{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001038064,0.0002848128,0.0004868396,0.0001822835,0.0005398581,0.00007752418,0.00067373,0.0001763833,0.0001764798],"category_scores_gemma":[0.0003481025,0.0002418013,0.00003018729,0.001186053,0.001035619,0.0007134611,0.001718632,0.0005072947,0.00001272851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579994,"about_ca_system_score_gemma":0.0002071402,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240761,"about_ca_topic_score_gemma":0.06408907,"domain_scores_codex":[0.9961742,0.0004170778,0.0004311488,0.001660183,0.0003679871,0.0009493887],"domain_scores_gemma":[0.9974785,0.001032677,0.0001839595,0.001057685,0.00003278162,0.0002144262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005549297,0.000235756,0.9737378,0.00005974624,0.0001164148,0.000054877,0.005169943,0.001714693,0.006431071,0.000006242373,0.003020565,0.008897919],"study_design_scores_gemma":[0.006435955,0.001523562,0.5758384,0.0001068244,0.0002420021,0.00002355937,0.004241598,0.1034435,0.002080395,0.0001466541,0.3052388,0.0006787073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929663,0.000196101,0.001519183,0.000985489,0.0001711903,0.00124789,0.002618069,0.00001991001,0.0002758786],"genre_scores_gemma":[0.9928226,0.0005123701,0.003982211,0.001719229,0.0001581904,0.00006929055,0.0006746563,0.00004351715,0.00001793196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3978994,"threshold_uncertainty_score":0.9860371,"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."}}