{"meta":{"query_hash":"af92f15ec891","filters":{"venue":"IEEE Transactions on Semiconductor Manufacturing"},"cohort_total":14,"direct_labels_cover":0,"predictions_cover":14,"exported":14,"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/af92f15ec891","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Semiconductor+Manufacturing"},"results":[{"id":"W1983334556","doi":"10.1109/tsm.2012.2192143","title":"NBTI and Process Variations Compensation Circuits Using Adaptive Body Bias","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":48,"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 Waterloo","funders":"","keywords":"Negative-bias temperature instability; CMOS; Transistor; Reliability (semiconductor); Static random-access memory; Microprocessor; Electronic engineering; Circuit reliability; Threshold voltage; Process (computing); Process corners; Chip; Computer science; Electronic circuit; Engineering; Voltage; Electrical engineering; Embedded system; Power (physics)","score_opus":0.06086920214315679,"score_gpt":0.26023601851624534,"score_spread":0.19936681637308856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983334556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9638642,0.00018896894,0.032778792,0.0000068820787,0.0020642474,0.00029088088,0.00006550321,0.00042111357,0.0003194057],"genre_scores_gemma":[0.999097,0.000043194268,0.00040891397,0.00004938569,0.00026163078,0.00003291924,0.0000069976336,0.000075496275,0.000024488021],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998659,0.000052393203,0.00035245874,0.0002681579,0.00021257091,0.00045538758],"domain_scores_gemma":[0.99933386,0.000119802025,0.000080979225,0.0002320913,0.000041595664,0.0001916609],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017696709,0.000326475,0.00029122052,0.000250088,0.00026578552,0.0001059126,0.0001129835,0.00015621295,0.00024141835],"category_scores_gemma":[0.0000040773507,0.0003342937,0.00006592939,0.0001241876,0.00004939964,0.0010680273,0.0000017934565,0.00026771484,0.00003699861],"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":[0.000012696493,0.000080613354,0.000102549675,0.0001556718,0.00015268833,0.0000014837191,0.002164797,0.037825182,0.95764965,0.000054277873,0.000012961937,0.0017874336],"study_design_scores_gemma":[0.00035860267,0.00002589681,0.0009447919,0.00007600732,0.00010138474,0.000054212505,0.00046046084,0.0098741315,0.9874637,0.00010332679,0.000114082795,0.0004234129],"about_ca_topic_score_codex":0.00006263748,"about_ca_topic_score_gemma":0.000008811749,"teacher_disagreement_score":0.035232767,"about_ca_system_score_codex":0.00014488396,"about_ca_system_score_gemma":0.000020535887,"threshold_uncertainty_score":0.9999109},"labels":[],"label_agreement":null},{"id":"W1996722897","doi":"10.1109/tsm.2005.858518","title":"A Novel Approach for the Patterning and High-Volume Production of Sub-40-nm Gates","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Advancements in Photolithography Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Resist; Logic gate; Materials science; Amorphous solid; Layer (electronics); Electronic engineering; Nanotechnology; Optoelectronics; Engineering; Chemistry; Crystallography","score_opus":0.016577341407359936,"score_gpt":0.22522342590681776,"score_spread":0.20864608449945782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996722897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45899412,0.000102745784,0.5398247,0.000028707793,0.00031024552,0.00044698472,0.000034455556,0.00023744087,0.000020564828],"genre_scores_gemma":[0.96703076,0.00018869141,0.032205768,0.000021870099,0.00011665383,0.0002979885,0.0000048824777,0.000059533144,0.00007386081],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990094,0.000010799069,0.00028993443,0.00028922257,0.00014446626,0.00025620218],"domain_scores_gemma":[0.99946564,0.000079371086,0.00006635302,0.00031557906,0.000030705047,0.000042336396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013724889,0.00023071842,0.00020324612,0.0001786485