Integration of remote sensing and morphometric data for geomorphologic mapping
Bibliographic record
Abstract
The evaluation of the efficiency of multisource remote sensing and morphometric data for geomorphologic mapping has been made. The authors have developed a method based on the synergistic relation between surface objects mapable with remote sensing data, morphometric information and mapping of surficial deposits. Maximum likelihood classification along with various vegetation indices (VI) including NDVI and TSAVI, RI and SI were used to identify and discriminate the geomorphological surface units using Landsat 5 and SPOT 4 images. The elevation, slope and aspect data were derived from a DEM. Linear discriminant analysis models were used to determine the level of synergy between these variables and their effectiveness as geomorphological mapping tools. Average classification accuracies ranges between 85 and 95 percent. Correlation between the classification results and the geological maps units was 87% and a combination of morphometric variables, principally elevation, and VI variables, principally TSAVI, were dominant in the numerical models.
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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 | 0.004 |
| 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 | 0.001 |
| 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".