Bibliographic record
Abstract
It is incontrovertible that, with the ravages inflicted on the world by the Covid-19 pandemic, travel by air has been vastly reduced amidst strict quarantine measures imposed on passengers. The resuscitation of air services to the volume that existed in 2019 will not only take a long time but will also require new approaches to connectivity. Trading in air transport will involve more reliance on digital technology and platforms such as the Internet that would promote communication of data and relevant details of route structures and threats posed thereto. Artificial intelligence and the Internet will be essential in providing data and details in a timely manner for both States and their airlines to take effective measures against the spread of another pandemic, the occurrence of which scientists are saying is probable in the foreseeable future. Against this backdrop of ominous reality, the aviation community has no alternative but to lean heavily on spontaneity in the exchange of information to suspend or terminate air services that connect potential hotspots that are likely to spread a virus which infects a particular city and could settle in other cities that are connected by air. This article inquires into the relevance and applicability of technology and the role that the Internet could play in what some call The New Normal.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".