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Record W2809883519 · doi:10.1080/1828051x.2017.1404942

Effect of fish oil supplementation and forage source on Holstein bulls performance, carcass characteristics and fatty acids profile

2018· article· en· W2809883519 on OpenAlexaff
Hossein Zakariapour Bahnamiri, Mahdi Ganjkhanlou, A. Zali, Wen Yang

Bibliographic record

VenueItalian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDry matterSilageFish oilFeedlotHayAnimal scienceForagePolyunsaturated fatty acidBiologyRuminantFood scienceBeef cattleChemistryFatty acidFish <Actinopterygii>AgronomyPastureBiochemistry

Abstract

fetched live from OpenAlex

Thirty-six Holstein bulls (initial body weight, 345 ± 61 kg) were randomly assigned to six dietary treatments with a 2 × 3 factorial arrangement, with two levels of AH (alfalfa hay) (10 and 20% of AH) combining with three levels of FO (fish oil) (0, 1 and 2.1% of DM) to investigate the effects of AH proportion and FO supplementation on performance, carcase characteristics, and meat fatty acids profile. DMI (dry matter intake) (kg/day) was lower (p < .01) for high (8.0) than for low (8.7) AH. Highest level of FO reduced DMI (p < .01) regardless of AH level. Dietary inclusion of FO increased the concentration of VA (p < .01), CLA (p < .01) and n-3 (p < .01) fatty acids which subsequently reduced n-6: n-3 (p < .01). The results indicate that AH can be replaced by corn (zea mays) silage to mitigate the detrimental effect of supplemented fat on dry matter intake. Moreover, FO can be supplemented to feedlot diet to enrich ruminant products without deleterious effects on carcase characteristics.

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 score0.655
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.241 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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