Individual differences in rat sensitivity to CO2
Bibliographic record
Abstract
Feelings of fear, anxiety, dyspnea and panic when inhaling carbon dioxide (CO2) are variable among humans, in part due to differences in CO2 sensitivity. Rat aversion to CO2 consistently varies between individuals; this variation in aversion may reflect CO2 sensitivity, but other personality traits could also account for individual differences in aversion. The aims of this study were to 1) assess the stability of individual differences in rat aversion to CO2, 2) determine if individual differences in sweet reward motivation are associated with variation in aversion to CO2, and 3) assess whether variation in aversion to CO2 is related to individual differences in motivation to approach gains (promotion focus) or maintain safety (prevention focus). Twelve female Sprague Dawley rats were exposed multiple times at three different ages (3, 9 and 16 months old) to CO2 in approach-avoidance testing to assess motivation to avoid CO2 against motivation to gain sweet rewards. Rats were also tested for motivation to find hidden sweet rewards, and for their motivation to approach rewards or darkness. Tolerance to CO2 increased with repeated exposures and was higher at older ages. Individual differences in aversion to CO2 were highly repeatable but unrelated to motivation for sweet rewards or the strength of promotion and prevention focus. These results indicate that individual differences in aversion to CO2 reflect variation in CO2 sensitivity.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".