{"id":"W2114598663","doi":"10.1109/icarcv.2006.345256","title":"Genetic Algorithm for Silhouette Matching","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crossover; Genetic algorithm; Algorithm; Matching (statistics); String searching algorithm; Operator (biology); Silhouette; Computer science; Population; Point (geometry); String (physics); Approximate string matching; Pattern matching; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; 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":[0.0007069356,0.0008260125,0.001144908,0.001438141,0.0006508603,0.0008090271,0.001325763,0.001595134,0.003117907],"category_scores_gemma":[0.002538487,0.0003674417,0.0007315591,0.001629482,0.0006708727,0.001026684,0.0006049307,0.001044504,0.0007639622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171529,"about_ca_system_score_gemma":0.001261027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005368715,"about_ca_topic_score_gemma":0.003194893,"domain_scores_codex":[0.9993887,0.0001406571,0.00003590176,0.000149863,0.0002324357,0.00005241505],"domain_scores_gemma":[0.9995114,0.000249241,0.00004732951,0.00004985947,0.0001249132,0.00001734609],"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":[0.00007105031,0.00005107215,0.0006003686,0.000121387,0.0000762241,0.0001163153,0.0001105858,0.6984307,0.005373772,0.03167978,0.003355486,0.2600134],"study_design_scores_gemma":[0.00001801275,0.00002250523,0.0001367104,0.00001086283,0.00001372582,0.00006523007,0.00001286209,0.9813585,0.00139154,0.01329402,0.003664165,0.0000118312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004551463,0.0005026903,0.9924082,0.00009417289,0.00004086518,0.00004650435,0.00003460171,0.0004363621,0.001885173],"genre_scores_gemma":[0.1635541,0.0008079623,0.8286552,0.0001480334,0.00006672656,0.0003319995,0.0003240926,0.0001608499,0.005950903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005368715,"threshold_uncertainty_score":0.01067489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005109887793635,"score_gpt":0.240441576739495,"score_spread":0.2303904778615587,"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."}}