A comparison of locomotor performance of the semiarboreal Pacific marten (<i>Martes</i><i>caurina</i>) and semiaquatic mustelids
Bibliographic record
Abstract
The relatively long body and short limbs of mustelids allow them to exploit resources from a diversity of habitat types. This body plan also has important implications for energetics because of increased heat loss from a high surface to volume ratio and muscular support of an elongated spine. Past research suggests that dorsal flexion of the spine enables semiaquatic mustelids to be relatively economical runners at faster speeds. We evaluated locomotor performance in a semiarboreal mustelid, the Pacific marten (Martes caurina (Merriam, 1890)), and compared our results from three females and one male to those previously observed in semiaquatic mustelids. At slower speeds, when martens used a walking or trotting gait, they were less economical than predicted; at higher speeds, martens were as economical as predicted. Nonetheless, martens did not switch to a bounding gait earlier than expected based on an allometric relationship between body mass, running speed, and gait. At the highest speed, martens increased stride length and decreased stride frequency. These observations suggest that unlike the semiaquatic river otters (Lontra canadensis (Schreber, 1777)) and mink (Neovison vison (Schreber, 1777)), martens do not use spinal flexion but instead employ other adaptations that result in energy savings at high speeds.
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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 | 0.000 | 0.000 |
| 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".