FEATURED PHOTO: SOUTHERNMOST BREEDING OF THE NORTHERN HAWK OWL IN THE UNITED STATES
Bibliographic record
Abstract
In North America, the Northern Hawk Owl (Surnia ulula) breeds across the boreal forest of Canada and Alaska and is generally rare and irruptive in the coterminous United States (Duncan and Duncan 2014). Over the last two decades, multiple instances of breeding have been documented in northern Montana and Washington (Jessica Larson, Owl Research Institute, in litt., 2015; Washington Bird Records Committee [WBRC] 2015), suggesting that it is a rare but regular part of the breeding avifauna of the interior Northwest. In the last two decades 32 attempts at nesting have been documented in Glacier National Park and the nearby Flathead National Forest in northwestern Montana (J. Larson, in litt., 2015), as have two in Okanogan County in north-central Washington (WBRC 2015). Prior to 2014, there was a single breeding record in northern Idaho in 2001, at Snow Lake in Boundary County, and two additional summer (July and August) reports from northern Idaho (Idaho Bird Records Committee [IBRC] 2015). Most of the prior reports from Idaho (22 in all, nine confirmed; IBRC 2015) were in late fall or winter, supporting its status in the state as an irruptive visitor, predominantly in the nonbreeding season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.021 |
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".