Bibliographic record
Abstract
Climate change challenges need to be considered in various dimensions. Aviation industry has multiple impacts on human lives such as impacts on the urban and natural environments. Various dimensions of the issue and its importance have been reported by the IPCC, following a request from the ICAO and the Parties to the Montreal Protocol on Substances that Deplete the Ozone Layer in 1999. In this paper different related topics have been investigated. Aviation: Development and Improvement, Climate changes as main environmental crisis, causative source of pollutions: Air pollution (GHGs, aerosol, smoke and particulate, dust), water pollution, biodiversity, hazardous materials, and aeronautical noise. Link between aviation impacts and environmental crisis have been discussed. Different perspectives of the aviation challenge briefly are presented: I- Human dimension, II- Urban environment (local, regional, and global), III- Natural environments (terrestrial, aquatic, and atmospheric) and IV- Birds killed by intervention. In concluding remarks two aspects of the issue, A) benefits, and B) impacts have been considered, and in the end some recommendations have been made on Emissions Trading, Environmental Performance, and Technological Developments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".