Analysis of Remote Sensing and Geographic Information System Technologies to Enhance Geological Mapping in Eagle Plain, Northern Yukon
Bibliographic record
Abstract
This study presents an integrated remote sensing and GIS-based approach for the geological mapping in the study area based on field work. Landsat ETM+ and ASTER images were used. The image data were transformed using Principal Component Analysis (PCA), band ratioing and texture measurements (GLCM). Classification was performed on the original data and datasets of the combination of transformed data. Classification accuracy assessment and class signature separability analysis show that PCA-Ratio-GLCM dataset has the highest overall accuracy (62.27%, 63.29%) and class signature separability for both images. This result indicates that the integration of PCA, band ratioing and GLCM made a great contribution to improving lithological classification in the study area compared to original data. Based on classified images and transect analysis result from ASTER data, the lithological contacts of the previous geological map were revised and a new geological map and the corresponding geodatabase were produced for the study area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".