{"id":"W6942172035","doi":"10.1371/journal.pone.0162406.g002","title":"&lt;i&gt;R&lt;/i&gt;&lt;sub&gt;0&lt;/sub&gt;-centralities at the major stations in the Tokyo metropolitan area.","year":2016,"lang":"en","type":"other","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Table (database); Population; Line (geometry); Quarter (Canadian coin)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003881718,0.0008387255,0.00039127,0.001171126,0.0004649513,0.0008230614,0.00112978,0.000313409,0.4174251],"category_scores_gemma":[0.002732362,0.0003429207,0.0004192427,0.002354719,0.0002677428,0.001169291,0.0007208494,0.000540095,0.1430715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006633188,"about_ca_system_score_gemma":0.0009634624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01356082,"about_ca_topic_score_gemma":0.04161378,"domain_scores_codex":[0.9996663,0.00004707682,0.0000290554,0.0001057124,0.00008638956,0.00006541519],"domain_scores_gemma":[0.9986718,0.0002778329,0.0001458231,0.000192248,0.0004891037,0.000223258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003017256,0.00007868325,0.01024057,0.001035336,0.0000578143,0.00008430016,0.0003275448,0.0005787573,0.001333083,0.004558262,0.9259288,0.05547525],"study_design_scores_gemma":[0.0001032171,0.00006421215,0.06691921,0.0001646726,0.00003953884,0.00007504127,0.0006084348,0.0005088536,0.001220213,0.001762092,0.9285097,0.00002476933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01308269,0.0006565601,0.006486421,0.0005424921,0.001586585,0.0004130196,0.7375216,0.00506519,0.2346454],"genre_scores_gemma":[0.1003707,0.00121342,0.01632303,0.0003320315,0.0002222717,0.0007495072,0.5661634,0.002937393,0.3116881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4174251,"threshold_uncertainty_score":0.8309724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517024908678419,"score_gpt":0.2341388846844193,"score_spread":0.2089686355976351,"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."}}