Bibliographic record
Abstract
The global aviation industry is changing. Increasingly, the industry will be driven by, and judged on, its environmental record. The international aviation community, through ICAO/CAEP has made significant progress addressing the most visible environmental issues in aviation; airframe and engine noise and air emissions (NOx, CO2). This initiative anticipates the next wave of environmental concerns in aviation, the role and contribution of individual aerospace manufacturing processes towards sustainability.The Ontario Aerospace Council (OAC) has embarked on a province-wide project known as the MOSAIC Initiative (Manufacturers of Sustainability, Aerospace Industry Catalyst). This initiative proactively addresses issues related to aerospace manufacturing and the environment by: Surveying the current state of the industry in Ontario, Canada; Identifying issues, barriers and concerns related to sustainability; Establishing environmental benchmarks for the Ontario Aerospace Industry; Creating a blueprint for small and medium-sized aerospace businesses to launch their environmental programs; Identifying the top-ten sustainability objectives for the OAC, and a plan to meet those objectives; Creating an environmental sustainability vision statement for the OAC. This EIC Climate Change presentation will discuss the industry trends and philosophy that led us to develop the MOSAIC initiative, the goals and measurable outcomes of the initiative and the status of the project to date.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".