Greenhouse Microclimate Flow Simulation: Influence of Inlet Flow Conditions
Bibliographic record
Abstract
Greenhouse (GH) has been demonstrated as a profitable technology for food production with low demand of irrigation water.In this work, a numerical model is developed to study the micro-climatic environmental conditions inside a greenhouse distillation system for optimize operation.The system performance (temperatures, flow velocities, relative humidity) is presented and improvement factors for the system performance are suggested.The result shows that the inlet velocity and plant transpiration have a more pronounced effect on the relative humidity than the incoming temperature variation.As temperature increases by 8C o the relative humidity decreases with few percentiles (~2%).When velocity varies between 0.2-0.7m/s,and within the diurnal operation of the GH, an increase of up to 5 points in the humidity is observed.Finally, when the transpiration increases from 0.2 to 1.2 g/m 3 the relative humidity observes a drastic jump of over 15 points.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".