Typology of the transformations occurred in the peri-urban space of huerta de Valencia. Evidence from north arch of Valencia (Spain)
Bibliographic record
Abstract
This paper present the outputs obtained from the measurement and classification of the different types of changes happened in the last 70 years on the Northern area of expansion of the city of Valencia. The city has progressively been covering, with different rhythms and intensities, the space of La Huerta. We can identify between 1944 and 2014 a group of transformations that occur repetitively, building a change pattern identified as common on the city's expansion evolution. The methodology is based on the analysis and measurement of changes occurred on land structure, land use, buildings occupation and on the traditional structure of non-urban roads. The key sources to measure such changes have been the use of the Cadastre of 1929Cadastre of -1944;; 1972; and 1989; the orthophoto collections from the Valencian Regional Library and the evolution of SIOSE mapping. The most outstanding results refer to the surprising resilience of some elements from the structure of La Huerta de Valencia and the discovery and identification of the main transformations patterns that could be generalized to the rest of La Huerta de Valencia.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".