{"id":"W3043027343","doi":"10.3390/min10070628","title":"Incorporating Far-Infrared Data into Carbonate Mineral Analyses","year":2020,"lang":"en","type":"article","venue":"Minerals","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Université Bordeaux Montaigne; Weizmann Institute of Science","keywords":"Aragonite; Calcite; Portlandite; Mineral; Carbonate; Carbonate minerals; Calcium carbonate; Mineralogy; Infrared; Geology; Analytical Chemistry (journal); Chemistry; Materials science; Environmental chemistry; Optics; Metallurgy; Cement","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009788545,0.0002091176,0.0003341488,0.0001176383,0.00005982933,0.0000896914,0.0005336365,0.00007449028,0.0001313903],"category_scores_gemma":[0.0001311689,0.0001866195,0.00008357159,0.0007351905,0.00003158394,0.0003711751,0.0001761733,0.0001608345,0.00009222821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002394301,"about_ca_system_score_gemma":0.00002184833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001301795,"about_ca_topic_score_gemma":0.0001527633,"domain_scores_codex":[0.998773,0.00004968781,0.000398236,0.0003292138,0.0002445148,0.0002053738],"domain_scores_gemma":[0.9991041,0.00002759766,0.00005373813,0.0005583013,0.00004359789,0.0002126853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007218484,0.00001370245,0.0003097069,0.00007323801,0.000275038,0.00003939987,0.00106809,0.2615848,0.6943629,0.00007180528,0.04159838,0.0005956712],"study_design_scores_gemma":[0.0002347333,0.00001580962,0.00003713223,0.000007992008,0.00006358184,8.504085e-7,0.0001995589,0.9752122,0.004613021,0.0001503154,0.0192129,0.0002519355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8508602,0.007044218,0.02601424,0.008720227,0.00063677,0.0005505608,0.0001704348,0.002742764,0.1032605],"genre_scores_gemma":[0.9944855,0.0000639234,0.003020874,0.0007699133,0.0003453652,0.00001155087,0.0005725662,0.000040329,0.0006899813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7136273,"threshold_uncertainty_score":0.7610122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.091124244959763,"score_gpt":0.3080608154996867,"score_spread":0.2169365705399237,"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."}}