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Record W1889107504 · doi:10.7202/1015956ar

Napâttuit: Wood use by Labrador Inuit and its impact on the forest landscape

2013· article· en· W1889107504 on OpenAlexafffundvenueabout
Isabel Lemus-Lauzon, Najat Bhiry, James Woollett

Bibliographic record

VenueÉtudes/Inuit/Studies · 2013
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsGeographyShrubAbundance (ecology)Tree lineEcologyForest ecologyTundraAgroforestryClimate changeEcosystemEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

In Nunatsiavut, recent studies have shown that major changes to forest tundra ecosystems have occurred over the past two centuries, including a shift in the abundance and range of tree/shrub species. Although this trend could be due to the highly variable climate of this period, we should also consider anthropogenic factors, such as wood harvesting, when conducting ecological studies of forest dynamics. Based on a literature review, interviews, and field observations, this article documents the interactions between residents of Nain (Nunatsiavut) and the forest landscape since the late 18th century. Nain is one of the few Inuit communities south of the tree line, and its inhabitants seem to have had an ambivalent and changing relationship with their forest landscape. Thus, though probably perceived initially as potentially dangerous, the forest has gradually been integrated into land use patterns and helped shape some aspects of Labrador Inuit culture. For Nain’s inhabitants, wood use has been continuous but not homogenous over time. Patterns of use and harvesting have changed with the socio-economic setting and have left their traces on the region’s forest stands, as is evident from the abundance of cut stumps and the scarcity of naturally dead trees.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.063
GPT teacher head0.382
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations10
Published2013
Admission routes4
Has abstractyes

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