Boreal Owl (<i>Aegolius funereus</i>) and Northern Saw-whet Owl (<i>Aegolius acadicus</i>) breeding records in managed boreal forests
Bibliographic record
Abstract
During the 2016 breeding season we monitored 169 nest boxes suitable for Boreal Owl (Aegolius funereus) and Northern Saw-whet Owl (Aegolius acadicus) in high-latitude (>55°N) boreal forests of northwestern Alberta affected by partial logging. Despite the large number of boxes deployed, the number of boxes used by Boreal and Northern Saw-whet Owls was small. Boreal Owls used nest boxes (n = 4) in conifer-dominated stands with three being in uncut blocks and the other in a 50% green tree retention cut-block. In contrast, Northern Saw-whet Owls used boxes (n = 4) in a broader range of cover types, breeding in boxes placed in stands with at least 20% post-harvest tree retention. Although both species successfully bred in the same landscape, Boreal Owls produced fewer eggs (mean = 2.5) and raised fewer young (mean = 0.5) than Northern Saw-whet Owls (5 and 2.25, respectively). Furthermore, our observed Boreal Owl egg production was lower than has been found for the same species nesting in nest boxes in different regions or forest types. In contrast, breeding parameters of Northern Saw-whet Owls were similar to that found in nest boxes in the eastern boreal region of Canada and in the southern part of its range.
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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 | 0.000 |
| 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) | 0.000 | 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".