Research on developing a methodto estimate fugitive CH4 emissionfactors of coal surface mining in Quang Ninh province
Bibliographic record
Abstract
This article presents the approach and the results of estimating CH4 emission from surface mining in Quang Ninh province to develop its fugitive CH4 emission factors. The two largest active surface mines were selected named Nui Beo and Cao Son. By applying a direct measurement method with samples using a specific chamber, CH4 emission was collected and calculated for the whole mine. Results showed that CH4 emission varied from mine to mine both the total amount and its range of maximum and minimum. In detail, the total amount of CH4 emission from Cao Son is 19,032.87 m3with the range of 73.14% while these numbers of Nui Beo are 2,684.47 m3 and 76.38%, respectively. The CH4emission factors of surface mines then were estimated in the range of 0.0850 m3/ton and 0.0225 m3/ton respect to high emission level and low emission level. These values are close to the emission factors generated from Germany (0.015 - Lignite), Canada (0.088 - Lignite; 0.19 - Bituminous), South Africa (0.002÷0.064 - Bituminous). These emission factors are considered as scientific evidence to propose the national factors of CH4 emission of surface mining.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".