Caracterización y diagnóstico de las cadenas de carne porcina, carne aviar y apicultura
Bibliographic record
Abstract
resultados de una Evaluación de Diseño, realizada sobre la convocatoria del Fondo de Promoción de Tecnología Agropecuaria (FPTA) 2019.El Plan Estratégico Institucional (PEI) 2016-2020 de INIA y su Agenda de Investigación, basada en una matriz de Problemas/ Oportunidades (P/O), constituyen herramientas para la identificación y formulación de los proyectos de investigación.Dado que la Agenda de Investigación definida no incluyó líneas específicas vinculadas a los rubros de avicultura, apicultura y suinos, la Junta Directiva de INIA solicitó que se realizara en 2019 una convocatoria del FPTA específica para apoyar la investigación e innovación en dichos rubros.Durante octubre y noviembre de 2018, INIA realizó una serie de consultas y reuniones con representantes y referentes de esos tres sectores, a efectos de identificar los problemas y oportunidades más relevantes que enfrentan cada una de las cadenas.De este modo, la convocatoria FPTA 2019 se propuso contribuir a solucionar dichos problemas, a través del apoyo a proyectos de investigación/innovación.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".