{"id":"W4293066543","doi":"10.1016/j.acags.2022.100094","title":"Applying machine learning methods to predict geology using soil sample geochemistry","year":2022,"lang":"en","type":"article","venue":"Applied Computing and Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"AdaBoost; Artificial intelligence; Support vector machine; Gradient boosting; Machine learning; Random forest; Naive Bayes classifier; Boosting (machine learning); Artificial neural network; Computer science; Pattern recognition (psychology); Radial basis function; Classifier (UML); k-nearest neighbors algorithm; Quadratic classifier; Data mining","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00227877,0.0002237164,0.0002842124,0.0001192642,0.002283948,0.0001768637,0.001155246,0.00005692307,0.00004603756],"category_scores_gemma":[0.0002679394,0.0002266434,0.00004770735,0.0009233529,0.0001504791,0.00007947099,0.00289061,0.0004800808,0.000002068146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000383989,"about_ca_system_score_gemma":0.00009025257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004685525,"about_ca_topic_score_gemma":0.000003560993,"domain_scores_codex":[0.9975526,0.0001675771,0.0003161959,0.0009423493,0.0003623544,0.0006589344],"domain_scores_gemma":[0.9986258,0.0005998993,0.000185907,0.0003566367,0.00004685806,0.0001848671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000192235,0.0000974575,0.01145706,0.00009279554,0.00003513414,0.0000218207,0.003273237,0.5981079,0.09353571,0.01397733,0.0002101223,0.2791722],"study_design_scores_gemma":[0.0001976574,0.00007783491,0.0003593453,0.00000863872,0.00000718819,0.0001524101,0.0007643049,0.9336517,0.00470243,0.008212499,0.05150608,0.0003599189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1467264,0.0002099122,0.8467235,0.0008668025,0.000305576,0.0002459174,0.00000331809,0.0003394744,0.004579068],"genre_scores_gemma":[0.7714917,0.000002547242,0.22764,0.0005016089,0.00007109554,0.00005636043,0.000006368629,0.00000357147,0.0002266668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6247653,"threshold_uncertainty_score":0.9990149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247288364010301,"score_gpt":0.282398412374845,"score_spread":0.259925528734742,"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."}}