{"meta":{"query_hash":"034104237fe6","filters":{"venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy"},"cohort_total":40,"direct_labels_cover":0,"predictions_cover":40,"exported":40,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/034104237fe6","api":"https://metacan.xera.ac/api/v1/cohort?venue=Mining+Technology+Transactions+of+the+Institutions+of+Mining+and+Metallurgy"},"results":[{"id":"W2599310696","doi":"10.1080/14749009.2017.1308690","title":"Quantifying the influence of geotechnical borehole inclination on collecting joint orientation data","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Borehole; Joint (building); Geology; Magnetic dip; Geotechnical engineering; Orientation (vector space); Drilling; Population; Inclination angle; Directional drilling; Geometry; Structural engineering; Engineering; Mathematics; Geophysics","score_opus":0.1015648527283284,"score_gpt":0.31580772776614424,"score_spread":0.21424287503781586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599310696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9081219,0.00013749168,0.09000812,0.000074385905,0.000017686814,0.00006634058,0.0005231978,0.00020235521,0.00084866636],"genre_scores_gemma":[0.97411,0.00008162046,0.025257716,0.000012021042,0.000004926735,0.000030765885,0.00042254486,0.000017859258,0.000062590974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974849,0.0010612718,0.0001969708,0.0003723246,0.00068473956,0.00019986951],"domain_scores_gemma":[0.9692258,0.022575479,0.0034089994,0.0028090884,0.0016450332,0.0003355285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055273375,0.0006494079,0.00055731006,0.0007065968,0.0004741973,0.00091523567,0.000683505,0.0006083362,0.0003199891],"category_scores_gemma":[0.026932806,0.00048070418,0.0004957431,0.0010998108,0.0007905248,0.0016834193,0.0009221823,0.0005208113,0.00009808462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025180037,0.00015331407,0.44784802,0.00020657745,0.00018174507,0.00023746902,0.00034901642,0.48685846,0.014670698,0.00087069697,0.00024626878,0.048126012],"study_design_scores_gemma":[0.000020265781,0.00039166326,0.39240938,0.00007169046,0.00011910531,0.00031105548,0.0005888594,0.58743423,0.015316585,0.0020149248,0.0012151916,0.00010700761],"about_ca_topic_score_codex":0.008484971,"about_ca_topic_score_gemma":0.022825226,"teacher_disagreement_score":0.008484971,"about_ca_system_score_codex":0.0005613963,"about_ca_system_score_gemma":0.0010429408,"threshold_uncertainty_score":0.029231668},"labels":[],"label_agreement":null},{"id":"W2616283577","doi":"10.1080/14749009.2017.1323172","title":"Risk-resilient mine production schedules with favourable product quality for rare earth element projects","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"BHP Billiton; AngloGold Ashanti; Vale Canada Limited; Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; Barrick Gold Corporation","keywords":"Production (economics); Quality (philosophy); Product (mathematics); Element (criminal law); Rare-earth element; Business; Rare earth; Environmental science; Risk analysis (engineering); Geology; Earth science; Economics; Mathematics; Political science","score_opus":0.03825335066058689,"score_gpt":0.2682176731067852,"score_spread":0.2299643224461983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616283577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9485256,0.00007227503,0.04572218,0.00017139087,0.000014955586,0.00013029482,0.00028481215,0.00012982287,0.0049487767],"genre_scores_gemma":[0.9897776,0.000027536444,0.009383988,0.000008180738,0.000001224947,0.00006003046,0.00010026305,0.000012725773,0.00062847906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982685,0.00006708422,0.0000073720726,0.000023946552,0.000031775377,0.00004291024],"domain_scores_gemma":[0.9991021,0.00048767184,0.00018082757,0.00005187664,0.00010118793,0.00007636237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093641,0.000385752,0.00032746347,0.0005202321,0.00024060755,0.00046373403,0.00052145857,0.0006257115,0.0021447896],"category_scores_gemma":[0.0032649096,0.0003332702,0.0003822471,0.0003209469,0.00034203866,0.00041350586,0.0004774172,0.0004521589,0.00013964513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004894762,0.000020913712,0.0012203545,0.000008693648,0.0000047041426,0.00004103185,0.000015149792,0.9960938,0.00041347044,0.0005004932,0.00007264068,0.0015597478],"study_design_scores_gemma":[0.000028335859,0.00013097083,0.0012027244,0.0000039817214,0.0000071880922,0.000013522065,0.000047236364,0.99701726,0.00052507874,0.0008084927,0.0002098174,0.0000054090556],"about_ca_topic_score_codex":0.0055450397,"about_ca_topic_score_gemma":0.005506393,"teacher_disagreement_score":0.0055450397,"about_ca_system_score_codex":0.00070889137,"about_ca_system_score_gemma":0.00084292144,"threshold_uncertainty_score":0.011025548},"labels":[],"label_agreement":null},{"id":"W2624929277","doi":"10.1080/14749009.2017.1341142","title":"Adaptive policies for short-term material flow optimization in a mining complex","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Heuristic; Term (time); ENCODE; Production (economics); Data mining; Reduction (mathematics); Data stream mining; State (computer science); Artificial intelligence; Algorithm","score_opus":0.05291948212047829,"score_gpt":0.2703244426418384,"score_spread":0.21740496052136013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624929277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08035282,0.0002321016,0.9152992,0.00047029697,0.000053881442,0.000094168085,0.000091629205,0.00029736204,0.003108549],"genre_scores_gemma":[0.9100677,0.00020029115,0.08713309,0.00008886264,0.000023731225,0.00017293202,0.00008491341,0.00006851949,0.0021598865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994449,0.00018779538,0.000029977537,0.00013620565,0.00010832431,0.00009276309],"domain_scores_gemma":[0.99770784,0.0015859167,0.00030834426,0.00010750603,0.00018243615,0.00010788193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018749037,0.00089056394,0.0010247002,0.00066175865,0.0006614424,0.0015170692,0.001036185,0.0012930983,0.0017212059],"category_scores_gemma":[0.004137845,0.0006737582,0.0006509685,0.00046694113,0.001488408,0.0014001509,0.0011426612,0.0014784152,0.0001824226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021269148,0.000018261091,0.00018690943,0.000009637044,0.000008254596,0.000012543198,0.00001773156,0.99354,0.0003790401,0.0029538781,0.000085495056,0.002767104],"study_design_scores_gemma":[0.0000037930733,0.000008909707,0.000045967437,0.0000018495061,0.000002305845,0.0000017540491,0.0000054111324,0.9978758,0.00015608307,0.0018048966,0.0000907774,0.000002578017],"about_ca_topic_score_codex":0.00960589,"about_ca_topic_score_gemma":0.007360476,"teacher_disagreement_score":0.00960589,"about_ca_system_score_codex":0.0021333518,"about_ca_system_score_gemma":0.0022180977,"threshold_uncertainty_score":0.01909995},"labels":[],"label_agreement":null},{"id":"W2734633914","doi":"10.1080/14749009.2017.1351115","title":"Cave fragmentation in a cave-to-mill context at the New Afton Mine part I: fragmentation and hang-up frequency prediction","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cave; Fragmentation (computing); Excavation; Mining engineering; Geology; Hang; Context (archaeology); Archaeology; Geography; Engineering; Geotechnical engineering; Computer science; Structural engineering","score_opus":0.024659234225019495,"score_gpt":0.2549537768069362,"score_spread":0.2302945425819167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734633914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991358,0.000017523998,0.0003748787,0.0000086085665,7.539633e-7,0.0000069276407,0.00012611215,0.000012578538,0.00031691353],"genre_scores_gemma":[0.9989322,0.000020105226,0.00064730854,0.0000022076122,0.0000011942487,0.000005737015,0.00022691386,0.0000034540194,0.00016084389],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998265,0.000018608795,0.000012723456,0.00004586002,0.000050445113,0.00004592755],"domain_scores_gemma":[0.99927884,0.000280757,0.00015935446,0.000043750693,0.00012944691,0.00010778678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032616046,0.00023829938,0.00033981926,0.0015203094,0.00060547265,0.0007685631,0.00062684447,0.0006134836,0.0007051838],"category_scores_gemma":[0.0011543416,0.00022580202,0.0003663216,0.0010800945,0.00047709374,0.00044441107,0.0005939772,0.00037636512,0.00014742544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025597404,0.00012567209,0.92686373,0.00004121472,0.00003559256,0.001317607,0.000530909,0.04983649,0.00421062,0.00019254298,0.00026628768,0.016323444],"study_design_scores_gemma":[0.000010665123,0.00010439614,0.8959571,0.000016908916,0.000011469582,0.00031278623,0.00050842937,0.10182504,0.0008922767,0.00013012609,0.00020839412,0.000022540116],"about_ca_topic_score_codex":0.06419272,"about_ca_topic_score_gemma":0.090270944,"teacher_disagreement_score":0.06419272,"about_ca_system_score_codex":0.0010228081,"about_ca_system_score_gemma":0.00048391343,"threshold_uncertainty_score":0.12763816},"labels":[],"label_agreement":null},{"id":"W2736500113","doi":"10.1080/14749009.2017.1296669","title":"Interpreting the results of <i>in situ</i> pull tests on Friction Rock Stabilizers (FRS)","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Rock bolt; Engineering; Geotechnical engineering; Probabilistic logic; Quality assurance; Quality (philosophy); Rock mass classification; Mining engineering; Geology; Computer science","score_opus":0.01894232182600526,"score_gpt":0.24301144751605566,"score_spread":0.2240691256900504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736500113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91040945,0.0015627058,0.07484214,0.00017855845,0.00015207437,0.00021364067,0.0007644648,0.00037994364,0.011497084],"genre_scores_gemma":[0.97362465,0.0008203882,0.02242061,0.000102397,0.00002191166,0.00008491423,0.00030078998,0.000093572016,0.0025308419],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99723697,0.00055185606,0.0003469007,0.00024434502,0.0014464277,0.00017354805],"domain_scores_gemma":[0.9911793,0.003654818,0.0018241018,0.00049652177,0.0027672441,0.00007804696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003931258,0.0008432705,0.000515032,0.0016056647,0.0004004511,0.0010065626,0.0009185886,0.0008470611,0.001974183],"category_scores_gemma":[0.00735561,0.00031317392,0.000625643,0.0010620159,0.00060124823,0.0007595866,0.0004530548,0.00041263743,0.0007612177],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011970485,0.00020162096,0.121388055,0.003009107,0.00023250292,0.0013271549,0.0016814017,0.013401661,0.7542783,0.0011920468,0.0012144345,0.10087659],"study_design_scores_gemma":[0.00004992808,0.003641467,0.15530108,0.0004185357,0.00043796125,0.0013955595,0.003282353,0.010382219,0.81411403,0.0011137826,0.009733976,0.00012911882],"about_ca_topic_score_codex":0.0012552411,"about_ca_topic_score_gemma":0.0036350696,"teacher_disagreement_score":0.003931258,"about_ca_system_score_codex":0.00048363995,"about_ca_system_score_gemma":0.0004670855,"threshold_uncertainty_score":0.020790696},"labels":[],"label_agreement":null},{"id":"W2747960616","doi":"10.1080/14749009.2017.1363991","title":"Improved