Three-dimensional photogrammetry as a tool for estimating morphometrics and body mass of Steller sea lions (<i>Eumetopias jubatus</i>)
Bibliographic record
Abstract
A technique was developed to estimate morphometrics and body mass of Steller sea lions (Eumetopias jubatus) using three-dimensional (3D) photogrammetry. 3D photogrammetry reduces many of the problems associated with camera and body position encountered with two-dimensional photogrammetric techniques, allowing body mass estimation of free-ranging, active sea lions, without sedation, heavy weighing equipment, and disturbance. 3D computer wireframes of 53 Steller sea lions of various age classes were generated from multiple time-synchronous digital photos and used to estimate length, girth, and volume. Average estimates of standard length and axillary girth were within ±2.5% and ±4.0% of physically measured dimensions, respectively. Average estimates of standard length and axillary girth using only wireframes based on ideal body postures were within ±1.7% and ±3.1% of physically measured dimensions, respectively. Regressions of physically measured mass on photogrammetrically estimated body volume yielded a predictive model. Body mass estimates using this model were on average within 9.0% (95% confidence interval = ±1.7%) of the physically measured mass. This technique was also successfully applied to reptiles and fish.
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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 | 0.002 | 0.001 |
| 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".