Metodología para la eco-innovación en el diseño para desensamblado de productos industriales
Bibliographic record
Abstract
En esta memoria de tesis se presenta la metodología de eco-innovación ECOINDES. Esta metodología tiene el fin de generar nuevos conceptos de producto más respetuosos con el medio ambiente a partir del diseño para desensamblado. ECOINDES se basa en un método de generación de conceptos denominado DESTRIZ y en dos métodos de evaluación ambiental: Eco-EPI (Eco- Evaluación del Potencial Innovador) y PR-EOL (End-Of-Life de un PRoducto). La generación de conceptos -método DESTRIZ- se sustenta a su vez sobre dos pilares: el diseño para desensamblado o diseño para desensamblaje (DFD) y la metodología TRIZ (acrónimo ruso de la Teoría de Resolución de Problemas Inventivos). El DFD se emplea como punto de partida para la innovación. El impacto ambiental se obtiene aplicando los métodos Eco-EPI y PR-EOL desarrollados en la tesis. Con Eco-EPI se selecciona el concepto más eco-innovador, y finalmente con PR-EOL se evalúa la mejora ambiental del producto en diferentes escenarios de EOL. La metodología ECOINDES se ha validado aplicándola en una lavadora de la empresa Fagor Electrodomésticos S.Coop.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".