Exploring the effect of pain on response to reward loss in calves
Bibliographic record
Abstract
Negative emotional states are known to interact, potentially aggravating one another. In this study, we used a well validated paradigm (successive negative contrast, SNC) to determine if pain from a common procedure (disbudding) influences responses to a reward downshift. Holstein calves (n = 30) were trained to approach a 0.5 L milk reward. Latency to approach, number of vocalisations and pressure applied on the bottle were recorded during training. To assess how pain affected responses to reward downshift, calves were randomly assigned to one of three treatments before the downshift. Two groups were disbudded and provided the 'gold standard' of pain mitigation: intraoperative local anesthesia and analgesia. One of these disbudded groups was then provided supplemental analgesic before testing. The third group was sham disbudded. All calves were then subjected to the reward downshift by reducing the milk reward to just 0.1 L. Interactions were detected between test session and daily trials on pressure applied for the Disbudded group (estimate ± SEM: 0.08 ± 0.05), and on vocalisations for the Sham (0.3 ± 0.1) and Disbudding + Analgesia (0.4 ± 0.1) groups. Our results indicate that SNC is a promising paradigm for measuring negative affect in calves and suggests that pain potentially affects the response to a reward downshift.
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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) | 0.001 | 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".