A preliminary assessment of cisco (Coregonus spp.) diversity in Yellowknife Bay, Great Slave Lake, Northwest Territories
Bibliographic record
Abstract
Northern freshwater fi shes, particularly members of the genus Coregonus, have been used as a model for studying microevolution due to their well known colonization history coupled with parallel patterns of phenotypic differentiation. We assessed and described the phenotypic diversity among ciscoes in the Yellowknife Bay region of Great Slave Lake using 1) traditional taxonomic methods for cisco species and 2) contemporary linear and geometric morphometric methods. Four morphs were identifi ed using a qualitative visual assessment of gross morphology and/or location of capture in comparison to published taxonomy for the genus Coregonus. The morphs identifi ed were assigned the following colloquial names: A) 'adfl uvial', B) 'lacustrine', C) 'shortjaw', and D) 'least'. Univariate analyses on taxonomic measures indicated that cisco varied in terms of age, number of lateral line scales, number of anal fi n rays, middle gill raker length, and lower gill arch length. By contrast, fi ve morphs were identifi ed using cluster analysis of linear and geometric morphometric data combined with age and gill raker number. The additional phenotypic cluster entailed a splitting of large and small lacustrine morphs into two groups. Multivariate analyses indicated a strong relationship between body shape and gill raker number, eye size, age and mouth morphology. These results suggested that the segregation among morphs was based on differential usage of trophic and/or habitat resources as well as life history variation such as age at maturity. Our results indicated that the level of phenotypic diversity was greater than previously documented for Great Slave Lake and that this diversity was similar to that reported for ciscoes in the Laurentian Great Lakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".