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Record W2022043878 · doi:10.4141/cjps07133

Development of a method to determine carotenoid composition of fresh forages

2008· article· en· W2022043878 on OpenAlexvenueno aff
Nicolas Cardinault, Bernard Lyan, M. Doreau, Béatrice Chauveau, Edmond Rock, Pascal Grolier

Bibliographic record

VenueCanadian Journal of Plant Science · 2008
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsnot available
Fundersnot available
KeywordsXanthophyllLuteinCarotenoidAntheraxanthinForageViolaxanthinFood scienceSaponificationBiologyChlorophyllBotanyRuminantChemistryAgronomyZeaxanthinBiochemistryPasture

Abstract

fetched live from OpenAlex

Due to the limited interest in carotenoids in ruminant diets until recently, analyses of forages are often incomplete, focusing mainly on β-carotene and lutein. Carotenoid composition of green forage from middle mountain meadow was analyzed by HPLC after extraction and elimination of chlorophylls by mild saponification. This method of analysis uses two C18 columns in series with a quaternary gradient system. Our method allowed, for the first time, the identification and quantification of several xanthophylls other than lutein (i.e., violaxanthin, antheraxanthin, epilutein) in chlorophyll-free extracts from carotenoid-rich forage. The intra-day (3.5–7.5 %) and inter-day (1.2–3.5 %) coefficients of variation are suitable for routine determination of carotenoids in green forage. This method could also be used in metabolic studies of these micronutrients in ruminants. Key words: Xanthophylls, carotenoids, fresh forage, HPLC

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.047
GPT teacher head0.292
Teacher spread0.245 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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