Common Musk Turtles (Sternotherus odoratus) select habitats of high thermal quality at the northern extreme of their range
Bibliographic record
Abstract
Abstract In ectotherms, variation in body temperature (Tb) affects physiological performance and, ultimately, fitness. Therefore, reptiles regulate Tb behaviourally by choosing habitats of optimal temperature. The main goal of this study was to determine the link between patterns of thermoregulation and habitat selection in Common Musk Turtles inhabiting a thermally challenging region. We expected habitat selection to be based on the fulfillment of thermoregulatory requirements, which can be accomplished by selecting thermally superior habitats. From early May to late August 2007, we tracked 22 Common Musk Turtles with temperature-sensitive radio-transmitters and collected daily Tb profiles with automated radio-telemetry data loggers. In addition, temperature data loggers were placed in the study area to measure the range of environmental operative temperatures (Te) available to musk turtles. The habitats with the highest thermal quality were aquatic habitats with surface cover (i.e., lily pads, macrophytes, etc.) followed by shallow water. As expected, musk turtles used habitats non-randomly and had a strong preference for thermally superior habitats. This is consistent with the typical aquatic basking behaviour observed in musk turtles, suggesting that there is a strong link between thermal quality of habitats and habitat selection, even in this almost entirely aquatic turtle.
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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 | 0.000 | 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) | 0.001 | 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".