{"id":"W2037646952","doi":"10.2316/journal.201.2007.1.201-1654","title":"ON THE SIMPLIFICATION OF AN EXAMPLES-BASED CONTROLLER WITH SUPPORT VECTOR MACHINES","year":2007,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Controller (irrigation); Set (abstract data type); Data set; Data mining; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001262456,0.0001451525,0.0002454387,0.00008254695,0.0001191084,0.0001044783,0.0003511345,0.00004114565,0.00001079423],"category_scores_gemma":[0.00006340177,0.00007613053,0.00004250717,0.0001304457,0.00005812499,0.00008279429,0.00001286211,0.0001219687,0.00001065325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001366551,"about_ca_system_score_gemma":0.00002820664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002228006,"about_ca_topic_score_gemma":0.00001253499,"domain_scores_codex":[0.9987884,0.0001610834,0.0003168501,0.0002680286,0.0002633818,0.0002023193],"domain_scores_gemma":[0.9985862,0.0006265707,0.0001991607,0.0003818332,0.0001132396,0.00009302959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001352721,0.0007301,0.01162014,0.0002192547,0.0003188485,0.00003559001,0.001971483,0.0232998,0.008895636,0.6908992,0.0007455042,0.2599117],"study_design_scores_gemma":[0.001200195,0.001293598,0.008975406,0.00005827637,0.00001829442,0.0000176397,0.0001006654,0.9825762,0.0009301843,0.0002375474,0.004405154,0.0001868561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06566448,0.0001857384,0.9321133,0.0005373231,0.0002195508,0.0004749283,0.000006116268,0.00007065813,0.0007278805],"genre_scores_gemma":[0.9990528,0.000002201481,0.0002921437,0.0003380721,0.00010316,0.00002260268,0.000004080299,0.000008866154,0.000176029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9592764,"threshold_uncertainty_score":0.3104513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427746610295611,"score_gpt":0.2486002289958136,"score_spread":0.2343227628928575,"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."}}