{"meta":{"query_hash":"b6d5d9280458","filters":{"venue":"Synthesis lectures on mechanical engineering"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/b6d5d9280458","api":"https://metacan.xera.ac/api/v1/cohort?venue=Synthesis+lectures+on+mechanical+engineering"},"results":[{"id":"W4389257734","doi":"10.1007/978-3-031-46866-7_1","title":"Sources of Data","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Fault Detection and Control Systems","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":"University of Toronto","funders":"","keywords":"Data acquisition; Instrumentation (computer programming); Process (computing); Digital data; Sampling (signal processing); Computer science; Experimental data; Data mining; Computer hardware; Data transmission; Detector; Statistics; Mathematics; Telecommunications","score_opus":0.024198015041973824,"score_gpt":0.21670901262383194,"score_spread":0.1925109975818581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257734","genre_codex":"other","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":[0.0015979811,0.010984905,0.09650216,0.00061836164,0.022576274,0.004512985,0.0042887554,0.029262096,0.8296565],"genre_scores_gemma":[0.9146974,0.0011728727,0.001207617,0.00009152137,0.0023908757,0.00016477898,0.00012456582,0.0017643232,0.07838603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982917,0.0000113301685,0.0005184421,0.00044125528,0.00043171772,0.00030558865],"domain_scores_gemma":[0.9980465,0.00065645925,0.00007871208,0.0010760422,0.000023797344,0.00011847882],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026352418,0.0004772386,0.0007436107,0.00033585395,0.000028920824,0.000030487501,0.00074199366,0.00054030115,0.00018576907],"category_scores_gemma":[0.00028352946,0.00045712362,0.00021090769,0.000072560375,0.000014966258,0.000052634416,0.000111330664,0.0005944496,0.00023775754],"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":[0.000102405036,0.00003144626,1.8906103e-7,0.0028255424,0.0029463733,0.00014693673,0.00006603967,0.6570927,0.043381564,0.14161764,0.0071137855,0.14467537],"study_design_scores_gemma":[0.00030173615,0.00011158929,0.0000028603645,0.002082508,0.00030899062,0.000030164738,0.00000934036,0.6965603,0.04485725,0.0017838037,0.25254285,0.0014085633],"about_ca_topic_score_codex":0.0000059562744,"about_ca_topic_score_gemma":0.000012132099,"teacher_disagreement_score":0.9130994,"about_ca_system_score_codex":0.00006326401,"about_ca_system_score_gemma":0.000014979382,"threshold_uncertainty_score":0.99978805},"labels":[],"label_agreement":null},{"id":"W4389257736","doi":"10.1007/978-3-031-46866-7_4","title":"Data-Based Modelling for Control","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Advanced Control Systems 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":"University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Control engineering; Automation; Control (management); Machine learning; Artificial intelligence; Control system; Engineering","score_opus":0.03302932424845161,"score_gpt":0.21931800216470548,"score_spread":0.18628867791625386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.3608346e-7,0.0003219218,0.99076504,0.00006367479,0.0009962465,0.0010159576,0.0011707812,0.0023102195,0.0033556048],"genre_scores_gemma":[0.57425153,0.0010848067,0.33505163,0.0011350838,0.011893024,0.006222451,0.0043748426,0.013332884,0.05265377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767226,0.000011898055,0.00063000026,0.0007372469,0.000397562,0.0005510589],"domain_scores_gemma":[0.9963151,0.002171148,0.00010856628,0.0011711924,0.000072709474,0.00016124388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035426565,0.0007412839,0.0009392496,0.00035635318,0.00007088435,0.00006294317,0.0006901514,0.0007078739,0.00003590626],"category_scores_gemma":[0.00050