Using meristic and morphometric techniques to identify juvenile <i>Catostomus</i> suckers (Largescale Sucker <i>C. macrocheilus</i> , Longnose Sucker <i>C. catostomus</i> , White Sucker <i>C. commersonii</i> —Teleostei: Cypriniformes: Catostomidae) within the Peace River watershed, Alberta
Bibliographic record
Abstract
Catostomid suckers are an important but understudied component of North American freshwater ecosystems. Alberta populations of Catostomus macrocheilus Girard, 1856; Catostomus commersonii (Lacepède, 1803); and Catostomus catostomus (Forster, 1773) have yet to be described for common meristic phenotypes and can be difficult to differentiate as juveniles. Given the relative rarity of C. macrocheilus in Alberta, site-specific phenotypic data are needed to ensure proper long-term monitoring of this species. Sample populations of juveniles from the Peace River Drainage were acquired and assessed for meristic data, including the fin rays, lateral line scale series, and vertebrae. Additionally, mouth shape differences were recorded between the three Catostomus species. Meristic data were analyzed through a series of non-parametric Mann–Whitney U tests and a principal component analysis, while morphometric data were analyzed using a canonical variate analysis, a Procrustes ANOVA, a discriminant function analysis, and a cross-validation test. Results indicated statistically significant differences between species at several meristic features, with the number of dorsal fin rays being the most useful metric for species-level field identification . Furthermore, mouth shape was determined to be significantly different between all species but was most distinct for C. commersonii. This study contributes to an improved understanding of catostomids in Alberta , provides statistically significant identification methods, and helps establish a baseline for future evolutionary research, which will inform conservation.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".