Energy consumption and commercial applications of liquid foam insulation technology for greenhouses
Bibliographic record
Abstract
It is well known fact that operating a commercial greenhouse in northern Latitudes requires large amounts of energy. As energy prices continue to fluctuate, it is critically important to provide growers with a tool that gives them greater control of their micro-climate. Sunarc of Canada has developed an energy saving system for commercial greenhouse growers. The liquid foam insulating system was installed at site 1 over an area of 14,700 ft² (Chatam, ON, Canada), as well as at site 2 (Leamington, ON, Canada) over an area of 43,000 ft². Both facilities were monitored for energy use during the 2007 winter period. Night-time energy savings ranged from above 60% to below 10% depending on outdoor temperatures with greater savings occurring during colder outdoor temperatures. Monthly average night-time energy savings resulted in values from February, March and April 2007 of 46.6, 42, and 32.3% respectively. Following initial commercial testing the liquid foam system was reengineered to improve and optimize operations, reduce fill time, and improve liquid foam formulas. The new system was installed at site 3 (Laval, QC, Canada) as a demonstration unit. The company is presently negotiating international distribution writes with several partners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".