ABORDAGEM DIAGNÓSTICA E TERAPÊUTICA DA SÍNDROME VESTIBULAR PERIFÉRICA SECUNDÁRIA A OTITE INTERNA EM CÃO DA RAÇA LABRADOR RETRIEVER: RELATO DE CASO
Bibliographic record
Abstract
Coffee production in Colombia, during the third quarter of 2021, grew 21.6% compared to the same period in 2022, reaching 8.8 million bags in the first nine months of the year.This positive behavior was driven by the good climate that favored the productivity peaks of the coffee park, and by the entry into production of approximately 117 thousand hectares that were renewed in recent years.The Colombian coffee zone is characterized by high cloud cover during the day, which can be estimated from the records of sunlight and by the availability of water in the soil, a variable quantified through regional water balances (Jaramillo, 2005).These two conditions, determinants of coffee production, must be taken into account to guide cultivation practices, including shading.In this research project, the behavior of different physiological variables was evaluated, under different levels of shading 0%, 35%, 50% and 65%, the variables of net assimilation index and relative growth index, the treatment sown with 35%. of shading, presents the highest values in the physiological index variables in relation to the 50 and 65% shading treatments.The crop growth indices, leaf area and leaf area index had higher values in the treatments planted under shade of 65% (0.48), 35 and 50% (0.48) than the treatment planted under free sun exposure.( 0
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".