Microaprendizaje para estudiar bioeconomía y economía circular en sistemas agroalimentarios - [Microlearning to study bioeconomy and circular economy in agri-food systems]
Bibliographic record
Abstract
La bioeconomía y la economía circular son aspectos novedosos que están despertando un creciente interés. A pesar de ello, existen escasos materiales didácticos relacionados con estos conceptos, especialmente en el ámbito agroalimentario. El proyecto BIOCIR (Aprendizaje activo en bioeconomía y economía circular en sistemas agroalimentarios) pretende suplir esa carencia creando contenido audiovisual mediante técnicas microlearning. El proyecto involucra a alumnos de Grado y Máster de 9 asignaturas de la Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB) en la elaboración vídeos cortos (2-5 minutos) sobre bioeconomía agraria y economía circular con el objetivo de consolidar una nueva fuente de material didáctico atractivo y transversal. Mediante encuestas a alumnos y profesores se evalúan las competencias adquiridas y la utilidad del material didáctico creado. Blanco-Gutiérrez, Irene; Gutiérrez, Alberto; Ferrer, Juan Ramón; López, Carmen; Soriano, Bárbara; Blanco, María; Arce, Augusto; García; Almendros; Hernández, Carlos Gregorio; Bardají, Isabel; Esteve, Paloma
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".