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Record W4362698510 · doi:10.1038/s41598-023-32756-5

Calves peak-end memory of pain

2023· article· en· W4362698510 on OpenAlexafffund
Thomas Ede, Raphaela E. Woodroffe, M.A.G. von Keyserlingk, Daniel M. Weary

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDairy Farmers of ManitobaDairy Farmers of Canada
KeywordsAnalgesicMedicineAnesthesiaRecallConditioningReflexPsychologyMathematicsStatisticsCognitive psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.331
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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