Use of Modern Multi- and Hyperspectral Space Images for Mapping Hydrothermal Alteration and Lithological Units in the Arctic
Bibliographic record
Abstract
Abstract— The article reviews and analyzes domestic and foreign literature on the use of modern multi- and hyperspectral space images from Earth remote sensing satellites for mapping hydrothermal alteration and lithological units in the Arctic territory of Russia and other countries. It is shown that in foreign countries (mainly the United States and Canada) and in Russia today there are many publications on mapping of hydrothermal alterations using spectral bands of remote sensing satellites. At the same time, there are almost no such articles for northern territories. Possibly, this is due to the labor-intensive process of data collection and analysis, the short summer period, dense vegetation, bogginess, etc. The maps obtained using remote sensing technologies have advantages over geological maps. They can be used to identify promising areas in order to optimize further prospecting and exploration works, to build geological and genetic models of deposits, and to supplement traditional methods of mineral exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".