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Record W4401444546 · doi:10.1144/egc1-2024-13

Exploring for hydrogen, helium and lithium: is it as easy as 1, 2, 3?

2024· article· en· W4401444546 on OpenAlexaff
Jon Gluyas, Madeleine C. S. Humphreys, Rūta Karolytė, Anran Cheng, Barbara Sherwood Lollar, C. J. Ballentine

Bibliographic record

VenueEnergy geoscience conference series. · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLithium (medication)HeliumHydrogenPsychologyAtomic physicsPhysicsPsychiatryQuantum mechanics

Abstract

fetched live from OpenAlex

Hydrogen, helium and lithium, elements one two and three of the periodic table, are in demand to enable and enhance low-carbon energy technologies. Anthropogenic hydrogen is manufactured from water via methane reforming or from electrolysis. Both are costly and have environmental impacts. Helium is commonly found in low concentrations in association with petroleum gases. Lithium is mined by brine pumping or from igneous rocks, with consequential serious environmental impacts. Were it possible to economically find hydrogen in its molecular state, then surely such hydrogen would dominate the market. Similarly, helium generated without associated greenhouse gases would also be a market stimulant for a helium industry. What if hydrogen and helium could be co-produced from a single composite discovery? And what if the water leg to such hydrogen and helium deposits were rich in lithium? It, too, would be produced with costs for all elements shared. Helium is a natural product of crystalline rocks including granite and its generation can liberate hydrogen from interstitial water. These same rocks can be rich sources of lithium and may also deliver geothermal resources. The energy transition may therefore shift what we consider to be important for energy geoscience. The basement may become as important as the basin.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0010.002
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 head0.054
GPT teacher head0.274
Teacher spread0.220 · 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 designTheoretical or conceptual
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

Citations4
Published2024
Admission routes1
Has abstractyes

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