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Record W3187280934 · doi:10.3997/2214-4609.202182005

Geothermal Potential of Positive Temperature Anomalies above Salt Structures in Nova Scotia

2021· article· en· W3187280934 on OpenAlexaffabout
C. Skinner, Grant Wach

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeothermal gradientGeothermal energyGeologyTemperature gradientGeothermal explorationRenewable energyGeothermal heatingEnvironmental sciencePetroleum engineeringEarth sciencePetrologyGeophysicsMeteorologyGeographyEcology

Abstract

fetched live from OpenAlex

Summary Low carbon renewable energy is required to support the energy transition away from hydrocarbons while meeting rising global energy demands. Geothermal energy is a proven system, capable of electricity production and direct heat, with over 95% availability. Normally geothermal energy requires a high geothermal gradient, however technological advances are improving the opportunities for deployment in lower gradient regions. In sedimentary basins the geothermal gradient is normally lower, however the presence of large salt deposits can provide localized regions with an increased gradient due to the unique characteristics of salt. Salt is able to mobilize and flow under suitable conditions, and form structures; it also has a thermal conductivity two to four times higher than clastics and carbonates. Therefore, sediments above salt structures are expected to have a higher geothermal gradient - positive temperature anomalies. This research focusses on assessing the geothermal potential associated with positive temperature anomalies above salt structures in selected areas of the Scotian and Maritime basins.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.206
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes2
Has abstractyes

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