{"id":"W3159387031","doi":"10.3390/app9163245","title":"Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Postdoctoral Science Foundation","keywords":"Maceral; Liptinite; Inertinite; Computer science; Artificial intelligence; Vitrinite; Pattern recognition (psychology); Segmentation; Coal; Identification (biology); Cluster analysis; Data mining; Engineering","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":[0.0002147338,0.00004320181,0.00006374265,0.00008052661,0.00002935337,0.00001926942,0.00006357271,0.00001682281,0.00001064763],"category_scores_gemma":[0.000003062283,0.00003861101,0.000008454999,0.0001188305,0.00007567031,0.0000639567,0.000005246942,0.00002746635,0.000009575034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001147118,"about_ca_system_score_gemma":0.0000051512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003786784,"about_ca_topic_score_gemma":6.698757e-7,"domain_scores_codex":[0.9995301,0.000005666149,0.0001582212,0.00009749153,0.0001514684,0.00005710544],"domain_scores_gemma":[0.9998217,0.00002890975,0.0000658183,0.00005698801,0.00001323069,0.00001336118],"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":[0.00000547944,0.00001173036,0.002281555,0.0000811007,0.000001641757,1.215878e-8,0.0001543287,0.04549791,0.9489443,0.0007184945,0.00001552779,0.002287911],"study_design_scores_gemma":[0.0001244092,0.00002551773,0.03470557,0.00002583353,0.000003583877,1.239195e-7,0.0002766924,0.570207,0.3943999,0.0001689603,0.000008926862,0.00005349698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899313,0.00001171776,0.006350712,0.00001620727,0.00006896155,0.0001003197,0.000002517936,0.00001787768,0.003500312],"genre_scores_gemma":[0.9989468,0.000003632702,0.001004207,0.000006311463,0.000005442559,0.000004359618,0.000008891386,0.000002916276,0.00001741107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5545444,"threshold_uncertainty_score":0.1574511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02857254850750418,"score_gpt":0.2668554731084631,"score_spread":0.238282924600959,"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."}}