{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"dbdd18a513cc","filters":{"venue":"IET Collaborative Intelligent Manufacturing"}},"results":[{"id":"W3201190361","doi":"10.1049/cim2.12041","title":"Design for mass customisation, design for manufacturing, and design for supply chain: A review of the literature","year":2021,"lang":"en","type":"review","venue":"IET Collaborative Intelligent Manufacturing","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Design for manufacturability; Supply chain; Variety (cybernetics); Product design; Product (mathematics); Quality (philosophy); Manufacturing engineering; Process management; Computer science; Strategic design; Implementation; Process (computing); Systems engineering; Design review (U.S. government); Risk analysis (engineering); Engineering; Management science; Business; Marketing; Operations management; Software engineering; Product testing","retraction":null,"screen_n_in":null,"score":{"opus":0.04875020524029409,"gpt":0.2863108224720184,"spread":0.2375606172317243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002561516,0.001021285,0.002124633,0.0004546062,0.0005773563,0.0006788234,0.0007497544,0.0004174161,0.00006009981],"category_scores_gemma":[0.001180677,0.0007443272,0.0006015113,0.0008134015,0.00009327554,0.0007490574,0.0001979944,0.0003034551,0.000007173849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002539767,"about_ca_system_score_gemma":0.0006295796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002444533,"about_ca_topic_score_gemma":0.000002476491,"domain_scores_codex":[0.9960566,0.0002684397,0.001495104,0.001122988,0.0004238731,0.0006330503],"domain_scores_gemma":[0.9944715,0.001362434,0.001977168,0.0006272533,0.001520494,0.00004114907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004617313,0.0001424757,0.000001737582,0.3814799,0.001359344,0.000006097986,0.0006889771,0.001778686,0.00001856781,0.002030809,0.09203101,0.5200007],"study_design_scores_gemma":[0.000545199,0.00005378331,8.832922e-7,0.06672291,0.001490295,0.000004999013,0.000105046,0.0003618883,0.02294208,0.002072209,0.9048536,0.000847085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001459215,0.5880937,0.3950036,0.0003884387,0.00065365,0.0156751,0.0001051284,0.00005239954,0.00002658642],"genre_scores_gemma":[0.00002553213,0.9465583,0.04260043,0.0006644909,0.001090637,0.006828167,0.0008728076,0.0001925649,0.001167056],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8128226,"threshold_uncertainty_score":0.9995008,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4396612978","doi":"10.1049/cim2.12103","title":"Early fault detection for rolling bearings: A meta‐learning approach","year":2024,"lang":"en","type":"article","venue":"IET Collaborative Intelligent Manufacturing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"China Scholarship Council","keywords":"Fault (geology); Computer science; Artificial intelligence; Geology; Seismology","retraction":null,"screen_n_in":null,"score":{"opus":0.01712776946900508,"gpt":0.2750411936097175,"spread":0.2579134241407124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004815092,0.0004963144,0.0005036513,0.0004602149,0.0002071923,0.0005725719,0.0002658197,0.0002029661,0.00005845472],"category_scores_gemma":[0.0001005479,0.0004617056,0.0003087655,0.0005065948,0.00003864824,0.0004942345,0.00006579591,0.0006796911,0.00004826725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002895012,"about_ca_system_score_gemma":0.00002739192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004661722,"about_ca_topic_score_gemma":0.00002762389,"domain_scores_codex":[0.9980228,0.00007107573,0.0004982012,0.0005885535,0.0002985551,0.0005207908],"domain_scores_gemma":[0.999055,0.0003733844,0.00006042619,0.0002508604,0.0001396537,0.0001206574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009530462,0.0001061899,0.00007186818,0.00205498,0.006041723,0.00004340047,0.007640253,0.8464103,0.01909791,0.001077997,0.001468257,0.1158918],"study_design_scores_gemma":[0.0000896901,0.0001276213,0.0000360204,0.0000856669,0.0004030241,0.000006479849,0.0002190392,0.2086085,0.7276798,0.0005937124,0.06170843,0.0004420444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2047591,0.004999818,0.7791879,0.00004867887,0.000733024,0.001693126,0.00005315156,0.004393519,0.004131638],"genre_scores_gemma":[0.9854774,0.0003633133,0.01174976,0.00002227691,0.0002937921,0.001600836,0.00003050828,0.0001686821,0.0002934163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7807183,"threshold_uncertainty_score":0.9997835,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410265055","doi":"10.1049/cim2.70021","title":"Bridging the gap: Empowering manufacturing and production small medium enterprises through industrial Internet of Things adoption model","year":2025,"lang":"en","type":"article","venue":"IET Collaborative Intelligent Manufacturing","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Bridging (networking); Business; The Internet; Production (economics); Industrial Internet; Industrial organization; Knowledge management; Internet of Things; Computer science; World Wide Web; Economics; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.03020541728966085,"gpt":0.254695092603868,"spread":0.2244896753142072,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000309712,0.0003795206,0.0003439952,0.0002432468,0.0001380778,0.0002385843,0.0003329593,0.0002017754,0.00002086141],"category_scores_gemma":[0.00007450059,0.0003290112,0.0000795538,0.0002759068,0.0001606943,0.001178539,0.000142708,0.0006251466,0.00000573484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002255195,"about_ca_system_score_gemma":0.00006819264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003357748,"about_ca_topic_score_gemma":0.0000164336,"domain_scores_codex":[0.9982023,0.00005070871,0.0007421961,0.000360705,0.0002897664,0.0003543838],"domain_scores_gemma":[0.9992185,0.0001287637,0.0001628799,0.0003110099,0.0001210699,0.0000577361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003155379,0.0001184672,0.0005279504,0.001236519,0.0008722693,0.00001057472,0.03486652,0.8350437,0.005137819,0.002363255,0.003543534,0.1159638],"study_design_scores_gemma":[0.0002565573,0.0000305433,0.0001165398,0.0006951583,0.0000509615,0.000007469902,0.00344309,0.02330768,0.9673729,0.001526913,0.002902478,0.0002896925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7698495,0.0002957985,0.212263,0.000771215,0.001832092,0.0008947753,0.00002163199,0.0003473959,0.01372455],"genre_scores_gemma":[0.9979088,0.0003713078,0.0009513153,0.00007268616,0.0001423418,0.00007219297,0.00001574262,0.00004031107,0.0004253099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9622351,"threshold_uncertainty_score":0.9999162,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}