Dogs’ Shampoos for the Coat Care
Bibliographic record
Abstract
The majority of cosmetics for dogs are shampoos. These are used not only to wash the coat and improve its structural properties; but also as adjuvant treatment in various skin disease The dogs’ owners in Lithuania are beginning carefully to take care of their pet well-being, skin health and appearance of the coat. There are many cosmetic devices for this reason and the market is expanding by the addition of new products. So, the purpose of this work was to evaluate shampoos effects to dogs‘ hair for daily coat care. There were three dogs in the research work involved, and three shampoos for daily coat care for research work selected randomly: X1, X2 and X3. Some hair of each dog were used for analysis by scanning electron microscope FEI Quanta 200 FEG. There were taken hair photos‘ of cross-section. Other hair was used to evaluate hair‘ ratio of convolution. The maximum visible changes of hairs cross-section are seen in photos made after bathing of every dog with X3 shampoo. The ratio of convolution have increased in german jagdterriers breed case with all exploratory shampoos. Labrador retriever and german shepherd ridge‘s hair ratio of convolution reduced after bathing with all exploratory shampoos. After the research, there were established which exploratory shampoo is the most economical (X3), which is the most suitable for dogs’ skin pH (X3). Also, the influence of shampoos’ for hair surface, cross-section diameter and ratio of convolution was observed. In conclusion, the maximum visible changes were observed in photos after bathing with X3 shampoo, which was evaluated like the best by the dogs‘owners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".