Sink or swim: Risk stratification of preweaning mortality in harbor seal pups (<i>Phoca vitulina richardii</i>) admitted for rehabilitation
Bibliographic record
Abstract
Abstract To date, few consistent relationships between survival in rehabilitation programs and diagnostic measures recorded upon admission have been identified for harbor seal pups. Veterinary records for 718 unweaned Pacific harbor seal pups (Phoca vitulina richardii) admitted to a rehabilitation center were examined to identify clinical factors associated with preweaning survival and develop a triage tool to stratify pups according to their risk of mortality. Physical, serum chemical, and hematological variables were examined and their relationship with survival to weaning was assessed by logistic regression and classification and regression tree (CART) analysis. Survival to weaning was 85.1% and many clinical variables reflecting the pups’ age, size, growth, injuries, and blood parameters were associated with the likelihood of survival. A decision tree model, consisting of serum concentrations of phosphorus, sodium, and calcium, successfully stratified harbor seal pups into clinical subgroups according to their preweaning mortality risk. For both the derivation and validation cohorts, pups classified as “high risk” had significantly lower odds of survival, while those classified as “low risk” had significantly greater odds of survival. This simple decision tree could serve as a practical triage tool to help identify and direct care towards pups at higher risk of preweaning mortality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".