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Record W2362633395

Point Cloud Density Extraction Based on Stochastic Distribution Estimation

2009· article· en· W2362633395 on OpenAlexaff
YE Ai-fen, Shengrong Gong, Xuancang Wang, Chunping Liu

Bibliographic record

VenueJisuanji gongcheng · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsComputer scienceHomogeneity (statistics)Point cloudDensity estimationCombingPoint distribution modelPoint (geometry)Distribution (mathematics)Plot (graphics)Feature (linguistics)AlgorithmMathematical optimizationStatistical physicsData miningArtificial intelligenceStatisticsMathematicsEstimatorMachine learningMathematical analysisGeometryPhysics
DOInot available

Abstract

fetched live from OpenAlex

Density extraction method has difficulty in representing local distribution and its stochastic feature from the extracted density information.This paper proposes a solution to solve this problem,combing density method with stochastic distribution estimation.The method computes the density of each single small plot,and combines it with the overall density of the point cloud.A parameter is obtained,which can reflect the local aggregation feature.Tests show that this parameter can satisfactorily provide reliable data on estimating stochastic distribution and homogeneity of the point cloud.

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)0.000
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 score0.519
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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
GenreEmpirical

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

Citations2
Published2009
Admission routes1
Has abstractyes

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