Análise de variáveis hematológicas e bioquímicas em cães de busca e resgate
Bibliographic record
Abstract
Exercise can induce a series of physiological and laboratory changes depending on some factors such as type of exercise, intensity, frequency, and level of training of the dog. This study aimed to evaluate changes in blood count, leukogram, and biochemical parameters before and after each search performed by the animals. Samples were collected before starting the first search (T0), right after the first search (T1), after the lunch break before starting the second search (T2) and right after the second search (T3). The study was based on verifying changes between these intervals, to also assess possible muscle injuries resulting from efforts made during exercise, with the hypothesis of changes in the blood count due to splenic contraction and gas exchange, we sought to verify changes. Six dogs were studied, four of the Bloodhound breed, a male Belgian Shepherd and a female Labrador Retriever that were submitted to search work. Clinical signs of exhaustion or exercise intolerance were not observed in the dogs during the study. In the course of the studies, no alterations were identified that deviate from the normal physiological pattern.
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 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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".