,0.00016022167,0.000026286276,0.00015270693,0.00008225853,0.000015832366],"category_scores_gemma":[0.000003068732,0.00019691628,0.00008931948,0.000087697386,0.00008593863,0.00032353136,0.0000020036625,0.0002678245,7.853379e-7],"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":[0.000035582903,0.000087386834,0.00002174651,0.00022316231,0.00016149215,1.187818e-7,0.0003935699,0.21400252,0.7330351,0.000009640887,0.00007863245,0.051951043],"study_design_scores_gemma":[0.0002633574,0.000035445242,0.00027004702,0.00004321898,0.00006446627,0.000016320837,0.00012355208,0.0127670225,0.9852834,0.000033266468,0.0008938009,0.0002061019],"about_ca_topic_score_codex":0.000025330644,"about_ca_topic_score_gemma":0.00000936303,"teacher_disagreement_score":0.5080366,"about_ca_system_score_codex":0.00006768767,"about_ca_system_score_gemma":0.000005210823,"threshold_uncertainty_score":0.8030013},"labels":[],"label_agreement":null},{"id":"W2102704639","doi":"10.1109/tsm.2007.914388","title":"A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":50,"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","funders":"","keywords":"Workcell; Wafer fabrication; Job shop scheduling; Computer science; Scheduling (production processes); Semiconductor device fabrication; Schedule; Distributed computing; Mathematical optimization; Engineering; Artificial intelligence; Wafer; Robot; Mathematics","score_opus":0.02399545512018482,"score_gpt":0.23318466116225164,"score_spread":0.2091892060420668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102704639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44267684,0.000025777328,0.5550335,0.0000092189675,0.0009863235,0.00042853173,0.00009681585,0.00070270785,0.00004025317],"genre_scores_gemma":[0.85586643,0.000009177699,0.14355093,0.00005843255,0.00012501783,0.00018400486,0.000023731332,0.000119320124,0.00006297778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981292,0.00003297005,0.00050280476,0.00052310695,0.00034486104,0.0004670837],"domain_scores_gemma":[0.99875665,0.00033981682,0.0001101446,0.00046352422,0.00015161774,0.00017826866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013502156,0.00043851748,0.00038672434,0.00038835217,0.00042493053,0.0000732815,0.00023213857,0.00020363965,0.0001518999],"category_scores_gemma":[0.000012625945,0.00041197392,0.00018453144,0.00019985111,0.00012061808,0.0003236465,0.0000011770978,0.0003681052,0.000048712453],"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":[0.00013863586,0.00007557605,0.00004617607,0.0001860526,0.00015249892,0.00001888744,0.00043151452,0.9734082,0.014774831,0.0000026323055,0.00011583166,0.010649136],"study_design_scores_gemma":[0.0019898333,0.00008404263,0.00010539998,0.00046390478,0.00008742011,0.00012709897,0.0005174509,0.28221783,0.7135956,0.000004214505,0.00016401394,0.0006431881],"about_ca_topic_score_codex":0.0000107340675,"about_ca_topic_score_gemma":0.000007680053,"teacher_disagreement_score":0.69882077,"about_ca_system_score_codex":0.00038644287,"about_ca_system_score_gemma":0.00009317641,"threshold_uncertainty_score":0.9998332},"labels":[],"label_agreement":null},{"id":"W2109495827","doi":"10.1109/tsm.2007.907613","title":"A Fab-Wide APC Sampling Application","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Metrology; Reliability engineering; Sampling (signal processing); Event (particle physics); Process (computing); Computer science; Process variation; Duration (music); Fault detection and isolation; Engineering; Semiconductor device fabrication; Microprocessor; Manufacturing engineering; Real-time computing; Embedded system; Wafer; Artificial intelligence; Operating