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Record W4411618051 · doi:10.51847/8hnqvbiuye

10.51847/8hNQvBIuye

2000· article· en· W4411618051 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDevelopment (topology)Transport engineeringComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.877
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9920.993

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.

Opus teacher head0.008
GPT teacher head0.158
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2000
Admission routes1
Has abstractyes

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