Bibliographic record
Abstract
Carbon dioxide and other greenhouse gases have been causing global temperature rising and climate change. According to the National Geographic Society, the average global temperature is predicted to increase by 0.36 degrees Fahrenheit per decade strictly due to greenhouse gas emissions. A large amount of these gases are released into the atmosphere by aviation aircraft. Sustainable Aviation Fuel (SAF) is an eco-friendly alternative to traditional aviation fuels, which significantly reduces carbon emissions. SAF is compatible with existing aircraft and infrastructure, offering us a path in the aviation industry to combat climate change. Studies have shown that blending SAF at a mere 1% ratio and uniformly distributing it to all transatlantic flights would reduce both the annual contrail energy and the total energy forcing by ~0.6%. Dating back to as early as 2016, airports such as the Trudeau International Airport in Montreal have been utilizing SAF. Now, airlines such as Alaska and United Airlines have flights with 100% sustainable aviation fuel usage. The U.S. Department of Energy, Transportation, Agriculture, and the Environmental Protection Agency have come together to make the SAF Grand Challenge Roadmap. In their guideline, they state that they are working towards expanding production to achieve 3 billion gallons per year of domestic SAF and achieve a minimum of a 50% reduction in life cycle greenhouse gas emissions by 2030 and 100% by 2050. The future looks bright with SAF, we are on the brink of a new age.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.232 | 0.121 |
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".