{"id":"W2487959321","doi":"10.1158/1538-7445.am2016-3632","title":"Abstract 3632: Adaptive operations and technology platform for nation-scale precision oncology","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Workflow; Scalability; Computer science; Precision medicine; Analytics; Data science; Scale (ratio); Predictive analytics; Big data; Data mining; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009158062,0.0001041133,0.0001349276,0.0002410588,0.0002535833,0.00003225461,0.0002949002,0.0003457525,0.0001424068],"category_scores_gemma":[0.0006886821,0.00006986273,0.00003606434,0.0002038867,0.0006739168,0.00001163078,0.0002481823,0.0001750822,0.00004755485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120501,"about_ca_system_score_gemma":0.000625085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005205286,"about_ca_topic_score_gemma":0.00057334,"domain_scores_codex":[0.9985639,0.00002482873,0.0002404502,0.000330736,0.0003522544,0.0004878538],"domain_scores_gemma":[0.9984993,0.0001605432,0.00003026375,0.0002629273,0.0008660244,0.000180938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002284699,0.00005748617,0.0001605272,0.00003116533,0.00003318195,7.553531e-7,0.00006219808,0.000003090569,0.638685,0.0004304982,0.009342066,0.3509656],"study_design_scores_gemma":[0.00198503,0.002211369,0.0008416272,0.00009230909,0.00000711976,0.000008005964,0.0004722049,0.0003057143,0.5403782,0.003076842,0.4503891,0.0002324813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732131,0.001666259,0.006844318,0.01294217,0.0002049378,0.001489062,0.0002636255,0.00002520749,0.003351354],"genre_scores_gemma":[0.987232,0.00407848,0.002344147,0.00008741786,0.0003472578,0.0005843924,0.00004772162,0.00002109724,0.005257432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.441047,"threshold_uncertainty_score":0.2848919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07564222026282387,"score_gpt":0.40862806947769,"score_spread":0.3329858492148661,"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."}}