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Record W3102249225 · doi:10.1371/journal.pone.0242100

Pessimistic dairy calves are more vulnerable to pain-induced anhedonia

2020· article· en· W3102249225 on OpenAlexafffund
Benjamin Lecorps, Emeline Nogues, M.A.G. von Keyserlingk, Daniel M. Weary

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnhedoniaMedicinePessimismOptimismAnesthesiaAnimal sciencePsychologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Pain induces deficits in appreciation of rewards (i.e. anhedonia) and variation in response to pain may be partly explained by individual differences in general expectations (i.e. optimism). Dairy calves are routinely subjected to painful procedures such as hot-iron disbudding. We tested if female Holstein calves (n = 17) display signs of anhedonia (as evidenced by reduced consumption of a sweet solution) after hot-iron disbudding (performed under general and local anesthesia), and whether individual differences in optimism explain the variation in this response. Individual variation in optimism was measured using responses to two judgment bias tests (performed when calves were 25 d old), and anhedonia was measured by comparing consumption of a sweet solution before and after hot-iron disbudding. We found that intake of the sweet solution declined (by mean ± SD: 48.4 ± 44.3%) on the day after disbudding, and that more pessimistic calves were more affected. Sweet solution consumption did not return to baseline for the duration of the study (i.e. 5 days). Calves reduced their intake of a sweet solution after hot-iron disbudding, consistent with pain-induced anhedonia, and more pessimistic calves showed stronger evidence of anhedonia, suggesting that they were more affected by the procedure. However, our results cannot rule out the possibility that calf responses were driven by anorexia.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.209
GPT teacher head0.321
Teacher spread0.112 · 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 designObservational
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

Citations21
Published2020
Admission routes2
Has abstractyes

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