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Record W2359380423

Effects of dietary energy and lysine on the performance and biochemical traits in growing-finishing pigs during constant high temperature period

2004· article· en· W2359380423 on OpenAlexaff
Ye Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsGlobulinInternal medicineLysineAlbuminEndocrinologyChemistryTriglycerideLarge whiteHemoglobinCreatine kinaseAnimal scienceBiologyBiochemistryCholesterolMedicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the experiment was to study the effect of the digestible energy and lysme on theperformance and biochemical traits of growing-finishing pigs during constant high temperature period.Thtrty-twocross bred (Duroc×Long white×Large white)fatterting pigs.weighing an average of 44.1kg±1.8kg.wererandomly assigned to four treatments.The level of the crude protem was 15.5%.The levels of energy were 13.23MJ·kg~1.13.73 MJ·kg~1,14.93 MJ·kg~(-1).14.73 MJ·kg~1,and the,levels of lysine were 0.676%.0.726%,0.764%.0.800% respectively.Results showed that:①High dietary energy and lysine level increased the bodyweight sigmficantly(P0.05)and decreased the Fecd/Gain ratio(P0.05).②With the increase of the dietaryenergy and lysine.the concentration of thyroxine(T_).total protein,globulin in the serum increased significantly(P0.05),and the concentration of Thyroxine(T_4).glucose.albumin.increased insignificantly(P0.05).butthe content of creatine kinase.triglyceride.Albumin/Globulin decreased significantly(P0.05).③With theincrease of the dietary energy and lysine.serum K~+ increased significantly(P0.05)and serum C1 decreased(P0.05),The energy and lysine had little effect on the serum Na~+.④The energy and lysine increased the levelsof blood antibody and lymphocyte ratio,and had a significant effect on Serum IgM(P0.05).and had ainsignificant effect on the serum IgA.IgG and lvmphocvte ratio(P0.05).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.456

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)0.0000.000

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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