Forecast and Analysis of Coal Traffic in Daqin Railway Based on the SARIMA-Markov Model
Bibliographic record
Abstract
With the continuous advancement of China’s supply-side structural reform, the country’s energy consumption structure has undergone considerable changes, including an overall reduction in fossil energy use and a rapid increase in clean energy application. In the context of China’s coal overcapacity, port and rail capacities are difficult to change in the short term. This study forecasts the monthly coal traffic of Daqin Railway on the basis of the seasonal autoregressive integrated moving-average Markov model and then uses the monthly coal transport data of this railway from September 2009 to November 2019 as samples for model training and verification. Coal traffic from December 2019 to September 2020 is accurately predicted. This study also analyzes the effects of China’s industrial structure adjustment, clean energy utilization, and low-carbon usage on the coal transport volume of Daqin Railway. In addition, the characteristics of seasonal fluctuation and the development trend of Daqin Railway’s coal traffic are explored. This study provides a reference for adjusting the train operation chart of Daqin Railway’s coal transport and developing a special coal train operation plan. It can determine the time of coal transport peak warning, improve the efficiency of coal transport management, and eventually realize a reasonable allocation of resources for Daqin Railway.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".