{"id":"W4407602924","doi":"10.32764/saintekbu.v1i2.83","title":"KOREKSI BIAS ESTIMATOR KERNEL DENGAN BOOTSTRAP","year":2016,"lang":"id","type":"article","venue":"SAINTEKBU","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Kernel (algebra); Mathematics; Discrete mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007106538,0.0004551866,0.0003875194,0.0002658462,0.0002590174,0.0005849407,0.002484668,0.0001529576,0.0001164708],"category_scores_gemma":[0.0002029506,0.00033199,0.0002518325,0.0007463447,0.0002176573,0.001141057,0.0008928361,0.0002652869,0.002666524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002058711,"about_ca_system_score_gemma":0.0002430736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005670122,"about_ca_topic_score_gemma":0.00002274726,"domain_scores_codex":[0.9966,0.00008732147,0.0005173436,0.001023634,0.000629199,0.00114251],"domain_scores_gemma":[0.9976024,0.0002547077,0.0001467059,0.001347225,0.0001276485,0.000521339],"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":[0.00002379793,0.0003050113,0.006683868,0.0001936077,0.0001743745,0.0007438151,0.003879407,0.0005446268,0.02101895,0.06374303,0.03882565,0.8638639],"study_design_scores_gemma":[0.003249424,0.001148326,0.06792253,0.003019386,0.00009042378,0.0009636625,0.0002020147,0.486074,0.04757946,0.008756242,0.3769473,0.004047174],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06665921,0.0006694972,0.9205464,0.003751871,0.005224518,0.0002109983,0.00000772562,0.000531417,0.002398405],"genre_scores_gemma":[0.967114,0.0001250353,0.02239521,0.000430887,0.000627731,0.00001311251,9.112832e-7,0.0000432618,0.009249832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9004548,"threshold_uncertainty_score":0.9999132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04756644896249635,"score_gpt":0.2555181765643019,"score_spread":0.2079517276018056,"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."}}