Differences between thermal preference and thermal performance in a wintry spider <i>Mecicobothrium thorelli</i>: are the spiders under evolutionary pressures on their seasonal activity?
Bibliographic record
Abstract
Thermal preference and thermal performance are used to describe the thermal biology of an ectothermic organism through parameters, i.e., estimating locomotor performance by maximum running speed. In this study, we assessed the thermal preference and locomotor performance of the spider Mecicobothrium thorelli Holmberg, 1882, a wintry mygalomorph spider endemic to the native mountainous grasslands of central Argentina and Uruguay. The preferred temperatures of the 72.4% of the individuals were in the range of 10–20 °C. The highest frequencies of preferred temperatures were 10–15 °C in males and 15–20 °C in females. The sprint speed showed significant differences between all the temperatures evaluated and showed the highest speeds at 25 °C and the lowest at 3 °C. The optimal temperature was 26.09 °C, which was significantly higher than the preferred temperature in both males and females. We concluded that M. thorelli selects a wide range of temperatures and prefers to stay in medium and low temperatures, which are correlated with winter activity in the wild. However, the species showed maximum speed at higher temperatures, which implies that spiders would perform even better in nature and maximize their locomotion by being active during a warmer period.
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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".