grade control in open pit mines","year":2017,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Open-pit mining; Mining engineering; Geology; Forensic engineering; Engineering","score_opus":0.030801170670529707,"score_gpt":0.2630235048995308,"score_spread":0.2322223342290011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747960616","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72868913,0.0003154808,0.25925997,0.00018890608,0.000067378445,0.00010783672,0.00012954803,0.0010188217,0.010222901],"genre_scores_gemma":[0.9853135,0.000040656578,0.012813436,0.00000880215,0.0000040405316,0.000009457594,0.00004563167,0.000026837704,0.0017376993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993723,0.00008992775,0.00003096607,0.00010022201,0.0002997946,0.00010683748],"domain_scores_gemma":[0.9990792,0.00027951246,0.00018117037,0.00015360296,0.00023765165,0.000068884176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005978149,0.0004375838,0.00074781844,0.00048730357,0.0004541791,0.0014383402,0.0015918593,0.00081542856,0.0021353217],"category_scores_gemma":[0.002201601,0.0003755945,0.0003038235,0.00067528273,0.00079119107,0.0012450033,0.0014064092,0.00061867124,0.0003765799],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000365548,0.00018510078,0.0046331743,0.000076319135,0.000015013932,0.0003690568,0.000105041545,0.8970289,0.01949164,0.002911375,0.0005730544,0.074245796],"study_design_scores_gemma":[0.00007293349,0.0003122196,0.0027820691,0.000010717589,0.000015122386,0.000086173925,0.00011315633,0.9793819,0.011053755,0.0042591356,0.0018827079,0.000030053698],"about_ca_topic_score_codex":0.0065124743,"about_ca_topic_score_gemma":0.008519298,"teacher_disagreement_score":0.0065124743,"about_ca_system_score_codex":0.0006488619,"about_ca_system_score_gemma":0.0006970758,"threshold_uncertainty_score":0.012949109},"labels":[],"label_agreement":null},{"id":"W2769282422","doi":"10.1080/25726668.2019.1577596","title":"Responding to new information in a mining complex: fast mechanisms using machine learning","year":2019,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"AngloGold Ashanti; Vale Canada Limited; Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; BHP Billiton; Canada Excellence Research Chairs, Government of Canada; Barrick Gold Corporation","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.023069284161523592,"score_gpt":0.24388996442606192,"score_spread":0.22082068026453833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769282422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12470911,0.00047231207,0.8699176,0.0008531946,0.00009517824,0.00012812213,0.00004560961,0.001187021,0.0025918926],"genre_scores_gemma":[0.9240321,0.00015195821,0.074384056,0.00009759555,0.000034065117,0.00009361455,0.000035402227,0.000057467427,0.0011137796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993585,0.0001976374,0.000037711627,0.00015551886,0.00017218031,0.0000784338],"domain_scores_gemma":[0.99565965,0.0027697792,0.000570381,0.000426799,0.000392533,0.0001807641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021406352,0.0008504445,0.0011417918,0.0007154564,0.00076082535,0.0015929246,0.0018735549,0.0013501337,0.0021080612],"category_scores_gemma":[0.008053668,0.0006178834,0.00048106007,0.00060660765,0.0014191268,0.0024671368,0.0016081762,0.0016413727,0.00034562155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015167569,0.00014964964,0.0020344132,0.00005180056,0.00004353641,0.00007245299,0.00011218967,0.89245355,0.003299075,0.0069670416,0.00081355264,0.0938512],"study_design_scores_gemma":[0.0000070300616,0.00001669336,0.00010150042,0.0000024954613,0.0000028047623,0.0000054224515,0.000007092466,0.9960334,0.00041148308,0.0033037167,0.00010421568,0.0000041833264],"about_ca_topic_score_codex":0.0053467746,"about_ca_topic_score_gemma":0.0042845407,"teacher_disagreement_score":0.0053467746,"about_ca_system_score_codex":0.001240113,"about_ca_system_score_gemma":0.0014303935,"threshold_uncertainty_score":0.011320949},"labels":[],"label_agreement":null},{"id":"W2791286876","doi":"10.1080/25726668.2018.1437334","title":"Cave fragmentation in a cave-to-mill context at the New Afton mine Part II: implications to mill performance","year":2018,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Comminution; Mill; Muck; Cave; Shovel; Mining engineering; Engineering; Fragmentation (computing); Breakage; Geology; Metallurgy; Mechanical engineering; Materials science; Archaeology; Geography; Soil science; Computer science; Composite material","score_opus":0.022212763259941867,"score_gpt":0.24947037174223516,"score_spread":0.2272576084822933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791286876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998262,0.00003455633,0.00014221201,0.0000664177,0.0000012503835,0.0000066721377,0.00010994764,0.0000057257025,0.0013711499],"genre_scores_gemma":[0.99939406,0.000033125718,0.00017996469,0.000008143938,0.0000015649026,0.0000029905696,0.000059376114,0.0000032359892,0.0003174683],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99959964,0.00004767355,0.000023116234,0.00008819549,0.00012706306,0.00011437535],"domain_scores_gemma":[0.99902487,0.00034319062,0.00023639575,0.000049020586,0.00021088126,0.00013555428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040724495,0.00013707712,0.00027880023,0.0013523671,0.0014890231,0.0015064506,0.0008116927,0.0006588713,0.00226063],"category_scores_gemma":[0.0019751405,0.00023130103,0.00020887413,0.0017428419,0.0012554617,0.0008935895,0.0011652107,0.0004401871,0.00015608649],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028801558,0.00009077881,0.9570295,0.00005601992,0.00003324006,0.0028833633,0.005105191,0.0054832348,0.0052041467,0.0006553226,0.00043954654,0.022731505],"study_design_scores_gemma":[0.0000031745708,0.000041768784,0.99314135,0.000015633179,0.000005190934,0.00029011094,0.0033826053,0.0021900113,0.00029673477,0.00012926717,0.00049049995,0.000013715107],"about_ca_topic_score_codex":0.13715576,"about_ca_topic_score_gemma":0.3436233,"teacher_disagreement_score":0.13715576,"about_ca_system_score_codex":0.0031542191,"about_ca_system_score_gemma":0.0009714141,"threshold_uncertainty_score":0.27271485},"labels":[],"label_agreement":null},{"id":"W2907379475","doi":"10.1080/25726668.2018.1563742","title":"Long-term production scheduling optimization and 3D material mixing analysis for block caving mines","year":2019,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Block scheduling; Schedule; Block (permutation group theory); Production schedule; Engineering; Block structure; Mixing (physics); Software; Computer science; Structural engineering; Mathematics; Operations management; Finite element method","score_opus":0.015068719603036794,"score_gpt":0.22521234813371635,"score_spread":0.21014362853067955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907379475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4614014,0.0004473884,0.5285723,0.00028960357,0.00004495373,0.0001334772,0.0004283316,0.0003010248,0.008381504],"genre_scores_gemma":[0.93434536,0.00016188253,0.0626531,0.000020210988,0.000008920053,0.00011749259,0.0002301673,0.000065376145,0.002397448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998215,0.000059052218,0.000006966188,0.00003069198,0.000044427346,0.00003748586],"domain_scores_gemma":[0.9995819,0.00025404466,0.00006563437,0.000018700477,0.000051269097,0.000028513734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062541576,0.0006495137,0.0006135719,0.00046956787,0.00038359023,0.00066602184,0.000539292,0.00079033576,0.0017252892],"category_scores_gemma":[0.0009446051,0.0005658511,0.000797678,0.0004212461,0.00038929645,0.00040960222,0.0004463392,0.00054199767,0.0001312286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007839032,0.000005872877,0.0001680507,0.000006716151,0.0000027301937,0.000010479085,0.0000032187618,0.99811184,0.00037603968,0.00022956227,0.00003085784,0.0010467325],"study_design_scores_gemma":[0.0000019172605,0.000011222111,0.000121571444,8.56111e-7,0.0000016431039,0.000002772906,0.000003592228,0.99941707,0.00018191387,0.00016883385,0.00008736451,0.0000012124816],"about_ca_topic_score_codex":0.016785568,"about_ca_topic_score_gemma":0.010981147,"teacher_disagreement_score":0.016785568,"about_ca_system_score_codex":0.0010795266,"about_ca_system_score_gemma":0.0014621177,"threshold_uncertainty_score":0.03337574},"labels":[],"label_agreement":null},{"id":"W2914974217","doi":"10.1080/25726668.2019.1575053","title":"Application of simultaneous stochastic optimization with geometallurgical decisions at a copper–gold mining complex","year":2019,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Canadian Institute of Mining, Metallurgy and Petroleum","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Production (economics); Stochastic optimization; Computer science; Robust optimization; Mathematical optimization; Comminution; Multi-objective optimization; Work (physics); Data mining; Reliability engineering; Engineering; Mathematics; Economics","score_opus":0.01628775845657905,"score_gpt":0.22034760187909824,"score_spread":0.2040598434225192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914974217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34767804,0.00026121514,0.6428958,0.000565878,0.00004564525,0.00012435741,0.00012628961,0.00019012742,0.00811273],"genre_scores_gemma":[0.98168725,0.000051621962,0.016491987,0.000025284839,0.0000103357415,0.000060905873,0.000031172236,0.0000139196445,0.0016274499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932516,0.00024032922,0.000026368645,0.00012364895,0.00015408217,0.00013047807],"domain_scores_gemma":[0.9987754,0.0008247723,0.00016568687,0.000054492753,0.00011660451,0.000062947045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012501065,0.0010392155,0.0012398232,0.0004848493,0.0005276102,0.0013639118,0.0007940279,0.0012376497,0.0013062349],"category_scores_gemma":[0.0019862063,0.0007542015,0.0012537242,0.000558572,0.0009711773,0.0007724705,0.0013218889,0.0009723794,0.00010850033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019527148,0.00000898987,0.00016898058,0.000007140367,0.000009809609,0.000021857377,0.000005860245,0.997424,0.00033802624,0.0008232205,0.000031310028,0.0011413425],"study_design_scores_gemma":[0.0000032337018,0.00001834216,0.00008435229,6.611208e-7,0.0000036137287,0.0000033256326,0.0000041134276,0.9992078,0.00017718785,0.00045779377,0.000037323098,0.0000023218981],"about_ca_topic_score_codex":0.012083194,"about_ca_topic_score_gemma":0.007144933,"teacher_disagreement_score":0.012083194,"about_ca_system_score_codex":0.0013137558,"about_ca_system_score_gemma":0.0016507846,"threshold_uncertainty_score":0.024025738},"labels":[],"label_agreement":null},{"id":"W2918678357","doi":"10.1080/25726668.2019.1583843","title":"Approximate