Original Title: On value and using of TAWS/FMS alert data in examination of air accidents, the case of Warsaw-Smolensk flight on 10 April 2010. Polish Title: O wartosci i wykorzystaniu danych TAWS/FMS do badania wypadkow lotniczych, sprawa lotu Warszawa-Smolensk, 10 kwietnia 2010 roku
Bibliographic record
Abstract
Through an analysis of TAWS/FMS data collected and registered during the last minute of TU-154M aircraft flight from Warsaw to Smolensk, we show the value and existence of space for undertaking research works on enhancing standard functionality of TAWS/FMS systems to enable their effective use in examination of the course and causes of air accidents. The flight ended up in the total destruction of the aircraft and the death of all passengers and crew on board. The TAWS/FMS flight altitudes and spatio-temporal data, i.e. geographical location and speed of the aircraft motion, were inspected for their internal and external consistency with the data from the ATM QAR service recorder. Using the data from ATM QAR, records from the cockpit voice recorder (CVR), jointly with data from the TAWS/FMS systems, it was possible to reconstruct the most probable horizontal and vertical trajectory of the TU- 154M aircraft during the last minute of flight before its complete destruction, as well as the likely scenario of accompanying events. The data available from recorders enabled the authors to gain information on the preliminary stage of the course of accident, and the first phase of the aircraft’s destruction, resulting in serious damage of the left wing of the aircraft. Enhanced standard functionality of TAWS/FMS systems, incorporating use of their data in the post-accident situations examination, would have improved results and simplified the present analysis considerably.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".