Scanning Snakes to Measure Condition: A Validation of Quantitative Magnetic Resonance
Bibliographic record
Abstract
Body composition is a measure of an animal's energetic state that can inform many research fields, yet the analysis traditionally requires individuals to be killed, and chemical analysis is labor intensive. Quantitative magnetic resonance (QMR) measures body composition noninvasively in live and nonanesthetized animals. Our aim was to validate QMR analysis for snakes by comparing it with gravimetric chemical analysis. We collected Northern Watersnakes (Nerodia sipedon sipedon) and Eastern Massasaugas (Sistrurus catenatus catenatus) that were found dead on roads, analyzed their body composition using the QMR scanner, and then by gravimetric chemical analysis. We compared fat mass, wet lean mass, and total water mass between the two methods, and then calculated bias, absolute error (g), and relative error (%) of the QMR analysis. Body composition values from the QMR analyses were highly correlated with the values obtained by gravimetric chemical analysis. Bias and errors were reasonable for wet lean and total water mass values, but the raw QMR data overestimated fat mass. When we calibrated the QMR using the chemical extraction data, it nearly eliminated bias and greatly reduced absolute and relative error. Therefore, following calibration, QMR analysis is an effective method to measure body composition of snakes. QMR very accurately measures wet lean and total water masses and can be used to detect changes in fat mass particularly in longitudinal studies of individuals across seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".