Investigation of Differences of Topographical Map and GIS-derived Spatial Map with Actual Ground Data in Peninsular Malaysia
Bibliographic record
Abstract
In a geographical information system (GIS), digital maps usually used to show multiple views of geographical objects either through two-dimensional or three-dimensional, which topographical parameters are digitally generated. Digital maps are often used in extensively environmental application without quantifying the effect of their errors. This study was carried out to investigate the difference of elevation and slope of topographical map and GIS-derived spatial map with actual ground data. The analyses of differences were quantified from interpolation process, sampling and measurement in the field. The RMSE of the DEM creation for the test site was 0.62. The result was based on the 10 m DEM resolutions and 20 m contour interval. From the analysis of differences (elevation and slope) of topographical map and actual ground data, it’s showed that the difference is only about 2 % and 28%, respectively. The great differences on slope may be due to error during data collection by different enumerators and also inconsistent reading of slope measurement and target. Despite the difficulty occurs during ground data collection, estimation method was applied and this relatively simple procedure but appears acceptable in regard to sufficient data sets at nominal map scale 1:50000.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".