Bibliographic record
Abstract
The industry is one of the main pillars of a strong economic city. Unfortunately, third world countries' industrial cities face environmental threats to the point that sustainable environments are considered a luxury (Pugh, C, 2013). According to a report issued by the Ministry of Agriculture and Land Reclamation in 2015 Egypt lost approximately 8618 acres of the finest farmland in the Delta and the Nile Valley as a result of urban sprawl on farmland to take advantage of employment opportunities and services in cities (Ministry of Agriculture land protection and land reclamation, 2015). The paper attempts to monitor different cases in the Egyptian context, trying to conclude the effective factors for their environmental and urban form as a result of industrial use. The Aim is to conclude key factors for sustainable industrial cities. The paper's results are based on a designed questionnaire that is analysed using SPSS. The questionnaire is completed with the help of experts and executives in order to specify the main factors in the sustainable urban form regarding industrial cities. It is followed by cluster analysis to determine the positive or negative effects of each element in relation to the rest of the elements concluding the most effective factor affecting the environment in every group as a tool to help the urban planning decision makers (environmental - urban - economic and social).
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".