Tracking movement in overwintering songbirds: An RFID approach
Bibliographic record
Abstract
Globally, habitat loss and changes in land use are causing an increase in habitat fragmentation, which can negatively impact some songbird species. Urbanization in particular can create highly fragmented habitats with landscape corridors that vary greatly in their propensity to be crossed. In the study of animal movement, there are several factors that limit the quantity and quality of data that can be collected on individuals. In birds, movements occur quickly and often high in trees or in heavy brush, making detailed observations of the movements of known, colourbanded, individuals challenging. I utilized a Wi-Fi enabled radio-frequency identification (RFID) bird-feeder system in order to passively and autonomously track movement events of banded permanent resident songbirds (i.e., House Finches, Song Sparrows, Dark-eyed Juncos, and Mountain Chickadees) in the area of Kamloops, BC, Canada. I tracked banded and RFID tagged songbirds over a 63 day period from 1 January 2016 to 3 March 2016, recording 21732 visitation events by 28 individuals. From these visitation events I determine 817 movements by House Finches (n = 23 individuals), 176 movements by Mountain Chickadees (n = 2 individuals), and 15 movements by individuals of other songbird species. I then used ArcGIS to create resistance landscapes to ask whether movement patterns were best predicted by the “resistance” characteristic of the habitat between feeder locations or by straight-line distance. The movement patterns exhibited by tagged birds were not predicted by any of straight-line distance, least-cost distance, straight-line pathway resistance, or least-cost pathway resistance. However, feeders with a higher proportion of shrubs and trees were visited marginally more frequently. I also found that females traveled between feeders more frequently than males.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".