Vertical Flight Infrastructure DATA Quality Shortcomings
Bibliographic record
Abstract
This paper represents an assessment of how vertical flight infrastructure data is accounted for, the criteria and standards used to accomplish this, what shortcomings may exist in both the data as well as the process, and what changes need to be implemented in preparing for the next generation of vertical flight transportation. The primary focus of this paper will be to review the regulations, criteria, standards, and processes that apply to aviation infrastructure data Collection, Accuracy, Resolution, Integrity, Traceability, Timeliness, Completeness, and Format and to what extent it is successfully being accomplished in the vertical flight industry. Based on these findings, this paper will strive to identify the current gaps in data accountability for vertical flight infrastructure and provide recommendations for remediating these issues. While this assessment will be specific to current and traditional helicopter infrastructure, it is intended to provide insights into identifying best practices as it applies to the Advanced Air Mobility (AAM) industry and its supporting infrastructure as well.
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 | 0.109 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".