Do boating and basking mix? The effect of basking disturbances by motorboats on the body temperature and energy budget of the northern map turtle
Bibliographic record
Abstract
Abstract Basking is the primary mechanism used by many freshwater turtles to maintain their body temperature (Tb) in a range that maximizes physiological performance. Basking turtles are easily disturbed by motorboats, but the consequences of the increasingly popular use of motorboats on turtles is largely unknown. In this work, predictive models built from field and laboratory data were used to assess the effects of the frequency of basking disturbance by motorboats on Tb and metabolic rate (MR) of female northern map turtles (Graptemys geographica), a species of conservation concern. Simulations revealed that the effects of boat disturbance vary seasonally. In early May, a conservative estimate of the disturbance rate (0.15 per hour) resulted in a 0.34°C decrease in mean daily Tb, which translated to a 7.8% reduction in mean MR. In June, July and August, owing to warmer lake temperatures, the effect of disturbance was less marked and the observed disturbance rates (0.32, 0.96 and 1.23 per hour, respectively) reduced the mean MR of an adult female by 2.1%, 0.5%, and 0.4 %, respectively. Reduction in MR decreases the rate of energy assimilation, which could translate into sublethal effects on turtles, such as reduced growth and reproductive output. Motorboat usage is increasing in many areas and is probably affecting other species of freshwater turtles that use aerial basking. This study offers important insights on the implications of disturbances for species that bask. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".