Herramientas SIG para el estudio de la Carmona Romana = GIS tools to study Roman Carmona
Bibliographic record
Abstract
A partir de herramientas SIG analizamos diversas cuestiones sobre la Carmona romana. Tratamos de definir el ager a partir del analisis conjunto de la cuenca visual y el coste de la distancia en tiempo partiendo de la ciudad. Igualmente, analizamos la red de caminos contrastandola con la ruta optima obtenida mediante la herramienta de ruta de coste. Proponemos un patron que explique el porque de la distribucion de las estructuras funerarias en la necropolis occidental de Carmona, a partir de las herramientas de cuenca visual acumulada y area de influencia de las vias de acceso a la ciudad. Finalmente, estudiamos la estructura urbana de la ciudad con la herramienta de ruta de coste y la posible funcion del bastion de la Puerta de Sevilla. -------------------------------------------------------------- From GIS tools we analyze several questions about Roman Carmona. We try to define the ager from a joint analysis of the cumulative viewshed from the city and the shortest path away from the city. Similarly, we analyze the road network contrasting with the optimal route obtained by the cost path tool. We propose a pattern that explains the distribution of funerary structures in the western necropolis of Carmona, from the cumulative viewshed tool and a buffer of the access roads to the city. Finally, we study the city urban’s structure with the cost path tool and the possible role of the Puerta de Sevilla bastion.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".