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Record W2953151572 · doi:10.36829/63cts.v4i2.520

Ventajas de los jardines verticales sobre edificios de concreto en clima cálido-seco de Guatemala

2017· article· es· W2953151572 on OpenAlexaff
Claudia Taracena

Bibliographic record

VenueCiencia Tecnologí­a y Salud · 2017
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgriculture and Social Issues
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArtGeography

Abstract

fetched live from OpenAlex

El Centro Universitario de El Progreso de la Usac se encuentra en Guastatoya en el dominado corredor seco de Guatemala, ya que posee una temperatura anual de 27-34°C, en un clima cálido-seco. Por incidencia solar que reciben las aulas durante todo el año, se decidió utilizar un jardín vertical como sistema de enfriamiento pasivo, que se hizo con estructura de pino tratado, neumáticos de caucho reutilizados como macetas y vegetación local. Se tomaron datos de temperatura y humedad relativa ambiente, la temperatura y humedad relativa del salón cubierto por el jardín vertical y un salón sin cubierta adicional al muro de concreto, durante 30 días dispersos de enero a abril, para cubrir la época más fría y más caliente del año. La máximo disminución de temperatura fue 2.3°C y un incremento de humedad relativa de 4.4%. El confort higrotérmico se midió por una encuesta entre estudiantes de agronomía con jornada de 7:00 a 16:00 h, dando como resultado que solamente el 8.4% de los estudiantes encuestados percibieron mayor confort higrotérmico, esto se cree que fue porque el jardín vertical se encontraba en su etapa inicial y con poca proliferación vegetal para producir el efecto deseado. Se recomienda repetir el estudio durante el 2018, cuando el jardín vertical esté más maduro.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.299
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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