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The occurrence, size and geometry of geothermal resources in volcanic terrains

2019· article· en· W2983919935 on OpenAlexaff
Albert F. Waibel

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsGeothermal gradientGeologyGeothermal explorationEarth scienceVolcanoTectonicsTerrainCalderaGeothermal energyMining engineeringGeophysicsGeochemistryPaleontologyGeographyCartography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.167
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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