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Record W2789502011 · doi:10.1080/11956860.2018.1436244

Tree-ring evidence of changes in the subarctic forest cover linked to human disturbance in northern Labrador (Canada)

2018· article· en· W2789502011 on OpenAlexafffundvenueabout
Isabel Lemus-Lauzon, Najat Bhiry, Dominique Arseneault, James Woollett, Ann Delwaide

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à RimouskiUniversité LavalCenter for Northern Studies
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsSubarctic climateDisturbance (geology)GeographyLarchEcologyTree linePopulationHabitatForestryAgroforestryClimate changeArchaeologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

We combined dendroecological analyses with historical and ethnographic information to document connections between forest use patterns since the 18th century and stand composition and structure in the Nain region of Labrador, Canada. The highest recruitment periods for both eastern larch and spruce, pulses in growth releases, and decades with greatest harvesting evidence (cut stumps) all occurred primarily between 1910 and 1970. The strongest disturbance signal occurred after 1940, resulting in the absence of old trees and increased larch recruitment. The 1910–1970 period coincides with significant shifts in human settlement and land use patterns. Most notably, the increased demand for fur in the 1920s and 1930s changed Inuit land use: the Inuit spent more time inland where fur-bearing animals and wood resources were available. Moreover, population growth in Nain, which was accelerated by the relocation of Inuit communities in northern Labrador between 1950 and 1960, increased local harvesting intensity. We argue that long-term land use needs to be accounted for as a driver of forest dynamics in this subarctic forest landscape.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.256
Teacher spread0.225 · 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

Citations11
Published2018
Admission routes4
Has abstractyes

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