525775,0.00077638344,0.00027430727,0.00006227544,0.00001095094,0.00009113688,0.000040791143,0.0005875367,0.000078799945],"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":[0.000043435142,0.0000037333741,3.0783798e-9,0.00032871845,0.00025856242,0.0000071552968,0.0000015706091,0.96847385,0.0005810304,0.027345171,0.00030921266,0.0026475699],"study_design_scores_gemma":[0.00045092497,0.000039778242,2.2153774e-8,0.00063282,0.00021526356,0.0000014801269,4.2055058e-7,0.9682491,0.0011615789,0.0021904258,0.02635324,0.0007049424],"about_ca_topic_score_codex":0.0000011514993,"about_ca_topic_score_gemma":0.0000047173057,"teacher_disagreement_score":0.65571344,"about_ca_system_score_codex":0.0002216059,"about_ca_system_score_gemma":0.00003433361,"threshold_uncertainty_score":0.9994687},"labels":[],"label_agreement":null},{"id":"W4389257761","doi":"10.1007/978-3-031-46866-7","title":"Data Analytics for Process Engineers","year":2023,"lang":"en","type":"book","venue":"Synthesis lectures on mechanical engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Analytics; Process (computing); Data science; Computer science; Data analysis; Data mining","score_opus":0.027581280729133316,"score_gpt":0.24700417765167013,"score_spread":0.2194228969225368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257761","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003112473,0.004410405,0.82689047,0.00074795226,0.03240477,0.009957062,0.013111992,0.039599963,0.07256617],"genre_scores_gemma":[0.4606299,0.0016405777,0.0069323108,0.0008390648,0.024631642,0.007964984,0.004988765,0.011217185,0.48115554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974412,0.000015210343,0.00061504863,0.00074951473,0.0005089615,0.00067011185],"domain_scores_gemma":[0.9970225,0.0013458406,0.000077371726,0.0012650024,0.000059241353,0.0002300391],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043118285,0.0007600875,0.00094657653,0.0005008145,0.00006782161,0.00010433378,0.0011068989,0.00082357775,0.00005455041],"category_scores_gemma":[0.001274986,0.0007591887,0.00030763718,0.00024923935,0.000012006685,0.00007998527,0.00007014891,0.0008288316,0.00015388548],"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":[0.00003485599,0.000012924386,1.2931105e-8,0.0019373547,0.00088507164,0.000018083932,0.000019584711,0.94765794,0.0011040863,0.0019930848,0.037955448,0.0083815595],"study_design_scores_gemma":[0.00020042888,0.000049171067,2.4999983e-7,0.00055463164,0.00021613608,0.000006604186,0.000008125153,0.8463985,0.0027570436,0.00045355625,0.14862953,0.00072606385],"about_ca_topic_score_codex":9.942207e-7,"about_ca_topic_score_gemma":0.000012759273,"teacher_disagreement_score":0.81995815,"about_ca_system_score_codex":0.00033630032,"about_ca_system_score_gemma":0.000090532565,"threshold_uncertainty_score":0.9994859},"labels":[],"label_agreement":null},{"id":"W4389257780","doi":"10.1007/978-3-031-46866-7_6","title":"Final Remarks","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Fault Detection and Control Systems","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":"University of Toronto","funders":"","keywords":"Sketch; Computer science; Data science; Analytics; Data analysis; Field (mathematics); Process (computing); Exploratory analysis; Meaning (existential); Cultural analytics; Exploratory data analysis; Artificial intelligence; Data mining; Semantic analytics; Epistemology; Semantic