MétaCan
Menu
Back to cohort
Record W2006481279 · doi:10.3390/rs5031066

Multisensor NDVI-Based Monitoring of the Tundra-Taiga Interface (Mealy Mountains, Labrador, Canada)

2013· article· en· W2006481279 on OpenAlexafffundabout
Élizabeth L. Simms, Heather Ward

Bibliographic record

VenueRemote Sensing · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsStantec (Canada)Memorial University of Newfoundland
FundersGovernment of Canada
KeywordsTundraNormalized Difference Vegetation IndexTaigaEnvironmental scienceRemote sensingPhysical geographyAdvanced Spaceborne Thermal Emission and Reflection RadiometerVegetation (pathology)ClimatologyGeographyGeologyClimate changeArcticForestryDigital elevation model

Abstract

fetched live from OpenAlex

The analysis of a series of five normalized difference vegetation index (NDVI) images produced information about a Labrador (Canada) portion of the tundra-taiga interface. The twenty-five year observation period ranges from 1983 to 2008. The series composed of Landsat, SPOT and ASTER images, provided insight into regional scale characteristics of the tundra-taiga interface that is usually monitored from coarse resolution images. The image set was analyzed by considering an ordinal classification of the NDVI to account for the cumulative effect of differences of near-infrared spectral resolutions, the temperature anomalies, and atmospheric conditions. An increasing trend of the median values in the low, intermediate and high NDVI classes is clearly marked while accounting for variations attributed to cross-sensor radiometry, phenology and atmospheric disturbances. An encroachment of the forest on the tundra for the whole study area was estimated at 0 to 60 m, depending on the period of observation, as calculated by the difference between the median retreat and advance of an estimated location of the tree line. In small sections, advances and retreats of up to 320 m are reported for the most recent four- and seven-year periods of observations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.225
Teacher spread0.202 · 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

Citations23
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueRemote SensingSame topicClimate change and permafrostFrench-language works237,207