{"id":"W3021193189","doi":"10.1101/2020.05.05.024323","title":"Omics BioAnalytics: Reproducible Research using R Shiny and Alexa","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure; University of British Columbia","funders":"","keywords":"Computer science; Omics; Data science; Analytics; Coding (social sciences); World Wide Web; Bioinformatics; Biology","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.02218355,0.002726564,0.002957464,0.004785494,0.002000212,0.006648717,0.00490256,0.001867606,0.04554531],"category_scores_gemma":[0.05881284,0.001812505,0.003326316,0.005165763,0.002813497,0.004329549,0.008045828,0.004336815,0.05831402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011313,"about_ca_system_score_gemma":0.006293023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003788519,"about_ca_topic_score_gemma":0.00387182,"domain_scores_codex":[0.9846261,0.003838061,0.001092113,0.003415524,0.006170461,0.0008577317],"domain_scores_gemma":[0.9640805,0.01075128,0.002021773,0.01455372,0.007143376,0.001449301],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001772625,0.000238105,0.008034797,0.002111567,0.001031212,0.0009019487,0.001027756,0.01087411,0.03544677,0.08473615,0.6338966,0.2199284],"study_design_scores_gemma":[0.0005551835,0.0001488631,0.00789362,0.0006967746,0.0003345461,0.0008678483,0.0003014275,0.1081811,0.07016744,0.1650997,0.6452369,0.000516562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005268092,0.0006313089,0.6824206,0.002054867,0.0007916039,0.0004096945,0.01936635,0.2760593,0.0129982],"genre_scores_gemma":[0.07118939,0.0007295422,0.7584206,0.001345196,0.0004097752,0.002105281,0.03708492,0.1182363,0.01047904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9950975,"threshold_uncertainty_score":0.1523643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04735374273203197,"score_gpt":0.2833111681241432,"score_spread":0.2359574253921112,"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."}}