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Record W1918668959 · doi:10.4141/cjas-2015-100

Review: Recent advances in evaluation of bags made from different textiles used in situ ruminal degradation

2015· article· en· W1918668959 on OpenAlexvenueno aff
Tiago Neves Pereira Valente, Edênio Detmann, Cláudia Batista Sampaio

Bibliographic record

VenueCanadian Journal of Animal Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsRumenNeutral Detergent FiberIn situDry matterDegradation (telecommunications)Organic matterIncubationChemistryAnimal scienceEnvironmental sciencePulp and paper industryFood scienceBiologyFermentationBiochemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

Valente, T. N. P., Detmann, E. and Sampaio, C. B. 2015. Review: Recent advances in evaluation of bags made from different textiles used in situ ruminal degradation. Can. J. Anim. Sci. 95: 493–498. Textile bags are used in the laboratory to analyze the indigestible contents (internal markers) of feedstuffs after in situ ruminal incubation. Information is needed on the rate and extent of degradation in the rumen using bags made from different materials. In situ techniques have been used extensively to measure the degradation of feedstuffs in the rumen. However, in situ techniques are prone to variability. This paper reviews the effects of particle size, the material from which bags are made, pore size, tensile strength of the bags, in situ estimation of the levels of indigestible compounds [indigestible dry matter (iDM), indigestible neutral detergent fiber (iNDF), and indigestible acid detergent fiber (iADF)], rumen degradation profiles, and the use of bags made from nylon (50 µm), F57 (Ankom®), and non-woven textile (100 g m−2).

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.999
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.083
GPT teacher head0.303
Teacher spread0.221 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations44
Published2015
Admission routes1
Has abstractyes

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