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Record W1827960186 · doi:10.3168/jds.2008-1173

Letter to the Editor: A Response to the Comments of

2008· letter· en· W1827960186 on OpenAlexaff
H.V. Petit, Marie‐France Palin

Bibliographic record

VenueJournal of Dairy Science · 2008
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsNEFAForageAnimal scienceSaturated fatFood scienceChemistryBiologyEndocrinologyBiochemistryCholesterolAgronomyInsulin

Abstract

fetched live from OpenAlex

We appreciate the opportunity to respond to the Letter to the Editor by Rastani and Kertz, 2008Rastani R.R. Kertz A.F. Letter to the Editor: Differences in forage to concentrate ratios can confound results: A comment on Petit et al. (2007).J. Dairy Sci. 2008; 91: 2533Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar in response to our paper published in the Journal of Dairy Science (Petit et al., 2007Petit H.V. Palin M.F. Doepel L. Hepatic lipid metabolism in transition dairy cows fed flaxseed.J. Dairy Sci. 2007; 90: 4780-4792Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar). We acknowledge the fact that dietary NDF concentrations ranged from 33.4 to 39.4% of the DM, which is a difference of 6 points. Moreover, greater forage to concentrate ratio and NDF concentration in the diet of cows fed saturated lipids led to lower postpartum DMI compared with cows fed whole flaxseed or a control diet. Therefore, the response to fat treatments was partly confounded by differences in the forage to concentrate ratios and dietary NDF concentrations of the 3 postpartum diets. Feed intake might have been at least partly limited by gut fill, especially for the 39.4% NDF diet that contained saturated lipids. Furthermore, much lower starch concentration in the diet containing saturated lipids very likely contributed to lower plasma glucose and greater NEFA concentrations for this diet. Comparisons of control (CO, no supplemental fat) and unsaturated lipids supplied as whole flaxseed (FL) to saturated lipids supplied as Energy Booster (EB; MSC, Dundee, IL) were confounded by different forage to concentrate ratios that likely affected response variables, although the comparison between CO and FL may still be valid. Indeed, cows fed the CO and FL diets had similar DMI after calving (P > 0.05), and NDF concentrations in CO and FL diets were also similar but some blood parameters related to fatty liver syndrome differed between these diets. Multiparous cows fed FL had lower concentrations of triglycerides than those fed CO or EB in wk 4 after calving. Moreover, liver glycogen concentration after calving was significantly greater for multiparous cows fed FL compared with those fed CO. There was no confounding effect between diets CO and FL because similar DMI for both resulted in differences for some parameters related to fatty liver syndrome. Finally, the objectives of the present experiment were to “determine the effects of feeding flaxseed, a rich source of n-3 FA, on liver concentrations of TG, glycogen, and total lipids, liver and blood profiles of FA, and plasma concentrations of NEFA, BHBA, FA, and glucose. Energy Booster, a source of saturated and rumen inert lipids, was compared with flaxseed to determine the effects of dietary lipids with different profiles of fatty acids.” Therefore, these objectives were met by the results reported by Petit et al., 2007Petit H.V. Palin M.F. Doepel L. Hepatic lipid metabolism in transition dairy cows fed flaxseed.J. Dairy Sci. 2007; 90: 4780-4792Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar regarding the effects of flaxseed on blood and liver concentrations of different parameters and the conclusion goes along with the objectives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.078
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0050.005
Open science0.0040.003
Research integrity0.0380.036
Insufficient payload (model declined to judge)0.0140.017

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 head0.030
GPT teacher head0.261
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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
Published2008
Admission routes1
Has abstractyes

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