{"id":"W2138671360","doi":"10.1109/cvpr.1993.341183","title":"Partitioning range images using curvature and scale","year":2002,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Curvature; Range (aeronautics); Scale (ratio); Set (abstract data type); Interpretation (philosophy); Object (grammar); Computer science; Surface (topology); Artificial intelligence; Algorithm; Mathematics; Geometry; Engineering; Physics","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":[0.0000280708,0.00005293781,0.00006724783,0.00003203525,0.00004997482,0.00003503157,0.00002005746,0.00002803099,0.0002649027],"category_scores_gemma":[0.000002756312,0.00004802608,0.0000226082,0.00007384401,0.000009906126,0.00007830254,0.00000622982,0.00005553924,0.00001830742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006337688,"about_ca_system_score_gemma":3.583914e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001282655,"about_ca_topic_score_gemma":0.000006683868,"domain_scores_codex":[0.9997338,0.000003924413,0.00005993928,0.0000659956,0.00004297105,0.00009338136],"domain_scores_gemma":[0.9998915,0.000006520475,0.000004359651,0.00005709779,0.000009590509,0.00003097312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002369438,0.00005559227,0.0421547,0.0002196531,0.0002598404,0.00002387024,0.001848558,0.8432943,0.03166818,0.0001512912,0.02423532,0.05608628],"study_design_scores_gemma":[0.00006167923,0.000001434527,0.000164642,0.00001294328,0.00002194376,0.000003283324,0.0000226735,0.998598,0.0008054693,0.00005236101,0.0001824861,0.00007309176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7888375,0.00451339,0.1887755,0.0001447091,0.00006511846,0.00003006167,0.00000406702,0.0004137603,0.01721586],"genre_scores_gemma":[0.9923934,0.0001327506,0.006853022,0.00003428199,0.00005075473,9.143847e-7,9.592474e-7,0.000009571949,0.0005243684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2035559,"threshold_uncertainty_score":0.2900498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782043911839152,"score_gpt":0.1966122532407924,"score_spread":0.1787918141224009,"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."}}