A New Approach for Sustainable Bioconstruction in Coffee-Growing Environments, based on a Geothermal Bioclimatic System (Simulation)
Bibliographic record
Abstract
Abstract: Colombian coffee is a product recognized worldwide for its mildness, flavor and high-quality. Coffee growing in Colombia is an important sector that contributes to the country's economy. The department of Cauca, due to its geolocation, biodiversity and climatic conditions, has become the fourth largest coffee producer in the country, outstanding for the production of specialty coffees, and supporting rural economies. The objective of this research is to analyze the viability and functioning of the model of a Canadian well in a coffee environment, applying concepts of vernacular and biophilic architecture in a coffee growing house located in the municipality of Cajibío, Cauca. Initially, a bibliographic analysis was carried out to learn about the functioning of Canadian wells and their applications, in addition to a socioeconomic study, constructive analysis and proposal for housing improvement in the context of the plateau of Cajibío, Cauca. The place chosen for the study was the "Parque Tecnológico de Innovación del Café" (TECNICAFE), where the required physical and environmental data were recorded (construction of a device to measure variables), in order to carry out a simulation of the bioclimatic design through the Energy 2D software. The behavior of temperature during the day and night was analyzed using two materials in the proposed bioclimatic model: Hemp fiber and PEAD (High Density Polyethylene). Finally, hemp fiber was determined as a construction material for the thermal insulation process in approximately 1 hour of simulation, with a temperature variation of approximately 10 °C above and below the outside temperature, ensuring a level of thermal comfort for people using the house in the study area (living room). This proposal is an alternative to improve the thermal comfort and quality of life of coffee growers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".