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Record W2047628338 · doi:10.1093/japr/15.1.28

Midday and Nighttime Cooling of Broiler Chickens

2006· article· en· W2047628338 on OpenAlexaff
José ́Candelario Segura-Correa, J.J.R. Feddes, M.J. Zuidhof

Bibliographic record

VenueThe Journal of Applied Poultry Research · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsBroilerAnimal scienceStockingDiurnal temperature variationFeed conversion ratioBiologyEnvironmental scienceBody weightMeteorologyEndocrinology

Abstract

fetched live from OpenAlex

Hot weather along with high stocking densities can lead to high mortality and decreased performance of broilers, especially during the last week of rearing. Two trials were conducted to test the hypotheses that reduced nighttime and midday temperatures improve broiler live performance and reduce mortality under warm cyclic temperature conditions. In each trial, groups of 306 male broilers were placed in each of 6 environmentally controlled chambers. The warm temperature treatments were control, nighttime cooling, and midday cooling (4 replicates). In the control treatment, diurnal temperatures ranged between 15 and 25°C on d 29 and progressively increased to between 20 and 35°C on d 42. In trial 2, 2 additional chambers housed broilers under thermoneutral conditions. No differences in feed consumption were found within or between trials due to the temperature treatments. The cooling treatments did not improve BW, weight gain, feed conversion, or livability. The birds housed under thermoneutral conditions did not have improved BW. These results suggest that broilers subjected to regular warm cyclic temperature fluctuations for 2 wk prior to shipping are able to acclimatize with no negative impact on BW. Effective environmental temperatures predicted from broiler surface thermal resistance and thermal mass were within 5°C among the treatments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.284
Teacher spread0.240 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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