Bibliographic record
Abstract
Belgrade?s main characteristics are: a) high densities, b) extremely high concentration of jobs in CBD and its central zone, c) very high level of usage of urban public transport and of pedestrian movements. According to the Newman - Kenworthy classification, Belgrade is a typical ?public transport city?, while due to its transport strategy Belgrade can be best described as a hybrid of the Thomson?s ?low cost strategy? (very high usage of buses) and ?strong centre strategy? (high concentration of jobs in it?s CBD). This type of spatial development and urban transport strategy is extremely sensitive to the rise of personal motorization and automobile usage. Since in Belgrade: a) main mode of transport are buses, b) streets are very narrow, c) although public transport oriented, Belgrade doesn?t have rail systems with separated, ?exclusive? right of way (metros, and light rail systems - traffic jams at Belgrade?s streets are extremely pronounced - number of vehicles per 1 km of streets is - 277 vehicles/km. Hence, Belgrade has four times more vehicles per 1 km of street network than Australian cities, two times more than metropolises of the USA and Canada, and 25% more than the West European and wealthy Asian cities. In short, Belgrade is (for a very long time) mature for a rail (metro or LRT) system, with completely separated, exclusive right of way, and much more strict private motor vehicles limitation strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".