{"id":"W4409795017","doi":"10.61091/jcmcc127b-392","title":"Research on Union Adaboost Based on Sample Weight Updating Mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); AdaBoost; Sample (material); Artificial intelligence; Computer science; Pattern recognition (psychology); Chemistry; Chromatography; Support vector machine; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002784888,0.001448039,0.002514757,0.001846629,0.000927971,0.001973724,0.003363323,0.001768462,0.001760363],"category_scores_gemma":[0.004677128,0.0007813385,0.0014431,0.002443258,0.0009873888,0.0030804,0.001115192,0.002057488,0.0006664171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183046,"about_ca_system_score_gemma":0.00232698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009223979,"about_ca_topic_score_gemma":0.003564114,"domain_scores_codex":[0.9971469,0.0005318128,0.0002073991,0.0007662458,0.001038747,0.000308837],"domain_scores_gemma":[0.9982499,0.000456768,0.0001452294,0.0001437762,0.0009080144,0.00009630242],"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":[0.0003405395,0.0003890091,0.003516744,0.000327711,0.0002917977,0.00008083886,0.0001480144,0.2355992,0.005666658,0.008753087,0.005671567,0.7392148],"study_design_scores_gemma":[0.00003300407,0.00007513789,0.0005887733,0.00001755622,0.00005823519,0.00005780581,0.00002583555,0.9924979,0.002157771,0.002088761,0.002379801,0.00001945949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02085748,0.002216846,0.9718356,0.0003114064,0.0003552047,0.0001057119,0.00004739415,0.001435826,0.002834561],"genre_scores_gemma":[0.6326596,0.00306811,0.3509634,0.001066996,0.0007308108,0.0005777659,0.0005358557,0.000479035,0.009918422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009223979,"threshold_uncertainty_score":0.01834053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821092812600578,"score_gpt":0.2986889547165522,"score_spread":0.2704780265905464,"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."}}