Pathological and epidemiological investigation of skin lesions in belugas (<i>Delphinapterus leucas</i>) from the St. Lawrence Estuary, Quebec, Canada
Bibliographic record
Abstract
Abstract Following the evaluation of 26,020 photographs collected from 2003 to 2014 as part of a photo‐identification program in St. Lawrence Estuary belugas, an atlas of cutaneous anomalies, composed of 18 skin lesions categories (SLCs), is proposed. At least one SLC was present in 51%, 97%, and 94% of neonates, gray, and white belugas, respectively. The most common SLC observed were “single linear fissure” (22%), “single linear scar” (19%), and “ulcer‐like lesion” (17%) in neonates, and “rake mark” (77%; 70%), “single linear fissure” (31%; 24%) and “circular depression” (40%; 35%) in gray and white belugas, respectively. Logistic regression modeling revealed significant correlations between temporal and individual variables for most SLCs. Histological evaluation of cutaneous lesions from 111 belugas stranded between 1983 and 2017 were also performed. “Single linear fissure,” “single linear scar,” “crater‐like scar,” “rake mark,” and “Morse code lesions” appear to be of traumatic origin. Results from pathological and epidemiological analyses suggest that some of these SLCs, such as “yellow patch,” “circular depression,” and “map depression” are associated with molting. Postnatal molting could account for “ulcer‐like lesions” and “single linear fissures” in neonates. Urchin spines were found within “pinhole erosions” and a gamma‐herpesvirus was detected by PCR in a wound.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".