{"id":"W3103085915","doi":"10.1007/978-3-030-63820-7_43","title":"Edge Curve Estimation by the Nonparametric Parzen Kernel Method","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nonparametric statistics; Kernel density estimation; Kernel (algebra); Kernel smoother; Variable kernel density estimation; Mathematics; Kernel method; Statistics; Pattern recognition (psychology); Artificial intelligence; Computer science; Radial basis function kernel; Combinatorics; Support vector machine","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001613508,0.0002330968,0.0002424068,0.0007788804,0.0006580362,0.001098649,0.004380987,0.0001298624,0.000006466395],"category_scores_gemma":[0.0001336087,0.0001919677,0.00006115156,0.001129453,0.0005641693,0.006285104,0.002368098,0.0006295697,0.00009526192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001570253,"about_ca_system_score_gemma":0.0002172102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004103943,"about_ca_topic_score_gemma":0.000003239925,"domain_scores_codex":[0.9982151,0.00008290758,0.0006659711,0.0003204132,0.0005103758,0.0002052707],"domain_scores_gemma":[0.9966162,0.0004513473,0.000420001,0.002073315,0.0003442905,0.00009478467],"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":[0.000001096591,0.000007925142,0.000001890219,0.00001328841,0.000004429314,1.855152e-7,0.0008670173,0.00007783504,0.000003881084,0.1701486,0.005028975,0.8238449],"study_design_scores_gemma":[0.0001292271,0.00006011089,0.00008718069,0.00005314114,0.000006138097,0.00003006353,0.000009509784,0.7563978,0.0004311005,0.01801598,0.2245198,0.0002599992],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001025181,0.0003769964,0.8986541,0.002862606,0.0001551599,0.0004054123,0.00001243535,0.0001857745,0.09734649],"genre_scores_gemma":[0.01381663,0.002477731,0.9741333,0.007282042,0.0000570117,0.0001080955,0.00007682227,0.00001834419,0.00202998],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8235849,"threshold_uncertainty_score":0.9999383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03201028364622473,"score_gpt":0.3145265578158467,"score_spread":0.2825162741696219,"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."}}