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Record W4410612530 · doi:10.1080/00207233.2025.2507444

Fertility of indigenous “Atlas Brown” Algerian cattle under different heat stress levels

2025· article· en· W4410612530 on OpenAlexaff
Aziza Ferag, Djalel Eddine Gherissi, Tarek Khenenou, Amel Boughanem, Hafida Hadj Moussa, Amina Maamour, Christian Hanzen

Bibliographic record

VenueInternational Journal of Environmental Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsArtificial Insemination Center of Quebec
Fundersnot available
KeywordsHeat stressIndigenousAtlas (anatomy)FertilityGeographyFisheryEnvironmental scienceBiologyAnimal scienceEcologyDemographyPopulation

Abstract

fetched live from OpenAlex

This study investigates the impact of heat stress, measured by daily temperature-humidity index (THI), on the reproductive performance of native Algerian cows. We analysed fertility metrics from 3,847 artificial inseminations performed on 2,130 Atlas Brown cows. Results showed a total pregnancy rate (TPR) of 58.97%, a first-service conception rate (CR1stAI) of 24.41%, and a second-service conception rate (CR2ndAI) of 36.71%. Severe THI levels (>80) significantly decreased TPR by 26% and CR1stAI by 46%, but low and moderate THI had no significant impact. Heat stress did not significantly affect CR2ndAI and repeat breeding cows (RBC), though moderate and severe heat stress decreased CR2ndAI, and severe heat stress increased RBC. Moderate heat stress reduced the proportion of cattle with <30 days reproductive period. The study shows that Atlas Brown cattle are susceptible to high THI levels and perform well under low and moderate heat stress, suggesting the potential utility of indigenous breeds in high-THI regions.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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
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

Citations1
Published2025
Admission routes1
Has abstractyes

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