Integrating Landsat, Geologic, and Airborne Gamma Ray Data as an Aid to Surficial Geology Mapping and Mineral Exploration in the Manitouwadge Area, Ontario*
Bibliographic record
Abstract
For suq5cial mapping and mineral prospecting purposes, Landsat Thematic Mapper, airborne gamma ray spectrometry, and geological data were integrated for a 5000-km2 area near Manitouwadge, Ontario. Using characteristic Landsat data signatures for the main surficial units, the signatures for 6,250,000 30-by 30-m-pixel areas were evaluated, and each pixel area was assigned to a surficial unit category. When the predictive surficial geology map thus generated was compared to eight surficial geology units on a published suq5cial geology map, there were obvious visual similarities, with an overall pixel-by-pixel accuracy of 46 percent. Pixel areas with Landsat and gamma ray signatures comparable to those of training sites in base metal-enriched tills near the Manitouwadge area mines, commonly formed clusters or near-linear bands overlying Archean greenstone belts or in close contact with a carbonatite complex where Fe and Zn mineralization is known. Tests on these derivative pixel areas indicated that their distribution was controlled almost entriely by the gamma ray data signatures.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".