Comparison of genetic and visual identification of cisco and lake whitefish larvae from Chaumont Bay, Lake Ontario
Bibliographic record
Abstract
Cisco (Coregonus artedi) are an important component of native food webs in the Great Lakes, and their restoration is instrumental to the recovery of lake trout (Salvelinus namaycush) and Atlantic salmon (Salmo salar). Difficulties with visual identification of larvae can confound early life history surveys, as cisco are often difficult to distinguish from lake whitefish (Coregonus clupeaformis). We compared traditional visual species identification methods with genetic identifications based on barcoding of the mitochondrial cytochrome c oxidase I gene for 726 coregonine larvae caught in Chaumont Bay, Lake Ontario. We found little agreement between the visual characteristics of cisco identified by genetic barcoding and the most widely used dichotomous key, and the considerable overlap in ranges of traditionally utilized metrics suggests that visual identification of coregonine larvae from Chaumont Bay is impractical. Coregonines are highly variable and plastic species and often display wide variations in morphometric characteristics across their broad range. This study highlights the importance of developing accurate, geographically appropriate larval identification methods to best inform cisco restoration and management efforts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".