system; Detector; Mathematics; Telecommunications","score_opus":0.016039179816445755,"score_gpt":0.2311549802360548,"score_spread":0.21511580041960904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109495827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5003232,0.00005630274,0.49653783,0.000016403645,0.00061381166,0.00016210879,0.000017917235,0.0005798231,0.0016926421],"genre_scores_gemma":[0.99843985,0.000056936406,0.00069129077,0.00017821166,0.00017080359,0.000035918543,0.000016374877,0.000101090875,0.0003095249],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980731,0.000018206534,0.00053590967,0.00044803423,0.00029764313,0.00062712503],"domain_scores_gemma":[0.99895203,0.00019258515,0.00006563096,0.000524762,0.00005903917,0.00020593396],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028106748,0.00040473763,0.0003391227,0.00053141906,0.00023068103,0.000086716966,0.00028742335,0.0002545431,0.00030748494],"category_scores_gemma":[0.0000042975657,0.0004051582,0.00024980246,0.00031278955,0.000046126694,0.0003470327,0.0000010836969,0.00075107504,0.00024180247],"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":[0.000017356822,0.000055084198,0.000020174723,0.000054594042,0.0002662258,0.000008495263,0.0005262332,0.086265594,0.87013555,0.000051161493,0.0001925126,0.042407002],"study_design_scores_gemma":[0.00026186346,0.000022840688,0.000099407815,0.000038217546,0.00010127034,0.000020482257,0.00042601142,0.0026455983,0.9887403,0.00016506876,0.006995185,0.00048371116],"about_ca_topic_score_codex":0.00011125749,"about_ca_topic_score_gemma":0.00020507487,"teacher_disagreement_score":0.49811667,"about_ca_system_score_codex":0.0003274467,"about_ca_system_score_gemma":0.000018614614,"threshold_uncertainty_score":0.99984},"labels":[],"label_agreement":null},{"id":"W2119459533","doi":"10.1109/tsm.2003.822725","title":"An Optimal Residency-Aware Scheduling Technique for Cluster Tools With Buffer Module","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":22,"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":"Scheduling (production processes); Semiconductor device fabrication; Computer science; Time constraint; Distributed computing; Resource constraints; Job shop scheduling; Cluster (spacecraft); Throughput; Resource (disambiguation); Buffer (optical fiber); Wafer; Real-time computing; Mathematical optimization; Computer network; Engineering; Operating system; Mathematics; Routing (electronic design automation)","score_opus":0.019707882770221605,"score_gpt":0.2438777265884775,"score_spread":0.2241698438182559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119459533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35816148,0.000018097011,0.6402109,0.00003564858,0.00037635825,0.00051108946,0.00004649318,0.0006134361,0.000026467735],"genre_scores_gemma":[0.67934877,0.00001529203,0.32002667,0.000062503954,0.00010799695,0.00028269444,0.000013954234,0.00010678945,0.00003535932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985517,0.000017965282,0.00032109205,0.00044476378,0.00021809565,0.0004463477],"domain_scores_gemma":[0.99915373,0.00008835556,0.000044144075,0.00046128893,0.00007251672,0.00017996941],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014201601,0.00036895392,0.0002719791,0.0002635054,0.00025672992,0.00016059632,0.00024111172,0.00022547475,0.00008828054],"category_scores_gemma":[0.0000036067838,0.00035190617,0.00011380478,0.00013232439,0.000043628992,0.00078556483,0.0000011564233,0.00046410764,0.00001778298],"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":[0.00009133655,0.000055037643,9.533678e-7,0.00006672908,0.000058890513,0.000003198793,0.00023224836,0.9732743,0.024123976,0.0000055333503,0.000004137945,0.002083659],"study_design_scores_gemma":[0.00120157,0.00016781208,0.000010969854,0.00017609252,0.00004874167,0.000045330085,0.00021247729,0.07731926,0.920232,0.00008476775,0.000027390328,0.00047362407],"about_ca_topic_score_codex":0.000013678444,"about_ca_topic_score_gemma":0.000024607289,"teacher_disagreement_score":0.896108,"about_ca_system_score_codex":0.0002309252,"about_ca_system_score_gemma":0.00006598815,"threshold_uncertainty_score":0.9998933},"labels":[],"label_agreement":null},{"id":"W2131433807","doi":"10.1109/tsm.2002.801379","title":"Optimal scheduling techniques for cluster tools with process-module and transport-module residency