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Record W2044939985 · doi:10.4113/jom.2010.1087

The assessment of non visual maritime cognitive maps of a blind sailor: a case study

2010· article· en· W2044939985 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

VenueJournal of Maps · 2010
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCognitive mapCartographyGeographyCognitionMaritime safetyArtificial intelligencePsychologyComputer scienceNeuroscienceEnvironmental planning

Abstract

fetched live from OpenAlex

Abstract Please click here to download the map associated with this article. Nowadays, thanks to the accessibility of GPS, sighted people widely use electronic charts to navigate through different kinds of environments. In the maritime domain, it has considerably improved the precision of course control. In this domain, blind sailors can not make a compass bearing, however they are able to interact with multimodal electronic charts. Indeed, we conceived SeaTouch, a haptic (tactilekinesthetic) and auditory virtual environment that allows users to perform virtual maritime navigation without vision. In this study we attempt to assess if heading or northing "haptic" views during virtual navigation training influences non-visual spatial knowledge. After simulating a navigation session in each condition, a blind sailor truly navigated on the sea and estimated seamark bearings. We used the triangulation technique to compare the efficiency of northing and heading virtual training. The results are congruent with current knowledge about spatial frames of reference and suggest that getting lost in heading mode forces the blind sailor to coordinate his current "view" with a more global and stable representation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.313
Teacher spread0.302 · 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