Geobotanical Remote Sensing Applied to Targeting New Geothermal Resource Locations in the U.S. Basin and Range with a Focus on Dixie Meadows, NV
Bibliographic record
Abstract
This paper presents an overview of the work our collaboration is doing to increase the detailed mapped resource base for geothermal exploration in the Western US. We are imaging several large areas in the western US with high resolution airborne hyperspectral and satellite multispectral sensors. We have now entered the phase where the remote sensing techniques and tools we are developing are mature enough to be combined with other geothermal exploration techniques such as aeromagnetic, seismic, well logging and coring data. The imaging sensors and analysis techniques we have developed have the ability to map visible faults, surface effluents, altered minerals, subtle hidden faults. Large regions are being imaged at reasonable costs. The technique of geobotanical remote sensing for geothermal signatures is based on recent successes in mapping hidden faults, high temperature altered mineralization, clays, hot and cold springs and CO2 effluents the Long Valley Caldera and Mammoth Mountain in California. The areas that have been imaged include Mammoth Mountain and the Long Valley Caldera, Dixie Meadows NV, Fish Lake Valley NV, and Brady Hot Springs. Areas that are being imaged in the summer of 2003 are the south moat of the Long Valley Caldera, Mammoth Mountain western Pickles, Nash, Kasameyer, Foxall, Martini, Cocks, Kennedy-Bowdoin, McKnight, Silver, Potts, flanks, Mono Inyo chain north of Mammoth Mountain in CA, and the Humboldt Block in NV. This paper focuses on presenting the overview of the high-resolution airborne hyperspectral image acquisition that was done at Dixie Meadows NV in August 2002. This new imagery is currently being analyzed and combined with other field data by all of the authors on this paper. Results of their work up until the time of the conference will be presented in papers in the remote sensing session.
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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 | 0.001 | 0.000 |
| 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".