Residential versus Migratory Bird Flight: Classification by Trajectories Characterization
Bibliographic record
Abstract
This paper presents a proof of concept for classifying migratory birds from residential birds in Near-Field using an alternative method, by examining the nature of their flight paths, patterns, and trajectories. Multiple videos containing natural and artificial databases of flying birds were used to extract their flight trajectories. For them to fly over a long distance, migratory birds, Canadian Geese, for example, have much higher physical strength and lower body weight compared to residential birds. Therefore, due to their nature and physical limitations, migratory birds fly in folk, usually with considerably more predictable and periodic (due to their flapping motion) fight paths without drastic changes in their heading. Whereas residential birds, on the other hand, fly or sometimes glide in shorter distances and sections from point A to point B, so they can change their heading and acceleration very quickly or even in mid-air. Four (4) trajectories characteristics and observed from the bird's flight paths: turning angle, periodicity (frequency), and object pace (velocity and acceleration). Hereafter, principal component analyses were applied to reduce the number of these trajectory features from 4 to 2 parameters. Support vector machine (SVM) with Quadratic transformation kernel was then used for binary classification. Sample test results show that the prediction was$\geq$90% accurate. Note that classification accuracy can be improved with more true-to-life training data to cover more cases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".