Breeding habitat preference and nest success of Red-necked Phalaropes on Niglintgak Island, Northwest Territories
Bibliographic record
Abstract
Red-necked Phalaropes ( Phalaropus lobatus (L., 1758)) breed throughout arctic and subarctic wetlands. These wetlands provide Red-necked Phalaropes dense graminoid habitat that shelters and conceals nests, and freshwater ponds where phalaropes engage in social interactions and feed on small aquatic invertebrates. We studied breeding habitat preference of Red-necked Phalaropes at multiple scales and determined which, if any, nest-site characteristics influenced hatching success. Red-necked Phalaropes avoided habitat at the meso (home range) scale containing large areas of mud and shrub cover, and selected sites with greater cover of graminoids, aquatic emergents, and open water than that available in the environment. At the micro (nest) scale, phalaropes chose sites dominated by graminoids and water. In 2005, concealed nests experienced higher daily nest survival than exposed nests. In 2006, 40% of nests were destroyed during a summer storm surge and we detected no differences in habitat characteristics between the remaining successful and unsuccessful nests. We suggest that annual differences in the adaptive value of nest-site selection depend on the relative abundance of mammalian and avian predators. A habitat model using data at the meso scale correctly identified phalarope habitat and has the potential to be used broadly across the western Arctic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".