{"meta":{"query_hash":"51d560bebe17","filters":{"venue":"Statistics Optimization & Information Computing"},"cohort_total":8,"direct_labels_cover":0,"predictions_cover":8,"exported":8,"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/51d560bebe17","api":"https://metacan.xera.ac/api/v1/cohort?venue=Statistics+Optimization+%26+Information+Computing"},"results":[{"id":"W2886710868","doi":"10.19139/soic.v6i3.573","title":"Proportional Odds under Conway-Maxwell-Poisson Cure Rate Model and Associated Likelihood Inference","year":2018,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Weibull distribution; Poisson distribution; Inference; Statistics; Mathematics; Logistic regression; Odds; Estimator; Poisson regression; Applied mathematics; Econometrics; Computer science; Artificial intelligence; Medicine; Population","score_opus":0.04156561893067718,"score_gpt":0.3439772174284305,"score_spread":0.30241159849775334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886710868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073140017,0.0006899409,0.9898767,0.00042707135,0.000034329616,0.000078163095,0.0003115,0.00015404008,0.0011141679],"genre_scores_gemma":[0.45825574,0.0036702808,0.5236549,0.000527076,0.00045055628,0.0010349145,0.0019911153,0.00022004898,0.010195423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99513334,0.0028765763,0.00023504008,0.0009002097,0.000570173,0.00028468508],"domain_scores_gemma":[0.9816792,0.015009078,0.0013068435,0.0011525806,0.0006611629,0.0001911268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011498418,0.001160795,0.00220261,0.0019093724,0.0007491002,0.0021290914,0.003705383,0.0021920258,0.0048630727],"category_scores_gemma":[0.04274986,0.00075647864,0.002228783,0.0027000525,0.0015216578,0.0036335657,0.0024032348,0.0037687868,0.0008748348],"study_design_candidate":"theoretical_or_conceptual","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":[0.00029136933,0.00016256198,0.012863937,0.00052184553,0.00025290245,0.00079979305,0.00047843572,0.40871182,0.00078252645,0.40832222,0.004764282,0.16204832],"study_design_scores_gemma":[0.000033844473,0.000059038644,0.0012835895,0.00005016526,0.000063846834,0.00041532776,0.000080590966,0.822666,0.00045866065,0.17152183,0.0033306591,0.0000365349],"about_ca_topic_score_codex":0.0036969674,"about_ca_topic_score_gemma":0.0026934443,"teacher_disagreement_score":0.011498418,"about_ca_system_score_codex":0.0012780409,"about_ca_system_score_gemma":0.0019405484,"threshold_uncertainty_score":0.06081021},"labels":[],"label_agreement":null},{"id":"W3007645312","doi":"10.19139/soic-2310-5070-863","title":"Integral stochastic ordering of the multivariate normal mean-variance and the skew-normal scale-shape mixture models","year":2020,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Kurtosis; Mathematics; Skewness; Skew; Multivariate normal distribution; Univariate; Normal distribution; Stochastic ordering; Bivariate analysis; Multivariate statistics; Variance-gamma distribution; Skew normal distribution; Applied mathematics; Statistics; Computer science; Asymptotic distribution","score_opus":0.0287477710404654,"score_gpt":0.2792570264791077,"score_spread":0.25050925543864233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007645312","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":[0.013730023,0.0011180711,0.9813286,0.00048433396,0.000106276835,0.000070159476,0.0005586673,0.00024838466,0.0023553735],"genre_scores_gemma":[0.67602134,0.0076902914,0.2717761,0.0006764688,0.00072848063,0.0009393344,0.004345576,0.0005865205,0.03723594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996849,0.0013879853,0.00018281881,0.00075106113,0.00046945686,0.00035970897],"domain_scores_gemma":[0.9884459,0.007906978,0.001397104,0.00069335283,0.0011954874,0.00036115502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0078729475,0.0020452149,0.002805874,0.0021439123,0.00071221584,0.003197028,0.0033595278,0.0023316958,0.006958927],"category_scores_gemma":[0.020516891,0.00127736,0.00324136,0.0021845524,0.0030242342,0.0038237