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Record W7096718392

ABSTRACT SATELLITE GPS MEASUREMENTS OF LANDFAST ICE DISPLACEMENTS IN THE CANADIAN BEAUFORT DURING THE

2008· article· en· W7096718392 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemSea iceSatelliteBeaufort seaDrift iceDisplacement (psychology)Arctic ice pack
DOInot available

Abstract

fetched live from OpenAlex

Three GPS (Global Positioning System) beacons were placed on the landfast ice cover of the Southeastern Beaufort Sea in mid-February, 2003. Changes in the positions of these beacons, roughly 35 km offshore of Richards Island and the Tuktoyaktuk Peninsula, were deduced from data relayed through the System Argos satellite network.This data acquisition program was carried out for Devon Canada for input to a planned drilling program. Position data with sufficient continuity from two of the three beacons were used to document landfast ice movements at both the western and eastern ends of the area of interest. Consistent, but less detailed data, were acquired at the third, central, beacon site. The results showed capabilities forresolving movements with as small as 2m in both the north-south and east-west directions on time scales of 6 to 12 hours. All detected movements were limited to a period in March and involved a single primarily southward, ice displacement event, approximately 10 m in magnitude. Smaller (2-5m) eastward components of this displacement dissipated over a, roughly, week-long period immediately following the event. Satellite imagery and ice velocity data from mobile, more offshore, pack ice, allowed identification of the causal source of the displacements.

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 score0.013
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
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 head0.029
GPT teacher head0.212
Teacher spread0.183 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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