Application of Multiple Imputations to Freight Transportation Survey Data: A Case Study of Commodity Flow Survey
Bibliographic record
Abstract
Problem statement: Freight transportation data was indispensable input to transportation planning.In Thailand, efforts had been put to collect freight movement data by conducting road side survey and commodity flow survey.The results of these surveys did not produce consistent volume of shipment due to limited sampling coverage and non-response.Nevertheless, freight distribution patterns, which were derived from these surveys, were favorably consistent with each other.Approach: The objective of this study was propose an approach to improving quality of the commodity flow survey data in terms of total shipment weight.Our scope of study was limited to consumer goods and food stuffs.Multiple imputations were performed to correct non-response.The shipment weight was again adjusted by taking into account of the probability of no shipment in a particular quarter.Results: Comparison between the adjusted weight and road side survey data showed that the discrepancies in total weight of significantly reduced.Conclusion: Total shipment weights of the CFS after the adjustments are compares to those of road side survey.Plausible result is obtained for the case of consumer goods, while that of food stuffs is still notably different.
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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 | 0.092 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".