826,0.0022633485,0.0034909158,0.0017421086],"study_design_candidate":"theoretical_or_conceptual","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":[0.00018637264,0.00008308002,0.0053751883,0.00032246078,0.000299934,0.00026721737,0.00023217754,0.5070751,0.0008599463,0.4356945,0.0040430743,0.04556091],"study_design_scores_gemma":[0.0000125529195,0.000032954882,0.0008104504,0.000035905483,0.00003404845,0.000055229844,0.0000230967,0.91912174,0.00013608942,0.07813668,0.001568786,0.000032481763],"about_ca_topic_score_codex":0.014368743,"about_ca_topic_score_gemma":0.010963703,"teacher_disagreement_score":0.014368743,"about_ca_system_score_codex":0.0023882305,"about_ca_system_score_gemma":0.0025164916,"threshold_uncertainty_score":0.041636646},"labels":[],"label_agreement":null},{"id":"W3032905675","doi":"10.19139/soic-2310-5070-506","title":"Applying Multivariate and Univariate Analysis of Variance on Socioeconomic, Health, and Security Variables in Jordan","year":2020,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","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":"Humber Polytechnic; University of Guelph-Humber","funders":"","keywords":"Univariate; Multivariate statistics; Socioeconomic status; Multivariate analysis; Multivariate analysis of variance; Unemployment; Demography; Statistics; Geography; Descriptive statistics; Variance (accounting); Population; Mathematics; Economics; Economic growth; Sociology","score_opus":0.06453785820404344,"score_gpt":0.3825543744520991,"score_spread":0.31801651624805566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032905675","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":[0.9651694,0.0005134956,0.029359212,0.00032968738,0.00020360757,0.0005120755,0.0009405911,0.0001529413,0.002818847],"genre_scores_gemma":[0.9750447,0.0002876645,0.021980079,0.00006520083,0.00011636637,0.0008497359,0.0008043562,0.00005211045,0.0007998102],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9789834,0.015748082,0.00086608174,0.0018006832,0.001701987,0.0008998218],"domain_scores_gemma":[0.97092235,0.021784294,0.0025643862,0.0022204134,0.0019547427,0.00055379834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01800034,0.0009973429,0.0012970645,0.0037748124,0.00078324316,0.0015273227,0.0008072097,0.00040182966,0.0036323972],"category_scores_gemma":[0.036171522,0.0003485433,0.0029468099,0.0036747323,0.0010313365,0.001345972,0.0018295994,0.0013009016,0.0002291905],"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":[0.00057075085,0.0006744512,0.8760642,0.00022193769,0.0029417546,0.00038590442,0.004686075,0.0067076124,0.0008778608,0.007551957,0.0024081448,0.0969094],"study_design_scores_gemma":[0.000060720195,0.0021131483,0.88155746,0.0001992671,0.00066364877,0.00015487726,0.006367933,0.094393454,0.0011734373,0.0067864787,0.0063843634,0.00014525208],"about_ca_topic_score_codex":0.015005682,"about_ca_topic_score_gemma":0.008069095,"teacher_disagreement_score":0.01800034,"about_ca_system_score_codex":0.0009766541,"about_ca_system_score_gemma":0.0022766655,"threshold_uncertainty_score":0.09519601},"labels":[],"label_agreement":null},{"id":"W4408482362","doi":"10.19139/soic-2310-5070-2259","title":"Abnormal Behavior Detection in Surveillance Systems Using a Hybrid EfficientNet-Transformer Model","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Computer science; Engineering; Electrical engineering","score_opus":0.009189233084896655,"score_gpt":0.2591130718710099,"score_spread":0.24992383878611324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408482362","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":[0.09764098,0.00028374203,0.89688694,0.00029805073,0.00005441304,0.00006102766,0.00028522254,0.0024232564,0.002066311],"genre_scores_gemma":[0.9322118,0.0001865922,0.06404757,0.0001115956,0.000030631305,0.000060584196,0.0005236417,0.00007809269,0.0027494438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997986,0.00003652856,0.000011222691,0.0000673002,0.000056205095,0.000030091938],"domain_scores_gemma":[0.9997234,0.000109381304,0.000028