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Record W3195844304 · doi:10.3390/ani11092507

Dairy Heifer Motivation for Access to a Shaded Area

2021· article· en· W3195844304 on OpenAlexaff
Clarissa Silva Cardoso, M.A.G. von Keyserlingk, Luiz Carlos Pinheiro Machado Filho, María José Hötzel

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of British Columbia
FundersUniversidade Federal de Santa CatarinaCiência sem FronteirasFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsShadingAnimal sciencePastureHeat indexHeat stressLatency (audio)Environmental scienceGeographyBiologyHorticultureEcologyTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

= 18) were trained to push a weighted gate to access either an experimental area containing both a shaded (simple tree shade and shade cloth) and unshaded area (WITH SHADE) or an experimental area with no shade (BARREN). The latency to push the weighted gate, and the maximum weight pushed by each heifer, were recorded in both the summer and the autumn. Temperature and humidity were recorded continuously for the duration of the study and were used to calculate the heat index. The maximum weight pushed to enter the WITH SHADE area was greater in summer than in autumn, and was inversely related to the latency to push the weighted gate. Heifers refused to work for access to the BARREN environment. As expected, both the maximum ambient temperatures and heat index were higher in summer than in autumn, and also higher in the non-shaded areas than under the shade in both seasons. Heifers of higher social rank displaced other heifers more often, and spent more time in the shaded areas, particularly in the area with trees plus a shade cloth, than the intermediate and subordinate heifers. We conclude that shade is an important and valued resource for heifers reared on pasture-based systems in sub-tropical environments, particularly during the hot summer months.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.062
GPT teacher head0.274
Teacher spread0.212 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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