Examining the relationship between commercial and residential land uses and energy consumption using Gis and Vickor model (case study: District 6)
Bibliographic record
Abstract
The city is a living thing whose population determines its future. Given its administrative and political pole in Iran, Tehran has attracted a significant population and District 6, given the establishment of commercial-administrative centers is the administrative pole. The purpose of the study was to examine and compare the energy consumption in the field of transportation and administrative-commercial buildings. For this purpose, the research method was based on a researcher-made questionnaire based on 6 main variables and 49 items. The sample size of 384 people was selected to reach the results using Cochran's test to answer. The study was applied in terms of purpose and descriptive-analytical in terms of nature. Data collection was based on library documents, and Vikor test was used to rank energy consumption and reach the final results. It has to be acknowledged that the results showed a significant relationship between social and economic factors in the field of transportation and residential and commercial areas until the end of January 2019. Other cases followed a 5-year pattern with a not-so-low consumption rate. Moreover, there was a significant relationship between pollutant production and energy consumption in the second quarter of each year. The statistical results based on the Vickor model showed that the first and second conditions of the above statistical test were confirmed and Districts 2, 3 and 8 have the best rank in terms of Q value, respectively, and the final result is correct.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".