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Record W2074734621 · doi:10.1088/1748-9326/8/1/014037

Subsurface energy footprints

2013· article· en· W2074734621 on OpenAlexaff
Grant Ferguson

Bibliographic record

VenueEnvironmental Research Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Saskatchewan
FundersEarth Institute, Columbia UniversityNational Aeronautics and Space Administration
KeywordsCarbon sequestrationEnvironmental scienceGeothermal energyFossil fuelEnergy securityGeothermal gradientEnergy developmentEnergy supplyEarth scienceNatural resource economicsEnergy (signal processing)Renewable energyGeologyCarbon dioxideWaste managementEngineeringEconomics

Abstract

fetched live from OpenAlex

Anthropogenic climate change and energy security concerns have created a demand for new ways of meeting society’s demand for energy. The Earth’s crust is being targeted in a variety of energy developments to either extract energy or facilitate the use of other energy resources by sequestering emitted carbon dioxide. Unconventional fossil fuel developments are already being pursued in great numbers, and large scale carbon capture and sequestration and geothermal energy projects have been proposed. In many cases, these developments compete for the same subsurface environments and they are not necessarily compatible with each other. Policy to regulate the interplay between these developments is poorly developed. Here, the subsurface footprints necessary to produce a unit of energy from different developments are estimated to assist with subsurface planning. The compatibility and order of development is also examined to aid policy development. Estimated subsurface energy footprints indicate that carbon capture and sequestration and geothermal energy developments are better choices than unconventional gas to supply clean energy.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.975

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0850.025

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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations14
Published2013
Admission routes1
Has abstractyes

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