North American Bird Strike Advisory System
Bibliographic record
Abstract
The international aviation community recognizes the high human and economic costs associated with bird strikes. Hundreds of lives and millions of dollars have been lost in recent years because of this problem. Notably, aviation experts in North America recognize the importance and availability of potential solutions for this problem. Several models and systems such and the USAF’s Bird Avoidance Model (BAM) and the Avian Hazard Advisory System (AHAS) as well as the technological development of advanced radar and communications systems have made great progress in addressing the problem of bird strikes. However, many have argued that further and much greater advancement could be made if the current fragmented and competitive efforts could be consolidated in a single cooperative venture. This strategic plan is the initial step in a process of consolidating and integrating the various United States and Canadian civil and military efforts in order to develop and implement North American Bird Strike Advisory System. The plan has been developed based on the collected wisdom and technical knowledge of the top personnel and organizations in the field of aviation safety. If implemented, the plan will represent a critical first step leading to the realization of a North American Bird Strike Advisory System that will help protect aviators and their equipment from the deadly and costly effects of bird hazards. The plan outlines the architecture of a notional bird strike advisory system for North America. It identifies the key agencies that must be involved in the development of the system. It establishes a top level schedule and identifies six key goals in developing an integrated system. The plan describes more detailed objectives and activities required to accomplish these goals. Recommendations are made regarding which agencies might most effectively take the lead in integrating various activities needed to accomplish each goal. It proposes a 5 year budget of approximately $16,000,000 in order to support the initial phases of the effort. The strategic plan and its appendices also outline in considerable detail the key technical challenges, risks, and suggested organizational and technological solutions for these problems. While reviewing this strategic plan, it’s important to remember that it is not a detailed blueprint for developing and implementing the final system. Rather, it is a starting point for an evolving project and system that can be continuously developed and improved as technology and organizational systems become more advanced. The relatively modest budget proposed is essentially a “down payment” for the more robust system that will evolve based on this initial consolidation and integration effort. The plan represents an important first step in moving beyond fragmented competitive approaches to consolidated and integrated system that will save hundreds of lives and prevent a great deal of economic loss associated with destroyed or damaged aircraft and equipment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.059 |
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".