The Model of Digital Cartographic Layers of Different Scales to Calculate the Ratios of Cartographic Generalizations: An Applied Study to Anah City
Bibliographic record
Abstract
The research aims to give an accurate perception of the role of modern technologies, represented by geographic information systems, in extracting data and information, and then building a geographical database for urban land uses in an Ana city to reach generalized models based on the source map with a scale of 1/5000 and then building generalized models through The application of digital generalization elements according to the different standards adopted in the research (1/10000, 1/25000, 1/50000, 1/100000) in order to reach the end to the best results in the cartographic production of these models in a way that facilitates the process of understanding and perception by finding The most appropriate way to represent this data on a map according For the functional importance of each of the uses through disposal to eliminate overlap between uses Depending on the generalization elements of selecting, deleting, simplifying, smoothing, coding and exaggeration, and finally, as The research showed that there is a difference in the areas, uses and preparation of methods when moving from one scale to another. the research concluded by building a three-dimensional model to simulate the natural process of generalizing digital cartography according to different scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".