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Record W2342481911

Rho-Rho Loran-C Combined with Satellite Navigation for Offshore Surveys

2015· article· en· W2342481911 on OpenAlexaff
S. T. Grant

Bibliographic record

VenueThe International Hydrographic Review · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsCanadian Hydrographic Service
Fundersnot available
KeywordsSatelliteGeodesySubmarine pipelineRadio navigationRemote sensingSatellite navigationRange (aeronautics)Navigation systemGeologyMeteorologyGlobal Positioning SystemGeographyComputer scienceTelecommunicationsEngineeringAerospace engineeringReal-time computingOceanography
DOInot available

Abstract

fetched live from OpenAlex

The Loran-C system of navigation recently became usable in the rho-rho (range-range) mode with the installation of atomic clocks at all the Loran-C stations. This paper outlines briefly the principles of rho-rho Loran-C operation and the associated problems of geodesy and radio wave propagation. It describes the procedure for using rho-rho Loran-C alone and in combination with Satellite Navigation, as developed over the past two years at the Bedford Institute of Oceanography. Results of accuracy tests are given along with estimates of the magnitudes of the various sources of errors in Loran-C range measurements for both the standalone and satellite aided modes of operation. The results generally show a 2 \sigma \!\, absolute ranging accuracy of 270 m using rho-rho Loran-C alone with an improvement to about 180 m when combined with Satellite Navigation. The Bedford Institute of Oceanography rho-rho Loran-C system has been used routinely with Satellite Navigation at ranges of 2 500 km (1 250 n.m.). Based on this experience the rho-rho Loran-C coverage would include most of the North Atlantic Ocean and large areas o f the Pacific.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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