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Record W2053799990 · doi:10.1017/s1751731109990656

A comparison between the 2N and 4N HCl acid-insoluble ash methods for digestibility trials in horses

2009· article· en· W2053799990 on OpenAlexaff
Domenico Bergero, C. Préfontaine, N. Miraglia, Pier Giorgio Peiretti

Bibliographic record

Venueanimal · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHayDry matterDigestion (alchemy)FecesField trialAnimal scienceChemistryHorseAnimal feedAgronomyFood scienceBiologyChromatography

Abstract

fetched live from OpenAlex

The digestibility of horse feeds and rations can be determined using different techniques such as calculations based on the chemical composition, in vivo or in vitro methods. The marker methods overcome difficulties like discomfort for the animals and longer experimental times encountered using the ingesta/egesta method. In field conditions, a natural indigestible marker like acid-insoluble ash (AIA), with no changes in the normal ration, could be a very useful tool for digestibility trials. A group of six standardbred horses was used in a set of seven apparent digestibility trials. The diets were based on a first-cut meadow hay added to three different cereals (barley for trials 1 and 2; oats for trials 3 and 5 and corn for trials 6 and 7), the hay : concentrate ratio being 60 : 40 or 70 : 30 on a dry matter basis. Feedstuffs and faeces were analysed to determine the AIA content, using 2N HCl or 4N HCl technique. No differences about AIA concentration were found between the two methods for means and accuracy in each diet. Digestion coefficients for each diet did not differ with AIA method, even if in some trials interfering factors consistently lowered the overall values. Consequently, the AIA 2N HCl can be considered the easier and cheaper method to state apparent digestibility in field conditions, and a good tool for digestibility trials in horses fed hay-based diets.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.171
GPT teacher head0.434
Teacher spread0.262 · 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 designObservational
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

Citations21
Published2009
Admission routes1
Has abstractyes

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