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Record W2221377182 · doi:10.5539/mas.v9n13p36

Investigation of Nano Selenium Influence on Productivity and Hematological Exponents of Broiler Chickens

2015· article· en· W2221377182 on OpenAlexvenueno aff
A. A. Konkov, Ampleeva L.V., С. Д. Полищук, Г. И. Чурилов

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSeleniumBroilerFodderAnimal scienceBody weightNano-ChemistryProductivityBiologyEndocrinologyMaterials scienceEcology

Abstract

fetched live from OpenAlex

They have shown the influence of nano selenium water solution on intensification of growth and broiler chickens’ blood exponents change. Chickens’ metabolism modification with nano selenium causes the changes of blood biochemical exponents depending on nano selenium concentration. They have used nano selenium at concentration of 0.1, 0.01 and 0.001 mgr/kg of live weight a day. Independent of the concentration one can see the increase of blood platelets and chickens’ live weight and the decrease of the used fodder in all groups.

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 score0.001
Threshold uncertainty score0.002

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 head0.057
GPT teacher head0.268
Teacher spread0.211 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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