Prediction of Natural Volatile Organic Compounds Emitted by Bamboo Groves in Urban Forests
Bibliographic record
Abstract
Due to the COVID-19 outbreak, people in countries around the world including the United Kingdom, Denmark, Canada, and South Korea are seeking physiological and psychological healing by visiting forests as stay-at-home orders continue. NVOCs (natural volatile organic compounds), a major healing factor of forests, have several positive effects on human health. This study specifically researched the NVOC characteristics of bamboo groves. This study revealed that α-pinene, 3-carene, and camphene were observed to emit the most, and the largest amount of NVOC emitted was seen during the early morning and late afternoon within bamboo groves. Furthermore, NVOC emission was found to have normal correlations with temperature and humidity, and inverse correlations with solar radiation, PAR (photosynthetically active radiation), and wind speed. A regression analysis conducted to predict the effect of microclimate factors on NVOC emissions resulted in a regression equation with 82.9% explanatory power, finding that PAR, temperature, and humidity had a significant effect on NVOC emission prediction. In conclusion, this study investigated NVOC emission of bamboo groves, examined the relationship between NVOC emissions and microclimate factors, and derived a prediction equation of NVOC emissions to figure out bamboo groves’ forest healing effects. These results are expected to provide a basis for establishing more effective forest healing programs in bamboo groves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".