629958,0.000033820892,0.00008626226,0.000018505862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042251693,0.00059852644,0.00052856177,0.00063690223,0.00017860034,0.00057831657,0.0009730351,0.00038394184,0.0011949218],"category_scores_gemma":[0.00094882475,0.00029073848,0.0005100962,0.00039192406,0.00030844862,0.0010592822,0.0004940915,0.0005530132,0.000363097],"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":[0.00023017949,0.00016462631,0.0042101336,0.000050401908,0.000087071574,0.00010482074,0.000042754134,0.8542511,0.010734125,0.0052625863,0.0018013223,0.12306085],"study_design_scores_gemma":[0.0000018409849,0.000010198284,0.00009115735,5.6820414e-7,0.0000032466953,0.000007728184,0.0000014429675,0.99869233,0.00053773716,0.00058176613,0.00007075618,0.0000012031794],"about_ca_topic_score_codex":0.011889988,"about_ca_topic_score_gemma":0.016489057,"teacher_disagreement_score":0.011889988,"about_ca_system_score_codex":0.0008539781,"about_ca_system_score_gemma":0.00084769947,"threshold_uncertainty_score":0.023641586},"labels":[],"label_agreement":null},{"id":"W4412020141","doi":"10.19139/soic-2310-5070-2521","title":"A Hybrid Approach of Long Short Term Memory and Transformer Models for Speech Emotion Recognition","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Term (time); Transformer; Long short term memory; Computer science; Short-term memory; Cognitive psychology; Natural language processing; Psychology; Artificial intelligence; Cognition; Engineering; Artificial neural network; Working memory; Electrical engineering; Recurrent neural network; Neuroscience","score_opus":0.02329019575944726,"score_gpt":0.2514189199685227,"score_spread":0.22812872420907546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412020141","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":[0.046435997,0.0015482644,0.94178796,0.0003979523,0.0002997806,0.00009752975,0.00034462442,0.0041735508,0.0049143317],"genre_scores_gemma":[0.8234814,0.0012253037,0.16361667,0.00048571426,0.00012288711,0.0001439458,0.0010195232,0.00020053319,0.009704035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974924,0.000047649417,0.000019098476,0.00008564414,0.000059276226,0.000039215305],"domain_scores_gemma":[0.99973303,0.000093073344,0.00001861078,0.000038005768,0.00009989418,0.000017402466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005917244,0.0009953594,0.0005656333,0.0005566019,0.00020015445,0.0007897,0.0011334049,0.00061631063,0.002613223],"category_scores_gemma":[0.0010504213,0.0002656061,0.000957247,0.00050786114,0.00024531264,0.0016821377,0.00076853234,0.0012340295,0.0014799181],"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":[0.000589583,0.00033288985,0.0021745085,0.0002642629,0.00039004415,0.0002706053,0.0001700947,0.1095457,0.06660797,0.0064620795,0.0059143384,0.807278],"study_design_scores_gemma":[0.000016231248,0.00019819265,0.00056510145,0.00001769063,0.00010706802,0.00014497235,0.000043455428,0.9781279,0.015337447,0.0035956278,0.0018240706,0.000022198099],"about_ca_topic_score_codex":0.0039461376,"about_ca_topic_score_gemma":0.006255255,"teacher_disagreement_score":0.0039461376,"about_ca_system_score_codex":0.00041448974,"about_ca_system_score_gemma":0.0006028156,"threshold_uncertainty_score":0.008742094},"labels":[],"label_agreement":null},{"id":"W4413001899","doi":"10.19139/soic-2310-5070-2367","title":"Optimizing cell load regulation capability in dynamic cell manufacturing systems","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Advanced Manufacturing and Logistics Optimization","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":"Laurentian University","funders":"","keywords":"Cellular manufacturing; Computer science; Engineering; Manufacturing