{"id":"W2872913067","doi":"10.1002/9781118445112.stat07989","title":"Big Data in Biosciences","year":2017,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; University of Toronto; Western University","funders":"","keywords":"Big data; Data science; Computer science; Context (archaeology); Analytics; Wearable computer; Geography; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004962424,0.0004532589,0.000495989,0.0003443269,0.0001088096,0.0001418089,0.002761737,0.0006120064,0.0002502478],"category_scores_gemma":[0.001404856,0.0003823646,0.00003700857,0.0001375505,0.0009874585,0.000006707711,0.001452248,0.0004652765,0.0001062766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002944256,"about_ca_system_score_gemma":0.0009526212,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003543,"about_ca_topic_score_gemma":0.02560092,"domain_scores_codex":[0.9968803,0.00008725563,0.0005202589,0.0009346668,0.0008189447,0.0007585746],"domain_scores_gemma":[0.9965641,0.00004438743,0.0004476233,0.002470379,0.0001406575,0.0003328648],"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":[0.00003477328,0.0002298253,0.0003930505,0.0002619596,0.00005970398,0.00003487833,0.00001815495,5.996868e-7,0.0008856701,0.0001163173,0.7845875,0.2133776],"study_design_scores_gemma":[0.0005667355,0.0003434688,0.0004632226,0.0003908938,0.00002327053,0.00000496935,0.00006864661,0.0004491601,0.0001066173,0.0003214673,0.9967416,0.0005199392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.00104682,0.01976906,0.09909008,0.001002712,0.007082223,0.003134559,0.4434265,0.000229446,0.4252186],"genre_scores_gemma":[0.003440773,0.1163396,0.1248528,0.0004419395,0.002703859,0.00003518213,0.1135216,0.0004900857,0.6381741],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.329905,"threshold_uncertainty_score":0.9998628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105974680227034,"score_gpt":0.3778925562011252,"score_spread":0.2672950881784218,"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."}}