Bibliographic record
Abstract
To understand the effects of different acclimation temperature on the thermal tolerance of juvenile Silurus meridionalis Chen, the juveniles with a mass of 16.9±0.3 g were acclimated at water temperature 10 ℃, 20 ℃ and 30 ℃ for 2 weeks, and their thermal tolerance under a temperature variation rate of 1 ℃·h-1 was evaluated. The results showed that the critical thermal maximum (CTmax), lethal thermal maximum (LTmax), critical thermal minimum (CTmin), and lethal thermal minimum (LTmin) at acclimation temperatures 10 ℃, 20 ℃ and 30 ℃ were 34.13 ℃, 38.22 ℃ and 39.41 ℃, 34.84 ℃, 38.63 ℃ and 39.53 ℃, 4.88 ℃, 5.90 ℃ and 9.80 ℃, and 4.12 ℃, 5.03 ℃ and 8.29 ℃, respectively, i.e., the test parameters all increased with increasing acclimation temperature. The thermal tolerance amplitude at 10 ℃, 20 ℃ and 30 ℃ was 29.25 ℃, 32.32 ℃ and 29.61 ℃, respectively. Within the range of acclimation temperature 10 ℃-20 ℃, the acclimation response ratio (ARR) at high and low temperatures was 0.41 and 0.12; while within the range of acclimation temperature 20 ℃-30 ℃, the ARR was 0.10 and 0.39. The thermal tolerance polygon area of the juveniles within the range of acclimation temperature 10 ℃-30 ℃ was calculated as 617.5 ℃2. It was indicated that the thermal tolerance of S. meridionalis was dependent on acclimation temperature.
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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".