{"id":"W4399582117","doi":"10.32614/cran.package.tongfen","title":"tongfen: Make Data Based on Different Geographies Comparable","year":2020,"lang":"en","type":"dataset","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Environmental science","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":[0.004757373,0.002865446,0.001712594,0.006317617,0.001429142,0.003049461,0.004057185,0.001929693,0.06535152],"category_scores_gemma":[0.03029479,0.001407513,0.002660565,0.01013811,0.0008239838,0.002468653,0.004826535,0.0022874,0.05564176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002565435,"about_ca_system_score_gemma":0.007128664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1129751,"about_ca_topic_score_gemma":0.2185494,"domain_scores_codex":[0.9964274,0.0007782725,0.000439768,0.0009565204,0.0008490431,0.0005490797],"domain_scores_gemma":[0.9919498,0.002250137,0.0005917003,0.002448835,0.00224628,0.000513205],"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":[0.0001398326,0.00002371637,0.002239281,0.0005993567,0.0001337315,0.00002939933,0.00007711699,0.0005399447,0.000246957,0.001095541,0.988809,0.006066083],"study_design_scores_gemma":[0.0006472588,0.00003228605,0.01174199,0.0003134237,0.000138831,0.00008480857,0.0001710514,0.002054784,0.001151221,0.005834689,0.9777116,0.0001180543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004129746,0.00005687606,0.001406364,0.0001282207,0.00008163039,0.00006420327,0.9930475,0.003786754,0.001015439],"genre_scores_gemma":[0.001261882,0.00005755026,0.004420224,0.0001027234,0.00001442082,0.0003783574,0.9917004,0.001158352,0.0009061459],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1129751,"threshold_uncertainty_score":0.2246349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05059299392733206,"score_gpt":0.2804103697463968,"score_spread":0.2298173758190647,"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."}}