{"id":"W2119381029","doi":"10.1109/sipnn.1994.344978","title":"Shape characterization and its applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Characterization (materials science); Histogram; Computer science; Set (abstract data type); Shape factor; Artificial intelligence; Heat kernel signature; Feature (linguistics); Shape analysis (program analysis); Computer vision; Image (mathematics); Pattern recognition (psychology); Feature extraction; Spectrum (functional analysis); Mathematics; Active shape model; Geometry; 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":[0.00003474961,0.00003656532,0.00003308552,0.00003176199,0.00006039042,0.00007154067,0.0001618887,0.00002051568,0.000138904],"category_scores_gemma":[0.000005702482,0.00003155405,0.000008296683,0.0001894218,0.000008368866,0.0003221608,0.00004323665,0.00002442879,0.0001461643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004924587,"about_ca_system_score_gemma":0.000002061714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.917888e-7,"about_ca_topic_score_gemma":3.097446e-8,"domain_scores_codex":[0.999671,0.000007340857,0.00007002342,0.0001310184,0.0000618384,0.00005873082],"domain_scores_gemma":[0.9997562,0.00001115687,0.00002461414,0.0001339695,0.00004322291,0.00003082851],"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":[1.035657e-7,0.00002307327,0.00002700292,0.000004471275,0.000001092229,1.577414e-7,0.00004783884,4.33511e-9,0.07211228,0.2804922,0.00008897982,0.6472028],"study_design_scores_gemma":[0.0001032112,0.00003190997,0.005446283,0.000004770456,0.000002591482,0.00001558944,0.000004567943,0.5991313,0.1561514,0.001467474,0.2374418,0.0001990967],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005607416,0.00007726175,0.9918614,0.00204584,0.00001082299,0.0001235422,7.738486e-7,0.0003197206,0.00499992],"genre_scores_gemma":[0.9725139,0.0004973969,0.01525282,0.0007776481,0.00003925959,0.00009673669,0.000005030375,0.000004487588,0.01081275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9766086,"threshold_uncertainty_score":0.1878696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02699877845669062,"score_gpt":0.2355079601937993,"score_spread":0.2085091817371086,"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."}}