Using form analysis techniques to improve photogrammetric mass‐estimation methods
Bibliographic record
Abstract
Abstract Numerical characterization of animal body forms using elliptical Fourier decomposition may be a useful analytic technique in a variety of marine mammal investigations. Using data collected from the Weddell seal (Leptonychotes weddellii), we describe the method of body form characterization using elliptical Fourier analysis and demonstrated usefulness of the technique in photogrammetric mass‐estimation modeling. We compared photogrammetric mass‐estimation models developed from (1) standard morphometric measurement covariates, (2) elliptical Fourier coefficient covariates, and (3) a combination of morphometric and Fourier coefficient covariates and found that mass‐estimation models employing a combination of morphometric measurements and Fourier coefficients outperformed models containing only one covariate type. Inclusion of Fourier coefficients in photogrammetric mass‐estimation models employing standard morphometric measurements reduced the width of the prediction interval by 24.4%. Increased precision of photogrammetric mass‐estimation models employing Fourier coefficients as model covariates may expand the range of ecological questions that can be addressed with estimated mass measurements.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".