Enhancing solar insolation in agricultural greenhouses by adjusting its orientation and shape
Bibliographic record
Abstract
A controlled environment greenhouse requires a large amount of heating during the winter months, which is conventionally supplied by environmentally damaging fossil fuels. To lessen the detrimental effect of fossil fuels on the environment, it is beneficial to use clean solar energy for heating these greenhouses. This paper aims to enhance solar insolation in a greenhouse located in Toronto, Ontario by manipulating greenhouse orientations, roof inclinations, and greenhouse shapes. Different greenhouse models were designed on SketchUp software and then simulated in TRNSYS software to determine the pattern of solar insolation available on different greenhouse models. Greenhouse orientation considered for this study included east-west orientation, north-south orientation, and distinct angles between these orientations. Different roof inclinations of 15°, 30°, 45°, and 60° were examined to observe the pattern of solar insolation availability on the greenhouse roofs. Further to this, typical shapes of a greenhouse (i.e., even, uneven, vinery, semi-circular, elliptical or arch, single span, and quonset) were also investigated to determine solar insolation on greenhouse surfaces.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".