Measurement and Analysis of Exterior Light Emissions from Commercial Greenhouses
Bibliographic record
Abstract
Ontario greenhouse growers are moving towards year-round production supported by use of supplemental lighting. Light emitted by artificial sources at night scatters into the sky, contributing to skyglow. Some municipalities have implemented by-laws to limit light emissions, including Kingsville and Leamington Ontario. Methods for measuring sky brightness are widespread however, few are well tailored for measuring and comparing greenhouses light emissions. Therefore, one objective of the current research is to develop methods for measuring light emissions from commercial greenhouses. The present research also investigates the effect that greenhouse curtains have on greenhouse light emissions as well as the effect of greenhouse light emissions on sky brightness. Experimental methodology for measuring upwards nighttime greenhouse light emissions and sky brightness was developed. The primary mobile sensor platform was a drone carrying a visual RGB camera and a sky quality meter (SQM) light sensor. Upwards light emissions from greenhouses were measured with the greenhouse curtains open, with curtains gapped (partially opened), and curtains fully closed. Light abatement and blackout curtains were found to be very effective (95% - 99.99 %) at reducing light emissions from greenhouses when fully closed. Light emissions from gapped curtains were found to be approximately proportional to the area of curtain opening. Sky brightness near greenhouses was also shown to decrease, in varying degrees, when light abatement and blackout curtains were closed. The results show that light emissions from greenhouses will be affected by the amount and type of lighting used, which varies considerably between different greenhouse crops. Thus, the potential for light pollution could vary widely between operations. A sky brightness study on two separate nights using an upwards-looking car-mounted SQM observed large variations in sky brightness in locations such as cities, near greenhouses, and remote rural areas.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.012 | 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 teacher head, 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".