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Record W3082178193 · doi:10.1201/9780429059544

Animal-centric Care and Management

2020· book· en· W3082178193 on OpenAlexaff
Dorte Bratbo Sørensen, Sylvie Cloutier, Brianna N. Gaskill

Bibliographic record

Venuenot available
Typebook
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsCanadian Council on Animal Care
Fundersnot available
KeywordsAnimal welfareHuman animalAnimal-assisted therapySleep (system call)HUBzeroWelfarePet therapyAnimal scienceVeterinary medicinePsychologyBiologyMedicineEcologyPolitical science

Abstract

fetched live from OpenAlex

Even though pigs are relatively new as laboratory animals, the human-pig interface goes back a long time and is dominated by the use of pigs for human consumption. Pigs are not strongly territorial, they merely defend resources within their home range. The diurnal rhythm of domestic pigs kept in semi-natural conditions is characterized by relatively long periods of rest/sleep and long periods of activity. Keeping pigs in barren environments can lead to behavioral problems, lack of stimulation, and poor welfare, but these issues can be improved by the use of environmental enrichment. Most pigs used for research are meat-producing breeds purchased from farmers or special-purpose breeds like the Gottingen minipig. The use of positive reinforcement training will develop a positive human-animal relationship with safe and positive interactions, where pigs voluntarily cooperate. Pigs that are routinely trained and socialized are less fearful of humans and they are expectant and calm when people enter the room.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.211
Threshold uncertainty score0.707

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2110.233

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 head0.047
GPT teacher head0.304
Teacher spread0.257 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations8
Published2020
Admission routes1
Has abstractyes

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