Modelling and comparison of growth of the silver-lip pearl oyster Pinctada maxima (Jameson) (Mollusca : Pteriidae) cultured in West Papua, Indonesia
Bibliographic record
Abstract
A commonly used approach to quantifying growth is to fit mathematical models to length-at-age data. Growth of the silver-lip pearl oysters, Pinctada maxima, cultured at a commercial pearl farm in West Papua, Indonesia was expressed mathematically by fitting five growth models (Gompertz, Richards, Logistic, Special von Bertalanffy Growth Function (VBGF) and General VBGF) to length-at-age data. The criteria used to determine the best fit model were a low mean residual sum of squares (MRSS), high coefficient of determination (r2) and low deviation of the asymptotic length (L8) from the maximum length (Lmax). Using these criteria, the models were ranked accordingly: Special VBGF; General VBGF; Gompertz; Richards and Logistic models. The Special VBGF yielded the best fit (L8 = 168.38 mm; K = 0.930 year–1; t0 = 0.126; MRSS = 208.64; r2 = 0.802; Deviation of L8 from Lmax = 37.52 mm) and, accordingly, was used to model the growth of oysters cultured at three sites and two depths within the farm. Likelihood ratio tests were used to compare growth of oysters cultured at these sites and depths. Based on L8 and K values, favourable sites and depths could be determined that optimised growth requirements for the various stages of P. maxima culture. Sites with high K and L8 values were preferred sites for culturing juvenile oysters before pearl production, when high growth rate is essential to produce large numbers of oysters in the shortest time possible. In addition, high L8 may facilitate implantation of larger nuclei conducive to the production of larger, more valuable pearls. Conversely, sites with low K values were preferred sites for weakening P. maxima before pearl ‘seeding’, a process undertaken to minimise nucleus rejection after seeding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".