{"id":"W4398879717","doi":"10.7910/dvn/vmr7ms/3iormu","title":"biomarker_data.csv","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto","funders":"","keywords":"Biomarker; Computer science; Computational biology; Chemistry; Biology; Biochemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002002481,0.004455474,0.002566845,0.005153821,0.001094535,0.004137167,0.005508171,0.004086179,0.1256301],"category_scores_gemma":[0.009921559,0.001103782,0.001862623,0.006102109,0.0009139982,0.001993084,0.003824095,0.002244311,0.2050918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780039,"about_ca_system_score_gemma":0.002709075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01292752,"about_ca_topic_score_gemma":0.01898098,"domain_scores_codex":[0.9984377,0.0003199623,0.000149152,0.0005030166,0.0003302002,0.0002599831],"domain_scores_gemma":[0.9974399,0.0006649575,0.0002170218,0.0008946126,0.0004297311,0.0003537771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008421035,0.00002467076,0.0003900482,0.0004675066,0.0000337081,0.0000171345,0.00001123838,0.0002041054,0.00009871776,0.0003065046,0.9953531,0.003008985],"study_design_scores_gemma":[0.0005899385,0.00005673613,0.001820618,0.0003303561,0.00006727336,0.0001309105,0.0000498091,0.001200843,0.001025807,0.002717557,0.9919564,0.00005376585],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002303213,0.0003043444,0.0002307527,0.0002592658,0.000100353,0.00003312022,0.9943706,0.002895058,0.001576105],"genre_scores_gemma":[0.000853297,0.0001682295,0.000575542,0.0001706569,0.00003372363,0.0001322414,0.9966393,0.0002946069,0.001132505],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8743699,"threshold_uncertainty_score":0.4202747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678784401416591,"score_gpt":0.2885210998704766,"score_spread":0.2717332558563107,"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."}}