constraints","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":59,"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":"Correctness; Heuristics; Computer science; Scheduling (production processes); Process (computing); Turnaround time; Mathematical optimization; Linear programming; Benchmark (surveying); Distributed computing; Algorithm; Mathematics; Programming language","score_opus":0.023156218931489152,"score_gpt":0.23677106212008966,"score_spread":0.21361484318860052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131433807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.460635,0.00006730303,0.5378302,0.000049150145,0.00019231829,0.00041784864,0.000058000627,0.0005872471,0.0001629357],"genre_scores_gemma":[0.7969549,0.00009129785,0.20243108,0.000060210496,0.00007965191,0.00016445736,0.000006718755,0.00008688098,0.00012480334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985815,0.0000131904035,0.00035296523,0.00043812534,0.00020235259,0.0004118474],"domain_scores_gemma":[0.9993487,0.00012457135,0.00005241417,0.00025418153,0.00006537479,0.00015477915],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011594045,0.00035872246,0.000302898,0.0002324922,0.00023519147,0.000099421646,0.00014378398,0.00018903134,0.00023392943],"category_scores_gemma":[0.000005200535,0.00034303847,0.000086555374,0.00011677238,0.00012121776,0.00058560265,7.4673284e-7,0.00039585406,0.000008711151],"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":[0.00006607623,0.00006809641,0.000015885013,0.00027161057,0.00012075159,0.000005527724,0.0008501919,0.96167946,0.0033973893,0.000007572932,0.000018820469,0.033498645],"study_design_scores_gemma":[0.0011225971,0.00013640207,0.000031805004,0.00026551966,0.00008776726,0.000079470454,0.0003263534,0.19375317,0.80349785,0.00004143137,0.00007989991,0.000577725],"about_ca_topic_score_codex":0.000003654924,"about_ca_topic_score_gemma":0.000010085345,"teacher_disagreement_score":0.80010045,"about_ca_system_score_codex":0.00006734136,"about_ca_system_score_gemma":0.000018737876,"threshold_uncertainty_score":0.9999022},"labels":[],"label_agreement":null},{"id":"W2163177753","doi":"10.1109/66.857943","title":"Focus characterization using end of line metrology","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Advancements in Photolithography Techniques","field":"Engineering","cited_by":1,"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":"Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Metrology; Focus (optics); Optics; Offset (computer science); Line (geometry); Lens (geology); Coordinate-measuring machine; Physics; Repeatability; Mathematics; Computer science; Geometry","score_opus":0.017253769688146302,"score_gpt":0.24410398428239516,"score_spread":0.22685021459424887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163177753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70301366,0.0000280951,0.29580462,0.0000037605168,0.0002929668,0.00016284344,0.00007575618,0.00036063124,0.00025766954],"genre_scores_gemma":[0.99496645,0.00021838112,0.004580142,0.00002330226,0.00004659253,0.000026307327,0.000008193323,0.00006022721,0.00007040325],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989545,0.000026595459,0.00037028376,0.00022557181,0.00015186702,0.00027122413],"domain_scores_gemma":[0.99952084,0.000034878733,0.000054023854,0.0003120635,0.000019636693,0.000058538182],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000635095,0.0002308734,0.00026568052,0.000337141,0.00006783781,0.000011757407,0.00015225647,0.00012871348,0.0013903386],"category_scores_gemma":[6.7011086e-7,0.0002518752,0.000110332316,0.00016046513,0.000067451554,0.00030148384,7.436434e-7,0.00026489547,0.000008614483],"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":[0.000025632391,0.00004841029,0.000006027517,0.000038888003,0.000081572434,0.0000023666873,0.000095598756,0.0490438,0.8644353,0.0000036110544,0.0000015837013,0.08621721],"study_design_scores_gemma":[0.00022356966,0.000058928814,0.00008127393,0.00003782291,0.000043404638,0.000016348236,0.000010140674,0.004953149,0.9935903,0.00011683869,0.0006606018,0.00020764535],"about_ca_topic_score_codex":0.000026197025,"about_ca_topic_score_gemma":0.0000053267745,"teacher_disagreement_score":0.2919528,"about_ca_system_score_codex":0.000090391135,"about_ca_system_score_gemma":0.000008104419,"threshold_uncertainty_score":0.9999933},"labels":[],"label_agreement":null},{"id":"W2169286256","doi":"10.1109/tsm.2010.2080693","title":"Statistical