MétaCan
Menu
Back to cohort
Record W6931892806 · doi:10.5683/sp3/bb2kda

Can a social partner alleviate conditioned place aversion caused by isolation and pain in dairy calves?

2023· dataset· en· W6931892806 on OpenAlexaff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial isolationConditioningIsolation (microbiology)Social stressStressorSocial behaviourAnimal welfareSocial relation

Abstract

fetched live from OpenAlex

Social buffering occurs when the presence of a partner mitigates the stress response of an individual. In two experiments, we assessed the effects of social buffering in dairy calves, with and without a known conspecific, when either subjected to isolation from the larger group (Experiment 1; n = 12) or to recovery from the painful procedure of hot-iron disbudding (Experiment 2; n = 25). In Experiment 2, we also tested whether the level of fearfulness of each calf and the frequency of interactions between the two calves affected the degree of buffering. In both experiments the effects of buffering were assessed using a conditioned place aversion paradigm, with the prediction that calves would find experiences less aversive when exposed to the stressor with a conspecific. All calves were exposed to two conditioning treatments (i.e., exposed alone or with a ‘support’ partner), each session lasted 6 h each and were 48 h apart. Conditioned place aversion tests (with calves tested alone) occurred at 48, 72, 96 h after the second treatment. We found no evidence of social buffering for responses to either isolation from the group or the recovery from the painful experience. The number of physical interactions between the calves during treatment and the fearfulness of the calves also did not account for the individual variation observed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.004

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 head0.077
GPT teacher head0.373
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBorealisSame topicAdvanced Causal Inference TechniquesFrench-language works237,207