INFLUENCE OF MOLDING HOMOGENATES ON ELEMENT AND METABOLIC STATUS OF DOGS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".