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Record W2138215666 · doi:10.1177/0278364911433135

The Devon Island rover navigation dataset

2012· article· en· W2138215666 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.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Journal of Robotics Research · 2012
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsnot available
FundersCanadian Space AgencyNational Institute of Standards and TechnologyUniversity of Toronto
KeywordsTraverseComputer scienceGlobal Positioning SystemGround truthComputer visionArtificial intelligenceDigital elevation modelCompassDifferential GPSRemote sensingTheodoliteElevation (ballistics)Position (finance)GeologyGeodesyGeographyCartographyEngineering

Abstract

fetched live from OpenAlex

In this paper we present a rover navigation dataset collected at a Mars/Moon analogue site on Devon Island, in the Canadian High Arctic. The dataset is split into two parts. The first part contains rover traverse data: stereo imagery, Sun vectors, inclinometer data, and ground-truth position information from a differential global positioning system (DGPS) collected over a 10-km traverse. The second part contains long-range localization data: 3D laser range scans, image panoramas, digital elevation models, and GPS data useful for global position estimation. All images are available in common formats and other data is presented in human-readable text files. To facilitate use of the data, Matlab parsing scripts are included.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.057
GPT teacher head0.358
Teacher spread0.301 · 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