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Record W3200042178 · doi:10.33552/gjes.2020.04.000596

SRIN: A New Dataset for Social Robot Indoor Navigation

2020· article· en· W3200042178 on OpenAlexafffund
A.B. Rad

Bibliographic record

VenueGlobal Journal of Engineering Sciences · 2020
Typearticle
Languageen
FieldComputer Science
TopicMultimodal Machine Learning Applications
Canadian institutionsSimon Fraser University
FundersWestern Canada Research GridCompute Canada
KeywordsThe InternetComputer scienceRobotInternet of ThingsComputer securityTransport engineeringWorld Wide WebArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

With the rapid development of the Internet, our life is inseparable from it. The introduction of “smart transportation” is the embodiment of the Internet + transportation system. In the near future, the Internet + transportation system will become a central issue for development. Extract real-life examples from real life and show the changes that the Internet + transportation system has brought to our travels. At the same time, this passage will describe the problems existing in the existing transportation system and propose countermeasures for the corresponding problems.

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)0.001
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0030.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.024

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 head0.036
GPT teacher head0.320
Teacher spread0.284 · 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
GenreDataset

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

Citations6
Published2020
Admission routes2
Has abstractyes

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