Detection of Limestone Quarries in Jordan Through Remote Sensing Data to Achieve Sustainable Utilization in Vernacular Architecture
Bibliographic record
Abstract
All human societies seek stability and use of place and material in order to create architectural buildings. One of the most important materials that man used as a building material was natural stone, and in Jordan stone has a special architectural value, as Jordanian architects used it to form a unique architectural style, especially in the capital, Amman. But at the end of the last century and the beginning of the current century, alternative manufactured building materials appeared, and architects began using them for many reasons, including the lack of quality of natural stone and its defects, which resulted in deformation of the architectural facades. The research used remote sensing techniques to know the properties and quality of the stone. This research used the descriptive approach in studying the history of architecture in Jordan and its relationship to limestone, and relied on the analytical survey by obtaining data from satellite images, where they were analyzed and the properties of the stone in the ground were shown. This research aims to preserve the use of natural stone in construction as a building material that has characteristics in sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".