blast movement modelling for improved grade control","year":2019,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Movement (music); Optimization problem; Displacement (psychology); Computer science; Heuristic; Global optimization; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence","score_opus":0.017412857882040396,"score_gpt":0.21269887776860907,"score_spread":0.19528601988656868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918678357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016342066,0.000051815226,0.9797861,0.00004659332,0.00001548773,0.000027258317,0.0000775524,0.00037303442,0.0032800725],"genre_scores_gemma":[0.80869204,0.00017826693,0.18505096,0.00003561444,0.0000120196155,0.00016291824,0.00022611517,0.00016247711,0.005479505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998029,0.000038862672,0.000012310637,0.00004224264,0.00008280707,0.000020943815],"domain_scores_gemma":[0.99977845,0.0000790282,0.000043194334,0.000032520573,0.00005829375,0.000008540736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031490385,0.00050441694,0.00071759097,0.00030990446,0.00030878492,0.000926056,0.00080153573,0.00085681333,0.002094815],"category_scores_gemma":[0.0010314564,0.00041003255,0.00048364687,0.0004503102,0.00042842425,0.0006650327,0.0004579991,0.00066552375,0.00046220643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065645913,0.0000028640038,0.00006837859,0.0000062262707,0.0000015223394,0.000006155405,0.00000791686,0.99542856,0.0006640317,0.0012192365,0.00006338283,0.0025250933],"study_design_scores_gemma":[0.0000014252462,0.0000036177066,0.000028287794,8.6734246e-7,6.928187e-7,0.000002327837,0.0000012454369,0.999255,0.0001885104,0.00030153996,0.00021531909,0.0000011608051],"about_ca_topic_score_codex":0.012150254,"about_ca_topic_score_gemma":0.0074384734,"teacher_disagreement_score":0.012150254,"about_ca_system_score_codex":0.00073699723,"about_ca_system_score_gemma":0.0008891434,"threshold_uncertainty_score":0.024159074},"labels":[],"label_agreement":null},{"id":"W2922176645","doi":"10.1080/25726668.2019.1626169","title":"Simultaneous stochastic optimization of an open pit gold mining complex with supply and market uncertainty","year":2019,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Open-pit mining; Computer science; Econometrics; Business; Economics; Mathematical optimization; Mining engineering; Engineering; Mathematics","score_opus":0.01564222882868281,"score_gpt":0.22699523668398872,"score_spread":0.21135300785530592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922176645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58167857,0.00024743518,0.40129232,0.0006993464,0.000053000575,0.00013055565,0.00037100838,0.00017806643,0.015349735],"genre_scores_gemma":[0.97995466,0.000062302825,0.016784932,0.000029815808,0.0000076178258,0.000054078497,0.00007274462,0.000022205499,0.0030115556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996246,0.000099145844,0.000014450398,0.00008063907,0.000083272804,0.0000978463],"domain_scores_gemma":[0.9991788,0.00051458017,0.00012834065,0.000029164572,0.00008453741,0.00006458175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000905299,0.00066914957,0.0008565529,0.00043614162,0.0004643148,0.0013846337,0.0006291092,0.0010802544,0.001762102],"category_scores_gemma":[0.0016371368,0.0007346293,0.00081312057,0.00055031176,0.0008795291,0.00068545446,0.0011217619,0.00086255104,0.00012975735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014457272,0.0000082863335,0.00019008068,0.000006449803,0.0000054259413,0.00003177712,0.0000052093255,0.997886,0.0002535602,0.00082721445,0.000041120104,0.0007304122],"study_design_scores_gemma":[0.0000057527027,0.000022347427,0.00017525943,0.0000012620632,0.0000043994205,0.0000055445958,0.000012436733,0.99862015,0.00015559186,0.00090811646,0.00008604374,0.000002971351],"about_ca_topic_score_codex":0.012321131,"about_ca_topic_score_gemma":0.01083367,"teacher_disagreement_score":0.012321131,"about_ca_system_score_codex":0.0012797577,"about_ca_system_score_gemma":0.0019035728,"threshold_uncertainty_score":0.02449888},"labels":[],"label_agreement":null},{"id":"W3016780612","doi":"10.1080/25726668.2020.1749431","title":"An automated production targeting goal programming framework for oil sands mine planning considering organic rich solids","year":2020,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Asphalt; Production (economics); Petroleum engineering; Oil sands; Environmental science; Oil production; Scheduling (production processes); Enhanced oil recovery; Waste management; Engineering; Process engineering; Operations management; Materials science","score_opus":0.026471148251788104,"score_gpt":0.26845827991031956,"score_spread":0.24198713165853147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016780612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01051016,0.00008838055,0.98415,0.0001858066,0.000021347561,0.00015580535,0.00025076847,0.00079815753,0.0038397592],"genre_scores_gemma":[0.27279675,0.0002182508,0.7219835,0.00013702613,0.000025767671,0.00067365373,0.0005641733,0.00018065659,0.003420176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996338,0.000119694116,0.000018099428,0.00007194147,0.00008929384,0.00006723176],"domain_scores_gemma":[0.9993631,0.00042205479,0.000053168675,0.000017757551,0.00010381488,0.0000401339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010700012,0.0011093372,0.00080782676,0.00049861596,0.0005268315,0.0013844181,0.0012878334,0.001006018,0.003415029],"category_scores_gemma":[0.0014070837,0.00062596984,0.0011299911,0.00056045724,0.00055668363,0.00055082503,0.0011136476,0.0014938783,0.00040999305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018811694,0.000035600606,0.00015086393,0.000040324852,0.000012798228,0.00003527862,0.000026358313,0.98500466,0.0003995739,0.0051541706,0.00039989015,0.008721731],"study_design_scores_gemma":[0.000010494512,0.000015827876,0.000035263405,0.000005959184,0.0000048475135,0.0000055323244,0.00001252696,0.9966742,0.00014551781,0.0025039958,0.0005829257,0.0000029129537],"about_ca_topic_score_codex":0.02737439,"about_ca_topic_score_gemma":0.036193766,"teacher_disagreement_score":0.02737439,"about_ca_system_score_codex":0.0014157884,"about_ca_system_score_gemma":0.0036256623,"threshold_uncertainty_score":0.054430127},"labels":[],"label_agreement":null},{"id":"W3122955409","doi":"10.1080/25726668.2021.1872261","title":"Simultaneous multi-sector block cave mine production scheduling considering operational uncertainties","year":2021,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Comisión Nacional de Investigación Científica y Tecnológica","keywords":"Tonnage; Production (economics); Block (permutation group theory); Scheduling (production processes); Production schedule; Schedule; Context (archaeology); Flexibility (engineering); Engineering; Operational planning; Operations research; Computer science; Operations management; Business; Geology","score_opus":0.03079282333094633,"score_gpt":0.23872086123011035,"score_spread":0.20792803789916403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122955409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3612896,0.00025030895,0.6321383,0.00018268077,0.000031249925,0.00011343848,0.00034692514,0.00019973543,0.00544778],"genre_scores_gemma":[0.9820703,0.00006505464,0.016833983,0.000008804727,0.0000070193514,0.00003213125,0.00009254422,0.000021518375,0.00086872646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959546,0.00012994715,0.0000128835645,0.000081866725,0.00006979271,0.000110088],"domain_scores_gemma":[0.99934345,0.00038700495,0.000107843516,0.00003381658,0.00006927321,0.00005872965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007219253,0.0005237128,0.0010287346,0.00024022063,0.0003219616,0.0008929048,0.0006766037,0.0007889869,0.0016253716],"category_scores_gemma":[0.0014158598,0.00048715377,0.0004868618,0.00053786335,0.00034405504,0.0006740273,0.00070736994,0.00060001516,0.00015153573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005188992,0.000008095465,0.0003699795,0.000017997303,0.0000070509373,0.000061160055,0.000017116508,0.99472165,0.0007747235,0.000645933,0.00009201486,0.0032324134],"study_design_scores_gemma":[0.000004797899,0.000030622785,0.00029737694,0.00000159609,0.000002902689,0.000012708118,0.000015476557,0.99864024,0.00021090201,0.00064406253,0.00013627653,0.000003054434],"about_ca_topic_score_codex":0.008558784,"about_ca_topic_score_gemma":0.006984647,"teacher_disagreement_score":0.008558784,"about_ca_system_score_codex":0.0006251605,"about_ca_system_score_gemma":0.0015376192,"threshold_uncertainty_score":0.017017901},"labels":[],"label_agreement":null},{"id":"W3157100525","doi":"10.1080/25726668.2021.1919374","title":"Truck fleet size selection in open-pit mines based on the match factor using a MINLP model","year":2021,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sizing; Loader; Truck; Selection (genetic algorithm); Copper mine; Key (lock); Engineering; Factor (programming language); Open-pit mining; Integer programming; Integer (computer science); Computer science; Mathematical optimization; Operations research; Industrial engineering; Automotive engineering; Algorithm","score_opus":0.046492804367613424,"score_gpt":0.25911126020788866,"score_spread":0.21261845584027522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157100525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14595112,0.0002786252,0.8461008,0.00024221327,0.000025095123,0.00012072005,0.00028349,0.00012570126,0.006872169],"genre_scores_gemma":[0.9462237,0.00022149784,0.04902744,0.000041185158,0.000013539627,0.00016024309,0.0001679125,0.000048430422,0.004096103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935883,0.000341244,0.000018831228,0.00009712756,0.000083857216,0.00010012188],"domain_scores_gemma":[0.9981927,0.00136261,0.00020247712,0.000042648277,0.00012566168,0.00007387636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016255582,0.0008339436,0.0011514243,0.0006515684,0.0002860018,0.0015671604,0.0015159537,0.0010825647,0.002430819],"category_scores_gemma":[0.0032216895,0.0007402632,0.0010103658,0.00080585893,0.0005847131,0.0013044302,0.000697118,0.0008828212,0.00021231693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015119108,0.000011558464,0.00020981206,0.000015328347,0.0000073448145,0.000020272819,0.000007070361,0.9969939,0.00014380264,0.0012218155,0.000055149398,0.0012988426],"study_design_scores_gemma":[0.000001618045,0.000011145563,0.000057620757,0.0000018385173,0.0000025415468,0.0000033847323,0.000007473364,0.99923885,0.00005650379,0.00056945655,0.000048320668,0.0000013042138],"about_ca_topic_score_codex":0.008896884,"about_ca_topic_score_gemma":0.007156968,"teacher_disagreement_score":0.008896884,"about_ca_system_score_codex":0.0012015032,"about_ca_system_score_gemma":0.0010986939,"threshold_uncertainty_score":0.017690182},"labels":[],"label_agreement":null},{"id":"W3194027934","doi":"10.1080/25726668.2021.1886544","title":"Procedure