Technical Closure of the Humberto Molina Astudillo Hospital and Its Implications for Sustainability, Zaruma-Ecuador
Bibliographic record
Abstract
Zaruma, a heritage city of Ecuador with a rich geological mining history, it suffers from technical problems of stability.The Humberto Molina Hospital and its surroundings have been affected by different geodynamic events, causing damage to buildings.The aim of this study is to analyze the technical components that led to the Hospital's closure through engineering considerations of studies carried out and a matrix that includes the implications of the inhabitants' perception of the closure for the proposal of remediation measures.The methodology considers: a) Surveys and analysis of citizen perception; b) Analysis of the technical studies and its components; and c) Remediation proposal for the rehabilitation and sustainability of the Hospital.Citizen perception is aware of the imperative need for a hospital for a city recognized as a "magic town" (2019) by the Ecuadorian Ministry of Tourism.The stability analysis shows that the steep slopes decrease the safety factor.The study area has low susceptibility to landslide, also are present extreme precipitation conditions and fill areas.The design of berms and the construction of rainwater collection channels are recommended, so that they do not infiltrate, and do not saturate the saprolite in the area (generating instability on slopes).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".