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Record W4200222932 · doi:10.1675/063.044.0204

High-density Yellow Rail (Coturnicops noveboracensis) Population Beyond Purported Range Limits in the Northwest Territories, Canada

2021· article· en· W4200222932 on OpenAlexaboutno aff
Logan J. T. McLeod, Samuel Haché, Rhiannon F. Pankratz, Erin M. Bayne

Bibliographic record

VenueWaterbirds · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsRange (aeronautics)MarshHabitatDistance samplingBogGeographyPopulation sizePopulation densityAbundance (ecology)PopulationEcologyBorealWetlandDistribution (mathematics)Aerial surveyFisheryPhysical geographyBiologyDemographyPeatCartography

Abstract

fetched live from OpenAlex

The Yellow Rail (Coturnicops noveboracensis) is a secretive marsh bird of conservation concern in Canada. However, the status of this species in northern boreal regions remains largely unknown given uncertainty about population abundance and distribution. This knowledge gap is mainly due to limitations of traditional survey methods to detect this species. In this study, avian point count data collected from autonomous recording units and augmented by detections from a machine-learning recognizer were used to generate a species distribution model to provide habitat-specific density estimates and population size estimates for Yellow Rail breeding in the Edéhzhíe Dehcho Protected Area, Northwest Territories. This protected area is ∼150 km beyond the currently established northern range limit. A large population estimated at 906 (± 146) pairs was discovered. Yellow Rail were found at high densities in marshes (0.063 ± 0.004 males/ha), but were also observed in fens and bogs, albeit at much lower densities (0.003 ± 0.002 males/ha and < 0.001 ± 0.002 males/ha). Our results suggest both the range and the population size of Yellow Rail are much larger than currently reported. Further studies are required to provide better population size and distribution estimates to conserve this species at risk in Canada.

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 score0.102
Threshold uncertainty score0.486

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 head0.008
GPT teacher head0.188
Teacher spread0.181 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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