Relationship between nutritional condition and causes of death in beluga whales Delphinapterus leucas from the St. Lawrence estuary, Quebec, Canada
Bibliographic record
Abstract
Carcasses of endangered beluga whales Delphinapterus leucas from the St. Lawrence Estuary, Canada, have been examined consistently since 1983 to determine causes of death. The objective of this study is to compare the nutritional condition of belugas that died of different causes. Previously published categories of death were refined to discriminate acute from chronic pathological processes. Bayesian linear models were used to predict cause of death from the scaled mass index (SMI). Causes of death were as follows: 'bacterial diseases', 'verminous pneumonia', 'toxoplasmosis', 'other parasitic diseases', 'other infectious diseases', 'trauma-entrapment', 'other noninfectious diseases', 'dystocia-postpartum complications', 'neonatal mortality', 'cancer', 'primary starvation' and 'undetermined'. The models predicted a lower nutritional condition for the 'neonatal mortality' in belugas <290 cm in length and for 'primary starvation' and 'verminous pneumonia' categories for belugas ≥290 cm. Belugas that died from 'dystocia-postpartum complications' or from 'undetermined causes' had a higher-than-average SMI. Animals in the 'trauma-entrapment' category did not exhibit the highest nutritional condition, which was unexpected since individuals that died from trauma or entrapment are often used as references for optimal nutritional condition in other cetacean populations. Females that died from dystocia and postpartum complications were in similar nutritional condition as females dead from other causes during, or shortly after, pregnancy. This suggests that these females are not obese, ruling out a possible cause of dystocia. Although studying dead animals biases results toward low nutritional condition, our findings support the link between chronic pathological processes and poorer nutritional condition in belugas.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".