Initial approach for knowing the impact of informal trade on freight trips attraction estimates
Bibliographic record
Abstract
The knowledge of the characteristics of a commercial area allows a better understanding of its urban freight trips, then a better freight attraction estimation and better selection and implementation of urban freight transport initiatives. In developing countries, many commercial areas have informal trade. Informal trade has not been considered either for freight trip attraction estimation or for initiatives implementation, despite it could attract freight trips and block streets and sidewalks. This paper aims to estimate and compare freight trip attraction with and without considering informal establishments, to get an initial impact of informal trade in commercial areas of developing countries. A comparison of freight trip attraction in an area estimates with and without informal trade is made considering two supply situations, the first one considers that formal and informal trade share suppliers and the second considers that formal and informal trade have different suppliers. The results indicate that informal trade must be considered in freight trip attraction in an area estimate depending on the amount of informal trade presence in a commercial area, since it impacts freight trip attraction in an area estimates according to it. Also, the supply form of informal trade in the commercial areas must be considered such as sharing or not of suppliers with formal trade, which impacts directly the trips attracted due to the additional trips made exclusively for informal trade.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".