{"id":"W2809494470","doi":"10.1093/neuonc/noy059.715","title":"TBIO-27. GABRIELLA MILLER KIDS FIRST DATA RESOURCE CENTER ADVANCING GENETIC RESEARCH IN CHILDHOOD CANCER AND STRUCTURAL BIRTH DEFECTS THROUGH LARGE SCALE INTEGRATED DATA-DRIVEN DISCOVERY AND CLOUD-BASED PLATFORMS FOR COLLABORATIVE ANALYSIS","year":2018,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Context (archaeology); Psychological intervention; Resource (disambiguation); Medicine; Early childhood; Translational research; Data science; Psychology; Biology; Computer science; Pathology; Developmental psychology; Psychiatry","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01226543,0.001445017,0.001784072,0.003764099,0.00215191,0.008241962,0.00402274,0.001951611,0.1391322],"category_scores_gemma":[0.02356913,0.001151628,0.001526991,0.005742558,0.001045172,0.003294412,0.008258825,0.004050239,0.108352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00323881,"about_ca_system_score_gemma":0.01052262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03581432,"about_ca_topic_score_gemma":0.06297102,"domain_scores_codex":[0.9948638,0.0009064727,0.0004125093,0.001040886,0.002104606,0.0006717834],"domain_scores_gemma":[0.9717703,0.003919318,0.001319505,0.005534668,0.005892977,0.01156329],"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":[0.0002437668,0.00005117579,0.001573018,0.0001255536,0.00002498584,0.00007137503,0.00008306895,0.000160687,0.000694372,0.002193564,0.9758548,0.01892367],"study_design_scores_gemma":[0.0003013578,0.00006833238,0.005124093,0.0002309167,0.00002347893,0.0000786801,0.0001336953,0.001126941,0.000724824,0.003827173,0.988308,0.00005252118],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.00224263,0.001990232,0.01550861,0.0232983,0.003229479,0.001024698,0.7959971,0.04899237,0.1077166],"genre_scores_gemma":[0.009965069,0.002777471,0.04923317,0.006588767,0.001665518,0.001621643,0.8654757,0.009927772,0.05274481],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9959773,"threshold_uncertainty_score":0.4654436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04469235666187672,"score_gpt":0.3519724041823993,"score_spread":0.3072800475205226,"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."}}