Bibliographic record
Abstract
Liquid fuels will remain valued energy carriers well into any upcoming period when CO2 reductions are sought. Biofuels are the presumed replacement for the petroleum-based transportation fuels that dominate liquid fuel use. Lifecycle analysis embeds a closed-loop model of biofuel-related carbon flows, making net CO2 uptake an assumption to be refuted. However, evaluating net CO2 uptake through dynamic industrial and agriforestry supply chains at real-world commercial scales is extremely difficult. All such estimates carry a great deal of doubt and cannot be verified empirically. A different perspective follows by anchoring analysis in the certainty that end-use CO2 emissions from biofuels are essentially the same as those of the petroleum fuels they replace. A first-order model of the globally coupled bio- and fossil-fuel system reveals conditions for biofuel use to provide an atmospheric benefit. No benefit occurs in the energy sectors where biofuels are used, but rather must be found elsewhere in locations of carbon absorption or retention. The implication is that climate mitigation efforts should focus on such locations and include any mechanisms through which net uptake (an enhanced sink or verifiable offset) can be achieved by biological, chemical, geological or other means. Although biofuels can play a mitigation role when certain conditions are met, deemphasizing biofuel production in favor of terrestrial carbon management may offer more immediate and effective ways to counterbalance the CO2 emitted when using carbon-based liquid fuels of any origin. Climate policies for transportation fuels should be reconsidered accordingly.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".