Correlation between concentration of particulate matter 2.5 and solar energy production in Brooklyn, NY
Bibliographic record
Abstract
Many people have started using solar panels in recent years. Switching to renewable energy sources can greatly decrease the amount of carbon emissions that contribute to climate change. One effect of climate change is a rise in wildfires. Smoke from wildfires can be detrimental to human health by increasing the air quality index (AQI). In June 2023, the AQI drastically increased in Brooklyn, New York due to fires in British Columbia from the west to Quebec and Nova Scotia in the east. We hypothesized that particulate matter (PM) 2.5 would negatively affect solar energy production because when the sun’s rays are blocked by clouds or smoke, there is usually less solar energy production. Our hypothesis was supported given that during the month of the fires across British Columbia, Quebec, and Nova Scotia, the PM 2.5 concentration had a strong negative correlation to solar energy production (p = 0.05). Throughout the rest of the year, the PM 2.5 concentration generally did not have an effect on solar energy production. PM 2.5’s positive correlation with solar production was measured against cloud cover and wind speed as benchmarks for a strong and weak negative correlation. Our findings can help determine what factors should be considered when deciding where to install solar panels or to place solar fields. By making renewable energy more effective we hope that more individuals will choose to switch away from carbon fuel sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".