Method of identifying urban morphologies from cadastral attributes. Evidence from the littoral municipalities of the Valencian community (Spain)
Bibliographic record
Abstract
This paper is a partial result of the Research Project 'Strategies for sustainable regeneration in tourism settlements on the mediterranean coast (ERAM)' (ref.BIA2011-28297-C02-01) Spain National Plan of Research, 2011.The method has been designed with the purpose of identifying the different settlements pattern of the littoral municipalities of the Valencia Region, using the alphanumeric data of Cadastre to achieve this goal.The method has been applied to 59 littoral municipalities, after which the outputs were checked with those from the SIOSE project and from the ERAM's Project partial results 'Typology Map of touristic settlements of the Valencia region'.As the main output obtained, it should be highlighted how the methodology allows a good identification of the parameters in contrast with other reference-based projects.Moreover, we can get a more accurate clustering of morphology thanks to the use of lots divisions elaborated by the Cadastre.This method is possible to be extended to other municipalities allowing some clustering operations by mixing attributes to achieve more general morphologic patterns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".