Managing greenhouse humidity with ground air heat exchange systems
Bibliographic record
Abstract
Greenhouses are essential for ensuring food security in Canada, as the harsh climate makes outdoor agriculture impossible. Preventing excessive humidity levels in Canadian greenhouses during winter is challenging and increases energy consumption. This challenge arises because natural ventilation, a common method for reducing humidity, brings in cold outdoor air that must be reheated, leading to higher energy demands. This study investigates the potential of Ground Air Heat Exchangers (GAHEs) as an energy-efficient solution for greenhouse dehumidification. In this approach, greenhouse air is circulated through horizontal GAHEs buried underground. As warm, humid greenhouse air flows through the GAHE, it transfers heat to the cooler underground environment. When the air temperature drops below its dew point, water vapor condenses on the internal surfaces of the exchanger, reducing the absolute humidity of the air before it is recirculated back into the greenhouse. This process effectively removes excess humidity while minimizing energy losses compared to conventional ventilation methods. A numerical model is developed to assess the performance of GAHEs in controlling greenhouse humidity. This model is then used to optimize the system and conduct a parametric study. Since Canada has different climate zones, and climate conditions affect greenhouse air properties and GAHE performance, the optimization in this study is tailored to various climate zones. Greenhouses also come in a variety of sizes, so this optimization is designed to ensure that the optimized GAHE is applicable to greenhouses of all sizes. Finally, the results of this study provide a practical, energy-efficient dehumidification strategy applicable to any greenhouse, regardless of its size or the climate zones in which it is established
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".