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Record W2169013821 · doi:10.1648/0273-8570-71.1.46

DENSITY INFLUENCES CENSUS TECHNIQUE ACCURACY FOR CERULEAN WARBLERS IN EASTERN ONTARIO

2000· article· en· W2169013821 on OpenAlexaffabout
Jason Jones, William J. McLeish, Raleigh J. Robertson

Bibliographic record

VenueJournal of Field Ornithology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsQueen's University
Fundersnot available
KeywordsRADIUSStatisticsPopulation densityPopulationMathematicsDensity dependenceVariable (mathematics)GeographyDemography

Abstract

fetched live from OpenAlex

This study investigated the accuracy of 50-m fixed-radius, 100-m fixed-radius, and variable circular-plot point counts to estimate the actual density of breeding Cerulean Warblers (Dendroica cerulea) during the 1997 and 1998 breeding seasons, in Ontario, Canada. Density estimates were compared to actual densities as measured from intensive field observation of pairing and nesting behavior. Estimates of density from each of the techniques were positively correlated with actual density in both years. Both the technique used to census a population as well as the actual density of the population itself affected the accuracy of the derived density estimations. In both years, the 50-m fixed-radius technique overestimated density. In contrast, the 100-m fixed-radius technique and variable circular-plot technique underestimated density; the degree of the underestimate of the 100-m fixed-radius increased as actual density increased. There was no correlation between the degree of underestimation and actual density for the variable circular-plot technique. Although all three methodologies provide relative measures of density, the variable circular-plot technique provides the best absolute assessment of Cerulean Warbler density and is considered most suitable for broad-scale surveys.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.997

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)0.0040.000

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 head0.021
GPT teacher head0.278
Teacher spread0.258 · 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.

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

Citations52
Published2000
Admission routes2
Has abstractyes

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