Gas killing of rats: the effect of supplemental oxygen on aversion to carbon dioxide
Bibliographic record
Abstract
Abstract High concentrations of carbon dioxide (CO2), used for killing laboratory rodents, are known to be more strongly aversive to rats than sweet food items are attractive. This study investigated whether the maintenance of a high oxygen (O2) concentration, using a gas mixture of 70% CO2and 30% O2, would reduce aversion to CO2during a gradual-fill procedure. Eight male Wistar rats, aged 10 months, were housed individually in an apparatus consisting of two cages, one higher than the other and joined by a tube. In a series of trials, subjects entered the lower cage for a reward of 20 sweet food items. The gas was turned on at the moment the rat started eating the reward items and flowed into the lower cage at a fixed rate. There were four treatments: 1) 100% CO2at 14.5% cage volume min–1; 2) gas mixture at 14.5% min–1; 3) gas mixture at 21.0% min–1, which delivered CO2at approximately 14.5% min–1and 4) air, with each subject tested with each treatment four times. Measures of willingness to stay and eat in the lower cage (latency to stop eating, latency to leave and the number of reward items eaten) were much lower in all three gas treatments than in air, indicating that the CO2and the CO2+ O2mixture were both more strongly aversive than sweet food items were attractive. Comparing the gas mixture with 100% CO2, the latency to leave and the number of reward items eaten were slightly higher in the CO2+ O2mixture at 21% min–1than in CO2at 14.5% min–1, indicating that the addition of O2slightly reduced the aversiveness of CO2in the gradual-fill procedure. This reduction is not enough to warrant recommending the use of CO2+ O2mixtures for killing rats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".