{"id":"W6930884367","doi":"10.5281/zenodo.154059","title":"ICGC-TCGA-PanCancer/CGP-Somatic-Docker: 2.0.0-cwl1","year":2016,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Product (mathematics); Process (computing); Identification (biology); Work (physics)","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":[0.002024604,0.002122908,0.001799201,0.00349382,0.000919043,0.004537133,0.00385324,0.002703878,0.2277133],"category_scores_gemma":[0.005580711,0.00157473,0.001626329,0.003525683,0.0005043314,0.002267187,0.003437757,0.002740338,0.2484156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002766796,"about_ca_system_score_gemma":0.002710994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009873229,"about_ca_topic_score_gemma":0.01214231,"domain_scores_codex":[0.9988478,0.0002077923,0.0001261477,0.0001719762,0.0004508645,0.0001955203],"domain_scores_gemma":[0.9986079,0.0002475508,0.000144416,0.0003778358,0.0003439818,0.0002782208],"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":[0.0002269243,0.00002780843,0.0005245449,0.0005226878,0.00005563635,0.00006424673,0.00003208049,0.0006396865,0.0007561021,0.001825426,0.9681469,0.027178],"study_design_scores_gemma":[0.0001862679,0.00003045758,0.0008192023,0.0002002761,0.00005435319,0.0002606919,0.00001585557,0.0005976557,0.001947015,0.002609895,0.9932387,0.00003959981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.004419135,0.01165834,0.02445944,0.004911691,0.002556195,0.0005823895,0.6399157,0.08294138,0.2285557],"genre_scores_gemma":[0.01780718,0.005456241,0.02305792,0.003148416,0.0004522505,0.001041674,0.8011033,0.04531491,0.102618],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.7722867,"threshold_uncertainty_score":0.761777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838291123052335,"score_gpt":0.2821915503012922,"score_spread":0.2638086390707689,"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."}}