The Ontario Great Gray Owl Invasion of 1983-84: Habitat, behaviour, food, health, age and sex
Bibliographic record
Abstract
The following article is a summary of observations made during the 198~invasion of Great Gray Owls in Ontario, with dates and locations of birds.Because of the general nature and short length of most reports I could seldom make correlations between different types of information; and obvious gaps result because no further data were provided.However, some very useful observations were recorded. Perch sitesDeciduous trees (50) were noted as perch sites more frequently than coniferous trees (16).This may reflect the higher availability of the former, or the increased visibility of owls among bare branches.However, it may be that hun ting owls select perches in deciduous trees for easier manoeuvrability and better acoustics (R. W. Nero, pers.comm.).Owls seemed to make little effort to conceal themselves.They were noted perched on bare branches in trees 105 times, usually well out on branches, and almost as often in even less concealing situations.These included dead trees (32), utility poles (24), fence posts (13), overhead wires (9), snags (8), the tops of bushes (8), buildings of various sorts (6), stumps (2), guard rails (1), and stop signs (l).Low perches were favoured over high sites.Small trees or bushes (21) were noted more often than tall trees (4); heights of perches were below 5m 12 times, between 5 and 10m ten times, and above 10m only three times.Utility poles and wires, fence posts, and stumps (62) could also be considered low perches.These low perches probably facilitated the location of prey by sound (Norberg 1987).On a couple of occasions, owls were noted flying closer to a place where they ultimately dropped to the ground,'apparently getting closer to and locating the sound source more precisely.
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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 | 0.001 | 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) | 0.002 | 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".