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Record W2766313995 · doi:10.1657/aaar0016-075

Vegetation Greening Trends at Two Sites in the Canadian Arctic: 1984–2015

2017· article· en· W2766313995 on OpenAlexafffundabout
Rebecca Edwards, Paul Treitz

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsQueen's UniversityDucks Unlimited Canada
FundersUmeå UniversitetArcticNet
KeywordsNormalized Difference Vegetation IndexArcticGreeningVegetation (pathology)Environmental scienceWatershedLand coverPhysical geographyClimate changePrecipitationEnhanced vegetation indexTundraClimatologyGeographyLand useEcologyGeologyOceanographyVegetation IndexMeteorology

Abstract

fetched live from OpenAlex

ABSTRACT This study examined vegetation greening at two arctic sites: the Apex River Watershed (ARW), Baffin Island, Nunavut (a Low Arctic site) and the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (a High Arctic site). The vegetation at both study sites was characterized using a supervised land-cover classification approach using high spatial resolution satellite remote sensing data (i.e., IKONOS [4 m] and WorldView-2 [2 m]). Meanwhile, Normalized Difference Vegetation Index (NDVI) data spanning the past 30 years were derived from intermediate spatial resolution data (i.e., Landsat TM/ETM/OLI [30 m]). The land-cover classifications were used to partition the Landsat NDVI time series by vegetation type. Climate variables (i.e., temperature, precipitation, and growing season length [GSL]) were examined to explore potential relationships of NDVI to climate warming trends. The results of the land-cover classifications demonstrated inherent trends of vegetation types along elevation and moisture gradients. The NDVI time series for the CBAWO (1985–2015) demonstrated an overall significant increase in greening, specifically in the dry and mesic vegetation types. Conversely, similar greening (overall or by vegetation type) was not observed for the ARW (1984–2015). Based on climate data from the nearest permanent weather station (Mould Bay, Nunavut), the overall increase in NDVI at the CBAWO was largely attributed to a significant increase in July temperatures and GSL.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.153
GPT teacher head0.379
Teacher spread0.226 · 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.

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

Citations28
Published2017
Admission routes3
Has abstractyes

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