An Efficient Method for Capturing Barred Owls
Bibliographic record
Abstract
trapping techniques were tested to capture, mark, and release Barred Owls (Strix varia)from 2009 to 2011 in southeastern Manitoba.These proved to be inefficient; therefore, we developed and are reporting an improved method.A simple setup consisting of one mist net, two extended sectional mist net poles, high visibility string, tWo carabiners and a remotely controlled electronic game caller with recorded Barred Owl calls was developed and used in conjunction with a live Barred Ow/lure bird.Our setup differed from other mist net setups used for Barred Owls by its height (6.1 m) and its shape (a straight line).We achieved a 77% success rate when using this setup and experienced few disadvantages in its operation.We strongly encourage published research that involves the capture of wild animals to better report the effort and efficiency (success rates per unit time) of capture methods so that researchers using them to initiate research on species new to them can avoid inefficient methods.Barred Owl (Strix varia) is a relatively understudied species of owl in North America.Difficulties in capturing this species for measure-.ment, banding and/or radio marking may contribute to the scarcity of published data.Published accounts on capture techniques for Barred Owls often lack details about how trapping methods were implemented or omit time requirement per owl (Nicholls and Warner 1972, Hamer et al. 2007, Singleton et al. 201 0).Furthermore, the success rate of each trapping technique is seldom reported (Berger and Mueller 1959, Mazur et al. 1998).This disparity leaves novice Barred Owl researchers with little information on how to capture Barred Owls efficiently and what equipment is most effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".