TANGGUNG JAWAB NEGARA ATAS TERGANGGUNYA FREKUENSI RADIO PENERBANGAN DI WILAYAH UDARA PANTAI UTARA JAWA GUNA MENJAMIN KESELAMATAN PENERBANGAN
Bibliographic record
Abstract
Indonesia is an archipelagic country that can be divided into land, water and airspace. For the sake of realizing a national insight, it requires a transportation system. Transportation can be divided into land, sea and air transportation. Air transportation is a very efficient transportation for humans because it has comfort and can save time. Air transportation growth in Indonesia are rising highly time by time, law must provide the rule of operation of air transport. Passenger have right to get to destination that ruled on air ticket. Beside of the destination, passenger must get delivered by the time that written on ticket. Airline must provide aircraft that could carry all passenger that have the ticket, but in practice, some passenger can’t be carried by airline because there was changes of the type of aircraft that carry less passenger than planned. Passenger must get compensation from the loss. Airline shall responsible from causing the trouble. There some responsibility system in Indonesia law, but for the case of the above, the responsibility system that should airline take is unclear. This journal will discuss about the responsibility system that can be used for the case and how law govern to clear the problem.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".