{"id":"W1986004030","doi":"10.1109/icmew.2012.109","title":"Non-rigid 3D Model Retrieval Using Set of Local Statistical Features","year":2012,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Set (abstract data type); Polygon (computer graphics); 3d model; Manifold (fluid mechanics); Algorithm; Feature (linguistics); Statistical model; Artificial intelligence; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001153248,0.0001017333,0.0001800194,0.00005941782,0.00002400791,0.000009182503,0.00006201076,0.00007398583,0.00009104028],"category_scores_gemma":[0.000008431683,0.00008747071,0.00004996225,0.0001083579,0.0000305119,0.00007991986,0.00001819058,0.0001142177,0.00001378241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003163803,"about_ca_system_score_gemma":0.0000128077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003832327,"about_ca_topic_score_gemma":0.000002725947,"domain_scores_codex":[0.9993527,0.000006214551,0.0001659014,0.00007912717,0.0001585588,0.0002375154],"domain_scores_gemma":[0.9997061,0.00002466842,0.00001290075,0.0001304012,0.000029423,0.00009647033],"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":[0.000005721788,0.000009477882,0.0001418933,0.00003233589,0.00004084416,5.983923e-7,0.0001191446,0.9955028,0.002732687,0.0002963018,0.0005604109,0.0005577918],"study_design_scores_gemma":[0.00008600308,0.000005289052,0.00007133988,0.000009965001,0.00005868206,0.000002735095,0.00006080061,0.992608,0.006885737,0.00008845522,0.00001423576,0.0001087699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1616578,0.0001062165,0.8364091,0.000005295477,0.00005210711,0.00001878564,0.00002469329,0.00006349733,0.001662485],"genre_scores_gemma":[0.9520334,0.000007779337,0.0476736,0.0000161179,0.0000789671,1.930995e-7,0.00001298667,0.0000190671,0.0001579022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7903755,"threshold_uncertainty_score":0.3566952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427755166963968,"score_gpt":0.2691589524591027,"score_spread":0.244881400789463,"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."}}