Web; Algorithm","score_opus":0.01318322462853436,"score_gpt":0.19683521082616157,"score_spread":0.1836519861976272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257780","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001504458,0.0017412407,0.03724057,0.00039581014,0.01442547,0.0016681127,0.00024508324,0.01788619,0.92624706],"genre_scores_gemma":[0.48989668,0.0011978428,0.0011056728,0.00038734914,0.0054815826,0.0007820825,0.00006375028,0.0031392307,0.4979458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.998047,0.000013651976,0.0004972463,0.00047261198,0.00048431862,0.00048515285],"domain_scores_gemma":[0.9985836,0.00058350153,0.000055726356,0.0005490896,0.000027442635,0.00020061975],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022482839,0.0006815181,0.0007468491,0.0004042708,0.000059687743,0.000068990266,0.00033173722,0.0008476048,0.00045180134],"category_scores_gemma":[0.00030036294,0.0006738499,0.0004232618,0.000080433696,0.000012018428,0.00003370867,0.00003846073,0.0010921365,0.0012411079],"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":[0.0001162177,0.000020399715,4.6947097e-8,0.0012319811,0.0015460795,0.00037126653,0.00003668074,0.65393907,0.026561802,0.21220738,0.015745332,0.08822373],"study_design_scores_gemma":[0.0004325907,0.00015687902,0.000004102896,0.0022220747,0.00023890346,0.00007063175,0.00000496739,0.47746372,0.020443305,0.003950753,0.49280664,0.0022054159],"about_ca_topic_score_codex":0.0000030642943,"about_ca_topic_score_gemma":0.000011841841,"teacher_disagreement_score":0.48974624,"about_ca_system_score_codex":0.00021333754,"about_ca_system_score_gemma":0.000017420916,"threshold_uncertainty_score":0.99957126},"labels":[],"label_agreement":null},{"id":"W4389257804","doi":"10.1007/978-3-031-46866-7_3","title":"Data-Based Modelling for Prediction","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Contrast (vision); Set (abstract data type); Process (computing); Machine learning; Linear regression; Data set; Artificial intelligence; Data mining","score_opus":0.06619471281009445,"score_gpt":0.26074548230218897,"score_spread":0.19455076949209452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257804","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007574699,0.000189779,0.9857113,0.00011500473,0.0007923195,0.00077160355,0.0028567077,0.0058066435,0.0037491133],"genre_scores_gemma":[0.20466052,0.0069157523,0.6286172,0.0007620939,0.010471302,0.009439527,0.012525028,0.018272582,0.108335964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975456,0.0000066326397,0.00047076022,0.00080347696,0.0005173071,0.00065623387],"domain_scores_gemma":[0.99656403,0.0017094405,0.000052976873,0.0014954865,0.000054223252,0.00012387105],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031706275,0.0006393552,0.00061785267,0.00059446157,0.000078789475,0.000054881533,0.0010954029,0.0009135332,0.000066096705],"category_scores_gemma":[0.0007128594,0.0006592702,0.00020422455,0.00010123809,0.000026104906,0.0000901422,0.00018032841,0.0011559366,0.00010136426],"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":[0.000025215906,0.0000051261195,9.2575485e-9,0.00040576345,0.00015331733,0.000011590323,9.949146e-7,0.97026885,0.0015594257,0.01247553,0.0013562771,0.013737896],"study_design_scores_gemma":[0.0001266199,0.00006626483,9.874919e-8,0.00053042884,0.000068028705,0.000001810621,8.0983955e-7,0.91949546,0.018503282,0.0058183535,0.05488261,0.0005062439],"about_ca_topic_score_codex":5.3270145e-7,"about_ca_topic_score_gemma":0.0000026461532,"teacher_disagreement_score":0.35709402,"about_ca_system_score_codex":0.00029885105,"about_ca_system_score_gemma":0.000032152937,"threshold_uncertainty_score":0.99958587},"labels":[],"label_agreement":null},{"id":"W4389257862","doi":"10.1007/978-3-031-46866-7_2","title":"Exploratory Data Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Exploratory data analysis; Outlier; Computer science; Dimensionality reduction; Cluster analysis; Visualization; Data mining; Missing data; Exploratory analysis; Data visualization; Artificial intelligence; Machine learning; Data