Bibliographic record
Abstract
In order to realize the 3Dsituation display of airport based on the automatic dependent surveillance-broadcast(ADS-B)data,its smoothing method is developed in the first step of airport 3Ddisplay.The ADS-B data must be pretreated because it is unsmooth with low accuracy for 3Ddisplay of the moving aircraft on airport surface.After smooth pretreatment,ADS-B data can be interpolated to high-frequency track data which is used for 3Ddisplay.So the interacting multiple model Kalman filter(IMMKF)algorithm is used to smooth the track.First,according to the actual movement of aircraft,three motion models with respect to constant acceleration,constant turn and constant velocity are constructed separately.Second,the IMMKF algorithm which combines IMM and Kalman Singer filter is used to track and smooth ADS-B data.Compared with other several classical filters,the experiment results indicate that this method achieves the lower failure probability of tracking with enough smoothness,realtime calculation and high accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".