Stock structure analysis of the Arabian red shrimp (<i>Aristeus alcocki</i> Ramadan, 1938) in the Indian coast with truss network morphometrics
Bibliographic record
Abstract
The Arabian red shrimp (Aristeus alcocki Ramadan, 1938) is a deep-sea penaeoid shrimp that forms a major commercial fishery in the Indian coast. However, the spawning population of this species along the Indian coast is poorly known. To study this, stock structure of A. alcocki using truss morphometry was employed. A total of 1842 matured specimens were collected from five geographical locations (Tuticorin (SET), Chennai (SEC), Nagapattianam (SEN), Sakthikulangara (SWS), and Kalamuku (SWK)) along the Indian coast. Thirty-nine truss distances were extracted from each specimen and analyzed by multivariate methods (i.e., principal component analysis (PCA), discriminant functions (DF), and hierarchical cluster analysis). The results of the PCA indicated that the first two components cumulatively explained >70% (female: 72.1%; male: 71.5%) of the total morphometric variation. Stepwise DF analysis indicated that abdominal variables significantly discriminated the populations at different locations. The results clustered the five samples into a minimum of two groups: samples from SWK clustered in group I, whereas rest of the samples clustered in group II. Morphometric variation between the groups was significant for each sex. Significant differences between the groups may be attributed to geographical and environmental conditions, suggesting separate management strategies for resource sustainability.
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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 | 0.002 | 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".