{"id":"W2536640939","doi":"10.1109/tgrs.2016.2617819","title":"Road Curb Extraction From Mobile LiDAR Point Clouds","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lidar; Point cloud; Point (geometry); Extraction (chemistry); Correctness; Feature extraction","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":[],"consensus_categories":[],"category_scores_codex":[0.0002237533,0.0001903368,0.0001557725,0.00007176469,0.0006499544,0.00007295408,0.0001144239,0.0001076631,0.0001960251],"category_scores_gemma":[0.000008314817,0.0001364548,0.00007979346,0.0003437324,0.0004623666,0.0003326689,0.00000476418,0.0001789576,0.0004808775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001441662,"about_ca_system_score_gemma":0.0000200825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004269696,"about_ca_topic_score_gemma":0.0003503835,"domain_scores_codex":[0.9983947,0.00006144131,0.000235321,0.0006198479,0.0003461574,0.0003424988],"domain_scores_gemma":[0.9992048,0.00009778242,0.00008324306,0.0004153276,0.00001717058,0.0001816847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009472024,0.00002358262,0.000004725371,7.559335e-7,0.00000356029,0.000003592894,0.0002020258,0.000117543,0.1726304,8.35927e-7,0.00004364763,0.8269598],"study_design_scores_gemma":[0.00234656,0.0007591859,0.03216569,0.0006730688,0.0002233587,0.0006997468,0.001411969,0.2922353,0.5940335,0.005880686,0.06718648,0.002384476],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4666156,0.000009198826,0.531136,0.000503678,0.0003429639,0.0001317363,0.00000977558,0.00008376759,0.001167326],"genre_scores_gemma":[0.9672222,0.0001644954,0.03063969,0.0002102258,0.00006499931,2.45145e-7,8.125955e-7,0.00001801725,0.001679377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8245754,"threshold_uncertainty_score":0.6454532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009914237363220947,"score_gpt":0.2399610230494847,"score_spread":0.2300467856862638,"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."}}