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Record W4406798610 · doi:10.6000/1927-520x.2025.14.01

Differences in Physiological Characteristics and Heat Shock Protein Expression in Taiwan Swamp Buffaloes During Winter and Summer Seasons

2025· article· en· W4406798610 on OpenAlexvenueno aff
Pei Chen, Pi Hua Chuang, Bin Dai, Liang Yuan Wei

Bibliographic record

VenueJournal of Buffalo Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsSwampBiologyHeat shock proteinShock (circulatory)Hsp70EcologyGeneticsInternal medicineMedicine

Abstract

fetched live from OpenAlex

Background: This study examined the respiratory rate, rectal temperature, and expression levels of heat shock protein 70 (HSP70) and heat shock protein 90 (HSP90), as revealed by ELISA, in Taiwan swamp buffaloes (Bubalus bubalis, swamp-type) during the winter (February) and summer (August) seasons of 2022 in Taiwan. Methods: Data were collected from Taiwan swamp buffaloes during the winter and summer seasons. Respiratory rate, rectal temperature, and protein expression levels were measured and analyzed. Results: The results revealed age-related differences in response to changes in environmental temperature. In winter, buffaloes aged <1 year exhibited significantly higher respiratory rates, rectal temperatures, and heat tolerance coefficients than female buffaloes aged 14 to 20 years (P < 0.05). In the summer season, buffaloes aged <1 year had significantly higher rectal temperatures (P < 0.05) and higher expression levels of HSP70 (from ELISA) than female buffaloes aged 6 to 9 years and 14 to 20 years (P < 0.05). Conclusion: The findings suggest that the age of Taiwan swamp buffaloes affects their physiological responses to heat stress, with younger buffaloes exhibiting greater physiological reactions to heat stress than older buffaloes.

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 score0.004
Threshold uncertainty score0.007

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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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