Design Framework of Submicron Flip-Flop Circuits Considering Process Variations","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Low-power high-performance VLSI design","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 Waterloo; Carleton University","funders":"","keywords":"Flip-flop; Electronic engineering; Transistor; FLOPS; Electronic circuit; Circuit design; Process (computing); Leakage (economics); Engineering; Voltage; Transistor count; Integrated circuit design; Computer science; Electrical engineering; Parallel computing; CMOS","score_opus":0.017690058136904767,"score_gpt":0.2398221444221017,"score_spread":0.22213208628519693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169286256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49036026,0.000014033895,0.5071052,0.000010508196,0.001676651,0.00028376412,0.00006525326,0.0003683674,0.00011601275],"genre_scores_gemma":[0.97964495,0.000029764824,0.01991294,0.00003121217,0.00012660782,0.00008799492,0.0000066281864,0.00012496448,0.000034935147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979883,0.00004802602,0.0006033558,0.00042485638,0.0003606688,0.0005748238],"domain_scores_gemma":[0.99844986,0.00059662753,0.000095225085,0.0005721222,0.00008110196,0.00020504868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025022615,0.00042328786,0.00042448984,0.00039047876,0.00021039083,0.00007646278,0.00033234255,0.00034882588,0.0007636987],"category_scores_gemma":[0.000027998678,0.00045554544,0.00009955425,0.00024514677,0.0001318777,0.0005247296,0.0000021629605,0.0013503712,0.000102891216],"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":[0.000022073318,0.00011358954,0.000035463498,0.00025086186,0.00017445866,0.000011986413,0.0016044085,0.23490568,0.755298,0.00014731922,0.00009080845,0.0073453113],"study_design_scores_gemma":[0.00038772717,0.000055576656,0.0003821251,0.00010826705,0.00008134808,0.00004075313,0.00009015786,0.009182553,0.9884015,0.00066217803,0.0001257533,0.00048209392],"about_ca_topic_score_codex":0.000019617353,"about_ca_topic_score_gemma":0.000017578353,"teacher_disagreement_score":0.4892847,"about_ca_system_score_codex":0.0001132053,"about_ca_system_score_gemma":0.00009043412,"threshold_uncertainty_score":0.99978966},"labels":[],"label_agreement":null},{"id":"W3007295782","doi":"10.1109/tsm.2020.2976714","title":"Color Difference Detection of Polysilicon Wafers Using Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm With Elitist Strategy","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Industrial Vision Systems and Defect Detection","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 Manitoba","funders":"Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology; Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Support vector machine; Wafer; Artificial intelligence; Feature (linguistics); Algorithm; Computer science; Pattern recognition (psychology); Engineering","score_opus":0.01826643047564976,"score_gpt":0.20895079848174258,"score_spread":0.1906843680060928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007295782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53525084,0.000026803807,0.46337637,0.0000063675698,0.00055630575,0.0003421264,0.00019082506,0.00022594658,0.000024385059],"genre_scores_gemma":[0.9972874,0.000046123358,0.0023306215,0.000022958548,0.00011669531,0.00003511798,0.000025547171,0.000091115064,0.000044434753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982723,0.00008287051,0.0005784562,0.00041877478,0.00030360933,0.00034396944],"domain_scores_gemma":[0.9992843,0.000058184