for estimating broken ore density distribution within a draw column during block caving","year":2021,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Cave; Geology; Block (permutation group theory); Spark plug; Geotechnical engineering; Mining engineering; Engineering; Geometry; Mathematics; Geography; Mechanical engineering","score_opus":0.013240229114971294,"score_gpt":0.23053645932618064,"score_spread":0.21729623021120933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194027934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1795565,0.00007996509,0.8128955,0.000043424097,0.0000150263695,0.0015615608,0.0018677025,0.001242116,0.0027382288],"genre_scores_gemma":[0.3065742,0.00009746213,0.6874762,0.000022588503,0.0000052681494,0.0015277487,0.0020590157,0.000097439544,0.002140174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990546,0.00013819874,0.00008515883,0.00023169482,0.000413421,0.000076984055],"domain_scores_gemma":[0.99757797,0.00096097967,0.00034824864,0.00019306615,0.00085318316,0.00006671118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515491,0.0005008672,0.0005668384,0.0027451562,0.0007575687,0.00095453026,0.0009239035,0.0005572575,0.003927699],"category_scores_gemma":[0.006689463,0.00036209592,0.00043328045,0.0016487946,0.00047759362,0.00045940728,0.0009782689,0.0005928885,0.0013350422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058884616,0.0005101959,0.23961289,0.00043803902,0.0001113479,0.0004555087,0.0013357996,0.043584514,0.09357851,0.0050567472,0.0022809368,0.6124466],"study_design_scores_gemma":[0.00008016704,0.0014296426,0.36964804,0.00013888646,0.00012746628,0.0007014501,0.0029173747,0.5060694,0.10252528,0.0053550457,0.010708094,0.0002991993],"about_ca_topic_score_codex":0.010856849,"about_ca_topic_score_gemma":0.016210467,"teacher_disagreement_score":0.010856849,"about_ca_system_score_codex":0.00061616034,"about_ca_system_score_gemma":0.001728199,"threshold_uncertainty_score":0.021587312},"labels":[],"label_agreement":null},{"id":"W4311620417","doi":"10.1080/25726668.2022.2151112","title":"Quality assurance considerations for friction rock stabilizers","year":2022,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quality assurance; Quality (philosophy); Geology; Geotechnical engineering; Forensic engineering; Mining engineering; Engineering; Operations management; Physics","score_opus":0.03557490630789266,"score_gpt":0.25718951843025945,"score_spread":0.22161461212236677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311620417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5019682,0.020566903,0.41693687,0.009011753,0.0011241044,0.001608806,0.0010215607,0.0022133482,0.045548495],"genre_scores_gemma":[0.8820567,0.003021567,0.10540553,0.0010479141,0.0001852722,0.00030555634,0.0007027216,0.0002680163,0.0070067225],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9847685,0.003455682,0.0007669683,0.00083741714,0.009643554,0.0005278774],"domain_scores_gemma":[0.9769603,0.006136304,0.0028239007,0.0025791759,0.011173899,0.00032641916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017672462,0.0005495436,0.0006558638,0.0015893136,0.0011028306,0.002869319,0.0014983175,0.001559405,0.0035989233],"category_scores_gemma":[0.02749241,0.00033077147,0.0006907063,0.0008540225,0.001234905,0.0013457937,0.0009449828,0.00088927214,0.000868163],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001369146,0.0005052669,0.01743699,0.0025423851,0.00012212903,0.0018807822,0.0020435927,0.016799519,0.5593422,0.025759187,0.007322495,0.36487627],"study_design_scores_gemma":[0.0002700416,0.006976324,0.036731113,0.0015151406,0.00032012194,0.0050499802,0.002760304,0.034825984,0.6550895,0.01385068,0.24240403,0.00020683868],"about_ca_topic_score_codex":0.0034162723,"about_ca_topic_score_gemma":0.003294559,"teacher_disagreement_score":0.017672462,"about_ca_system_score_codex":0.0016643037,"about_ca_system_score_gemma":0.0019696502,"threshold_uncertainty_score":0.09346205},"labels":[],"label_agreement":null},{"id":"W4368370660","doi":"10.1080/25726668.2023.2205080","title":"Assessing stope performance using georeferenced octrees and multivariate analysis","year":2023,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mining engineering; Undercut; Rock blasting; Principal component analysis; Coal mining; Georeference; Geology; Engineering; Civil engineering; Computer science; Geography; Coal; Mechanical engineering; Physical geography; Artificial intelligence","score_opus":0.05057146576541826,"score_gpt":0.2890088762175019,"score_spread":0.2384374104520836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368370660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7543945,0.00007301402,0.23818833,0.000055745673,0.00001876581,0.00009535928,0.00193946,0.0008967984,0.0043380056],"genre_scores_gemma":[0.9032414,0.00004069453,0.094269656,0.000008831262,0.0000057307457,0.00004480507,0.0015657123,0.00009643236,0.00072674506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99890625,0.00029984204,0.00006729119,0.00017296904,0.00044887734,0.0001047188],"domain_scores_gemma":[0.99754816,0.0007065075,0.00049047446,0.00044885188,0.0007039313,0.000102001766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012227154,0.00062291825,0.00043338706,0.0024741585,0.00025320996,0.0011775958,0.00040714382,0.00034576163,0.0011503915],"category_scores_gemma":[0.0033096122,0.00018034688,0.00041939414,0.0020957573,0.00029709612,0.00070652505,0.0006390431,0.00034245403,0.00059368694],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006660409,0.00037276096,0.37855154,0.0002362513,0.00032874398,0.00033412673,0.0009313414,0.18324327,0.0849711,0.0039040826,0.0020011903,0.34445965],"study_design_scores_gemma":[0.000015678379,0.00055675424,0.5746213,0.00003178521,0.000064435095,0.00031374462,0.0011706257,0.39072606,0.024442231,0.001610968,0.0062951013,0.00015132886],"about_ca_topic_score_codex":0.0051727663,"about_ca_topic_score_gemma":0.015925609,"teacher_disagreement_score":0.0051727663,"about_ca_system_score_codex":0.0003237237,"about_ca_system_score_gemma":0.00037719912,"threshold_uncertainty_score":0.010285318},"labels":[],"label_agreement":null},{"id":"W4379880667","doi":"10.1080/25726668.2023.2218170","title":"Review of recent developments in short-term mine planning and IPCC with a research agenda","year":2023,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University; University of Alberta","funders":"","keywords":"Term (time); Truck; Greenhouse gas; Shovel; Engineering; Crusher; Operations research; Environmental planning; Civil engineering; Environmental science; Mechanical engineering; Geology","score_opus":0.12642842494998402,"score_gpt":0.3415130071878528,"score_spread":0.2150845822378688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379880667","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005172405,0.98657763,0.0036138718,0.002131984,0.00089431985,0.000021601578,0.00010197331,0.00003019711,0.006111252],"genre_scores_gemma":[0.005694516,0.9892464,0.0029461675,0.0004744643,0.00077713607,0.000026185957,0.00015668181,0.000011387289,0.00066706206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986883,0.00035167532,0.00018258358,0.00020612878,0.00049190724,0.00007936604],"domain_scores_gemma":[0.9929675,0.004585063,0.0006439621,0.00016443098,0.0014781257,0.00016095192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032910644,0.0011626385,0.0012438128,0.004680438,0.00056354963,0.00294573,0.0019955633,0.0017473141,0.0062957876],"category_scores_gemma":[0.007280739,0.0006857929,0.0009425881,0.013573126,0.0010091686,0.0037795855,0.0010754183,0.0018947616,0.0012905354],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007324609,0.00008995205,0.0007315997,0.03280653,0.00015155664,0.00028717454,0.00023704678,0.014771669,0.00039936946,0.0544462,0.05044405,0.8455616],"study_design_scores_gemma":[0.0000086416685,0.00010042447,0.0018190863,0.016421886,0.00019089064,0.00040875672,0.0003777963,0.0027578701,0.0003297826,0.01760445,0.9599173,0.000062924446],"about_ca_topic_score_codex":0.008014609,"about_ca_topic_score_gemma":0.0071580797,"teacher_disagreement_score":0.008014609,"about_ca_system_score_codex":0.003071317,"about_ca_system_score_gemma":0.0070864093,"threshold_uncertainty_score":0.02228409},"labels":[],"label_agreement":null},{"id":"W4380609503","doi":"10.1080/25726668.2023.2219128","title":"Development of site specific blasting index parameters based on single hole blast test cratering","year":2023,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rock blasting; Impact crater; Range (aeronautics); Geology; Inverse; Dimension (graph theory); Breakout; Mathematics; Geometry; Geotechnical engineering; Engineering; Physics; Combinatorics","score_opus":0.03553066676058833,"score_gpt":0.21947236029587944,"score_spread":0.1839416935352911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380609503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7153765,0.0001240701,0.27736,0.000030534775,0.000019360961,0.00034569413,0.0007941307,0.0015464815,0.0044032666],"genre_scores_gemma":[0.9493537,0.00007589202,0.049316052,0.000009143537,0.0000033473634,0.00013750489,0.00049490266,0.000044703032,0.0005646751],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992686,0.00008232278,0.00006252212,0.00012252777,0.0004309906,0.00003302464],"domain_scores_gemma":[0.99775225,0.00042967184,0.0004803435,0.00037033565,0.00090288615,0.0000645762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087808847,0.0006855779,0.00050635304,0.0013596048,0.00018467219,0.0005899517,0.00090873253,0.00046766608,0.0011633366],"category_scores_gemma":[0.002582004,0.00028975695,0.00022722917,0.0008499868,0.00032244602,0.000700398,0.000448077,0.00032124936,0.0004325997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037842515,0.00044808106,0.08158287,0.00048111659,0.0000562914,0.00023618428,0.0002934017,0.123851575,0.63875645,0.0012363328,0.00095006876,0.15172915],"study_design_scores_gemma":[0.00005207565,0.0010857354,0.108726904,0.000040285482,0.000055066776,0.00032084316,0.00027162588,0.46654776,0.4201308,0.0005282996,0.0021398745,0.00010076161],"about_ca_topic_score_codex":0.0015795015,"about_ca_topic_score_gemma":0.0036322728,"teacher_disagreement_score":0.0015795015,"about_ca_system_score_codex":0.00046344503,"about_ca_system_score_gemma":0.00063324405,"threshold_uncertainty_score":0.0046438575},"labels":[],"label_agreement":null},{"id":"W4384297472","doi":"10.1080/25726668.2023.2233230","title":"Sustainable