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Record W2981335134 · doi:10.4095/289578

Seasonal surface displacement derived from InSAR, Yellowknife and surrounding area, Northwest Territories

2011· report· en· W2981335134 on OpenAlexaffabout
N Short, Christopher W. Stevens, S A Wolfe

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsInterferometric synthetic aperture radarGeologyDisplacement (psychology)GeodesyPhysical geographySeismologyRemote sensingGeographySynthetic aperture radar

Abstract

fetched live from OpenAlex

Seasonal surface displacement was mapped by the Canada Centre for Remote Sensing and the Geological Survey of Canada for Yellowknife and the surrounding area using satellite interferometric synthetic aperture radar (InSAR). RADARSAT-2 Ultra-fine scenes acquired on May 21, July 8, August 1, August 25 and September 18, 2010, were interferometrically stacked and the total amount of summer vertical displacement was calculated. This open file presents the relative surface displacement in both map and digital ArcGIS form. The calculated values are portrayed in terms of relative displacement that has been classified as stable, low and medium downward displacement and upward displacement. At some locations a significant change in surface characteristics results in a loss of interferometric coherence and no values were calculated. Surface displacement may be from natural and human-induced processes. This dataset provides spatial context for the variability and relative magnitude of seasonal surface displacement within the Yellowknife area.

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)0.000
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 score0.394
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.081
GPT teacher head0.263
Teacher spread0.182 · 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
Published2011
Admission routes2
Has abstractyes

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