Calves peak-end memory of pain
Bibliographic record
Abstract
In humans, the 'peak-end' rule states that recollection of an experience is most often influenced by the peak (the most intense moment) and end of the experience. We investigated whether calves followed the peak-end rule in their memory of a painful procedure: disbudding. As proxies for retrospective and 'real-time' reports of pain, we used conditioned place aversion, and reflex pain behaviours. In two separate trials, calves were subjected to two disbudding conditioning sessions (one horn per treatment), acting as their own control. In the first trial, calves (n = 22) were disbudded and remained in a pen for 4 h, and disbudded and left in another pen for 4 h with an additional 2 h following an analgesic treatment. In the second trial, calves (n = 22) were disbudded and left in pens for 6 h during both treatments, receiving the analgesic at either 2 h or 4 h after disbudding. Calves were then tested for place aversion. For both trials we did not observe a preference for the pens where calves received analgesic treatment towards the end of the session. We did not find an association between aversion and the sum, peak or end of pain behaviours. Results are not consistent with a peak-end effect in calves' memory of pain.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".