Bibliographic record
Abstract
The Federal Methanol Fleet Project concluded with the termination of data collection from the three fleet sites in February 1991. The Lawrence Berkeley Laboratory (LBL) completed five years of operation, Argonne National Laboratory (ANL) completed its fourth year in the project, and Oak Ridge National Laboratory (ORNL) completed its third. Twenty of the thirty-nine vehicles in the fleet were powered by fuel methanol (typically M85, 85 % methanol, 15 % unleaded gasoline, although the LBL fleet used M88), and the remaining control vehicles were comparable gasoline vehicles. Over 2.2 million km (1.4 million miles) were accumulated on the fleet vehicles in routine government service. Data collected over the years have included vehicle mileage and fuel economy, engine oil analysis, emissions, vehicle maintenance, and driver acceptance. Fuel economies (on an energy basis) of the methanol and gasoline vehicles of the same type were comparable throughout the fleet testing. Engine oil analysis has revealed higher accumulation rates of iron and other metals in the oil of the methanol vehicles, although no significant engine damage has been attributed to the higher metal content. Vehicles of both fuel types have experienced degradation in their emission control systems, however, the methanol vehicles seem to have degraded their catalytic converters at a higher rate. The methanol vehicles have required more maintenance than their gasoline counterparts, in most cases, although the higher levels of maintenance cannot be attributed to ``fuel-related`` repairs. According to the daily driver logs and results from several surveys, drivers of the fleet vehicles at all three sites were generally satisfied with the methanol vehicles.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.977 | 0.982 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".