{"id":"W2097162546","doi":"10.1109/icpr.1994.576328","title":"Recognizing volumetric objects in the presence of uncertainty","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; A priori and a posteriori; Artificial intelligence; Parametric statistics; Context (archaeology); Object (grammar); Set (abstract data type); Process (computing); Key (lock); Machine learning; Probability density function; Cognitive neuroscience of visual object recognition; Conditional probability distribution; Pattern recognition (psychology); Data mining; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002038013,0.000632657,0.00131567,0.001521508,0.0006116952,0.002504476,0.001973882,0.001127,0.0008494382],"category_scores_gemma":[0.008913076,0.0009019283,0.0009915096,0.0009168102,0.002006647,0.003727461,0.002603112,0.001111817,0.0003041995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000789776,"about_ca_system_score_gemma":0.0005339022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001758113,"about_ca_topic_score_gemma":0.00198496,"domain_scores_codex":[0.998163,0.0003078002,0.00008003021,0.0003798454,0.0009105272,0.0001587216],"domain_scores_gemma":[0.9961105,0.002453623,0.0004586186,0.000523087,0.000359565,0.00009463786],"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":[0.0001801071,0.00004243042,0.003287589,0.0001915589,0.0001165404,0.0008933462,0.0006034504,0.5872606,0.03346577,0.07808886,0.001189694,0.2946801],"study_design_scores_gemma":[0.000004568786,0.00004062259,0.0007734642,0.0000185837,0.00002322038,0.0003573518,0.00007303612,0.935241,0.006583342,0.05454483,0.002303199,0.00003682538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01083262,0.0001547183,0.9882357,0.00006598738,0.0000106326,0.00001143126,0.00002287108,0.0002082784,0.0004578364],"genre_scores_gemma":[0.5589838,0.0005968494,0.4383804,0.0001169685,0.0001319707,0.00007475172,0.0002553898,0.0001335918,0.00132624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002504476,"threshold_uncertainty_score":0.01077819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562943105250544,"score_gpt":0.2360566652436295,"score_spread":0.2104272341911241,"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."}}