Bibliographic record
Abstract
It has been observed that local ozone concentrations can be impacted by both local emissions and by emissions that were transported from distant source regions. Thus, changes in ozone concentration in a particular region can only be understood by analyzing the precursor emission sources, such as nitrogen oxides (NOx), carbon monoxide (CO), and volatile organic compounds (VOC), over multiple regions. In this study, the primary sources of ozone concentration in seven receptor regions within North and Central America were quantitatively described by employing the GEOS-Chem model and its adjoint. The model results across all regions showed that the mean contribution of natural emissions to ozone concentration was 57% less than the contribution of anthropogenic emissions. It was also observed that local emissions have a larger contribution (at least 60%) to ozone concentrations than long-range transport in all observed regions except for eastern Canada. Further results show that peak ozone concentration and transport between regions in the model mostly occur during the spring and summer months. The exception to this trend was seen in Mexico, which had its largest ozone concentration and intake of transported emissions during Northern Hemisphere winter. Overall, this study concludes that ozone concentration and transport depend on a number of factors including emission type, season and geographical location.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".