{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002754536,0.0005878661,0.0008605673,0.0006045959,0.0001537563,0.0009208327,0.01387433,0.0001802796,0.0005314072],"category_scores_gemma":[0.00008742977,0.0004438864,0.000176341,0.001148671,0.00009448244,0.0003746043,0.004451256,0.0005904858,0.002262463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003111547,"about_ca_system_score_gemma":0.0001115139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000370189,"about_ca_topic_score_gemma":0.001176703,"domain_scores_codex":[0.9957738,0.000176453,0.0005215051,0.0018822,0.001150202,0.0004958753],"domain_scores_gemma":[0.9889465,0.0002774256,0.0002703704,0.01015712,0.00005469979,0.0002938955],"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":[0.000007809808,0.0001835461,0.00002487912,0.00006104663,0.0001078275,0.0000601088,0.000001556373,0.00006574278,4.181786e-7,0.0001798917,0.9986156,0.0006915944],"study_design_scores_gemma":[0.0002353594,0.00009822423,0.000147895,0.00005329725,0.0001104036,0.000001668577,0.000002505415,0.1165839,0.00000979226,0.00004084813,0.8822502,0.0004658356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.222381e-7,0.00004671717,0.03955998,0.002209242,0.0004991418,0.000218807,0.9567991,0.0002306155,0.0004358603],"genre_scores_gemma":[0.00002567956,0.0001007824,0.005360801,0.006757613,0.000139284,0.00001878587,0.9874321,0.0000147667,0.0001501964],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1165182,"threshold_uncertainty_score":0.9998013,"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."}}