Bibliographic record
Abstract
Unconventional fuels may present a viable partial replacement for conventional fossil fuel \nreservoirs (such as sandstone and limestone) in rocks onshore and offshore. These \nalternative fuels are obtained from distinct sources and employ extraction technologies \nwhich are very different to those used to extract conventional hydrocarbons. \nOil sands (also known as tar sands or bituminous sands) are loose sand or partially \nconsolidated sandstone containing viscous bitumen. Resources occur in Canada, Kazakhstan \nand Russia and estimated worldwide deposits represent 2500 billion barrels of oil in place. \nOil sands have only recently been considered to be part of the world's oil reserves, as higher \noil prices and new technology enable profitable extraction and processing. Converting oil \nsands to liquid fuels requires energy for steam injection and refining. \nMethane from coal includes gas recovered from active (coal mine methane or CMM) and \nabandoned mines (abandoned mine methane or AMM), as well as methane recovered from undisturbed or ‘virgin’ coal seams (usually known as coal bed methane or CBM). Gas from \nthese sources is already produced on a modest scale and exploration is ongoing for further \nprospects. Gas can also be derived from coal by combustion of underground coal seams in \nsitu to produce synthetic gas (‘syngas’). This process is usually known as 'underground coal \ngasification' (UCG). This technology is also in its infancy both in terms of engineering the \nsubsurface process and in the understanding of subsurface and surface environmental \nimpacts. Methane hydrates (methane gas trapped in ‘cages’ of water molecules, resembling ice) have \nbeen recovered from, or are postulated for, virtually all marine shallow sediment \ncontinental margins around the world and a few areas onshore. Volumes of about 2 x \n1014m3 methane in‐place have been estimated for this potential resource. To quantify \nreserve potential and to identify suitable methods of methane extraction, a full \nunderstanding of how hydrates are held within sediments is required. \nA less well known unconventional fuel is subsurface hydrogen. Small flows of hydrogen \nnaturally occur in some mines and in deep oceans associated with abiogenic and biogenic \nmethane, nitrogen and helium. The main geological environment that is promising for \nexploration is the tectonic remnants of ancient ocean floor known as ophiolites. The main \naccessible onshore areas are where ophiolites are found tectonically emplaced within fold \nbelts. \nThough unconventional fuels represent an enormous resource overall, some of the \ntechnology is immature and many of the environmental impacts of their exploitation are \nunknown. Apart from subsurface hydrogen, all are hydrocarbons and thus are constrained in \ntheir use in countries which may limit carbon emissions either now or in the future.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".