Exploring for hydrogen, helium and lithium: is it as easy as 1, 2, 3?
Bibliographic record
Abstract
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".