847,0.0001512956,0.0002390906,0.000061874496,0.00020525455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008619717,0.0004002152,0.00050073717,0.00020796258,0.000157225,0.00008694369,0.00013616151,0.00024331658,0.00042421184],"category_scores_gemma":[0.0000032815467,0.0003806208,0.00012761494,0.00028407335,0.000053309504,0.00030018465,0.000002038207,0.000433487,0.0000089466685],"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":[0.00015047462,0.000021123184,5.549823e-7,0.00005256332,0.00005515276,0.000002832768,0.00009996335,0.43943435,0.54883605,6.864293e-8,0.0000047960575,0.011342059],"study_design_scores_gemma":[0.0011006974,0.0005127013,0.000015538744,0.00004255402,0.00007939627,0.000023245242,0.00010795572,0.31592762,0.68186915,3.090161e-7,0.000045593155,0.00027521496],"about_ca_topic_score_codex":0.0003229796,"about_ca_topic_score_gemma":0.000016045004,"teacher_disagreement_score":0.46203652,"about_ca_system_score_codex":0.00020081684,"about_ca_system_score_gemma":0.000039935134,"threshold_uncertainty_score":0.9998646},"labels":[],"label_agreement":null},{"id":"W3107850185","doi":"10.1109/tsm.2021.3062943","title":"A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-Type Spatial Pattern Recognition in Wafer Bin Maps","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Industrial Vision Systems and Defect Detection","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":"University of Toronto","funders":"National Science Foundation","keywords":"Pattern recognition (psychology); Spatial filter; Bin; Metric (unit); Spatial analysis; Filter (signal processing); Common spatial pattern; Wafer; Pattern matching","score_opus":0.029727616802533646,"score_gpt":0.24600464012126463,"score_spread":0.216277023318731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107850185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.827054,0.000040323455,0.17024122,0.000007490981,0.0017841452,0.00047766033,0.00016991574,0.00015408106,0.000071145834],"genre_scores_gemma":[0.99928766,0.000051653064,0.00012205127,0.00002029152,0.00024867873,0.00010706229,0.00005386946,0.00007809841,0.00003063833],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848646,0.00009220383,0.0004074525,0.0004466374,0.00018994435,0.00037729062],"domain_scores_gemma":[0.99942255,0.00013742087,0.000055465774,0.0002194237,0.000051655596,0.000113512644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002015463,0.00034375887,0.00035826614,0.0004238536,0.00013062607,0.00009527941,0.00007051726,0.00027343733,0.00012957743],"category_scores_gemma":[0.000010422474,0.00033062682,0.00017773906,0.00017445418,0.000016498116,0.00019520333,0.0000022208926,0.0004939829,0.000019522598],"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":[0.0007086402,0.000247258,0.000028827595,0.00071514276,0.00028935706,0.000041609506,0.0009101324,0.25475672,0.44936508,0.0000019539425,0.000100649086,0.29283464],"study_design_scores_gemma":[0.0013965425,0.00023673604,0.00024165088,0.0001988102,0.000042691496,0.00005961063,0.00014876216,0.026867647,0.9702916,0.00006279763,0.00005597149,0.00039713964],"about_ca_topic_score_codex":0.00026301056,"about_ca_topic_score_gemma":0.00012764365,"teacher_disagreement_score":0.52092654,"about_ca_system_score_codex":0.00019216043,"about_ca_system_score_gemma":0.000022841934,"threshold_uncertainty_score":0.9999146},"labels":[],"label_agreement":null},{"id":"W4206400118","doi":"10.1109/tsm.2021.3053077","title":"IEEE Transactions on Semiconductor Manufacturing publication information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Semiconductor device fabrication; Manufacturing engineering; Computer science; Engineering; Semiconductor industry; Electrical