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Record W2056476685 · doi:10.4043/24601-ms

The Sensitivity of Ice Keel Statistics to Upward Looking Sonar Ice Draft Processing Methods

2014· article· en· W2056476685 on OpenAlexafffund
Ed Ross, David B. Fissel, Todd Mudge, Anudeep Kanwar, Dawn Sadowy, Ole-Christian Ekeberg

Bibliographic record

VenueOTC Arctic Technology Conference · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsASL Environmental Sciences (Canada)
FundersDivision of Ocean SciencesFisheries and Oceans Canada
KeywordsKeelSea iceHullSea ice concentrationArctic ice packDrift iceGeologySea ice thicknessFast iceSampling (signal processing)EquidistantSonarGeodesyRemote sensingMeteorologyOceanographyComputer scienceEngineeringGeographyTelecommunications

Abstract

fetched live from OpenAlex

Abstract Upward looking sonar (ULS) instruments have been used for several decades to provide continuous measurements of ice draft. The time resolution of the ice draft observations is typically 1–2 seconds. When fused with ice drift speed observations, a high horizontal spatial resolution can be realized. Such a high resolution allows for the identification of individual ice keel features and an analysis of their spatial characteristics. Many methods are available for transforming the ice draft series from an equispaced time domain to an equidistant spatial domain. This paper analyzed the sensitivity of ice keel statistics to three transformation methods applied to ULS sea ice measurements in the Beaufort Sea and North Chukchi Sea. Although differences were found between the methods, these were related to episodes when the sampling frequency is not high enough to profile an ice draft feature travelling with a high drift speed. Knowledge of maximum drift speeds in the region of a measurement location along with the enhanced power and storage capacities of modern ice profilers enable sampling configurations which avoid this scenario.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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 designOther design
Domainnot available
GenreMethods

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

Citations2
Published2014
Admission routes2
Has abstractyes

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