discernible by multivariate analysis: A case study of genus Coilia (Teleostei: Clupeiforms)
Bibliographic record
Abstract
Abstract – In order to understand the morphological differences between four populations of genus Coilia (Teleostei: Clupeiforms) and identify them conveniently, truss network data were used to conduct multivariate analysis. Nine-teen morphometric measurements were made for each individual. Burnaby’s multivariate method was used to obtain size-adjusted shape data. The cluster analysis and discriminant analysis were used to discriminate among popula-tions. The results indicated that 1) the four populations were clustered into three distinct groups; the first group in-cluded Changjiang C. mystus and Taihu C. ectenes, the second one included Zhujiang C. mystus, the last one included Changjiang C. ectenes, and 2) discriminant analysis with selected 4 morphological parameters showed that the iden-tification accuracy was between 88 % and 100%, and global identification accuracy was 95%. Our result showed that populations of different Coilia species living in geographic proximity to one another are more similar than conspecifics living farther apart. Separation and adaption are important to morphological difference. The taxonomy of genus Coilia should be reconsidered. This study also showed that the method to obtain size-adjusted data is important to acquire right conclusion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".