ICAO AVIATION OCCURRENCE CATEGORIES SIGNIFICANTLY AFFECTING AVIATION SAFETY IN POLAND FROM 2008 TO 2015
Bibliographic record
Abstract
Poland, as a member of the EU, is represented within the ICAO, by the European Aviation Safety Agency. However, this does not relieve our country from the responsibility of developing a state safety programme (SSP). The need to set up such a programme, which has to be specific to every country involved in aviation operation, was introduced by the ICAO's Annex 19. One of the important points in Annex 19 is: "5.2.1 Each State shall establish and maintain a safety database to facilitate the effective analysis of information on actual or potential safety deficiencies obtained, including that from its incident reporting systems, and to determine any actions required for the enhancement of safety". The Polish Civil Aviation Authority, along with other databases, manages the European Coordination Centre for Aviation Incident Reporting Systems (ECCAIRS). The authors (who are specialists dealing with exploitation processes in aviation) have conducted a laborious processing of the data contained in the ECCAIRS database, analysing them based on various criteria: aviation occurrence categories (as defined by the ICAO), phases of flight for different airports in Poland etc. Aircraft with an maximum take-off mass (MTOM) <5,700 kg (mainly general aviation) and for aircraft with an MTOM >5,700 kg (commercial aviation) were considered separately. It was found that the most events are those that relate to power plant (SCF-PP) airframes and related system (SCF-NP) failures, followed by collisions with birds (BIRD), events related to airports (ADRM) and events related to the required separation of aircraft (MAC). For lighter aircraft, the dominant categories are ARC, CTOL, GTOW and LOC-I events. The article presents a proposed method for predicting the number of events, determining the alert levels for the next years and assuming a normal distribution (Gaussian). It is one of the first attempts to use actual data contained in the database of events on airports in Poland. The results of this analysis may support the decisions of supervisory authorities in the areas where security threats are most important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".