MétaCan
Menu
Back to cohort

INFLUENCE OF MOLDING HOMOGENATES ON ELEMENT AND METABOLIC STATUS OF DOGS

2020· article· en· W3038802930 on OpenAlexaboutno aff
Н. В. Ефанова, Л. М. Осина, С. В. Баталова

Bibliographic record

VenueInnovations and Food Safety · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsSeleniumChemistryTriglycerideSodiumAnimal sciencePhosphorusFood scienceCalciumBlood sugarGlobulinPotassiumCholesterolBiochemistryEndocrinologyBiology

Abstract

fetched live from OpenAlex

In order to study the effect of the drone homogenate on the elemental, metabolic, hematological, and immune statuses of dogs, we created control and experimental groups of Labrador retrievers. The dogs were kept in the conditions of apartments in the village of Kochenevo, Novosibirsk Region. The diet of animals consisted of meat, cereal porridge, cottage cheese and vegetables. Dogs received daily vitamin and mineral supplements and apples. Labradors – retrievers of the control group daily, for two months, drone homogenate was drunk once a day at the rate of 15 mg per kg of body weight. The results of the studies showed that in the presence of a drone homogenate, dogs increase thyroxin synthesis, activate erythro and leukopoiesis, increase phagocytic activity and phagocytic index, synthesis of total blood protein, in particular globulins, and triglyceride levels. An analysis of the chemical composition of blood serum carried out before the start of the experiment revealed in dogs of both groups more or less pronounced microelementoses in terms of cobalt, chromium, iron, potassium, manganese, sodium, phosphorus, lead, selenium and zinc. After completion of the course of taking the homogenate, a correction of the macro- and microelement composition of the blood occurred. The concentration of lithium, chromium, sodium, iron, phosphorus was statistically significantly reduced, and the concentration of boron, calcium, copper, iodine, nickel and tin increased.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.095

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.036
GPT teacher head0.238
Teacher spread0.201 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInnovations and Food SafetySame topicFood Industry and Aquatic BiologyFrench-language works237,207