The occurrence, size and geometry of geothermal resources in volcanic terrains
Bibliographic record
Abstract
Abstract Volcanic terrains in general, and arc volcanic belts in particular, have been prime geothermal exploration targets as far back as the first geothermal power generation at Larderello, Italy, in 1904. The popularity of this terrain for geothermal exploration and development is based on the concept of young shallow magma bodies providing an abundance of shallow crustal heat and also on the presence of hot springs and fumaroles observed within these terrains. The success in developing these geothermal resources ranges from spectacular (e.g. Japan, Philippines, Indonesia, New Zealand, Italy, Iceland) to disappointing (e.g. Cascade Range of northwestern North America). The types, shapes and geometries of geothermal resources in volcanic terrain range in size from large broad three-dimensional fractured stockwork systems to narrow geothermal cell conduits. Effective and economic exploration and development of these resources is greatly improved by understanding the varying sizes and geometries of these resources and matching the exploration strategy design specifically for each exploration target rather than applying a single exploration formula and data interpretation model to all settings. Information from the mining industry provides valuable insight into the range in geometry and size of these resources. This body of knowledge should be used by the geothermal community: (1) for more effectively designing exploration programs specific; (2) to each prospect to interpret the body of exploration data in terms of site-specific geology and tectonics; (3) to integrate basic risk management best practices into exploration and development programs.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".