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Record W4318052091 · doi:10.1016/j.psj.2023.102526

Research Note: Impact of Eimeria on apparent retention of components and metabolizable energy in broiler chickens fed single or mixture of feed ingredients-based diets

2023· article· en· W4318052091 on OpenAlexaff
Emily Kim, William Lambert, Elijah G. Kiarie

Bibliographic record

VenuePoultry Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCoccidia and coccidiosis research
Canadian institutionsCanadian Animal Health InstituteUniversity of Guelph
Fundersnot available
KeywordsAmenBroilerEimeriaDry matterEimeria acervulinaChemistrySoybean mealAnimal scienceNutrientMealFood scienceNet energyBiology

Abstract

fetched live from OpenAlex

The effect of Eimeria on apparent retention (AR) of components and metabolizable energy corrected for nitrogen (AMEn) content in corn, wheat, soybean meal (SBM), and pork meal (PM) was investigated in broiler chickens. A total of 840 male d-old Ross 708 chicks were placed in 84 cages (10 birds/cage) and allocated either a nitrogen-free diet (NFD), or 1 of 6 test cornstarch-based semipurified diets: 1) corn, 2) wheat, 3) SBM, 4) PM, 5) corn, SBM, and PM (CSP) mixture, and 6) wheat, SBM, and PM (WSP) mixture (n = 12). Diets contained 0.3% titanium dioxide and nutrient digestibility was determined by difference method using NFD. On d 10, birds in half of replicates per diet were orally challenge with 1 mL of E. acervulina and E. maxima culture and the other half equal volume of saline. Excreta samples were collected from d 12 to 14. With exception of AR of Ca and P, there was no interaction (P > 0.05) between Eimeria and diet on AR of dry matter, crude fat (CF), crude protein and gross energy and AMEn of ingredients. Eimeria reduced AR of CF (P = 0.01) and had a tendency to reduce AR of DM (P = 0.09) and AMEn (P = 0.063) of ingredients. The data demonstrated exposure to Eimeria impacted nutrient retention and energy utilization irrespective to diet composition.

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 score0.728
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
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 head0.110
GPT teacher head0.362
Teacher spread0.253 · 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
Published2023
Admission routes1
Has abstractyes

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