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Record W2187666821

Observing pack ice properties with a helicopter-borne video-laser-GPS sensor

2000· article· en· W2187666821 on OpenAlexaffabout
L. Lalumiere, S.J. Prinsenberg, Ingrid Peterson

Bibliographic record

VenueThe Proceedings of the ... International Offshore and Polar Engineering Conference · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsGlobal Positioning SystemRemote sensingGeologySea iceRidgeAltimeterAssisted GPSGeodesyComputer scienceTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Various processing and display techniques were developed to display pack ice properties from data collected with a helicopter-borne Video-Laser-GPS Sensor System. The Video Sensor System collects digital video images, laser and radar altimeter profiles with GPS positioning. This report describes the sensors and the analysis performed on data collected in March 1998 and 1999 with the Video System in the southern Gulf of St. Lawrence, Canada. The ice features that can be determined from the video system are ice concentration, surface ice roughness, and ridge, floe and lead size distributions. Real-time display programs now provide quick-look and geo-referenced video plots, and plots of laser ice roughness, GPS flight track positions and flying heights. Further software development is needed to make ridge, floe and lead size distributions, and ice concentration available in real-time for operational use. Further field and analysis work is required to investigate the coherence seen between surface roughness, ridge frequencies and variations in image brightness.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.319

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 science0.0010.000
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 head0.011
GPT teacher head0.173
Teacher spread0.161 · 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

Citations4
Published2000
Admission routes2
Has abstractyes

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