science","score_opus":0.04583593783609774,"score_gpt":0.22528942665580806,"score_spread":0.17945348881971032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257862","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007107794,0.000428869,0.9565892,0.00040169904,0.00081485073,0.00022371691,0.00015120493,0.0018725619,0.03951076],"genre_scores_gemma":[0.24834214,0.0026898922,0.25097185,0.0019968278,0.006457581,0.0004116735,0.0013745043,0.0027748654,0.48498067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676067,0.000028629933,0.0005691253,0.0014137665,0.00073668204,0.0004911059],"domain_scores_gemma":[0.9953448,0.0009125423,0.00021652076,0.0032458608,0.0000608452,0.00021938974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061148347,0.00059405225,0.0010022456,0.0009049166,0.00013990878,0.00027305834,0.0031964292,0.00042566663,0.00019100771],"category_scores_gemma":[0.00055186846,0.0005387177,0.0005913055,0.000595002,0.000019567146,0.00023270558,0.0013259585,0.0006893759,0.0003049244],"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":[0.000010769374,0.000021973528,1.9772793e-7,0.00008422025,0.004729564,0.0002053451,0.000043434473,0.10158593,0.00045665522,0.8379026,0.001328016,0.05363129],"study_design_scores_gemma":[0.00005626617,0.000054366206,0.0000037306102,0.00023483914,0.0013468809,0.000005227435,0.0000035215082,0.9553896,0.0015709187,0.0050494936,0.035302516,0.000982623],"about_ca_topic_score_codex":0.000007605212,"about_ca_topic_score_gemma":0.00002358472,"teacher_disagreement_score":0.8538037,"about_ca_system_score_codex":0.000084257015,"about_ca_system_score_gemma":0.000052914114,"threshold_uncertainty_score":0.99970645},"labels":[],"label_agreement":null},{"id":"W4389257879","doi":"10.1007/978-3-031-46866-7_5","title":"Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian optimization; Computer science; Process (computing); Machine learning; Particle swarm optimization; Hyperparameter optimization; Artificial intelligence; Metaheuristic; Multi-swarm optimization; Engineering optimization; Process optimization; Inference; Optimization problem; Mathematical optimization; Algorithm; Engineering; Support vector machine; Mathematics","score_opus":0.010883780257469423,"score_gpt":0.18305276678113133,"score_spread":0.17216898652366192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389257879","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011104131,0.00035886923,0.7780841,0.00010985559,0.0024211523,0.0005687738,0.00008136477,0.00708288,0.2112819],"genre_scores_gemma":[0.30418134,0.016781598,0.104655914,0.00088983466,0.008238413,0.0014165743,0.0010838173,0.011048287,0.5517042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985054,0.0000052446744,0.00036521524,0.00040562495,0.00036288315,0.00035561447],"domain_scores_gemma":[0.9990915,0.00030161045,0.00005531742,0.00038455657,0.000034295816,0.00013271483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011829602,0.0005689478,0.00047855993,0.00037215743,0.000056942506,0.000073939256,0.00026814642,0.00064448087,0.00062527996],"category_scores_gemma":[0.00018869342,0.0005766557,0.00018935643,0.0000680762,0.000009379455,0.00005445176,0.000041935255,0.0006153295,0.00025417455],"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":[0.000005755835,0.0000028251097,5.490761e-9,0.0002450556,0.00009672924,0.000011397912,0.000005870524,0.9783483,0.000057271052,0.015640493,0.0004998581,0.005086405],"study_design_scores_gemma":[0.000073519994,0.000027529193,4.9273217e-7,0.00053439743,0.00007805539,0.0000035687635,5.332016e-7,0.9724681,0.010771533,0.0012537199,0.014107273,0.0006812808],"about_ca_topic_score_codex":8.822644e-7,"about_ca_topic_score_gemma":0.0000013710128,"teacher_disagreement_score":0.6734282,"about_ca_system_score_codex":0.00013715202,"about_ca_system_score_gemma":0.000013779247,"threshold_uncertainty_score":0.9996685},"labels":[],"label_agreement":null}]}