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Record W4255629800 · doi:10.24124/2020/59032

Pacific marten (Martes caurina) as an apex predator : the habitat and diet ecology of an insular population of mesocarnivore on Haida Gwaii

2020· dissertation· en· W4255629800 on OpenAlexaffabout
David Normand Breault

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMartenEcologyApex predatorHabitatIsotope analysisPredationGeographyInvertebratePredatorPopulationEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Pacific marten (Martes caurina) may benefit from invasive or non-native species that occur across some coastal areas of the Pacific Northwest. I used remote-camera trapping and stable-isotopes of carbon and nitrogen to infer resource-use strategies of marten on Haida Gwaii, British Columbia, Canada. Marten are more likely to be detected in 3 ha patches with less logging and optimal amounts of road and forest edge habitat, and areas close to marine shorelines and streams. Findings from bulk carbon and nitrogen stable-isotope analysis suggest that terrestrial fauna, including birds, deer, small mammals, and invertebrates, contribute the most to diet; marine invertebrates are the second-most important prey group. Marten consume salmon and berries seasonally, but these are a relatively minor component of the diet. Knowledge of habitat and diet ecology of this generalist, apex predator should be integrated into ecosystem-based management and conservation of the globally rare old-growth forests that remain relatively intact on Haida Gwaii.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.884
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.222
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes2
Has abstractyes

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