{"id":"W2969120545","doi":"10.3897/biss.3.38627","title":"Using Wikidata and Metaphactory to Underpin an Integrated Flora of Canada","year":2019,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Computer science; XML; Flora (microbiology); Information retrieval; World Wide Web; Database; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001049958,0.00005813187,0.0001019125,0.0001887417,0.0002059887,0.0001926218,0.0004175948,0.00001998179,0.000007186954],"category_scores_gemma":[0.000165648,0.00004804841,0.000006532612,0.0005127073,0.0001461008,0.004457599,0.0002736957,0.00003266182,0.000001722761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788742,"about_ca_system_score_gemma":0.001911139,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164084,"about_ca_topic_score_gemma":0.007041278,"domain_scores_codex":[0.9987755,0.00001310397,0.0001161819,0.0001220071,0.0008336376,0.00013953],"domain_scores_gemma":[0.9988851,0.00001613069,0.00006242332,0.0002048019,0.0007185505,0.0001130041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005377591,0.0001287702,0.1947707,0.0006015454,0.0001585087,0.00002204829,0.136107,0.002759757,0.01609684,0.06187982,0.05557952,0.5313578],"study_design_scores_gemma":[0.003749947,0.001774967,0.2813095,0.0001927041,0.00006395694,0.00008209649,0.1028085,0.2505284,0.03693792,0.000443247,0.3202466,0.001862205],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709101,0.0000120596,0.02766629,0.0002567512,0.0001925599,0.0001270596,0.0002260581,0.00001804535,0.0005910732],"genre_scores_gemma":[0.992879,0.000008323629,0.006629342,0.0004762775,0.00000138009,1.800281e-7,0.000002872088,2.256503e-7,0.000002404974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5294955,"threshold_uncertainty_score":0.9848741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118959542827469,"score_gpt":0.2620479848603429,"score_spread":0.2308583894320682,"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."}}