Bibliographic record
Abstract
The most serious energy, economic and environmental problems are related to the use of fossil and nuclear fuels, which are rapidly diminishing and highly polluting, and many distinguished atmospheric chemists, including Dr. James Hanson at NASA, Dr. Steven Chu, the director of Lawrence Livermore Laboratory, and Professor Ralph Cicerone, president of the National Academy of Sciences have documented that climate changes are now occurring much faster than predicted just a few years ago. The methane hydrates in the oceans and the permafrost in vast areas of the Artic regions of Siberia, Alaska and Canada are now starting to rapidly melt, and given this could release 50 to 100 times more carbon into the atmosphere than is now generated from the burning of fossil fuels, humanity is rapidly approaching an exponential 'tipping point' of no return. Given this sense of urgency, Hanson and others have warned that fossil fuels need to be phased-out by 2020 if irreversible damage to the earth's climate and food production systems is to be avoided. The Phoenix Project plan seeks to do exactly that by mass-producing wind-powered hydrogen production systems and simply modifying all the existing vehicles and power plants to use the hydrogen made from the sun, wind and water.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".