The assessment of non visual maritime cognitive maps of a blind sailor: a case study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".