Perfil hematológico de caninos recém-nascidos do município de São Luís – Maranhão
Bibliographic record
Abstract
The pediatrics as a medical clinical specialty of small animals has been increasing as the pet market grows, requiring new management techniques and better results. Newborns are a special challenge in veterinary therapy due to differences they present when compared to adult dogs. In order to determine a therapeutic protocol for a patient, it must be known their health condition, physiology and characteristics. Commonly veterinarians feel technically unprepared towards newborns, because this specialty is not part of the curriculum in most veterinary schools of our country. A complete blood count is an important tool that assists in directing clinical diagnosis, since it reflects the animal health status at the time of collection. It allows assessing whether there are changes in blood components of the animal body, and checking the organism’s defense response to any threat. Rottweiler, Poodle, Labrador and Shi Tzu breeds were selected for the study, The objective of this study is to provide grants to veterinarians who constantly in their work routine have contact with this age group and also to encourage further research into the consolidation area. This study demonstrated similarities between the results obtained and the hematological reference values available in the world literature.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".