open pit fleet management system: Integrating economic and environmental objectives into truck allocation","year":2023,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Truck; Sustainable management; Management system; Transport engineering; Sustainable development; Business; Engineering; Environmental resource management; Environmental planning; Civil engineering; Environmental science; Sustainability; Operations management; Automotive engineering; Ecology","score_opus":0.01077344954970555,"score_gpt":0.22240483624295523,"score_spread":0.21163138669324968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384297472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25905895,0.00029093632,0.72272485,0.0005874117,0.000039361443,0.00023658076,0.0002671734,0.000287885,0.016506836],"genre_scores_gemma":[0.95867723,0.00014618845,0.03627395,0.000024635825,0.000009490856,0.00011855445,0.00009489386,0.000019187733,0.004635897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999676,0.00009932233,0.000013222168,0.000050896357,0.000093954295,0.00006665656],"domain_scores_gemma":[0.99978083,0.00007587152,0.00004740733,0.0000149224015,0.000050025108,0.00003087756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007195035,0.0005202807,0.00048744338,0.00046459842,0.00044700896,0.0011469884,0.0009713535,0.0009849196,0.001568828],"category_scores_gemma":[0.0007395155,0.00032952256,0.0003794873,0.0008014082,0.00038074868,0.0014856828,0.00094184576,0.0004201885,0.0001590735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017063583,0.000026924114,0.0005653192,0.000022748396,0.000012313374,0.000057019766,0.000013597297,0.98383325,0.0011307738,0.0032468878,0.00015383275,0.010920294],"study_design_scores_gemma":[0.000004612046,0.00003819656,0.00031020326,0.0000044811945,0.000006473013,0.0000146132825,0.000023960878,0.9966635,0.00028799335,0.0020844012,0.00055616855,0.0000053246163],"about_ca_topic_score_codex":0.007821134,"about_ca_topic_score_gemma":0.012517772,"teacher_disagreement_score":0.007821134,"about_ca_system_score_codex":0.0010288067,"about_ca_system_score_gemma":0.0022517035,"threshold_uncertainty_score":0.015551209},"labels":[],"label_agreement":null},{"id":"W4390942475","doi":"10.1080/25726668.2022.2072559","title":"A stochastic mine planning approach to determine the optimal open pit to underground mining transition depth – case study at the Geita gold mine, Tanzania","year":2022,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; IAMGOLD; De Beers Group; AngloGold Ashanti; Vale Canada Limited","keywords":"Open-pit mining; Mining engineering; Gold mining; Mill; Engineering; Tanzania; Mining industry; Civil engineering; Environmental science; Environmental planning","score_opus":0.04996251173996534,"score_gpt":0.2691622631017754,"score_spread":0.21919975136181002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390942475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.657158,0.00030218944,0.3296639,0.00046106408,0.000020891994,0.00044072708,0.00057750836,0.0001356723,0.011239956],"genre_scores_gemma":[0.9610265,0.00008113378,0.037955273,0.00001535895,0.000004654984,0.00010892679,0.00010699747,0.000008396713,0.00069275603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994868,0.00025077845,0.000021631127,0.000055372042,0.00009473071,0.0000905795],"domain_scores_gemma":[0.9986467,0.0009758297,0.00014544306,0.00003150356,0.00014523417,0.000055336262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013293029,0.0005814681,0.000626492,0.0009891457,0.00049728766,0.0008969051,0.0007831988,0.0008369556,0.0016277405],"category_scores_gemma":[0.0023472446,0.0006335763,0.00066824874,0.00087271794,0.0005390006,0.00041237238,0.0005225146,0.00047326455,0.00006157281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015887952,0.000012887209,0.00080403086,0.000017438888,0.000008919194,0.000062497646,0.000014046095,0.995678,0.0002130174,0.0012133286,0.000057312573,0.0019027153],"study_design_scores_gemma":[0.00000697109,0.000042686246,0.00057615584,0.000004623279,0.0000061774335,0.000017571409,0.000033835167,0.9983633,0.00013065779,0.0006907833,0.00012273226,0.0000044163035],"about_ca_topic_score_codex":0.02618673,"about_ca_topic_score_gemma":0.0282013,"teacher_disagreement_score":0.02618673,"about_ca_system_score_codex":0.0019591984,"about_ca_system_score_gemma":0.002862281,"threshold_uncertainty_score":0.05206865},"labels":[],"label_agreement":null},{"id":"W4390942484","doi":"10.1080/25726668.2021.2001255","title":"Integration of simulation and dispatch modelling to predict fleet productivity: an open-pit mining case","year":2022,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of Alberta","funders":"","keywords":"Truck; Shovel; Productivity; Open-pit mining; Schedule; Engineering; Operations research; Computer science; Automotive engineering; Mining engineering","score_opus":0.049857666427461464,"score_gpt":0.2740160523679548,"score_spread":0.2241583859404933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390942484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94324577,0.00012700555,0.048536174,0.00036836608,0.000027875452,0.000102757025,0.00027513082,0.0001903663,0.0071266084],"genre_scores_gemma":[0.993936,0.000038441452,0.0049301563,0.000009273309,0.0000042303645,0.000029040772,0.00008005502,0.000009000375,0.0009638174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996718,0.00011830526,0.000016813534,0.000050283856,0.00006741538,0.00007534591],"domain_scores_gemma":[0.99868315,0.0008622307,0.000122736,0.00007890233,0.00014602502,0.00010689656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077529426,0.0007195001,0.0006992902,0.00055653264,0.0005838174,0.000924897,0.0011132641,0.0017371178,0.0017808175],"category_scores_gemma":[0.0018601231,0.000553517,0.00065165287,0.0007033253,0.0006613853,0.0008348496,0.0007013197,0.0009914723,0.00014679841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003731567,0.000056172437,0.0015042361,0.000005934947,0.000006706551,0.00012790781,0.000013444536,0.9963069,0.0002046608,0.00062823365,0.000049238937,0.0010593414],"study_design_scores_gemma":[0.0000054343595,0.000016278958,0.00021660942,7.9413036e-7,0.0000022540962,0.000006116614,0.000008954225,0.9994399,0.00011933572,0.00014625891,0.000035422418,0.0000025603933],"about_ca_topic_score_codex":0.043973837,"about_ca_topic_score_gemma":0.022265453,"teacher_disagreement_score":0.043973837,"about_ca_system_score_codex":0.0013693835,"about_ca_system_score_gemma":0.0016707821,"threshold_uncertainty_score":0.08743578},"labels":[],"label_agreement":null},{"id":"W4390942498","doi":"10.1080/25726668.2022.2046684","title":"Monitoring rock movement and—controlling ore loss and dilution associated with blasting at Geita and North Mara Gold mines, Tanzania","year":2022,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mining engineering; Geology; Gold ore; Borehole; Rock blasting; Movement (music); Block (permutation group theory); Shot (pellet); Geotechnical engineering; Metallurgy; Geochemistry","score_opus":0.014494772749310388,"score_gpt":0.1996078732198197,"score_spread":0.18511310047050933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390942498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99952507,0.000011426788,0.00018335452,0.0000033118677,3.2173904e-7,0.000009352895,0.000035319747,0.000003978268,0.00022780507],"genre_scores_gemma":[0.999099,0.00001421078,0.00055682386,0.0000019097754,3.6364918e-7,0.0000096837775,0.000040662548,0.0000011758021,0.0002761261],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998267,0.000025937026,0.000009221886,0.00003948845,0.00006454091,0.000034078887],"domain_scores_gemma":[0.99975306,0.000045513636,0.000101698824,0.000019177303,0.00005588458,0.000024670639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018456762,0.00020768438,0.00016245624,0.0005527212,0.00034725227,0.0002205155,0.0002346608,0.00019746761,0.0004813578],"category_scores_gemma":[0.0004817324,0.00015844015,0.000099487435,0.00041746278,0.00030611962,0.0001529886,0.00033721776,0.00013827531,0.00011421177],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005030166,0.00008568474,0.87362075,0.00008656605,0.000023313203,0.00083451223,0.0016363142,0.0018125824,0.099763684,0.00008709934,0.00012398949,0.021422435],"study_design_scores_gemma":[0.0000041623057,0.00016487025,0.99529356,0.0000048611178,0.0000053847807,0.000086030625,0.00041119664,0.00088355935,0.0029785708,0.000012449743,0.00015253999,0.000002715261],"about_ca_topic_score_codex":0.017285598,"about_ca_topic_score_gemma":0.05310063,"teacher_disagreement_score":0.017285598,"about_ca_system_score_codex":0.00035968915,"about_ca_system_score_gemma":0.00039175918,"threshold_uncertainty_score":0.034369946},"labels":[],"label_agreement":null},{"id":"W4390942507","doi":"10.1080/25726668.2022.2064261","title":"Benchmarking face support practice in seismically active mines","year":2022,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Geotechnical and Geomechanical Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmarking; Face (sociological concept); Legislature; Mine safety; Hazard; Mining engineering; Engineering; Business; Coal mining; Political science; Law","score_opus":0.009712872389185237,"score_gpt":0.217673554222321,"score_spread":0.20796068183313576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390942507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927927,0.000068117384,0.0018254826,0.00023281206,0.0000064072574,0.000109589026,0.00007644406,0.00003901381,0.0048495694],"genre_scores_gemma":[0.99727637,0.0000400919,0.0017529636,0.00003081731,0.0000026335158,0.000035602377,0.00006801462,0.0000065306454,0.0007870426],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9855657,0.0061703357,0.00073686696,0.0009124606,0.0050868653,0.0015277357],"domain_scores_gemma":[0.96657825,0.0080805225,0.004621405,0.0029836698,0.014914494,0.0028215619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0089631425,0.00024214417,0.000419372,0.0021931743,0.0018571289,0.0019161333,0.0016538185,0.0011843279,0.0022152944],"category_scores_gemma":[0.04261546,0.00020446577,0.0002148316,0.001667253,0.0014983881,0.0016211848,0.003218113,0.0006608202,0.00048904977],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012384702,0.0019133139,0.39454666,0.0004125054,0.00008655468,0.0011675693,0.040201735,0.019115493,0.006384785,0.004679854,0.0029098834,0.5273432],"study_design_scores_gemma":[0.00013789911,0.008597015,0.8298062,0.00043160212,0.00004341937,0.0012506413,0.09733379,0.029940115,0.007601364,0.0034663668,0.021211661,0.00017989878],"about_ca_topic_score_codex":0.014977516,"about_ca_topic_score_gemma":0.024861041,"teacher_disagreement_score":0.014977516,"about_ca_system_score_codex":0.004295944,"about_ca_system_score_gemma":0.0034078425,"threshold_uncertainty_score":0.047402143},"labels":[],"label_agreement":null},{"id":"W4391392990","doi":"10.1177/25726668231222998","title":"Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems","year":2024,"lang":"en","type":"review","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Belt