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Record W2050471449 · doi:10.3189/002214310793146179

An integrated lightweight ice-penetrating radar system

2010· article· en· W2050471449 on OpenAlexafffundabout
Laurent Mingo, Gwenn E. Flowers

Bibliographic record

VenueJournal of Glaciology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser UniversityBlue-O Technology (Canada)
FundersSimon Fraser UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSoftwareGeologyRadarRendering (computer graphics)TransmitterGlobal Positioning SystemRemote sensingComputer hardwareComputer scienceComputer graphics (images)TelecommunicationsOperating system

Abstract

fetched live from OpenAlex

Abstract We describe a portable low-frequency impulse radar system intended for ground-based surveys that employs off-the-shelf hardware integrated with custom-designed software. The hardware comprises a 1–200 MHz transmitter, digitizer, computer and GPS receiver, which together weigh ∼1.5 kg. The entire system, including waterproof enclosures and batteries suited for >8 hours of continuous operation, weighs <10 kg plus the weight of the antenna housing. The system design is flexible, permitting hardware components such as the digitizer or navigation device to be exchanged. The software includes acquisition parameter control, real-time visual ice-depth rendering and data management capabilities using a hierarchical data format. The system described here has been successfully used to sound polythermal ice up to ∼220 m thick in ski-based surveys in the Yukon, Canada, and temperate ice up to ∼550 m thick in machine-based surveys in Iceland.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.004

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 head0.229
Teacher spread0.215 · 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 designBench or experimental
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

Citations68
Published2010
Admission routes3
Has abstractyes

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