Composite model of multi-dimension multi-box model and Gauss model for atmospheric capacity study
Bibliographic record
Abstract
This paper aims to report its authors' new approach to testing the atmospheric environment and the testing of the local pollutant sources distribution in Beijing Municipality area by applying a newly developed composite simulation model. As a matter of fact, the so-called multi-dimensional multi-box model is an innovation brought forward by Beijing University of Technology and Regina University (Canada). The model is designated for studying the physical and chemical pollutant removing mechanism, testing of vertical and horizontal diffusion of the air pollutants and the changes of weather conditions in a 24-hour and all-weather basis. The model can also be used to forecast the weather and atmospheric quality in different cities, particularly designated for simulating PM_ 10 pollutant concentrations in Beijing. To establish a multi-dimensional multi-box model, we have divided the whole city into 38 sub-boxes according to the geographical locations and into 3 strata vertically according to the atmospheric spaces. Then, the model can further be divided into 114 sub-boxes, including the upper strata, and quality-balance equation groups to be measured in four directions(N, S, E and W), respectively. Gauss model is adopted also to forecast the air pollutant diffusion from all the main point sources. Comparing all the related study results with the indications in the international popular model-3, we have found that the expected pollutant distribution concentrations as well as the atmospheric environment capacity indicators gained from the composite model and multi-dimensional model are very much similar. The so-called atmospheric environmental capacity here mentioned is referred to as the optimal amount of the atmospheric emission if it can meet the state atmospheric environment quality standard at some areas and in some phases. Based on the forecasting of the atmospheric pollutant concentration, we have evaluated the atmospheric environmental capacity in Beijing which is indeed below the state atmospheric environment quality standard, and the atmospheric PM_ 10 capacities of different standard areas are retrieved. Compared with the ground monitoring data,it can be found that the evaluation results are reliable. Hence, the models illustrated above are expected to be effectively used to simulate China's atmospheric environmental capacity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".