Bibliographic record
Abstract
Avian conservation is imperative because birds provide many beneficial ecosystem services. Bird mortality is highest during the migratory period due to habitat loss from anthropogenic land cover change. On the way to and from breeding grounds, migrants make many stopovers to refuel and rest for the next leg of their journey. The abundance, distribution, and quality of the stopover habitat are important for a successful migration. The southern shore of Lake Ontario in Western New York has received attention for conservation, because it provides critical stopover habitats for migrants. Performing vegetation and bird surveys, at specific stopover locations, provides useful information for finding correlations between bird abundance and richness with specific habitat characteristics and provides insight to the presence of invasive plant species in an area. The field data also help validate the accuracy of the 2001 National Land Cover Database (NLCD), which supplied land cover information for the geographic information system model used to initially locate the sampling sites. Sampling site locations were predicted by the model using distance from the shoreline of the lake and percent woody cover within 5 kilometers. The model accurately predicted the location of forested habitat with only minor discrepancies between specific forested land cover types when comparing to the actual land cover at the sampling plots. The field surveys suggested that birds prefer stopover habitats with a higher abundance of saplings and large shrubs. Birds were observed to be higher in abundance and richness in more isolated habitats with less than ten percent wooded cover in a 5 km radius around the patch. They also seemed to prefer habitat near the shore (0-2 kilometers) or further away from the shore (32-75 kilometers). The identified preferences that migrants have for specific stopover characteristics in this study can be incorporated in the conservation plans for quality stopover habitats in the Western New York region. The model can serve as a template for identifying more stopover habitats in the future.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".