engineering","score_opus":0.01513047829741856,"score_gpt":0.2129287622320379,"score_spread":0.19779828393461935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206400118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46075746,0.00006165501,0.5308952,0.00019732279,0.0037776264,0.0005368628,0.00024387125,0.0015672401,0.001962782],"genre_scores_gemma":[0.99544364,0.0003822093,0.0013979577,0.00056608923,0.00018453717,0.00021008421,0.000121492005,0.00016066326,0.0015333194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99662817,0.00008353195,0.0010041193,0.0007332457,0.0006902894,0.00086064363],"domain_scores_gemma":[0.9980954,0.00016958619,0.00020524004,0.00095623283,0.00020472992,0.00036883238],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020708454,0.00080673763,0.00055265217,0.0008177047,0.0006055744,0.0005389174,0.00043006917,0.00048758453,0.002209037],"category_scores_gemma":[0.000008815641,0.0008837514,0.00035369585,0.00035035046,0.00006936014,0.002778993,0.0000019834774,0.0012887995,0.0005852246],"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":[0.000058630776,0.00017574368,0.0000010890807,0.00042557216,0.00024903246,0.000011926872,0.0010106852,0.90750825,0.026698045,0.000011098574,0.00072518236,0.06312473],"study_design_scores_gemma":[0.0009260163,0.00006265444,0.000056164496,0.00015815519,0.00011013902,0.00007127352,0.0002973405,0.013368205,0.97603935,0.00006310478,0.007949758,0.0008978393],"about_ca_topic_score_codex":0.00004167279,"about_ca_topic_score_gemma":0.00003888647,"teacher_disagreement_score":0.9493413,"about_ca_system_score_codex":0.00060927524,"about_ca_system_score_gemma":0.00011043091,"threshold_uncertainty_score":0.99936134},"labels":[],"label_agreement":null},{"id":"W4253781335","doi":"10.1109/tsm.2020.2988317","title":"IEEE Transactions on Semiconductor Manufacturing publication information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Semiconductor device fabrication; Manufacturing engineering; Computer science; Engineering; Electrical engineering","score_opus":0.01989564096123298,"score_gpt":0.20987562425277906,"score_spread":0.18997998329154608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253781335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37962142,0.000031547494,0.61331046,0.0004972928,0.0021825538,0.0007787098,0.00024741373,0.0021095094,0.0012211216],"genre_scores_gemma":[0.9964378,0.00020949027,0.0010074243,0.0013317754,0.00024564727,0.0001999895,0.000072670744,0.00016787897,0.00032732286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996734,0.00006390778,0.0010019772,0.0007121606,0.00067225803,0.0008156486],"domain_scores_gemma":[0.99833125,0.00013433654,0.00022345294,0.0006591624,0.00011546921,0.00053633825],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016426272,0.0008366099,0.0005588823,0.0006664037,0.0005257357,0.00044746837,0.0005568926,0.0004437986,0.0015839923],"category_scores_gemma":[0.000008411726,0.00088833744,0.00032504214,0.00032330098,0.00007180129,0.002903269,0.0000018370519,0.0013510196,0.0008191348],"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":[0.000104960345,0.000088202265,8.7421427e-7,0.00044243984,0.00018760133,0.0000040916775,0.0018629583,0.93239796,0.01661571,0.0000060957263,0.00095059874,0.047338523],"study_design_scores_gemma":[0.0009609109,0.00013362944,0.000033123924,0.00009919557,0.00009917313,0.00001880112,0.0002580711,0.039333664,0.9502902,0.000026936676,0.007854994,0.00089126744],"about_ca_topic_score_codex":0.000039187078,"about_ca_topic_score_gemma":0.000011796211,"teacher_disagreement_score":0.9336745,"about_ca_system_score_codex":0.000430039,"about_ca_system_score_gemma":0.0000614005,"threshold_uncertainty_score":0.9999588},"labels":[],"label_agreement":null},{"id":"W4392405845","doi":"10.1109/tsm.2024.3372521","title":"Fabrication of the Highly Ordered Silicon Nanocone Array With Sub-5 nm Tip Apex by Tapered Silicon Oxide Mask","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Anodic Oxide Films and Nanostructures","field":"Materials Science","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":"University of Waterloo","funders":"","keywords":"Silicon; Materials science; Fabrication; Optoelectronics; Apex (geometry); Silicon oxide; Oxide; Hybrid silicon laser; Nanotechnology; Silicon