{"id":"W2759098276","doi":"10.1101/191783","title":"Enabling Precision Medicine via standard communication of HTS provenance, analysis, and results","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hamilton Health Sciences Foundation; George Washington University","keywords":"Workflow; Computer science; Interoperability; Usability; Precision medicine; Personalized medicine; Documentation; Domain (mathematical analysis); Set (abstract data type); Data science; Data mining; World Wide Web; Bioinformatics; Database; Human–computer interaction; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.1032795,0.001780093,0.002213691,0.006219412,0.003078061,0.01850781,0.005123449,0.005114004,0.01090625],"category_scores_gemma":[0.2039234,0.001887822,0.001658517,0.004122596,0.005395056,0.01404994,0.02484213,0.008032922,0.01596796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003502337,"about_ca_system_score_gemma":0.0155621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003592431,"about_ca_topic_score_gemma":0.001740428,"domain_scores_codex":[0.8893297,0.05193735,0.01323622,0.009199822,0.03201443,0.00428248],"domain_scores_gemma":[0.6679171,0.0731572,0.02055085,0.16954,0.05988913,0.008945835],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002806742,0.0004482425,0.01225561,0.001351609,0.0005090294,0.002322274,0.006497036,0.01284184,0.01847978,0.3037479,0.2622987,0.3764413],"study_design_scores_gemma":[0.000372592,0.000266918,0.002891015,0.001790844,0.0001850717,0.0008127904,0.001018425,0.03726912,0.03639102,0.3421652,0.5763064,0.0005305546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006555574,0.001002912,0.8935311,0.01293986,0.001877724,0.001058444,0.00339149,0.0525607,0.02708205],"genre_scores_gemma":[0.2530541,0.002836933,0.6572276,0.01175822,0.005191199,0.003247496,0.01964791,0.02621059,0.02082597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9814922,"threshold_uncertainty_score":0.5462006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06025526407156832,"score_gpt":0.3332357356088759,"score_spread":0.2729804715373075,"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."}}