Managing the Risks: An Analysis of bird strike reporting at Part 139 Airports in Indiana 2001-2014
Bibliographic record
Abstract
Purpose: The purpose of the current study was fourfold: to identify bird strike reporting trends at Part\n\t\t\t\t 139 airports in Indiana (2001-2014) for comparison to national data; to determine which quarter of the\n\t\t\t\t year yields the most bird strike data; to gain a clearer understanding of the relationship between altitude\n\t\t\t\t and bird strikes, and to develop information based upon the data analyzed that can be used for the\n\t\t\t\t safety management of birds including comparisons to national data.\n\t\t\t\t Design/methodology: The researchers in this study answered the research questions by reviewing,\n\t\t\t\t sorting, and analyzing existing data. The data collection took place from March 01 to May 02, 2016.\n\t\t\t\t Two data sets were utilized for data collection. The National Wildlife Strike Database (NWSD) and the\n\t\t\t\t FAA Air Traffic Activity System (ATADS).\n\t\t\t\t Findings: When compared to national data, Indiana Part 139 airports have seen a faster increase in\n\t\t\t\t bird strike reporting during 2012 and 2014. Aggregate data indicated June through September (Quarter\n\t\t\t\t 3) had a significantly higher frequency of bird strikes reported. When examining bird strikes and\n\t\t\t\t altitude of occurrences, the exponential equation explained 95 % of the variation in number of strikes\n\t\t\t\t by 1,000-foot intervals from 1000 to 10,000 feet. Not surprisingly, the risk of bird strikes appears to\n\t\t\t\t decrease as altitude increases.\n\t\t\t\t Originality/value: This study adds to the body of knowledge by addressing the lack of published bird\n\t\t\t\t strike report analyses at a regional level. It also connects data analyses to safety management system (SMS) concepts and Wildlife Hazards Management Programs (WHMP). The aviation community can\n\t\t\t\t use regional bird strike data and information to develop or enhance existing wildlife hazard\n\t\t\t\t management programs, increase pilot awareness, and for refinements in the development and\n\t\t\t\t implementation of integrated research and operational efforts to mitigate the risk of bird strikes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".