Morphological differentiation among isolated populationsof dwarf snakehead fish, Channa gachua (Hamilton, 1822)using truss network analysis
Bibliographic record
Abstract
Truss-network analysis was applied to examine morphological differences among distinct populations of Channa gachua (Hamilton 1822). Ninety-eight fish individuals were collected from three-isolated habitats in north-eastern Thailand. Twenty-six truss variables were measured and then subjected to Burnaby’s size adjustment for removing size-dependent effect. The transformed data were processed for univariate and multivariate statistical analyses. The multivariate ANCOVA showed highly significant differences between populations (Pillai’s trace = 1.561, F = 10.102, p < 0.0001). The univariate ANCOVA presented the significant differences between 20 out of 26 truss variables (p < 0.05) representing morphological characters of head, trunk and caudal peduncle areas. The variability in body shape among three populations obviously showed by the first three components of PCA accounting for 49.98% of total variances. In addition, five variables from head area and three variables from body were selected to stepwise discriminant analysis, representing the first two discriminant functions (DF1 and DF2) accounted for 72.95 and 27.95% of shape variability, respectively. A high rate of correct classification of C. gachua to actual sampling locations was at 93.88%. The results support the existence of local adaptation of C. gachua across the isolated geographical locations, and indicate the presence of three phenotypic stocks of C. gachua regarding their habitat locations.
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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 | 0.001 | 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) | 0.001 | 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".