{"id":"W2801399313","doi":"10.12688/f1000research.14217.1","title":"ShinyDiversity - Understanding Alpha and Beta Diversity through Interactive Visualizations","year":2018,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital; Simon Fraser University","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Diversity (politics); Alpha diversity; Beta diversity; Alpha (finance); Index (typography); BETA (programming language); Sample (material); Biology; Visualization; Computer science; Statistics; Data mining; Ecology; World Wide Web; Mathematics; Species diversity; Biodiversity; Sociology; Physics; Anthropology","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":[],"consensus_categories":[],"category_scores_codex":[0.00183865,0.001316952,0.0008760903,0.00212605,0.0007976507,0.00239792,0.001550236,0.001349806,0.09449907],"category_scores_gemma":[0.008632312,0.0006433983,0.001179312,0.001534047,0.0005639695,0.002385337,0.004209523,0.001509572,0.01465891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000641295,"about_ca_system_score_gemma":0.001007043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004839952,"about_ca_topic_score_gemma":0.006903034,"domain_scores_codex":[0.9994942,0.000130657,0.0000506432,0.0001090929,0.0001526839,0.00006280147],"domain_scores_gemma":[0.9968213,0.001947693,0.0001215581,0.0003144995,0.0005459397,0.0002491082],"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":[0.001244835,0.0001708484,0.01012802,0.002579944,0.0003349799,0.001203039,0.003978299,0.00740611,0.03739037,0.01661524,0.7096129,0.2093354],"study_design_scores_gemma":[0.000916388,0.0001858243,0.01703592,0.001224871,0.0001472017,0.0008662744,0.001491491,0.0959199,0.04367992,0.06021133,0.7779418,0.0003790076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.04656098,0.00229113,0.3838229,0.007363903,0.001858924,0.0004184957,0.09721072,0.4159739,0.04449903],"genre_scores_gemma":[0.2468678,0.002593151,0.5903376,0.002008447,0.000486745,0.001959801,0.05128708,0.07709713,0.02736218],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.09449907,"threshold_uncertainty_score":0.3161309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1443000216120206,"score_gpt":0.4027050034683985,"score_spread":0.2584049818563779,"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."}}