La infraestructura verda en el sistema territorial metropolità. Anàlisi comparada de diferents àrees metropolitanes i elements estratègics per l’Àrea Metropolitana de Barcelona
Bibliographic record
Abstract
En aquest document es vol contribuir al debat actual sobre quin ha de ser el paper dels espais naturals protegits, de\nles zones agrícoles i dels verds urbans, per tal d’articular una Infraestructura Verda funcional a l’Àrea Metropolitana de\nBarcelona (AMB). En tenir en compte els exemples de cinc metròpolis seleccionades (Randstad, Montpeller, Paris,\nMilà, Toronto) es pretenen definir les possibles variants en la gestió territorial de la Infraestructura Verda en diferents\ncontexts, així com el caràcter canviant de la relació societat-naturalesa, tant en el propi espai urbà i com en el seu\nentorn proper. L’estudi de les experiències de cinc metròpolis de referència traça el camí per a descriure quins poden\nser els elements estratègics per a una Infraestructura Verda a la metròpoli de Barcelona. És d’interès concretar amb\nquin pes s’aplica el concepte de la Infraestructura Verda en diverses polítiques metropolitanes. Noves bases\nconceptuals com ara el metabolisme social, l’economia social solidària o la sobirania alimentària, definiran una línia\nd’actuació i participació que acompanyaran al planejament com a eina per la defensa d’aquests nous paradigmes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".