{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0003661434,0.0002112044,0.0002160294,0.0001032015,0.0008366746,0.0001032842,0.0004683449,0.0004012408,0.0001818897],"category_scores_gemma":[0.00007087828,0.0002259165,0.00009001836,0.00009920106,0.0003943483,0.00001079819,0.009119593,0.0004908583,0.00003316523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942294,"about_ca_system_score_gemma":0.0002649611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006695417,"about_ca_topic_score_gemma":0.0003761121,"domain_scores_codex":[0.9982997,0.0002055861,0.0001559251,0.0006839162,0.0002546228,0.0004002303],"domain_scores_gemma":[0.999063,0.00003664032,0.00009100047,0.0004432127,0.0002135055,0.0001526703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001715473,0.0009406111,0.2779619,0.002109921,0.002217684,0.00009178324,0.02021992,0.00006197004,0.1247182,0.009943622,0.5593843,0.0006346198],"study_design_scores_gemma":[0.01083779,0.006028412,0.4528685,0.002128772,0.001121568,0.0001806645,0.03241245,0.00192395,0.1258276,0.08449274,0.2748236,0.007353992],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9671731,0.0008437622,0.02122402,0.001022975,0.0005669373,0.0008849716,0.0006347978,0.00004688604,0.007602573],"genre_scores_gemma":[0.995244,0.001658553,0.000586427,0.0002354866,0.0003248821,0.000006407299,0.0007689584,0.00002727024,0.001148041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2845607,"threshold_uncertainty_score":0.9988945,"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."}}