A multiscale analysis of factors influencing Blackpoll Warbler occupancy and abundance during the non-breeding season in eastern Colombia
Bibliographic record
Abstract
The dataset includes two survey methods to monitor Blackpoll Warbler populations and assess habitat structure in tropical ecosystems across Colombia. The first method involved surveys conducted along 128 transects (regional-scale data), each 100 meters in length and 50 meters in width, across multiple sites during two sampling periods (2017 and 2018). The transects, situated in both uniform habitats (foothill tropical rainforests and terra firma tropical humid forests) and transitional/agricultural systems, were surveyed by trained observers using visual and auditory species identification techniques. Each transect was surveyed at least twice per season, with multiple repetitions to reduce biases. The second method, point counts (vegetation and landscape scale), was conducted across different habitat types in the Meta and Guaviare regions, with points strategically placed to ensure habitat homogeneity, at 26 sites using 160 point-count surveys, each within a 30-meter radius. Both transect and point count data were analyzed based on observer and repetition to determine species presence and absence. This combined methodology provides a comprehensive understanding of species distribution and habitat preferences across different landscape scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".