monitored for a 24 h period following density determination.We observed a strong, positive linear relationship between average larval density and average percent mortality in both experiments (r2 = 0.79 and r2 = 0.78 respect~vely).The regression slopes did not differ significantly between expenments.A linear regression on the pooled data yielded a highly significant relationship (rZ = 0.77, p < 0.0001).A second-order polynomial regression was also signlf~cant and improved the fit (R2 = 0.80, p < 0.0001).While the second-order polynomial regression better described the functional relationship between average density and average mortality, the simpler first-order model was judged preferable for predicting near-term mortality.The consistency and strength of the Linear regressions suggests that larval density can be a reliable predictor of larval mortahty.We determined the density of larvae associ.atedwith 50% mortality (potential 'critical' density value) using linear, polynomial and logistic regressions.All 3 produced similar 'critical' denslties and we used the linear model to estimate this value.Larval cod having densities >1.0338 g cm-3 have a 50% probability of dying within 24 h.The potential for using larval density to predict the probability of near-term mortality in laboratory and field studies is discussed.
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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) | 0.000 | 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".