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Record W4302589087 · doi:10.5957/icetech-2006-109

Evolution of an Inertial Measurement System called MOTAN: Summary of Installations on Five Ice-Strengthened Ships

2006· article· en· W4302589087 on OpenAlexaff
M. E. Johnston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCanadian Cardiovascular Society
Fundersnot available
KeywordsMarine engineeringInstrumentation (computer programming)Measure (data warehouse)Inertial frame of referenceInertial measurement unitUnits of measurementSystem of measurementComputer scienceMagnitude (astronomy)Aerospace engineeringGeologyEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper provides a description of MOTAN, an inertial motion a measurement system that has been used to measure ice-induced global impact forces on ships since the year 2000. Measurements from three ships are used to show that MOTAN measures whole-ship motions reliably, and that those motions can be used to determine global impact forces on ships with reasonable accuracy. Data from the CCGS Terry Fox are used to show that MOTAN and two other, independent instrumentation systems measured impact forces that were in good agreement. Having demonstrated that MOTAN is a viable means of measuring global impact forces on ships, the discussion focuses upon more recent efforts to develop an autonomous MOTAN, i.e. a system that operates unattended during a ship’s entire operating season. To date, the autonomous MOTAN has been installed on two ships: CCGS Henry Larsen and the M/T Véga Desgagnés, with the objective of using the data to determine statistical information about the magnitude and frequency of global loads that a ship experiences during its operating season.

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)0.003
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.189
Teacher spread0.176 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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