The effect of positively autocorrelated thermal variance on the reproduction and individual growth of nematode Caenorhabditis elegans
Bibliographic record
Abstract
The effect of uncorrelated and positively autocorrelated temperature variance on the offspring production and individual development of the nematode Caenorhabditis elegans was examined at two different temperature means: a mean closer to the optimal temperature (20°C) and a lower, more suboptimal mean (16°C). Variance in temperature was introduced with a computer-controlled incubators, which changed the target temperature every 40 minutes. Offspring production was measured as the number of offspring produced by five adults during a 72-hour period, and individual growth was measured via estimated body length when the individuals were 75-81 hours old, and through time to maturation, observed every 12 hours. \n \tUncorrelated variance had no effect on the offspring production, time to maturation, or body length for either mean temperature. However, when the temperature series was positively autocorrelated, where the condition at a certain time is dependent on previous conditions, it resulted in a decrease in offspring number, longer time to maturation, and shorter body length at mean 20°C. The negative effects of variance observed at mean 20°C were absent at mean 16°C, which is consistent with literature that suggests that mean temperature can influence the effect of variance on biological performance. The significant negative effect was only observed in positively autocorrelated treatments.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".