{"id":"W7117257462","doi":"10.1016/j.conengprac.2025.106715","title":"Interval PIP-based Adaboost-ELM for stratigraphic lithology identification in open-pit coal mining process","year":2025,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Science Fund for Distinguished Young Scholars of Hubei Province; Higher Education Discipline Innovation Project; Fundamental Research Funds for the Central Universities; China University of Geosciences; Natural Science Foundation of Hubei Province; Overseas Expertise Introduction Project for Discipline Innovation; National Natural Science Foundation of China","keywords":"Drilling; Feature (linguistics); Lithology; Measurement while drilling; Identification (biology); Feature extraction; Interval (graph theory); Process (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000470969,0.0006028828,0.0007908888,0.0008320188,0.0003025499,0.0006552822,0.001354229,0.0005448125,0.002779488],"category_scores_gemma":[0.0005795286,0.0003558048,0.0004602862,0.0007086722,0.0001927999,0.0007246183,0.0005670678,0.0005720208,0.0009249384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296509,"about_ca_system_score_gemma":0.0006402157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003901839,"about_ca_topic_score_gemma":0.004783739,"domain_scores_codex":[0.9997647,0.00002563314,0.0000122301,0.00005842981,0.00008780066,0.00005105869],"domain_scores_gemma":[0.9997513,0.00006436449,0.00002287758,0.0000183295,0.0001266586,0.00001643957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001555553,0.0005622411,0.007004435,0.0002008419,0.0001322843,0.0001561449,0.00009711451,0.1752261,0.02917237,0.0007881508,0.005126901,0.7799778],"study_design_scores_gemma":[0.00001111006,0.0000508101,0.001951077,0.000004877855,0.00001519219,0.00002796866,0.00002096608,0.9926283,0.004452409,0.0002519673,0.0005774578,0.000007822799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1551668,0.0005978205,0.8341095,0.0001219883,0.0001715249,0.00007598347,0.0003971545,0.00612867,0.003230493],"genre_scores_gemma":[0.8008146,0.0001844579,0.1932313,0.0001430116,0.00005038761,0.0001119496,0.0008385759,0.0002056612,0.004420103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003901839,"threshold_uncertainty_score":0.009298325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278250356240817,"score_gpt":0.3044133143072388,"score_spread":0.2916308107448306,"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."}}