Conveyor Systems Engineering","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reinforcement learning; Popularity; Truck; Open research; Computer science; Field (mathematics); Autonomy; Class (philosophy); Feature (linguistics); Operations research; Artificial intelligence; Risk analysis (engineering); Engineering; Business; Law","score_opus":0.04485996850144964,"score_gpt":0.31025638271959677,"score_spread":0.26539641421814714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391392990","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024170117,0.9975198,0.00089687004,0.00030882523,0.00014777813,0.000008620155,0.0000068743566,0.0000056642393,0.00086382683],"genre_scores_gemma":[0.004453336,0.99369556,0.0010474406,0.00020788235,0.00024060285,0.000012786971,0.000021835272,0.000003940676,0.0003166389],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933064,0.00017099356,0.00012207092,0.00012535595,0.00020551561,0.00004543414],"domain_scores_gemma":[0.99758863,0.0016717109,0.00018059928,0.0000495428,0.00045291855,0.00005655338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015838335,0.0008522979,0.0012356221,0.0019636312,0.0002847983,0.0014923129,0.0010791405,0.0014910238,0.0023797215],"category_scores_gemma":[0.003164995,0.0005110007,0.0008866193,0.0026420408,0.0006565777,0.0023903227,0.0007102337,0.0015811981,0.0007471047],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007797326,0.000112127396,0.00046569973,0.03691945,0.00016113672,0.00016155186,0.00021136447,0.0042081294,0.00078687817,0.01803943,0.010293904,0.92856246],"study_design_scores_gemma":[0.000028088336,0.00047224565,0.0020133185,0.021226589,0.00038429,0.00081749907,0.00041242258,0.0032435914,0.0012096479,0.009994587,0.96011794,0.00007981136],"about_ca_topic_score_codex":0.0022088983,"about_ca_topic_score_gemma":0.0019509678,"teacher_disagreement_score":0.0023797215,"about_ca_system_score_codex":0.0008409519,"about_ca_system_score_gemma":0.0019164371,"threshold_uncertainty_score":0.008376181},"labels":[],"label_agreement":null},{"id":"W4394784375","doi":"10.1177/25726668241241993","title":"High-order simulation of geological domains and effects on stochastic long-term planning of mining complexes","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"IAMGOLD; Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; De Beers Group; BHP; AngloGold Ashanti; Vale Canada Limited","keywords":"Categorical variable; Stochastic simulation; Data mining; Computer science; Set (abstract data type); Stochastic modelling; Footprint; Sample (material); Term (time); Statistics; Geology; Machine learning; Mathematics","score_opus":0.021561526636169698,"score_gpt":0.2600846885180101,"score_spread":0.2385231618818404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394784375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7858155,0.0002358169,0.20257889,0.0006194881,0.000059673563,0.00007166785,0.00051079463,0.00027541077,0.009832842],"genre_scores_gemma":[0.9888732,0.00007213096,0.009645629,0.000028808792,0.0000062885997,0.000036486937,0.00014443297,0.000028115925,0.0011650667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995739,0.00018502047,0.00002376878,0.000056999634,0.000079439524,0.00008087849],"domain_scores_gemma":[0.9962729,0.002740989,0.00032240534,0.00019568173,0.0002472038,0.00022082587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011018252,0.0003966712,0.0007063759,0.0005842277,0.00052852853,0.001217172,0.00090457604,0.0013332601,0.0021126592],"category_scores_gemma":[0.0048045786,0.000490264,0.0009266501,0.0006688512,0.0012540652,0.00077847205,0.000873388,0.00091626396,0.00013572564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045649963,0.0000038322382,0.0003417274,0.0000020675939,0.0000018700971,0.000009906946,0.000005272309,0.9983931,0.000034578377,0.0009936256,0.000014840536,0.00019449586],"study_design_scores_gemma":[0.0000030977053,0.0000047853086,0.00016803236,0.000001340628,0.0000015827899,0.0000035126884,0.0000063936864,0.99897623,0.000042519445,0.0007364587,0.00005385958,0.0000022607255],"about_ca_topic_score_codex":0.04133644,"about_ca_topic_score_gemma":0.021166109,"teacher_disagreement_score":0.04133644,"about_ca_system_score_codex":0.0016037113,"about_ca_system_score_gemma":0.0011692202,"threshold_uncertainty_score":0.082191706},"labels":[],"label_agreement":null},{"id":"W4394786340","doi":"10.1177/25726668241242230","title":"Joint stochastic optimisation of stope layout, production scheduling and access network","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integer programming; Joint (building); Schedule; Stochastic programming; Scheduling (production processes); Mathematical optimization; Net present value; Linear programming; Integer (computer science); Production schedule; Computer science; Production (economics); Stochastic modelling; Engineering; Mathematics; Civil engineering; Statistics","score_opus":0.03553590507445064,"score_gpt":0.25160914547368884,"score_spread":0.2160732403992382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394786340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07611135,0.00017651322,0.91371137,0.00022860029,0.000046059402,0.00012286677,0.00033628885,0.0002632212,0.009003758],"genre_scores_gemma":[0.89986086,0.00017084174,0.09079799,0.00004467801,0.000022274346,0.00021311517,0.00033667433,0.000118763026,0.008434726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914885,0.00030717623,0.000027770715,0.00016280668,0.00018339024,0.00017002641],"domain_scores_gemma":[0.9988279,0.000743195,0.00016522912,0.0000584637,0.00012576707,0.00007948887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263356,0.00082787446,0.0010443242,0.00057229534,0.00028924193,0.0014677529,0.00093990535,0.0010067606,0.003390718],"category_scores_gemma":[0.0025895084,0.00074478163,0.0009887632,0.0008918155,0.0006894098,0.0012003776,0.00082168693,0.0011447066,0.0003610255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016570042,0.000008692912,0.0001472012,0.00001501826,0.000007185527,0.000012215768,0.0000055784676,0.9953772,0.00031984842,0.0014602594,0.000091393376,0.0025387632],"study_design_scores_gemma":[0.0000037507796,0.000024312436,0.00020976677,0.0000021313208,0.0000034954307,0.0000062768945,0.000006428284,0.9976821,0.00024637632,0.0015264511,0.00028535054,0.0000034164982],"about_ca_topic_score_codex":0.007263709,"about_ca_topic_score_gemma":0.0070919683,"teacher_disagreement_score":0.007263709,"about_ca_system_score_codex":0.0012594794,"about_ca_system_score_gemma":0.0019013373,"threshold_uncertainty_score":0.014442861},"labels":[],"label_agreement":null},{"id":"W4395009937","doi":"10.1177/25726668241244930","title":"A reinforcement learning approach for selecting infill drilling locations considering long-term production planning in mining complexes with supply uncertainty","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"IAMGOLD; AngloGold Ashanti; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Infill; Term (time); Production (economics); Drilling; Production planning; Reinforcement learning; Reinforcement; Computer science; Engineering; Geology; Mining engineering; Artificial intelligence; Civil engineering; Structural engineering; Economics; Mechanical engineering","score_opus":0.029625442922309876,"score_gpt":0.2518113555716709,"score_spread":0.22218591264936102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395009937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07121698,0.00021887243,0.925054,0.0003359025,0.000039707982,0.00008816174,0.00006375598,0.0002471791,0.0027353824],"genre_scores_gemma":[0.9435097,0.00009798959,0.054394063,0.000079143894,0.000027973783,0.0001361932,0.00006232994,0.00003484253,0.0016576496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947256,0.00021316118,0.000024182873,0.00012893688,0.00008617445,0.00007505231],"domain_scores_gemma":[0.9968477,0.0021973501,0.00036813517,0.000070864044,0.00033863235,0.00017725975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016596713,0.00088879565,0.0014098302,0.0006132357,0.00038007324,0.00095551065,0.001447344,0.0013755687,0.0016597213],"category_scores_gemma":[0.004611152,0.00070756173,0.00054948754,0.0005270442,0.0011097372,0.0008536035,0.0010535643,0.0011723746,0.00017700941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021068689,0.000011322808,0.00024905454,0.000010953843,0.000011233879,0.000023012755,0.000012983768,0.99557483,0.00016321996,0.0007616588,0.000064866734,0.00309581],"study_design_scores_gemma":[0.0000043787136,0.000011608785,0.000044645334,0.0000017176177,0.0000023019516,0.000002421287,0.0000025475058,0.9993881,0.000039269875,0.00045961057,0.000041349493,0.0000019273975],"about_ca_topic_score_codex":0.01080499,"about_ca_topic_score_gemma":0.009856629,"teacher_disagreement_score":0.01080499,"about_ca_system_score_codex":0.0012787324,"about_ca_system_score_gemma":0.0015567822,"threshold_uncertainty_score":0.021484196},"labels":[],"label_agreement":null},{"id":"W4400902349","doi":"10.1177/25726668241255442","title":"Wet inrush susceptibility assessment at the Deep Ore Zone mine using a random forest machine learning model","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Inrush current; Mining engineering; Geology; Artificial intelligence; Computer