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OpenCL Implimentation of LiDAR Data Processing

2014· article· en· W9358471 on OpenAlexaff
Alexander Bussiere

Bibliographic record

VenueMolecular Medicine Today · 2014
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceObstacleObstacle avoidanceData processingLidarArchitectureCollision avoidanceProduct (mathematics)Real-time computingEmbedded systemArtificial intelligenceRobotDatabaseComputer securityCollision

Abstract

fetched live from OpenAlex

Oral leukoplakia is a white patch or plaque of the oral mucosa that cannot be characterized clinically or pathologically as any other diagnosable disease. Although oral leukoplakia is a well-known oral premalignant lesion with a high risk for development of oral cancer, little is known about its genetic basis. The development of oral cancer is a multistep process and it is believed that multiple genetic alterations are involved. Understanding this underlying genetic basis of oral cancer development will help us design better diagnosis and treatment plans in the cancer clinic. This review discusses recent progress in the identification of genetic and cytogenetic alterations in oral pre-malignant lesions and their potential clinical applications.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.370

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 science0.0010.000
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 head0.034
GPT teacher head0.319
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2014
Admission routes1
Has abstractyes

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