Geologic Mapping and Mineral Resource Assessment of the Healy and Talkeetna Mountains Quadrangles, Alaska Using Minimal Cloud- and Snow-Cover ASTER Data
Bibliographic record
Abstract
On July 8, 2003, ASTER acquired satellite imagery of a 60 km-wide swath of parts of two 1:250,000 Alaska quadrangles, under favorable conditions of minimal cloud- and snow-cover. Rocks from eight different lithotectonic terranes are exposed within the swath of data, several of which define permissive tracts for various mineral deposit types such as: volcanic-hosted massive sulfides (VMS) and porphyry copper and molybdenum. Representative rock samples collected from 13 different lithologic units from the Bonnifield mining district within the Yukon-Tanana terrane (YTT), plus hydrothermally altered VMS material from the Red Mountain prospect, were analyzed to produce a spectral library spanning the VNIR-SWIR (0.4 - 2.5 ?m) through the TIR (8.1 - 11.7 ?m). Comparison of the five-band ASTER TIR emissivity and decorrelation stretch data to available geologic maps indicates that rocks from the YTT display the greatest range and diversity of silica composition of the mapped terranes, ranging from mafic rocks to silicic quartzites. The nine-band ASTER VNIR-SWIR reflectance data and spectral matched-filter processing were used to map several lithologic sequences characterized by distinct suites of minerals that exhibit diagnostic spectral features (e.g. chlorite, epidote, amphibole and other ferrous-iron bearing minerals); other sequences were distinguished by their weathering characteristics and associated hydroxyl- and ferric-iron minerals, such as illite, smectite, and hematite. Smectite, kaolinite, opaline silica, jarosite and/or other ferric iron minerals defined narrow (< 250 m diameter) zonal patterns around Red Mountain and other potential VMS targets. Using ASTER we identified some of the known mineral deposits in the region, as well as mineralogically similar targets that may represent potential undiscovered deposits. Some known deposits were not identified and may have been obscured by vegetation- or snow-cover, or were too small to be resolved.
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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 | 0.000 |
| 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 | 0.001 |
| 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".