Seasonal Changes in Upper Thermal Tolerances of Freshwater Thai Fishes
Bibliographic record
Abstract
Seasonal change inferred to climate change inevitably influences Critical thermal maximum (CTmax) of riverine fishes. In this study, we investigated CTmax as thermal tolerance for four common riverine fishes, i.e., Danio regina, Channa gachua, Rasbora caudimaculata and Mystacoleucus chilopterus, in the Kwae Noi river system in western Thailand. The acute thermal tolerance was lower in the wet season (mean river temperature ∼25 °C) and higher in the dry season (mean river temperature ∼23 °C) with medians of wet season-CTmax for those four fishes of 35.3 ± 0.4, 36.2 ± 0.5, 37.3 ± 0.5 and 37.5 ± 0.6 °C, respectively, and high values of dry season-CTmax of 37.4 ± 0.5, 38.3 ± 0.5, 38.7 ± 0.7 and 39.1 ± 0.5 °C, respectively. The variations of CTmax for all of the four species in this study, throughout the wet and dry seasons, attribute to their seasonal plasticity in response to the dynamics of thermal stress. Under climate variability and climate change with increasing the higher temperatures of air and river, and altering the habitat, R. caudimaculata and M. chilopterus had higher capacities to tolerate the acute heat stress across wet and dry seasons.
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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".