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Record W4224869341 · doi:10.18280/ijsdp.170202

Technical Closure of the Humberto Molina Astudillo Hospital and Its Implications for Sustainability, Zaruma-Ecuador

2022· article· en· W4224869341 on OpenAlexvenueno aff
Paúl Carrión-Mero, Joselyne Solórzano, Fernando Morante-Carballo, Miguel Á. Chávez, Néstor Montalván-Burbano, Josué Briones-Bitar

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsClosure (psychology)SustainabilityPolitical scienceEcology

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.286
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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