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Record W1985638167 · doi:10.5539/jas.v5n2p150

Effect of Different Levels of Nanoparticles Chromium Picolinate Supplementation on Performance, Egg Quality, Mineral Retention, and Tissues Minerals Accumulation in Layer Chickens

2013· article· en· W1985638167 on OpenAlexvenueno aff
Nattapon Sirirat, Jin-Jenn Lu, Alex Tsubg-Yu Hung, Tu‐Fa Lien

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsChromiumYolkEggshellChemistryZincAnimal scienceFeed conversion ratioBody weightFood scienceEndocrinologyBiologyEcology

Abstract

fetched live from OpenAlex

This study was conducted to investigate the effects of various levels of nanoparticles chromium picolinate on performance, egg quality, minerals retention, and tissues accumulation of layer chickens. This study used 54 seventy-week old post-molt laying hens randomly allocated into 0 (control), 500 ppb (µg kg-1) Cr and 3000 ppb Cr groups for a 60-day experiment. The chromium was nanosize (80.8 ± 2.7 nm) chromium picolinate (NanoCrPic) and each treatment was undertaken with six replicates. In the meantime, a total of 18 birds (1 bird/replicate) were used for metabolic experimentation. The results of the experiment indicated that there were no significant effects on body weight, feed intake, feed efficiency, and egg production of layers. Supplemental NanoCrPic could significantly (p < 0.05) improve egg quality, or retention of chromium and zinc, but decrease shell ratio in the 60th day eggs. The addition of NanoCrPic resulted in increased minerals accumulation in tissues such as Cr, Ca, and P concentration in the liver, Cr concentration in the yolk and Ca concentration in the eggshell. In conclusion, supplemental NanoCrPic improved Cr and Ca accumulation in the liver and egg, improved Zn and Mn retention in layer chickens.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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 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

Citations43
Published2013
Admission routes1
Has abstractyes

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