Monitoring and Prediction Functional Change of Land Uses Toward Urban Sustainability
Bibliographic record
Abstract
Urban land uses are in a dynamic state that varies over time, the city of Karbala in Iraq has experienced functional changes over the past 100 years, as the city is characterized by the presence of significant tourist and socio-economic activity represented by religious tourism, and it occur due to various reasons such as urbanization.The purpose of this study is to apply a Markov model to analyze and predict the behavior of transforming the use of land in Karbala city over time.This can include the conversion of agricultural land, or other areas into residential, commercial, industrial land uses.The process of urbanization is typically driven by population growth, economic development, based on a set of probabilities and transitions between different states.They can help decision-makers understand the likely outcomes of different scenarios for the future.The research question is in which direction of the functional during the next 50 years in the case study?What are the values of the prediction of functional changes for future?The research Hypothesis: Urban functions are changed in different areas; agricultural land uses have decreased and land use functions have changed in an unplanned direction in the next 50 years.The study discovered that almost one-third of the agricultural land in Karbala has reduced.Additionally, there has been a 10% alteration in the usage of residential land in slums and other sectors.However, there has been a positive growth in transport, cemeteries, trade, industry, and services, with different degrees of progress.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".