Bibliographic record
Abstract
As realization grows of the damaging cumulative effects of CO2 on our biosphere, the prospect of substituting hydrogen for oil-based fuels attracts growing attention. Japan provides a leading example of remedial action with the expectation of five million fuel-cell-powered vehicles in operation by 2020. But where will the fuel for these and the rest of a “Hydrogen Age” come from? The hydrogen market used to be straightforward: small-scale or high-purity markets were supplied relatively expensively by electrolysis; the other 95% was supplied much more cheaply by reforming hydrocarbons — mostly using steam-methane reforming (SMR) and low-cost natural gas. The recent rise in the price of hydrocarbons — natural gas as well as oil — plus the need to sequester CO2 has disrupted this scenario. It seems likely that this is a permanent shift driven by growing demand for limited low-cost sources of fluid hydrocarbons. So the traditional SMR route to hydrogen will be in competition with reforming of heavier hydrocarbons (particularly coal and residual oils) as well as with electrolysis based on electricity produced from low-CO2-emitting sources. By 2025, new high-temperature thermochemical or thermoelectrolytic sources based on high-temperature nuclear reactors could be in contention. This paper assesses the economics of all these potential sources of hydrogen and their price sensitivities. It also considers their environmental footprints. Is hydrogen from “clean coal” or other lower value hydrocarbons cost-effective if it is also CO2-free? Is intermittent low-temperature electrolysis based on nuclear- and wind-produced electricity (NuWind©) the best way or does the hydrogen future belong to thermochemistry or thermoelectrolytic sources? How can one produce hydrogen to upgrade Canada’s vast oilsands resources without the detraction of a large CO2 processing penalty? Fortunately for our planet, switching to hydrogen is no more than a technical challenge with a range of possible solutions but we need to make that point clearly to the political decision-takers and be able to provide assurance that the preferred solution will not be a source of new problems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".