Bibliographic record
Abstract
City as an innovation of man noticed by civilization since early.The way of consisting locoting elements important divisions of City & their connection to eachother is offected by many factors such as economical-social managing -militarian factors & also features & needs & their relation to other factors.It is anticipated that by entrance to third millenniem , almost half of the world population being inhabitant in civic area.While civic development a aspace concept of changes in land using & aggregations surface, for meeting the need of citizen in the field of house, transportatin, free times & food & so on, but the important position of railway transportation is determined in relocation, in relocation , distribution & exchanges related to diffrent activities which leads to increasing traffic in city & physical development of city, because the effects of railway cause consisting development, field & organizing various activities in cities.We can say; complenity of each factors (economic, social, political, geografical, sociological) by self was sity procreator, as its spatial crystallization.Certainly, organizing city & the way of its development & evolution needs identifying city issue & civic problems & them planning for it this research is an analytic& descriptive approach.Gothering field information is done by using questionnaire & spss software & for priority of program, TOPSIS model eas used by calculating recent behaviour average &contrasting it with middle of saying surface which is estimated 108, by guessing railway effective role on Sari development inexistency, we can reject limit lower dawn middle, also by using TOPSIS omethod for arranging Sari locations according to calculating weights, dokhaniat & servinebagh were the most offerctable place respectively & bakh 8& peivandy were the least affectable place respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.956 | 0.949 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".