Bibliographic record
Abstract
The article examines international and national legislation regulating air transportation, in particular, international acts (the Montreal, the Warsaw Conventions, etc.), federal legislation (Air. Civil codes, etc.), and subordinate legislation (Federal Aviation Regulations and local acts of air carriers). One of the latest significant changes in the air transport area is related to the accession of the Russian Federation to the Convention for the Unification of Certain Rules for International Carriage by Air, adopted in Montreal. The relevance of the topic is beyond doubt, because in modern conditions, it is important for each individual and collectives to be mobile, and air transport is capable of providing the fastest and most comfortable delivery from one point to another in a short period of time. Much attention is paid to the analysis of the carrier's liability, because at the moment the carrier bears only fault-based liability for the flight delay and other damage caused, and is discharged from the liability only in case of force majeure circumstances, as confirmed by numerous examples of judicial practice. The author draws a conclusion about the increase in the amount of liability for the delay in delivery of goods, passengers and luggage.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".