The western screech-owl and habitat alteration in Baja California: a gradient from urban and rural landscapes to natural habitat
Bibliographic record
Abstract
We studied the western screech-owl (Otus kennicottii) in the desert of the southern Baja California peninsula to determine its status and habitat selection and whether it benefits from moderate human-caused habitat changes. Based on the response to tape-recorded call broadcasts, western screech-owls were more abundant in undisturbed vegetation than in human-altered habitat. In man-made environments, more owls responded in rural than in urban areas; indeed, they were practically absent in urban areas. In natural areas, a total of 1.6 owls/km was estimated in the nonbreeding season and 2.7 owls/km in the breeding season. The most important habitat features selected in natural areas were greater shrub cover in the strata containing shrubs 12 and 23 m high and greater canopy cover of trees >5 m high. In rural areas adjacent to cultivated areas, owls were present in areas with the greatest cover of shrubs 13 m high and a higher density of shrubs. The western screech-owl also occurs at lower densities in rural areas than in natural areas. Towns appear to be avoided in the arid desert of Baja California Sur. Western screech-owls may be still able to persist in rural areas that occupy an intermediate position on the gradient from a functional natural system (desert vegetation) to a completely altered ecological system, urbanized areas.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".