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Record W2808049685 · doi:10.1038/s41598-018-27669-7

Approach-aversion in calves following injections

2018· article· en· W2808049685 on OpenAlexaff
Thomas Ede, M.A.G. von Keyserlingk, Daniel M. Weary

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNasal administrationSalineMedicineAnesthesiaLatency (audio)Animal scienceBiologyPharmacology

Abstract

fetched live from OpenAlex

We assessed aversion to injections using an avoidance-learning paradigm. Holstein calves (n = 24) were randomly assigned to one of four routes of administration for 0.5 ml of saline: intramuscular (IM), intranasal (IN), subcutaneous (SC) and a null control. Calves were first trained to approach a milk reward of 1 L. Once the latency to approach the reward was consistent, calves received their assigned treatment when approaching the bottle. For the first 3 treatment sessions calves received a 1 L milk reward. This reward was then reduced to 500 mL, and then to 250 mL, and finally to 0 mL, each for 3 sessions. Compared to control calves, calves receiving the intramuscular injections showed a longer latency to approach the milk reward, but only when the milk reward was 0.25 L (P = 0.05) and 0 L (P < 0.01). Calves receiving the intranasal injections showed longer latencies relative to the controls only for the 0 L reward (P = 0.01). Calves receiving the subcutaneous injections did not differ from controls for any of the milk rewards (P > 0.2). We conclude that IM injections are aversive and that SC and IN routes are a refinement to be considered when feasible.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.058
GPT teacher head0.337
Teacher spread0.279 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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