Clustering Community Science Data to Infer Migratory Connectivity for Songbirds in the Western Hemisphere
Bibliographic record
Abstract
Migratory connectivity describes the spatial linkage between individuals through time and is necessary for full annual cycle conservation.However, conventional methods used to study migratory connectivity can be expensive and expertise intensive.In Chapter 2, we infer migratory connectivity patterns for songbirds using relative abundance models created from eBird, a global community science program, and an underlying broad-scale parallel migration assumption.We compare migratory connectivity inferences for two species with previously described connectivity estimates from the literature.We find that our method is a fast and inexpensive way to infer broad patterns on connectivity for these two species, though it cannot predict leapfrog migration or extreme deviations from parallel migration.In Chapter 3, a literature review of migration patterns shows that broad-scale parallel migration is commonly observed for songbirds in the Western hemisphere, and thus the methodology presented in Chapter 2 should be widely applicable to other species.This thesis would not have been possible without the guidance of Joseph Bennet, Richard Schuster, and Scott Wilson.I would like to thank my supervisor Joe Bennett for accepting me as a Master's student; I never thought that a graduate student experience could be so positive.I credit this in large part to the supportive and kind working environment that he fosters daily.Thank you for being such a responsive and motivating mentor.Thank you to Richard Schuster for his invaluable help with creating R code, and for troubleshooting any problems that I had.My coding skills have greatly improved since the begining of my Master's because of this
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".