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Record W2982210350 · doi:10.24908/iqurcp.11699

4. Problematic Overlap of Algonquin Park Eco-Tourism and Most Suitable Habitat for the Protection of Black Bear Biodiversity

2018· article· en· W2982210350 on OpenAlexvenueno aff
Celeste Barsony, Elise Bishop, Jill Mulveney, Aasif Patel

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityHabitatGeographyTourismEcotourismNational parkEcologyUrsusFlagship speciesEndangered speciesBiologyArchaeologyPopulation

Abstract

fetched live from OpenAlex

The purpose of our study is to identify problems arising or that have arisen within the biodiversity of the Algonquin to Adirondacks (A2A) region caused by ecotourism. Our full study will include examining three different species preferred habitat ranges and the possible anthropocentric impacts imposed on these habitats and biodiversity. Specifically, we look at the black bear (Ursus americanus) within the region of the Highway 60 corridor in Algonquin Park during the busiest months of the park (spring and summer). The main methodology for this study is using Geographic Information Systems (GIS) conduct a suitability analysis for each species, and then identify areas of overlap with Algonquin Park Eco-Tourism. By studying the impacts of Eco-Tourism on the bear populations, and their most suitable habitat regions, we hypothesize that ecotourism will have dangerous impacts on their habitat ranges and biodiversity potentials. To correct this, we will suggest specific times in the season to avoid using the said Eco-Tourism campsites, portage routes, trails and outpost cabins.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.976
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.071
GPT teacher head0.306
Teacher spread0.235 · 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
Published2018
Admission routes1
Has abstractyes

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