Application of Imputation Methods in the Analysis of Freight Trip Generation in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
Missing data is a common issue in empirical research, especially in complex surveys involving a large number of respondents. In 2012-13, a shipper-based, establishment survey was conducted of 1006 small and medium-sized firms and twelve large firms in the Greater Toronto and Hamilton Area; however, there exist complete records for only about 11 percent of the firms. In this study, several single and multiple imputation techniques were evaluated to determine the best method to impute this dataset. Once the entire dataset was imputed, freight trip generation models were developed using the imputed datasets and compared to models developed based on the complete data. The research concluded that imputation is useful when developing models as it allows for the usage of the full dataset, resulting in parameter estimates of greater power. It also allows for greater flexibility in modelling, as richer models with more explanatory variables can be considered.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".