{"id":"W2462333616","doi":"10.5194/isprs-archives-xli-b1-717-2016","title":"RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Lidar; Computer science; Preprocessor; Thresholding; Mobile mapping; Workflow; Artificial intelligence; Correctness; Computer vision; Remote sensing; Data mining; Geography; Database; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001577546,0.0004067466,0.0004270638,0.0006156227,0.001270404,0.0003588138,0.001426951,0.00009789936,0.0000314732],"category_scores_gemma":[0.0004206752,0.000236179,0.0003942576,0.001010782,0.004416703,0.0005156861,0.001132014,0.0003095022,0.000006882179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084101,"about_ca_system_score_gemma":0.0001569083,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7606444,"about_ca_topic_score_gemma":0.09417194,"domain_scores_codex":[0.9952672,0.0003013117,0.001558007,0.0004556695,0.001911542,0.0005062527],"domain_scores_gemma":[0.9962702,0.0008286605,0.001967114,0.0005886721,0.0001863087,0.0001589945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001014397,0.00002862198,0.0005268969,0.00002350413,0.00006620806,1.679186e-7,0.00245216,0.002387688,0.02616582,0.000006651397,0.0000535582,0.9681873],"study_design_scores_gemma":[0.0006912909,0.0001722855,0.004910334,0.0003497943,0.00004817309,0.000113472,0.001452057,0.9452572,0.03554223,0.004170466,0.007005689,0.0002870457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07082763,0.0000233987,0.915118,0.00270944,0.001248119,0.000764226,0.00008487236,0.00005138084,0.009172878],"genre_scores_gemma":[0.9939902,0.0002000862,0.004968798,0.0006043189,0.0001147653,3.915331e-7,0.00001504758,0.00001375238,0.00009267416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9679002,"threshold_uncertainty_score":0.9982927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517335771540808,"score_gpt":0.2465045685157803,"score_spread":0.2313312108003722,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}