{"id":"W2102141608","doi":"10.1144/geochem2011-106","title":"The ‘rgr’ package for the R Open Source statistical computing and graphics environment - a tool to support geochemical data interpretation","year":2013,"lang":"en","type":"article","venue":"Geochemistry Exploration Environment Analysis","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Graphics; Computer science; Interpretation (philosophy); Computer graphics (images); R package; Open source; Computational statistics; Computational science; Operating system; Programming language; Machine learning; Software","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103533,0.0002467087,0.0002545995,0.00003516378,0.0007432243,0.0008956049,0.0022859,0.00009208642,0.0002364305],"category_scores_gemma":[0.0003051245,0.0001744815,0.00008524652,0.0002538363,0.0001902926,0.0006363163,0.002751389,0.0001839644,0.0000530093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005101983,"about_ca_system_score_gemma":0.00002324645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003670152,"about_ca_topic_score_gemma":0.000004986655,"domain_scores_codex":[0.9977573,0.00007374941,0.0004975561,0.0008904875,0.0003878567,0.0003931125],"domain_scores_gemma":[0.9969913,0.0007608238,0.0002278959,0.001835886,0.00003867318,0.0001453907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002675031,0.0009841031,0.008812431,0.0002784964,0.005378495,0.00001706333,0.009391162,0.1035271,0.09378073,0.01642851,0.1156442,0.6454902],"study_design_scores_gemma":[0.0003013131,0.00005687451,0.001248832,0.000007563453,0.0003280983,0.000004214396,0.0006702402,0.9064505,0.005822295,0.004776655,0.07997006,0.0003633995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004580576,0.00005059002,0.981767,0.01242479,0.00002240536,0.0009029847,0.00004016243,0.00003453428,0.0001769624],"genre_scores_gemma":[0.9706931,0.0001462135,0.0253591,0.0006229638,0.00008594941,0.0006611528,0.001202826,0.000008686598,0.00122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9661126,"threshold_uncertainty_score":0.8636339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848200819654117,"score_gpt":0.2589150590544157,"score_spread":0.2304330508578745,"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."}}