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Record W2786373385

Error Budget Analysis for surface and underwater survey system

2016· article· en· W2786373385 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe International Hydrographic Review · 2016
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsThe Interdisciplinary Centre for the Development of Ocean Mapping
Fundersnot available
KeywordsSubseaHydrographic surveyBathymetryHydrographyDepth soundingUnderwaterMarine engineeringGeological surveyComputer scienceEnvironmental scienceEngineeringOperations researchGeographyGeologyOceanographyCartography
DOInot available

Abstract

fetched live from OpenAlex

For the installation of subsea infrastructures (pipelines, subsea wells, etc.) required for the extraction, storage and supply of hydrocarbon resources, the oil and gas company TOTAL regularly contracts hydrographic survey companies to provide positioning and hydrographic survey services. These companies mainly use two types of systems for these operations: surface and underwater survey systems. The error budget estimation identifies the parameters which affect the acquired data quality and to check if the measurement uncertainty of the sounding position meets the minimum survey specifications described by International Hydrographic Organization (IHO) as adopted by TOTAL. This paper gives an in-depth analysis on the error budget estimation of surface and underwater survey systems; describes briefly these state-of-the-art systems and proposes an estimation method of error budget of these systems. This work also contributes to improve bathymetric sounding position equations.

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.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.333
Teacher spread0.288 · 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