{"id":"W2038393361","doi":"10.1144/1467-7873/09-210","title":"The interpretation of geochemical survey data","year":2010,"lang":"en","type":"article","venue":"Geochemistry Exploration Environment Analysis","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":332,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"","keywords":"Data mining; Multivariate statistics; Computer science; Exploratory data analysis; Principal component analysis; Compositional data; Missing data; Visualization; Earth science; Artificial intelligence; Machine learning; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001141019,0.0001436997,0.0001920499,0.00004029103,0.0001712949,0.0000926162,0.001831612,0.0001076463,0.0001581881],"category_scores_gemma":[0.0006191321,0.0001157564,0.0001002739,0.0005020631,0.0001664437,0.0004601177,0.0006916365,0.000226131,0.00002100229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001519078,"about_ca_system_score_gemma":0.00003037498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002655831,"about_ca_topic_score_gemma":0.00006322611,"domain_scores_codex":[0.998446,0.00007274105,0.0004060506,0.0005202256,0.0003535716,0.000201437],"domain_scores_gemma":[0.9969279,0.0002978719,0.0002951587,0.002321187,0.00008781319,0.00007003702],"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":[0.0000961306,0.0005523983,0.05768369,0.00007466478,0.002061972,0.000007494266,0.001407294,0.02928015,0.8603032,0.002576501,0.006479993,0.03947647],"study_design_scores_gemma":[0.0001517353,0.00001071447,0.005322379,0.000004156838,0.0001637302,0.000002244201,0.0001064606,0.8306269,0.1515452,0.002770191,0.009052142,0.0002441399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07303698,0.00008501019,0.9182706,0.004128116,0.0001364465,0.0002035754,0.00007067288,0.00008403572,0.003984584],"genre_scores_gemma":[0.9946675,0.00005581387,0.003358498,0.00002058971,0.00003889645,0.00002727451,0.001181738,0.000002045479,0.0006476713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9216305,"threshold_uncertainty_score":0.4720407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02593977087994152,"score_gpt":0.2388714595501291,"score_spread":0.2129316886701876,"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."}}