{"id":"W7008529485","doi":"","title":"CLUSTERING: UNSUPERVISED LEARNING IN LARGE SCREENING BIOLOGICAL DATA","year":2010,"lang":"en","type":"other","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unsupervised learning; Biological data; Pattern recognition (psychology); Identification (biology); Statistical learning; Supervised 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003922676,0.001834458,0.002153407,0.004618477,0.002085478,0.003411722,0.004845336,0.002364959,0.1532614],"category_scores_gemma":[0.01957258,0.0017152,0.001709009,0.00456224,0.0008493853,0.003689063,0.003724678,0.002871115,0.1227977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033165,"about_ca_system_score_gemma":0.002978759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003026802,"about_ca_topic_score_gemma":0.008207667,"domain_scores_codex":[0.9974762,0.0004745257,0.0001516474,0.0006793757,0.001048994,0.0001692862],"domain_scores_gemma":[0.9908908,0.003540433,0.0003373437,0.002877294,0.001754596,0.0005994977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000228597,0.0001357733,0.001355659,0.000744604,0.0001481251,0.0001302694,0.0001245127,0.005713701,0.003863351,0.008571263,0.7687003,0.2102838],"study_design_scores_gemma":[0.0002928464,0.0001153931,0.005743491,0.000279917,0.000158402,0.0005673817,0.0001605423,0.2855268,0.0283658,0.06797636,0.6105848,0.0002282525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008709684,0.001098233,0.5468584,0.003073263,0.001180018,0.0007446048,0.07413368,0.3015564,0.06264552],"genre_scores_gemma":[0.04432675,0.0009584947,0.6721083,0.0005295774,0.0004357355,0.0009965076,0.1376074,0.04725357,0.09578352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1532614,"threshold_uncertainty_score":0.5127104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08589785513448546,"score_gpt":0.3119061820398414,"score_spread":0.226008326905356,"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."}}