Characterizing Northern Saw-Whet Owl (Aegolius acadicus) Winter Habitats in South-Central Indiana
Bibliographic record
Abstract
Northern Saw-whet Owl (Aegolius acadicus) winter habitat in south-central Indiana was assessed during two winters (2003/2004 & 2004/2005). Differences between locally occupied and unoccupied habitat were examined, and occupied habitat in Indiana was compared to occupied habitat in other regions. Using audio surveys and active voice detection, 40 locations were sampled in the first winter, 45 in the second winter, 35 in both winters and 50 total. The presence of Northern Saw-whet Owls was strongly related to the understory transparency, agreeing with previous studies in Maryland and Michigan showing a correlation with dense vertical structure. However, there was no evidence of a strong correlation between the presence of Northern Saw-whet Owls and either evergreen canopy cover, evergreen stem density or mid-canopy gap, contradicting results from other studies in Minnesota, Maryland and Michigan, though there was a minimum 40% evergreen canopy cover in occupied sites. The lack of consistency among studies indicates regional variability in the habitat structure of sites occupied by Northern Saw-whet Owls, but a dependable reliance on dense cover. Historic records show that Northern Saw whet Owls (Aegolius acadicus) occasionally breed in northern Indiana, and migrant popu lations spend the winter across the state (Cannings 1993). Christmas Bird Count records have also consistently (since 1985) documented the presence of Northern Saw-whet Owls wintering in south-central Indiana. Between fall and spring migration events, potential south-central Indiana forest sites were surveyed for Northern Saw-whet Owls to characterize occupied habitats in the winters of 2003/2004 and 2004/2005. Northern Saw-whet Owls undergo an annual fall movement from their primary breeding range in boreal forests along the U.S.-Canada border to points as far south as the Gulf Coast (Weir et al. 1980; Cannings 1993; Swengel & Swengel 1997). This partial migration has a high degree of variability due to irruptive years when the number of migrating Northern Saw-whet Owls can be ten times higher than the longterm average (Weir et al. 1980; Whalen & Watts 2002). Breeding Northern Saw-whet Owls are usu ally found in coniferous or mixed coniferous deciduous forests with a well-developed middle canopy of coniferous trees (Cannings 1993). Wintering and migrating Northern Saw-whet Owls appear to rely on dense vegetation for
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".