{"id":"W4394321151","doi":"10.6084/m9.figshare.23744002","title":"Construction of lung cancer serum markers based on ReliefF feature selection","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Lung cancer; Feature (linguistics); Computer science; Computational biology; Cancer; Pattern recognition (psychology); Artificial intelligence; Internal medicine; Biology; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008547747,0.0009053585,0.001266051,0.001538231,0.0002436126,0.0006345051,0.0005864145,0.0004847471,0.0006955098],"category_scores_gemma":[0.001728898,0.000267813,0.001115071,0.001113944,0.0002132831,0.0004329104,0.0005189909,0.0006062565,0.0005329067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002896238,"about_ca_system_score_gemma":0.0006738029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306762,"about_ca_topic_score_gemma":0.0009114002,"domain_scores_codex":[0.9994691,0.00007885184,0.00003768481,0.000138221,0.0001840624,0.00009216325],"domain_scores_gemma":[0.9996296,0.00007906603,0.00005191586,0.00002606401,0.0001819378,0.00003136523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009616697,0.0004278827,0.02626288,0.0003539,0.0001982573,0.0008701602,0.0001434767,0.05904519,0.1562359,0.001684612,0.006572346,0.7472438],"study_design_scores_gemma":[0.00007195754,0.0004924198,0.01260583,0.00002075908,0.0001238886,0.0005719483,0.00004944739,0.9319053,0.0500335,0.001059897,0.003009758,0.00005525327],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.2751804,0.001122777,0.7179759,0.00033348,0.0001184615,0.0004145835,0.001084622,0.002420564,0.001349221],"genre_scores_gemma":[0.7157105,0.0005431879,0.2777393,0.0001818752,0.00007922175,0.0005075841,0.003598016,0.00008757423,0.001552763],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.001538231,"threshold_uncertainty_score":0.004520535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099115047051201,"score_gpt":0.3067001875597479,"score_spread":0.2957090370892359,"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."}}