{"id":"W4241532958","doi":"10.1079/dmpd/20193204546","title":"<i>Neonectria neomacrospora</i> . [Distribution map].","year":2019,"lang":"en","type":"article","venue":"Distribution Maps of Plant Diseases","topic":"Plant Pathogens and Fungal Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Distribution (mathematics); Geography; Tsuga; Hypocreales; Forestry; Biology; Archaeology; Botany","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001441648,0.0008974723,0.000443874,0.004578043,0.0004602633,0.0005657813,0.0008243301,0.0003476725,0.08173448],"category_scores_gemma":[0.0003702588,0.0002609538,0.0002623371,0.007311483,0.000250508,0.0004579476,0.0004834199,0.0005826767,0.03501229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005869279,"about_ca_system_score_gemma":0.0006033404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03694823,"about_ca_topic_score_gemma":0.03131304,"domain_scores_codex":[0.9999065,0.000008980347,0.000007283126,0.00002488795,0.00003065977,0.00002173436],"domain_scores_gemma":[0.9996974,0.00003576052,0.00008172981,0.00001302068,0.0001239857,0.00004806866],"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":[0.0003439552,0.00007131955,0.0074309,0.003097118,0.00004344921,0.0006454079,0.0005351829,0.0006647274,0.009400936,0.001778392,0.8132229,0.1627656],"study_design_scores_gemma":[0.00004825718,0.00002507798,0.04432415,0.0001929665,0.00001309574,0.0003630332,0.0001304904,0.0002410143,0.0004121337,0.0002871647,0.9539456,0.00001709946],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0118792,0.003543873,0.002970897,0.001107875,0.0005914861,0.0006263332,0.8271428,0.001853594,0.1502839],"genre_scores_gemma":[0.02781375,0.002621265,0.008101346,0.0003424483,0.0001264665,0.0004674669,0.926591,0.0002247868,0.03371151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08173448,"threshold_uncertainty_score":0.2734291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005614259917813572,"score_gpt":0.2091917977415081,"score_spread":0.2035775378236945,"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."}}