¿Es posible la elección de una jurisdicción más favorable para la indemnización, por dañoz causados en un accidente aéreo por error no humano?
Bibliographic record
Abstract
This paper focuses on the analysis of the possibilities according to national and international law in order to determine the possibility of the affected in a plane crash or their heirs, who will be entitled to receive royalties generated from the incident, to obtain a better recognition of rights. An initial analysis of air law, which primary dispositions focus on the severance terms that are configured according to the plane crash, as well as an analysis of the differences between jurisdictions and how they apply the previously stated laws and treaties. Conflicts of jurisdictions to which we refer in this paper, respond to differences that arise according to international conventions and treaties, to which countries are signatories. So in this particular case we refer to the Warsaw Convention, as well as the Convention for the Unification of treaties, which contains the Montreal Convention regime......
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".