The Reported Shape, Size, Kinematics, Electromagnetic Effects, and Presence of Sound of Unidentified Aerial Phenomena From Select Reports, 1947-2016
Bibliographic record
Abstract
Publicly available witness reports, catalogued by military and civilian agencies, of Unidentified Aerial Phenomena (UAP) from 1947 to 2016 were hand-sorted for selection based on four criteria: reliability of witness testimonies, object angular size greater than 0.15 degrees, sufficient lighting, and sufficient information detail. The resultant database comprises the subset of historical UAP reports that were determined to likely represent unidentified aerial objects. Out of more than 100,000 reports amassed from one military database and four civilian databases, 301 reports spanning the same years were identified as meeting these criteria. From this selected set, the characteristics of shape, size, kinematics, electromagnetic effects, and sound emanation are examined. Detailed descriptions in the witness accounts allowed us to present scaled illustrations for the two most common UAP shape categories: disks (domed, elongated, shortened) and triangles (isosceles, equilateral). The largest shapes reported were diamond/rectangle and boomerang (median 300 ft (91 m)), and the smallest were spheres (median 20 ft (6 m)). Triangles (median 170 ft (52 m)) were consistently reported to hover, did not produce electromagnetic effects, and were often noted to have an absence of sound. The combination of unusual kinematic range and absence of sound was found in 16 reports which specifically mentioned objects that hovered, traveled faster than Mach 1, and exhibited an absence of sound: disk (5), triangle (8), oval (1), sphere (1), and boomerang (1). The dataset of UAP characteristics presented here, based on 301 reliable witness reports, can be used to inform the design of the various UAP field instrumentation, detection algorithms, and propulsion hypotheses that are critical to the advancement of our understanding of UAP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".