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Record W3180396042 · doi:10.1139/cjas-2021-0025

Bark charcoal powder containing wood vinegar liquid can shorten the time to shipping of broilers raised in tropical areas by activating performance and intestinal function

2021· article· en· W3180396042 on OpenAlexvenueno aff
Janjira Sittiya, Koh‐en Yamauchi, Kohsyo Yamauchi

Bibliographic record

VenueCanadian Journal of Animal Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsBark (sound)Dry matterFecesAnimal scienceCharcoalBroilerNutrientBiologyChemistryEcology

Abstract

fetched live from OpenAlex

The effect of dietary bark charcoal containing vinegar liquid (BCV) on the growth performance, carcass and visceral organs, intestinal histology, fecal ammonia nitrogen, and nutrient digestibility were investigated in a total of 350 one-day-old unsexed Ross 308 broilers raised under a high environmental temperature (heat stress) from 0 to 42 d of age. They were divided into 0% (control group), 1%, 2%, 3%, and 4% dietary BCV groups. Compared with the control group, the body weight gain (BWG) of the 1% dietary BCV group at 21–42 d of age and the feed efficiency of the 2% dietary BCV group at 0–42 d of age increased (P < 0.05). The carcasses of the 1% and 3% dietary BCV groups significantly decreased (P < 0.05). The digestibility of dry matter and gross energy of the 4% dietary BCV group improved at 21 d (P < 0.05). The duodenum of the 3% and 4% dietary BCV groups was significantly heavier than those in the control groups (P < 0.05). This result suggests that BCV can shorten the time to shipping date for broilers to before 42 d of age and can contribute to the development of poultry production in tropical regions.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Animal Science→Same topicAnimal Nutrition and Physiology→French-language works237,207→