Control de temperatura mediante un sistema geotérmico para un invernadero de altura
Bibliographic record
Abstract
The geothermal system implementation for temperature control and monitoring was performed into a greenhouse at 2,752 meters above sea level. This project takes advantage the thermal inertia, what provided by subsoil, through the Canadian well, for cooling, which is activated, when was found inside the greenhouse, there are high temperatures and for heating, when there are 01:00 to 06:00 am low temperatures. The greenhouse at being located in a height area presents drastic variations, such as: strong winds, frosts, hot days, cloudy days, the data recording was performed in each one these climatological conditions for knowing how varies the temperature, getting a 38°C data record into peak hours from 11:00 am to 02:00 pm, in the early morning, when there is frost, the temperature reaches 5 °C. At being working into a very low enthalpy zone, the subsoil temperature is stable from 15 °C to 17 °C, the Canadian well is made up a horizontal catchment at a 1.80 m depth, to perform the heat exchange, the air travels a 20 m distance, through the pipe, thus, causing a heat exchange between the circulating air and the surrounding land, it was got 2 °C to 3°C a temperature control inside, both heating and cooling, a project limitation is the construction area. This geothermal system has advantages, they do not emit greenhouse gases, are friendly to the environment, the electrical energy consumption is less, than, conventional heating.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".