{"id":"W2048170784","doi":"10.1016/j.gexplo.2014.11.014","title":"A comparative study of independent component analysis with principal component analysis in geological objects identification. Part II: A case study of Pinghe District, Fujian, China","year":2014,"lang":"en","type":"article","venue":"Journal of Geochemical Exploration","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Ministry of Land and Resources of the People's Republic of China","keywords":"Principal component analysis; Independent component analysis; Pattern recognition (psychology); Kurtosis; Cluster analysis; Identification (biology); Computer science; Artificial intelligence; Correspondence analysis; Data mining; Mathematics; Statistics; Biology","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":[0.001531478,0.0002585011,0.00123381,0.0007102491,0.0001429836,0.00006117251,0.0006705665,0.0000967759,0.00001531809],"category_scores_gemma":[0.000180694,0.0001952889,0.0002312854,0.003193865,0.00009133386,0.000457451,0.000321273,0.0004231735,5.836306e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009644176,"about_ca_system_score_gemma":0.00005935659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003576812,"about_ca_topic_score_gemma":0.0007525421,"domain_scores_codex":[0.995874,0.000606365,0.001778784,0.0004922004,0.001000927,0.000247746],"domain_scores_gemma":[0.9961159,0.0002285819,0.002087873,0.0006316815,0.0007847451,0.0001511679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005865819,0.02347129,0.3969286,0.00006325905,0.00501917,0.0007988379,0.06646737,0.4951349,0.01089741,0.000227927,0.00002468729,0.0003799671],"study_design_scores_gemma":[0.006040289,0.006061884,0.5491595,0.00007763904,0.003618065,0.0003662189,0.0451904,0.3742307,0.01416004,0.0004773868,0.00002437808,0.0005935165],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9555306,0.000020248,0.04371951,0.0002009376,0.00004391233,0.0004178513,0.000002638648,0.00001367551,0.0000506517],"genre_scores_gemma":[0.999186,0.000003669445,0.0007033141,0.000006976724,0.00003691512,0.00002996029,0.00002109773,0.000002419192,0.000009669937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1522309,"threshold_uncertainty_score":0.796365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553886843813912,"score_gpt":0.2769166123043883,"score_spread":0.2413777438662492,"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."}}