science; Engineering","score_opus":0.0246438569506687,"score_gpt":0.26712568492302674,"score_spread":0.24248182797235804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400902349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7855076,0.00047118086,0.2103496,0.00038930526,0.000054760956,0.00011031539,0.00071859354,0.0005129504,0.0018856775],"genre_scores_gemma":[0.9799523,0.00010375034,0.018500714,0.000034817163,0.000015352995,0.000055032957,0.0004371953,0.000010245173,0.000890637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997359,0.00006709124,0.000019687088,0.0000845268,0.000032684973,0.0000601449],"domain_scores_gemma":[0.9991241,0.0005499798,0.00009902035,0.000028045331,0.00016480655,0.00003404214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013562345,0.0006616363,0.0006633413,0.0007778099,0.00029885775,0.00065075635,0.0010109749,0.000915322,0.00090516446],"category_scores_gemma":[0.0018610619,0.0002277365,0.001014398,0.0004969306,0.00025994368,0.00047418734,0.00035543455,0.0007305016,0.00021021736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094470495,0.00010920304,0.0126410015,0.000031315943,0.000042989464,0.000112132904,0.000031123902,0.96406573,0.00075351464,0.00037042997,0.0004018712,0.021346264],"study_design_scores_gemma":[0.0000019652728,0.000015231887,0.0009622889,0.000002654818,0.000004953311,0.0000069507787,0.000006904842,0.9987494,0.000073534655,0.00013993258,0.000033097218,0.0000030910683],"about_ca_topic_score_codex":0.026961306,"about_ca_topic_score_gemma":0.016929487,"teacher_disagreement_score":0.026961306,"about_ca_system_score_codex":0.0006339557,"about_ca_system_score_gemma":0.0008166428,"threshold_uncertainty_score":0.053608716},"labels":[],"label_agreement":null},{"id":"W4401033185","doi":"10.1177/25726668241263408","title":"Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Copper mine; Open-pit mining; Copper; Copper mining; Mining engineering; Data mining; Computer science; Engineering; Metallurgy; Materials science","score_opus":0.03271246534937313,"score_gpt":0.2675548156573728,"score_spread":0.2348423503079997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401033185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80077374,0.00054828514,0.1843639,0.0005850539,0.00005917996,0.000104978295,0.0002516342,0.00018681843,0.013126419],"genre_scores_gemma":[0.9910131,0.000061706625,0.007706855,0.000014481297,0.000007212829,0.000024787305,0.000036349564,0.000014276024,0.0011211291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996598,0.000112170404,0.000014001892,0.00004816358,0.000095049196,0.0000708491],"domain_scores_gemma":[0.9986594,0.000994495,0.00012007212,0.00004271381,0.00011245984,0.00007080595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074583315,0.00075676345,0.0009194332,0.00047736196,0.00040709102,0.0009082642,0.00069959747,0.0011605677,0.001564435],"category_scores_gemma":[0.0020868618,0.00041513095,0.00068293366,0.0006850763,0.0005182874,0.00048161548,0.0009395199,0.00074696913,0.00008325553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019473839,0.0000141137925,0.00022654956,0.000012777992,0.000007189115,0.000049370337,0.000009410748,0.99755573,0.00028367672,0.000384926,0.000049065053,0.0013875678],"study_design_scores_gemma":[0.0000061439523,0.000036309644,0.00023996427,0.000001644228,0.000004622015,0.000008257576,0.0000140961,0.9990007,0.00021378044,0.0003681509,0.000102893755,0.0000033110396],"about_ca_topic_score_codex":0.013272378,"about_ca_topic_score_gemma":0.008727776,"teacher_disagreement_score":0.013272378,"about_ca_system_score_codex":0.0009561449,"about_ca_system_score_gemma":0.0010105328,"threshold_uncertainty_score":0.026390254},"labels":[],"label_agreement":null},{"id":"W4401578412","doi":"10.1177/25726668241270400","title":"Grade control drillhole spacing and mining selectivity determination using high resolution simulations applied on distinctly heterogeneous open pit mines","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Workflow; Infill; Mining engineering; Drilling; Profit (economics); Environmental science; Soil science; Geology; Computer science; Engineering; Database; Civil engineering","score_opus":0.026986523095581718,"score_gpt":0.25715938833181673,"score_spread":0.230172865236235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401578412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97613925,0.000063247535,0.017566659,0.00016708953,0.000016422748,0.000057514375,0.00047123275,0.000114208015,0.0054044807],"genre_scores_gemma":[0.9923234,0.00003126107,0.0069953958,0.000014386997,0.0000024720046,0.000023522898,0.00016777209,0.000013004784,0.00042874433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975723,0.00007379117,0.000017248134,0.000041378335,0.000048056918,0.00006232053],"domain_scores_gemma":[0.99853325,0.0009934215,0.0001350044,0.0000964236,0.0001445339,0.000097382006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062299456,0.0004867811,0.00065699714,0.0005607236,0.0004909608,0.0010583961,0.00075579045,0.0012011298,0.001767601],"category_scores_gemma":[0.0022270638,0.0004057579,0.0007356079,0.00071834226,0.0006503357,0.00062097545,0.0006576129,0.0007951989,0.00011354773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027230919,0.000025761341,0.0015858273,0.0000069511552,0.000006380856,0.00003906609,0.000010295465,0.9970951,0.00028968605,0.00027743742,0.000037425376,0.0005989203],"study_design_scores_gemma":[0.000010380717,0.000016552212,0.00051170855,0.000002047866,0.0000028514892,0.0000045655274,0.000023864324,0.9989569,0.00020702853,0.00019626316,0.00006461706,0.0000032191685],"about_ca_topic_score_codex":0.028686823,"about_ca_topic_score_gemma":0.025559627,"teacher_disagreement_score":0.028686823,"about_ca_system_score_codex":0.0010077779,"about_ca_system_score_gemma":0.0011349061,"threshold_uncertainty_score":0.057039678},"labels":[],"label_agreement":null},{"id":"W4402061962","doi":"10.1177/25726668241275434","title":"Implementing Gaussian process modelling in predictive maintenance of mining machineries","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive maintenance; Computer science; Process (computing); Gaussian process; Engineering; Gaussian; Reliability engineering; Physics; Operating system","score_opus":0.01728627419496074,"score_gpt":0.24461083749216772,"score_spread":0.227324563297207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402061962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06381893,0.00018252815,0.9334834,0.0001733814,0.000024198525,0.00004485724,0.000090543625,0.00038067592,0.0018014333],"genre_scores_gemma":[0.9584816,0.00020654882,0.039912287,0.000036670637,0.000021357791,0.00006952867,0.00012271166,0.000028724704,0.0011205943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948835,0.00017212355,0.00002808634,0.00009775183,0.0001472849,0.00006641772],"domain_scores_gemma":[0.99770576,0.0017406476,0.0001931373,0.00009135453,0.00023223602,0.000036819158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019481509,0.00066776364,0.0007535306,0.00050391816,0.0003365993,0.0010658106,0.0011517857,0.0011115305,0.00059101166],"category_scores_gemma":[0.004660141,0.0003635492,0.0007667942,0.0006019607,0.0006518911,0.0007268788,0.0008455149,0.0012882455,0.00014689026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002021359,0.000015197562,0.00087667286,0.0000148781955,0.000011315011,0.000031019234,0.000027229065,0.9900271,0.00031719406,0.0035613552,0.00006796728,0.005029839],"study_design_scores_gemma":[0.0000012795078,0.0000051618385,0.00007999659,0.0000011253367,0.0000016444975,0.0000025962408,0.000002289006,0.9988939,0.000112404006,0.0008524316,0.00004547717,0.0000017207416],"about_ca_topic_score_codex":0.020904418,"about_ca_topic_score_gemma":0.012229248,"teacher_disagreement_score":0.020904418,"about_ca_system_score_codex":0.0007749818,"about_ca_system_score_gemma":0.0011336721,"threshold_uncertainty_score":0.041565478},"labels":[],"label_agreement":null},{"id":"W4405323921","doi":"10.1177/25726668241301876","title":"An intelligent rule-based decision-making system for preliminary truck dispatching within open-pit mines","year":2024,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Truck; Adaptability; Shovel; Computer science; Process (computing); Engineering; Artificial neural network; Reduction (mathematics); Range (aeronautics); Real-time computing; Operations research; Artificial intelligence; Automotive engineering","score_opus":0.0229505359706728,"score_gpt":0.27966890318311505,"score_spread":0.25671836721244223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405323921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08499223,0.00015081682,0.9052598,0.00032380002,0.0001455099,0.00029122207,0.00020950589,0.0046409685,0.003986222],"genre_scores_gemma":[0.8512765,0.00008319182,0.14572138,0.00014117196,0.000032843356,0.00022578024,0.00023186035,0.00004205504,0.002245208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957746,0.00006267286,0.000049408063,0.00016140568,0.00009653631,0.000052530748],"domain_scores_gemma":[0.99903035,0.0003449128,0.00011858743,0.00007862234,0.00034032215,0.000087084125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010774641,0.00060384895,0.00076006993,0.0004502907,0.00059839664,0.0010633077,0.0015508847,0.00097948,0.0023874873],"category_scores_gemma":[0.0024017058,0.0003722984,0.0004357259,0.00034623116,0.00036637206,0.0008416445,0.0006266626,0.0008701121,0.00071274105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004901067,0.00058217073,0.0036253966,0.00012647784,0.000077222976,0.00052528776,0.00023705054,0.73855543,0.018688355,0.0031021729,0.003205965,0.23078437],"study_design_scores_gemma":[0.00002089142,0.000063189225,0.00025495223,0.000005872953,0.000012342089,0.000018928731,0.000010385788,0.9964156,0.0020021012,0.0006895507,0.0004969945,0.000009073655],"about_ca_topic_score_codex":0.0063206083,"about_ca_topic_score_gemma":0.0053870403,"teacher_disagreement_score":0.0063206083,"about_ca_system_score_codex":0.0007070842,"about_ca_system_score_gemma":0.0015184308,"threshold_uncertainty_score":0.012567639},"labels":[],"label_agreement":null},{"id":"W4408557526","doi":"10.1177/25726668251323986","title":"Data driven block discretisation method for predicting the bulk ore sorting benefits applied at distinctly heterogeneous open pit mines","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Block (permutation group theory); Sorting; Open-pit mining; Discretization; Block model; Mining engineering; Geology; Computer science; Algorithm; Mathematics; Geometry","score_opus":0.04226951182097654,"score_gpt":0.2970203860024876,"score_spread":0.25475087418151104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408557526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78652805,0.00019900096,0.20966284,0.000074287294,0.00002634428,0.000101383586,0.0013318585,0.0010254682,0.0010508272],"genre_scores_gemma":[0.9314322,0.00006418614,0.066157795,0.000014114598,0.0000055622772,0.00007016537,0.001363875,0.000033319157,0.00085870246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998406,0.000020827963,0.000014277614,0.000048873302,0.000055647273,0.000019633275],"domain_scores_gemma":[0.999126,0.00043845474,0.00012773214,0.000078039855,0.0001879879,0.00004181684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038033986,0.000366846,0.00041380516,0.0007243835,0.0001907717,0.00045740072,0.0005214971,0.00044784512,0.00081962196],"category_scores_gemma":[0.0015615032,0.000204418,0.00039430225,0.00071032706,0.0002592445,0.00040648784,0.00032452744,0.00050079107,0.0002703933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007003732,0.00020887348,0.057429824,0.00009944002,0.00006772398,0.00023929564,0.00012691456,0.8024213,0.04023676,0.0004919768,0.00065970776,0.097317934],"study_design_scores_gemma":[0.000004896616,0.000038216418,0.0068223453,0.0000022181662,0.0000037092284,0.000019430483,0.000016940345,0.98945564,0.0032821964,0.00015662858,0.00019079821,0.000006865673],"about_ca_topic_score_codex":0.01339261,"about_ca_topic_score_gemma":0.015023734,"teacher_disagreement_score":0.01339261,"about_ca_system_score_codex":0.00050049927,"about_ca_system_score_gemma":0.0005030797,"threshold_uncertainty_score":0.02662927},"labels":[],"label_agreement":null},{"id":"W4409954636","doi":"10.1177/25726668251337293","title":"Reassessing