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Record W2402120642 · doi:10.1017/s0030605315001039

Human impacts on two endemic cassowary species in Indonesian New Guinea

2015· article· en· W2402120642 on OpenAlexafffund
Jedediah F. Brodie, Margaretha Pangau‐Adam

Bibliographic record

VenueOryx · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGeorg-August-Universität Göttingen
KeywordsRainforestAbundance (ecology)EcologyBiological dispersalGeographyNew guineaDisturbance (geology)IndonesianSpatial ecologyRelative species abundancePopulationBiologyDemographyEthnology

Abstract

fetched live from OpenAlex

Abstract Cassowaries are important seed dispersers in tropical rainforests of New Guinea, but little is known about their population ecology or their responses to human disturbance. We used camera traps to measure the occurrence, local abundance, and activity patterns of northern cassowaries Casuarius unappendiculatus in lowland forests near Nimbokrang, Papua, and dwarf cassowaries Casuarius bennetti in the Arfak Mountains, West Papua. Our goals were to assess human impacts on cassowaries at multiple spatial scales and to measure their activity patterns over an elevational divide. At fine spatial scales local abundance of cassowaries was strongly reduced in areas frequented by humans. At larger spatial scales the distance to the nearest village or drivable road did not affect local abundance but altered the stage structure of the individuals detected, with a higher proportion of juveniles relative to adults. Local abundance of cassowaries was unrelated to site usage by introduced pigs. Both populations studied were strongly diurnal and their activity patterns were not significantly different. Efforts to control hunting remain critical to sustaining cassowaries and the seed dispersal services they provide.

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 score0.051
Threshold uncertainty score1.000

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)0.0010.001

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 head0.044
GPT teacher head0.273
Teacher spread0.229 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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