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Record W2369569586

Effect of Heat Stress on Physiological,Hormonal and Blood Biochemical Indexes in Labrador Dogs

2015· article· en· W2369569586 on OpenAlexaboutno aff
Qin Hai-bi

Bibliographic record

VenueProgress in Veterinary Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsSeral communityHeat stressHeat indexRectal temperatureHormoneInternal medicineChemistryHyperventilationEndocrinologyAnimal scienceMedicineBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper was to study physiological and biochemical status of dogs with heat stress and find the meanings of these indexes as the indicator of heat stress.The common physiological,hormonal and blood biochemical indexes of twenty-four Labrador dogs were surveyed at 14,18,28,35 ℃respectively.Compared with the normal state at 14 ℃ and 18 ℃,all the measured indexes indicated that dogs were under heat stress at 28 ℃ and 35 ℃.Breathing frequency and seral COR were significantly increased(P0.05)in heat stress,and this increase showed significant correlation(P0.05)with enviromental temperature(T)and temperature humidity index(THI).The rectal temperature and seral ATCH didn't changed significantly.The major seral enzymes such as ALT,ASP,CK,LDH increased significantly in acute heat stress period,then dropped to normal values.GLU and ALP maintained on normal levels.These results indicated that T,THI,COR and breathing frequency are meaningful indexes to study heat stress in dogs.But the rectal temperature,seral ACTH,GLU,ALP didn't changed significantly in heat stress,so these indexes are meaningless.In addition,studies on heat stress proved that ALT,ASP,CK,LDH would changed no rule in different animal modelsand the significance in studies on heat stress should be further studied.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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