MétaCan
Menu
Back to cohort
Record W2514238162 · doi:10.1139/juvs-2016-0012

Assessing the availability of aerially delivered baits to feral cats through rainforest canopy using unmanned aircraft

2016· article· en· W2514238162 on OpenAlexvenueno aff
Michael Johnston, Guy McCaldin, Andrew Rieker

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeThreatened speciesFeral catGeographyBiodiversityRainforestPredationFisheryHabitatWildlife conservationEcologyAgroforestryFelis catusBiology

Abstract

fetched live from OpenAlex

At least eight threatened wildlife species are at direct risk from predation by cats (Felis catus) on Christmas Island (Director of National Parks. 2014. Christmas Island biodiversity conservation plan. Canberra. Australia: Department of the Environment.). A range of strategies are now being used to manage cats across the island, including responsible ownership methods for domestic cats and lethal control tools to remove feral cats outside the township area. Unmanned aerial vehicles (UAVs) were used to drop non-toxic baits through the rainforest canopy to assess whether aerial baiting could be undertaken successfully on the island. Ground crews located 88% of baits, indicating that sufficient baits would be accessible to feral cats if broad-scale aerial baiting was to be undertaken in the future.

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)0.002
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 score0.131
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.284
Teacher spread0.253 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Unmanned Vehicle SystemsSame topicWildlife Ecology and ConservationFrench-language works237,207