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Record W2784247560 · doi:10.1080/09712119.2017.1422256

Treatment and post-treatment effects of dietary supplementation with safflower oil and linseed oil on milk components and blood metabolites of Canadian Holstein cows

2018· article· en· W2784247560 on OpenAlexafffundabout
Adolf Ammah, C. Benchaar, Nathalie Bissonnette, Nicolas Gévry, Eveline M. Ibeagha‐Awemu

Bibliographic record

VenueJournal of Applied Animal Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversité de SherbrookeAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsNEFALinseed oilAnimal scienceUreaFatty acidFood scienceBiologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

The treatment and residual effects of linseed oil (LSO) and safflower oil (SFO) supplementation on milk and blood metabolites of cows was investigated. Twenty-six cows were grouped according to parity and days in milk and assigned equally to one of two treatments: a control diet + 5%SFO or 5%LSO for 28 days (treatment period, TP). The TP was preceded by a control period of 28 days (all animals on control diet). After treatment, animals were returned to control diet for 28 days (posttreatment period, PTP). Blood and milk samples were collected weekly. Feed intake decreased with LSO and SFO (p <.05), while body weight (BW) increased steadily (p <.0001) throughout. Non-esterified fatty acid (NEFA) and triacylglyceride (TAG) increased (p <.0001) during treatments. Beta-hydroxybutyric acid increased (p <.0001) with SFO only. Milk urea nitrogen (MUN) and fat decreased (p <.0001) with both supplements. NEFA/MUN and milk fat content returned to control levels one week or three weeks after treatment, respectively. TAG did not return to initial concentrations by the end of PTP. The residual effects of feeding LSO or SFO on the physiology of cows were still active up to three weeks after cessation of treatments.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.997

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 head0.045
GPT teacher head0.292
Teacher spread0.247 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

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