{"id":"W2978775535","doi":"","title":"Application and study of Support Vector Regression based on optimization of weighted coefficient","year":2007,"lang":"en","type":"article","venue":"Journal of Jilin Institute of Chemical Technology","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"PCL Construction (Canada)","funders":"","keywords":"Support vector machine; Regression analysis; Regression; Linear regression; Mathematics; Correlation coefficient; Mathematical optimization; Statistics; Computer science; Artificial intelligence","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":[0.0001431555,0.00008125496,0.0002625896,0.0002617682,0.00001161489,0.000001007562,0.0001295906,0.0001260966,0.000003503897],"category_scores_gemma":[0.00005249007,0.00006771979,0.00003227924,0.0003796309,0.0001279111,0.00004362459,0.00001873752,0.0001605259,1.239101e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003184417,"about_ca_system_score_gemma":0.0000211152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.966789e-7,"about_ca_topic_score_gemma":5.362205e-7,"domain_scores_codex":[0.9991028,0.000003098805,0.0005732709,0.00008281661,0.0001580364,0.00008002017],"domain_scores_gemma":[0.9992343,0.0000413916,0.0003377081,0.000165202,0.0001881365,0.00003326322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001372691,0.001030136,0.0008287404,0.0001193059,0.00003425596,0.000003089184,0.00005513691,0.3904985,0.5836384,0.001288424,0.00002723895,0.02233951],"study_design_scores_gemma":[0.001069072,0.0003946602,0.0002094059,0.00008530753,0.00002985499,0.00001069622,0.00009182496,0.1472987,0.8502579,0.0001948303,0.0002881212,0.00006954122],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5881933,0.00003631958,0.4115162,0.00002789279,0.00004193415,0.00011691,0.000002758847,0.0000177467,0.00004698221],"genre_scores_gemma":[0.9417496,0.00002364431,0.0581892,0.000002248956,0.00001926581,0.000003703582,0.000003467655,0.000007967705,8.52254e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3535564,"threshold_uncertainty_score":0.2761533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006121044783480368,"score_gpt":0.2529738296118167,"score_spread":0.2468527848283363,"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."}}