Effects of Shading Using a Retractable Liquid Foam Technology on Greenhouse and Plant Microclimates
Why this work is in the frame
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Bibliographic record
Abstract
Climate control is an important aspect of greenhouse crop management. Shading is one popular method for reducing excess solar heat radiation and high air temperatures in the greenhouse during the summer season. A new innovative technology has recently been developed and is based on the injection of liquid foam between the double layers of polyethylene of the greenhouse roof. The foam can be used as a shading method during the warm days of the summer. This is the first investigation into the effect of shading using the liquid foam technology on greenhouse and plant microclimates. Our research was conducted over 2 years in two different areas of Canada. Experimental greenhouses were retrofitted with the new technology. Tomato ( Solanum lycopersicum ) and sweet pepper ( Capsicum annuum ) were transplanted. Two shading strategies were used: 1) comparison of a conventional nonmovable shading curtain to the liquid foam shading system and application of liquid foam shading based only on outside global solar radiation; and 2) application of foam shading based on both outside global solar radiation and greenhouse air temperature. Data on the greenhouse microclimate (global solar radiation, air temperature, and relative humidity), the canopy microclimate (leaf and bottom fruit temperatures), and ventilation (opening/closing) were recorded. Our study showed that the retractable liquid foam technology improved greenhouse climate. Under some conditions (very sunny and hot days), a large difference in air temperature (up to 6 °C) was noted between the unshaded and shaded greenhouses as a result of liquid foam application (40% to 65% shading). Foam shading also increased relative humidity by 5% to 12%. Furthermore, bottom fruit temperatures stayed cooler 3 h after shading treatment was stopped. As well, a reduction in ventilation needs was observed with liquid foam shading.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it