{"id":"W2058558324","doi":"10.1130/g31112.1","title":"Reading the mineral record of fluid composition from element partitioning","year":2010,"lang":"en","type":"article","venue":"Geology","topic":"Geological and Geochemical Analysis","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Citation; Library science; Computer science; Icon; Information retrieval; Reading (process); Mineral exploration; World Wide Web; Geology; Geochemistry; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003360667,0.0002381111,0.0002165916,0.001024844,0.0004798927,0.0008131066,0.0005004437,0.0005675391,0.002072706],"category_scores_gemma":[0.00275223,0.0001824715,0.0001399607,0.000718254,0.001839939,0.001605729,0.0007888126,0.0006379557,0.0006316822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002573554,"about_ca_system_score_gemma":0.0002601321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001568954,"about_ca_topic_score_gemma":0.002293326,"domain_scores_codex":[0.9998509,0.00003015743,0.000007183391,0.00004201429,0.00006323226,0.000006553589],"domain_scores_gemma":[0.999527,0.0001560364,0.00008740963,0.0001197691,0.0000895999,0.00002015874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004043196,0.00008236489,0.07104266,0.0007144854,0.000120107,0.0004702435,0.001155535,0.0193158,0.5957105,0.114415,0.003912676,0.1926562],"study_design_scores_gemma":[0.00006096151,0.000318167,0.09535676,0.0002139077,0.0001508235,0.001402836,0.001552055,0.08719646,0.571125,0.1560055,0.08645751,0.0001601686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6990257,0.004425663,0.2648359,0.002336515,0.0004896639,0.00005939017,0.001932587,0.0008387764,0.02605592],"genre_scores_gemma":[0.9293548,0.002541632,0.0636249,0.0002976347,0.00012856,0.00002019604,0.0009096202,0.0001353563,0.002987265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002072706,"threshold_uncertainty_score":0.006933868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008804795081649587,"score_gpt":0.193405814182819,"score_spread":0.1846010191011694,"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."}}