Weighing our measures: approach-appropriate modeling of body composition in juvenile Steller sea lions (<i>Eumetopias</i> <i>jubatus</i>)
Bibliographic record
Abstract
While many approaches to modeling body condition exist, ranging from arbitrary morphometric indices to sophisticated cone modeling, few approaches have attempted to develop a standardized, simplified method for determining total body fat and protein in otariids. Our goal was to develop a method for predicting the body condition of juvenile Steller sea lions (Eumetopias jubatus (Schreber, 1776)) using simple morphometrics such as measurements of girth, length, mass, and blubber depth. We compared a candidate set of models to determine which metrics best predicted total body water (TBW) measures obtained by deuterium isotope dilution. Furthermore, we used AICc (Akaike’s information criterion corrected for small sample size) model selection methods and cross-validation to choose and validate the best suite of predictors. TBW was best predicted by a model that included mass, standard length, axial girth with the addition of blubber depths on the lateral side of the neck and dorsal surface of the hip. The results presented here show that blubber depth is an important addition to modeling body composition and may improve upon nonlethal, population-level estimates of nonisotopically derived values of TBW in juvenile Steller sea lions. Additionally, our models present a model development framework for other research efforts for use in determining body condition in otariids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".