Identifying Kittlitz's Murrelet Nesting Habitat in North America at the Landscape Scale
Bibliographic record
Abstract
Abstract The Kittlitz's murrelet Brachyramphus brevirostris is a small, noncolonial seabird endemic to marine waters of Alaska and eastern Russia that may have experienced significant population decline in recent decades, in part because of low reproductive success and terrestrial threats. Although recent studies have shed new light on Kittlitz's murrelet nesting habitat in a few discrete areas, the location and extent of suitable nesting habitat throughout most of its range remains unclear. Here, we have compiled all existing nest records and locations to identify landscape-scale parameters (distance to coast, elevation, slope, and land cover) that provide potential nesting habitat in four regions: northern Alaska, Aleutian Islands, Alaska Peninsula Mountains and Kodiak Island, and Pacific Coastal Mountains (including nearshore interior Canada). We produced a final map classifying 12% (70,411 km2) of the lands assessed as potential Kittlitz's murrelet nesting habitat, with dense but distinct patches in northern Alaska and a more uninterrupted, narrow band extending across the Pacific Coastal Mountains, Alaska Peninsula Mountains, and Aleutian Islands. The extent of habitat-capable parameter values varied regionally, indicating that the Kittlitz's murrelet may be able to use a variety of habitats for nesting, depending on availability. Future nesting habitat studies could employ spatially random sampling designs to allow for quantitatively robust modeling of nesting habitat and predictive extrapolation to areas where nests have not been located but likely exist.
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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 | 0.001 | 0.001 |
| 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".