nitride; Metallurgy","score_opus":0.009341740011802609,"score_gpt":0.21040487721005435,"score_spread":0.20106313719825175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392405845","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99143326,0.0003075172,0.005397513,0.00026101532,0.0013875618,0.0005316531,0.00028988108,0.00026046927,0.00013115007],"genre_scores_gemma":[0.998233,0.000057324527,0.00021146593,0.00014257857,0.000072263014,0.000058669782,0.0000110063,0.000068215544,0.0011454737],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978165,0.00010624981,0.0004946471,0.0006813564,0.00047670052,0.00042452844],"domain_scores_gemma":[0.9987956,0.00017189003,0.00018580962,0.0006652689,0.00006653916,0.00011485907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017125288,0.00039010093,0.00037149029,0.00016550776,0.00027173705,0.00014319649,0.00045092448,0.00019273632,0.0005898392],"category_scores_gemma":[0.000008708408,0.00025979875,0.00017922552,0.0002657907,0.0002456634,0.00043693473,0.000005001573,0.0003924743,0.000077286386],"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":[0.00008706247,0.000053289506,0.000011208879,0.00011389553,0.00005297883,0.0000025295283,0.0003545271,0.0007903622,0.9962801,0.0000142571225,0.0006387848,0.0016010432],"study_design_scores_gemma":[0.00041244796,0.00009575872,0.0004269271,0.00019065272,0.00009750742,0.000032115422,0.00019504507,0.000056802935,0.99590474,0.00006730607,0.0021928782,0.00032779164],"about_ca_topic_score_codex":0.00038150564,"about_ca_topic_score_gemma":0.00008323349,"teacher_disagreement_score":0.0067997635,"about_ca_system_score_codex":0.0001409403,"about_ca_system_score_gemma":0.000121853634,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W4415002953","doi":"10.1109/tsm.2025.3619539","title":"A Condition Monitoring Method via a New Signal Expansion Strategy for the Crystal Lifting and Rotating Mechanism","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Vibration and Dynamic Analysis","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":"Concordia University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"SIGNAL (programming language); Monocrystalline silicon; Vibration; Benchmark (surveying); Process (computing); Condition monitoring; Control theory (sociology); Mechanism (biology)","score_opus":0.02030543493557713,"score_gpt":0.27447455005810745,"score_spread":0.2541691151225303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415002953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18742701,0.000080977574,0.81139284,0.00005290799,0.00045896895,0.0002942288,0.0000137880525,0.00023187738,0.000047376816],"genre_scores_gemma":[0.98734945,0.000038177586,0.012029208,0.000047069174,0.000111667214,0.0000551111,0.0000044938934,0.00003511373,0.0003297029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989711,0.000037485366,0.00031780434,0.0002800221,0.00013743342,0.0002561614],"domain_scores_gemma":[0.99926674,0.00039739153,0.000053387474,0.00017504866,0.000027554164,0.00007990063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001873689,0.00023418995,0.00021416265,0.00022297137,0.00044321592,0.0001345929,0.00010873252,0.00011356584,0.00006599451],"category_scores_gemma":[0.0000041616568,0.00020689663,0.0001294668,0.00014066749,0.000017791459,0.00029512838,0.0000021034698,0.0003100545,0.0000024727028],"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":[0.000019306386,0.000009123875,0.0000023827251,0.00006770145,0.00017480484,7.470447e-7,0.00040971523,0.6384107,0.29297265,0.000025619926,0.000019875824,0.0678874],"study_design_scores_gemma":[0.00031174763,0.000019467849,0.000019301531,0.000060417646,0.00011277014,0.00000494519,0.00064738357,0.47906214,0.51911885,0.00048054065,0.000033637523,0.00012881862],"about_ca_topic_score_codex":0.00008149574,"about_ca_topic_score_gemma":0.000031146104,"teacher_disagreement_score":0.79992247,"about_ca_system_score_codex":0.00008279889,"about_ca_system_score_gemma":0.000033363438,"threshold_uncertainty_score":0.8437},"labels":[],"label_agreement":null}]}