Growth trajectory analysis of Pacific whiteleg shrimp ( <i>Litopenaeus vannamei</i> ): Comparison of the specific growth rate, the thermal‐unit growth coefficient and its adaptations
Bibliographic record
Abstract
Empirical growth models have widespread application in the field of aquaculture. These models allow estimates of harvest size and waste outputs in addition to nutrient and feed requirements. In an effort to increase the ability to predict shrimp growth, the specific growth rate (SGR) and thermal-unit growth coefficient (TGC) models were fitted to 15 datasets encompassing growth of Pacific whiteleg shrimp (Litopenaeus vannamei). Shrimp were reared under commercial conditions in Southeast Asia with weights ranging from 0.01 g to 34 g. Growth rates were regressed against body weights to identify changes in growth pattern across life stages. Analysis identified two distinct patterns of growth, with a break point between stanzas at 7.5 g. The body weight exponent of the TGC model, traditionally assumed to be (1 − b) = 1/3, was solved for iteratively in each identified growth stanza in an effort to improve the goodness of fit of the TGC model. Average body weight exponents in the first and second stanzas were 0.416 and 0.952 respectively. Projected growth trajectories using these exponents resulted in significantly better fits in comparison to the traditional TGC and the SGR on the basis of statistical measures of goodness of fit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".