Thermal preferences and effects of temperature on fitness parameters of an endemic Argentinean tarantula (<i>Grammostola vachoni</i>)
Bibliographic record
Abstract
Mygalomorphs depend on thermal microhabitats for successful behavior, but their thermal biology is still poorly known. Grammostola vachoni Schiapelli and Gerschman, 1961 is an endemic tarantula from Argentina and it is listed as vulnerable in the IUCN Red List. However, little is known about its biology; therefore, we attempted to explore the thermal biology of juveniles and adult females of G. vachoni under laboratory conditions. We characterized the preferred temperatures, evaluated the relationship between temperature and locomotor performance, and calculated the thermal sensitivity. Individuals showed a peak temperature preference and spent 40% of the total time at 25–29 °C; we did not find any significant differences in temperature preference between juveniles and females. We found that locomotor performance is strongly affected by high temperatures. Different sprint speeds of juveniles and females were found at 5, 35, and 40 °C. The highest thermal sensitivity was recorded in the lowest temperature range and thermal sensitivity was lowest in the highest temperature ranges. Our results are relevant in the context of climate change, because thermal parameters constitute a useful tool to explore some possible effects of this change on body temperature and thus the physiological performance and vulnerability of ectotherms like G. vachoni.
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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".