Bibliographic record
Abstract
As a volatile organic compound existing in the atmosphere, methanol plays a key role in atmospheric chemistry due to its comparatively high abundance and long lifetime. Croplands are a significant source of biogenic methanol, but there is a lack of systematic assessment for the production and emission of methanol from crops in various phases. In this study, methanol emissions from spring wheat during the growing period were estimated using a developed emission model. The temporal and spatial variations of methanol emissions of spring wheat in a Canadian province were investigated. The averaged methanol emission of spring wheat is found to be 37.94 ± 7.5 μg·m<sup>-2</sup>·h<sup>-1</sup>, increasing from north to south and exhibiting phenological peak to valley characteristics. Moreover, cold crop districts are projected to be with higher increase in air temperature and consequent methanol emissions during 2020-2099. Furthermore, the seasonality of methanol emissions is found to be positively correlated to concentrations of CO, filterable particulate matter, and PM<sub>10</sub> but negatively related to NO<sub>2</sub> and O<sub>3</sub>. The uncertainty and sensitivity analysis results suggest that methanol emissions show a Gamma probabilistic distribution, and growth length, air temperature, solar radiation and leafage are the most important influencing variables. In most cases, methanol emissions increase with air temperature in the range of 3-35 °C while the excessive temperature may result in decreased methanol emissions because of inactivated enzyme activity or increased instant methanol emissions due to heat injury. Notably, induced emission might be the major source of biogenic methanol of mature leaves. The results of this study can be used to develop appropriate strategies for regional emission management of cropping systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.288 | 0.216 |
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".