El cauce bajo del río Santa Lucía: tres propuestas basadas en sus recursos patrimoniales
Bibliographic record
Abstract
En la última década se han realizado varias propuestas basadas en los recursos patrimoniales en torno al cauce bajo del río Santa Lucía en el área metropolitana de Montevideo. El origen de las iniciativas, el marco institucional en el cual se desarrollan y la gestión de las mismas es diverso, así como el éxito en la concreción de las acciones planteadas. A su vez, el concepto de recurso patrimonial, la identificación de los elementos que lo componen y su puesta en valor a través de un proyecto territorial concreto, han evolucionado de una propuesta a la otra, reflejando el estado del arte sobre esta temática en el ámbito nacional. El artículo presenta estas propuestas, realizando una descripción y evaluación de las mismas y de sus impactos y repercusiones, tanto territoriales como institucionales. During the last ten years several proposals have been done based on natural and cultural resources in the lower course of the Santa Lucía River, in the metropolitan area of Montevideo. The origin of these initiatives, the institutional frame in which they were developed, and their management are diverse, as well as the success in the concretion of the proposed actions. At the same time, the concept of heritage resource, the identification of the elements that compose it and its appreciation through a concrete land project have evolved, reflecting the state of the art on this topic in Uruguay. The article presents these proposals, carrying out a description and assessment of its land and institutional repercussions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".