{"meta":{"query_hash":"afa729d4a09c","filters":{"venue":"Journal of Machine Engineering"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/afa729d4a09c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Machine+Engineering"},"results":[{"id":"W2789646893","doi":"10.5604/01.3001.0010.8811","title":"INTELLIGENT MACHINING: REAL-TIME TOOL CONDITION MONITORING AND INTELLIGENT ADAPTIVE CONTROL SYSTEMS","year":2018,"lang":"en","type":"article","venue":"Journal of Machine Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Machining; Machine tool; Process (computing); Reliability (semiconductor); Engineering; Controller (irrigation); Computer science; Condition monitoring; Manufacturing engineering; Control engineering; Reliability engineering; Mechanical engineering","score_opus":0.007663274685495045,"score_gpt":0.23367233802729975,"score_spread":0.2260090633418047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789646893","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":[0.010612372,0.026649686,0.9417419,0.0013174302,0.0006727921,0.00009233259,0.00011785992,0.003218076,0.0155775985],"genre_scores_gemma":[0.7265573,0.02689384,0.22048415,0.0011699414,0.0020962476,0.00022546394,0.0006925446,0.00022242637,0.021658028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942195,0.00008236761,0.000029641116,0.00014974637,0.00027986712,0.000036448837],"domain_scores_gemma":[0.9996407,0.00010781533,0.00007583412,0.00005124156,0.00010372296,0.000020793792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004117488,0.00064514886,0.0005292552,0.0005327607,0.00016004831,0.0011560984,0.0009472074,0.0009825279,0.0017843473],"category_scores_gemma":[0.0008488509,0.00021121105,0.00023811315,0.00078918133,0.0005149203,0.0014617085,0.00056756934,0.0008554148,0.00075598934],"study_design_candidate":"bench_or_experimental","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":[0.00020655053,0.00011302707,0.0012728455,0.00069422735,0.000073428615,0.00022083074,0.00015205365,0.049117636,0.038611487,0.02669184,0.010320412,0.8725257],"study_design_scores_gemma":[0.000068559966,0.0006075609,0.00589071,0.0002444955,0.00010499398,0.0009093914,0.000118101605,0.7436993,0.026580548,0.05849509,0.16313684,0.00014431744],"about_ca_topic_score_codex":0.0008884845,"about_ca_topic_score_gemma":0.0005172594,"teacher_disagreement_score":0.0017843473,"about_ca_system_score_codex":0.0003189499,"about_ca_system_score_gemma":0.00028556504,"threshold_uncertainty_score":0.005969286},"labels":[],"label_agreement":null},{"id":"W3141262229","doi":"10.36897/jme/134244","title":"Tooling systems with integrated sensors enabling data based process optimization","year":2021,"lang":"en","type":"article","venue":"Journal of Machine Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ATS Automation Tooling Systems (Canada)","funders":"Österreichische Forschungsförderungsgesellschaft; Technische Universität Wien; Machine Tool Technologies Research Foundation","keywords":"Process (computing); Manufacturing engineering; Systems engineering; Computer science; Engineering; Engineering drawing; Operating system","score_opus":0.010265284076265604,"score_gpt":0.22598811615145423,"score_spread":0.2157228320751886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141262229","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":[0.029806215,0.0030148108,0.9539844,0.0002558183,0.0001809864,0.0002509773,0.0005542157,0.004909983,0.007042629],"genre_scores_gemma":[0.6821672,0.001686524,0.30984214,0.0002780123,0.00011842608,0.000319562,0.00083698705,0.00019314457,0.0045580906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989611,0.0000977627,0.00008079662,0.00021885688,0.00058323884,0.000058269645],"domain_scores_gemma":[0.99928075,0.00022980546,0.000112178044,0.00018247041,0.0001690591,0.000025784788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006351095,0.0008727542,0.0008360971,0.0007614572,0.00024303606,0.0014743024,0.0018297274,0.0011469207,0.003162712],"category_scores_gemma":[0.0017191719,0.0005458681,0.00031543238,0.0009462234,0.00038242532,0.0014131643,0.0012101388,0.0009680949,0.0010249731],"study_design_candidate":"bench_or_experimental","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":[0.0007911491,0.00035727108,0.002544332,0.0014408933,0.00021977421,0.00040563385,0.0002938518,0.15565906,0.3132238,0.018953806,0.005408266,0.50070214],"study_design_scores_gemma":[0.00011505539,0.00077899283,0.0023686439,0.00013445399,0.00009852083,0.0004024999,0.00004258678,0.77655965,0.1657341,0.011784869,0.041872866,0.000107697866],"about_ca_topic_score_codex":0.00047226096,"about_ca_topic_score_gemma":0.00052879506,"teacher_disagreement_score":0.003162712,"about_ca_system_score_codex":0.0004149687,"about_ca_system_score_gemma":0.00041315067,"threshold_uncertainty_score":0.010580301},"labels":[],"label_agreement":null},{"id":"W4410631077","doi":"10.36897/jme/203805","title":"Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints","year":2025,"lang":"en","type":"article","venue":"Journal of Machine Engineering","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ball (mathematics); Computer science; Scaling; Coincidence; Isotropy; Kinematics; Gradient boosting; Machine tool; Artificial neural network; Artificial intelligence; Algorithm; Mathematics; Geometry; Mechanical engineering; Engineering; Physics","score_opus":0.047343194175240015,"score_gpt":0.3065893756806312,"score_spread":0.2592461815053912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410631077","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":[0.6028243,0.0016634972,0.3877379,0.0005146341,0.00018233631,0.00014210324,0.0005472453,0.0030764004,0.0033115768],"genre_scores_gemma":[0.953537,0.00018078717,0.043973457,0.0001000824,0.000020263535,0.00007978488,0.0007594498,0.00005932152,0.0012899162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993667,0.000203413,0.000055594377,0.00015524187,0.00012283136,0.000096225245],"domain_scores_gemma":[0.99624157,0.002578821,0.0002503087,0.00016049865,0.00066522683,0.000103557344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002695133,0.0013272131,0.0009495757,0.0010704176,0.00031872647,0.0007255144,0.0010640419,0.0013354422,0.000848206],"category_scores_gemma":[0.005444142,0.000358979,0.00076869095,0.0006568938,0.00036665055,0.00081287615,0.0005591015,0.0011371705,0.00048471318],"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":[0.00042695765,0.00020521229,0.0059998482,0.000072705276,0.00007827658,0.000039785406,0.000034369164,0.9120261,0.0014389772,0.0002067234,0.0006748522,0.07879617],"study_design_scores_gemma":[0.000003434055,0.000045429868,0.0007260074,0.0000062334816,0.0000052920186,0.0000053039175,0.000007429743,0.99842703,0.0006404155,0.00007821451,0.00005202707,0.0000033085103],"about_ca_topic_score_codex":0.013882945,"about_ca_topic_score_gemma":0.008778976,"teacher_disagreement_score":0.013882945,"about_ca_system_score_codex":0.0006674519,"about_ca_system_score_gemma":0.0009816614,"threshold_uncertainty_score":0.027604282},"labels":[],"label_agreement":null}]}