Port Throughput Forecasting Based on Broad Learning System with Considering Influencing Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the development of the shipping industry, port throughput has increased significantly in recent years. Chinese ports have become increasingly important. The fluctuation of container throughput in ports is affected by many factors. The effective and accurate forecasting of port throughput provides a scientific reference for the development of the port. Based on the port throughput data of Lianyun Port in China from the first quarter of 2005 to the fourth quarter of 2016, this paper uses univariate linear regression, multiple linear regression, and broad learning system to forecast the container throughput of the port in each quarter of 2017 and 2018. Comparing the forecasting results with the actual throughput. The experimental results show that the broad learning system can consider two economic influencing factors about the throughput at the same time and forecast port throughput accurately and effectively. Therefore, broad learning system is more suitable for the forecasting of port throughput than other methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it