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Record W2052923828 · doi:10.5253/078.097.0418

Breeding and Population Density of the Eastern Screech Owl<i>Megascops asio</i>at the Northern Periphery of Its Range

2009· article· en· W2052923828 on OpenAlexaffabout
Christian Artuso

Bibliographic record

VenueArdea · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransectGeographyPopulation densityRiparian zoneRange (aeronautics)Nest (protein structural motif)PopulationBroodHabitatEcologyBiologyDemography

Abstract

fetched live from OpenAlex

Random-stratified spring nocturnal surveys using tape playback were conducted for Eastern Screech Owls Megascops asio from 2004 through 2007 in Winnipeg, Manitoba, Canada, at the northern periphery of its range. Surveys were stratified by (1) human density, (2) riparian vs. non-riparian habitat, and 3) the presence or absence of suburban greenspace. A total of 120 transects (each 1.75 km long) were surveyed, with 55 Eastern Screech Owls detected. Eastern Screech Owl densities peaked in moderate to high-density suburban areas (>20 persons/ha), where 36 (66%) detections occurred, and were lowest in wildlands (<1 pair/ha), where no detections occurred. Fifty-one (93%) Eastern Screech Owls were detected in riparian areas. Only 20 (36%) Eastern Screech Owls were detected in suburban greenspaces. In 4 years I located a total of 46 successful Eastern Screech Owl nests, 6 failed nests, and 37 territories with non-breeding owls. Excluding repeat cavity uses, a total of 61 nest sites and territory centres were located, of which 38 (62.3%) were in natural cavities, 21 (34.4%) in nest boxes, and 2 (3.3%) at sites where cavity choice was undetermined. Fledging dates ranged from 28 May – 3 July (mean 15 June, SE 1.4) over the 4-year period. Eastern Screech Owls were closely tied to riparian habitat, and showed larger average brood sizes and earlier average fledging dates in moderate and high-density suburban areas than in low-density suburban and rural areas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.215
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes2
Has abstractyes

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