Janssen's equation for cave stress estimation in block cave mining","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Cave; Block (permutation group theory); Stress (linguistics); Geology; Archaeology; Mining engineering; Computer science; Geography; Mathematics; Philosophy; Geometry","score_opus":0.023732584591754185,"score_gpt":0.2625727897647709,"score_spread":0.2388402051730167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409954636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13698874,0.002171677,0.8316349,0.0017695536,0.00045444162,0.00021734796,0.0007615348,0.00049560465,0.025506208],"genre_scores_gemma":[0.7470126,0.0013996382,0.241516,0.0002807677,0.00012326,0.00016363857,0.0003962572,0.00022854394,0.008879312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983986,0.00034200837,0.00021690772,0.00027385913,0.00069664256,0.00007207273],"domain_scores_gemma":[0.9967976,0.0016927643,0.0002966152,0.0002476817,0.0009231631,0.000042244348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024899964,0.00055588246,0.000610042,0.0014631957,0.00051080395,0.0013866442,0.0018481226,0.0011171742,0.0017578697],"category_scores_gemma":[0.009851173,0.0005639639,0.00068897335,0.0012189838,0.0008972641,0.0023424593,0.0010800024,0.00119842,0.00072436035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000815658,0.00011111763,0.07002483,0.00039185802,0.00008612658,0.00068210374,0.001047924,0.5851687,0.01177619,0.09026188,0.007889451,0.23247828],"study_design_scores_gemma":[0.000010576922,0.000063854626,0.014524171,0.0001610233,0.000037358168,0.00025751555,0.0002626432,0.94901454,0.0031814114,0.015843045,0.016560176,0.00008367151],"about_ca_topic_score_codex":0.025946798,"about_ca_topic_score_gemma":0.03445246,"teacher_disagreement_score":0.025946798,"about_ca_system_score_codex":0.0013460495,"about_ca_system_score_gemma":0.0015867567,"threshold_uncertainty_score":0.051591516},"labels":[],"label_agreement":null},{"id":"W4411657571","doi":"10.1177/25726668251348712","title":"Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integer programming; Selection (genetic algorithm); Period (music); Computer science; Linear programming; Integer (computer science); Operations research; Engineering; Artificial intelligence; Algorithm","score_opus":0.026146843166607467,"score_gpt":0.27770476089929025,"score_spread":0.25155791773268277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411657571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08915802,0.002092927,0.8699432,0.001894719,0.00037491292,0.00050267455,0.0017074073,0.00055193756,0.033774283],"genre_scores_gemma":[0.87584573,0.0013046359,0.09443355,0.0003650112,0.00008720799,0.0013084168,0.0009692139,0.00012250703,0.025563782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985247,0.00071247807,0.000046857334,0.00024851554,0.00017287445,0.0002945445],"domain_scores_gemma":[0.99720156,0.002114558,0.0002539904,0.000041475883,0.000248298,0.0001400901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002569114,0.002006796,0.0020534045,0.00097388093,0.0007692251,0.0030227553,0.0025168327,0.0031555237,0.007316712],"category_scores_gemma":[0.0038250738,0.001471079,0.0016580992,0.0015597608,0.0011772122,0.001363637,0.0016043357,0.0028354551,0.00076287455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003646671,0.00002807007,0.00021063277,0.00004180219,0.000019487621,0.000062529754,0.000019323303,0.9950624,0.000089562476,0.0027874853,0.00028263382,0.0013595695],"study_design_scores_gemma":[0.0000138081905,0.000024303887,0.00008701124,0.000008188115,0.000009616916,0.000006353721,0.00001732685,0.99837047,0.000035087025,0.0011361407,0.00028587467,0.000005827984],"about_ca_topic_score_codex":0.021102944,"about_ca_topic_score_gemma":0.018970044,"teacher_disagreement_score":0.021102944,"about_ca_system_score_codex":0.0027503176,"about_ca_system_score_gemma":0.0030102145,"threshold_uncertainty_score":0.04196018},"labels":[],"label_agreement":null},{"id":"W4413910654","doi":"10.1177/25726668251371946","title":"Automating and optimising pushback selection using reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Selection (genetic algorithm); Reinforcement; Reinforcement learning; Computer science; Psychology; Artificial intelligence; Social psychology","score_opus":0.017421065260775397,"score_gpt":0.24310818287578564,"score_spread":0.22568711761501026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413910654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15405706,0.0006157544,0.83547294,0.00053055544,0.00009097583,0.0002757243,0.00021210549,0.0039419406,0.0048028966],"genre_scores_gemma":[0.82948923,0.00011870646,0.16748768,0.00023071222,0.000029846884,0.00020270985,0.00032205446,0.00020360322,0.0019154472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904364,0.00030982483,0.000053745298,0.00023854848,0.00019413506,0.00016013662],"domain_scores_gemma":[0.99674594,0.002313064,0.00023939938,0.0002229212,0.00031850755,0.00016007517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020337,0.0014212081,0.0013115873,0.0006552244,0.00048794085,0.0010105695,0.0021950305,0.0011318879,0.002233495],"category_scores_gemma":[0.0052452195,0.00061555137,0.00077975605,0.0004672926,0.0011364932,0.0012434591,0.0013921461,0.0016649029,0.000543081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018061588,0.00022732632,0.003980227,0.00011718104,0.00006119023,0.00012862078,0.000087562235,0.89580107,0.0036333385,0.0020535763,0.001712271,0.092016935],"study_design_scores_gemma":[0.000019000006,0.000035969304,0.00013994618,0.0000049358346,0.00000674136,0.00001035653,0.000013209596,0.9979048,0.00055144646,0.0010755562,0.00023404186,0.0000040345735],"about_ca_topic_score_codex":0.007960707,"about_ca_topic_score_gemma":0.010300748,"teacher_disagreement_score":0.007960707,"about_ca_system_score_codex":0.0009990081,"about_ca_system_score_gemma":0.002203478,"threshold_uncertainty_score":0.015828729},"labels":[],"label_agreement":null},{"id":"W4415929271","doi":"10.1177/25726668251391540","title":"Predicting energy consumption SAG mills through Bayesian generalized linear model and random forest","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy consumption; Random forest; Energy (signal processing); Bayesian probability; Consumption (sociology); Copper mine; Random variable","score_opus":0.01957503386188411,"score_gpt":0.24948146798432433,"score_spread":0.22990643412244022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415929271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53572536,0.0011426903,0.45530462,0.0009363172,0.00010533278,0.00012813687,0.0027106276,0.001827472,0.0021194508],"genre_scores_gemma":[0.93743163,0.0002524231,0.057880837,0.000119652235,0.000043059445,0.000095154384,0.0029062035,0.000047893747,0.0012230988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.00037535763,0.00003623067,0.00017385997,0.00008997511,0.00009999776],"domain_scores_gemma":[0.99876106,0.0008761834,0.00011741621,0.000058822512,0.00015335064,0.000033185468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022668692,0.00095005264,0.0008562804,0.001182892,0.00029507047,0.00070253416,0.0010622932,0.00086485746,0.00091915467],"category_scores_gemma":[0.0032139109,0.000399901,0.0014205687,0.0011991406,0.0003028602,0.0007543697,0.0004622247,0.0010092902,0.00041737506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013629935,0.00010662558,0.0107013155,0.00004901337,0.00008981558,0.00007741353,0.000030722957,0.9494554,0.0004658707,0.0009531108,0.0013202028,0.036614206],"study_design_scores_gemma":[0.0000047207786,0.00001869417,0.0011435116,0.0000049097557,0.0000067756505,0.0000066296275,0.000008787664,0.99774027,0.00006864617,0.0008695351,0.000122184,0.000005308094],"about_ca_topic_score_codex":0.023247773,"about_ca_topic_score_gemma":0.026826588,"teacher_disagreement_score":0.023247773,"about_ca_system_score_codex":0.0005120846,"about_ca_system_score_gemma":0.000753691,"threshold_uncertainty_score":0.04622495},"labels":[],"label_agreement":null},{"id":"W7081936411","doi":"10.1177/25726668251376927","title":"Bridging gaps in intelligent truck dispatching: An underexplored PPO-based model with expanded feature integration within open pit mines","year":2025,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Truck; Bridging (networking); Key (lock); Scalability; Feature (linguistics); Convergence (economics); Schedule; Reinforcement learning","score_opus":0.033010958028991386,"score_gpt":0.27536083760404845,"score_spread":0.24234987957505705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7081936411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21044812,0.00036616306,0.773234,0.001066345,0.000122633,0.00015320313,0.00024743835,0.0003856591,0.01397639],"genre_scores_gemma":[0.96492976,0.00016499255,0.031069852,0.00007855018,0.000022280941,0.000080583675,0.000098131095,0.00004058468,0.003515138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997389,0.00008013163,0.000010116231,0.000062904015,0.00005120761,0.00005664837],"domain_scores_gemma":[0.9995969,0.00021922123,0.00005144845,0.000023502198,0.000060946866,0.000048001762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068472547,0.00045268724,0.0008034873,0.0002500594,0.00031117038,0.0010943881,0.001112672,0.0010187421,0.0018487622],"category_scores_gemma":[0.0014764216,0.00040964436,0.00060946494,0.0002943071,0.00069762074,0.0009227329,0.0009750387,0.0010686095,0.00021791685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013983504,0.0000100842335,0.00017280775,0.000010490183,0.0000039610595,0.000017669776,0.000010820247,0.99652725,0.00015765762,0.0012683256,0.000066864624,0.001740057],"study_design_scores_gemma":[0.0000030790952,0.000011208151,0.000042825126,0.0000013594632,0.0000017171757,0.0000026867565,0.000005405934,0.99913764,0.000034511355,0.00062439597,0.00013382119,0.0000012439815],"about_ca_topic_score_codex":0.013321199,"about_ca_topic_score_gemma":0.006939739,"teacher_disagreement_score":0.013321199,"about_ca_system_score_codex":0.00073395914,"about_ca_system_score_gemma":0.0015996294,"threshold_uncertainty_score":0.02648729},"labels":[],"label_agreement":null}]}