engineering","score_opus":0.003772466434183708,"score_gpt":0.2071512032245166,"score_spread":0.2033787367903329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413001899","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":[0.4062654,0.00053032057,0.57878244,0.00046842915,0.00006481795,0.00009509575,0.00027557547,0.00043743502,0.013080382],"genre_scores_gemma":[0.9843774,0.00010731432,0.014336447,0.000036366528,0.00000732189,0.000037462574,0.00006480921,0.000029782552,0.0010032549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997402,0.00006798429,0.00000803753,0.000046846588,0.000058147107,0.00007877866],"domain_scores_gemma":[0.999619,0.00020522076,0.00006569705,0.000023206429,0.000050440318,0.000036507096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005342257,0.0006391092,0.00053826487,0.0004273236,0.00043062482,0.0011558936,0.000676087,0.0006994088,0.0011155136],"category_scores_gemma":[0.0012517596,0.00034452506,0.00029132952,0.0007143609,0.00040907876,0.0007198337,0.0006430727,0.00044875962,0.00015361131],"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":[0.000015160761,0.000013403795,0.0001772198,0.000010876837,0.0000034571542,0.000019389263,0.000007055689,0.9947239,0.0013704642,0.0007974343,0.00011985758,0.002741807],"study_design_scores_gemma":[0.0000021633057,0.000016801016,0.00008109373,0.0000011276081,0.0000022544948,0.0000054140573,0.00000856672,0.9988066,0.0004346088,0.00047420934,0.00016535439,0.0000018800092],"about_ca_topic_score_codex":0.007269554,"about_ca_topic_score_gemma":0.005868331,"teacher_disagreement_score":0.007269554,"about_ca_system_score_codex":0.001137604,"about_ca_system_score_gemma":0.0011650132,"threshold_uncertainty_score":0.014454484},"labels":[],"label_agreement":null},{"id":"W4413001992","doi":"10.19139/soic-2310-5070-2319","title":"Improving Heart Disease Prediction Accuracy through Machine Learning Algorithms","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Algorithm","score_opus":0.04746319776124497,"score_gpt":0.4133984807299549,"score_spread":0.3659352829687099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413001992","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":[0.22637069,0.00785447,0.7498149,0.0029921837,0.00038169304,0.00017623261,0.0007370592,0.0040236483,0.0076490664],"genre_scores_gemma":[0.81258756,0.0015572588,0.182034,0.00046594997,0.00028072,0.000094968025,0.0009377139,0.00007517807,0.0019666436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911267,0.0003737552,0.00007222712,0.0001599421,0.00020892992,0.000072508905],"domain_scores_gemma":[0.9966947,0.002215842,0.0002858944,0.00020828916,0.0005313451,0.000063989566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030963891,0.0010147638,0.00094813114,0.0015910987,0.00028668155,0.0012205307,0.00086730556,0.0008674894,0.0012488537],"category_scores_gemma":[0.008686421,0.00025019582,0.00049820787,0.0010493321,0.00022644494,0.0011572123,0.00066710054,0.0010651179,0.0008975442],"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":[0.00030935518,0.00042586957,0.034158643,0.00015313902,0.00021821226,0.00008390517,0.00005567918,0.3486986,0.0022967204,0.0025194176,0.005504084,0.6055764],"study_design_scores_gemma":[0.0000160032,0.000080802856,0.002707352,0.000038834318,0.000027199912,0.000038894177,0.000014777326,0.9926448,0.0012403799,0.0024357513,0.0007449093,0.000010414275],"about_ca_topic_score_codex":0.004052157,"about_ca_topic_score_gemma":0.0036492415,"teacher_disagreement_score":0.004052157,"about_ca_system_score_codex":0.00051294145,"about_ca_system_score_gemma":0.0010949993,"threshold_uncertainty_score":0.016375482},"labels":[],"label_agreement":null},{"id":"W4415702155","doi":"10.19139/soic-2310-5070-2916","title":"Vehicle Routing Problem with Synchronization and Scheduling Constraints of support vehicles","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Vehicle Routing Optimization Methods","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":"Group for Research in Decision Analysis","funders":"","keywords":"Scheduling (production processes); Vehicle routing problem; Robustness (evolution); Synchronization (alternating current); Flow network; Job shop scheduling; Computation; Linear programming","score_opus":0.005838467422967215,"score_gpt":0.23433599663862803,"score_spread":0.22849752921566083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415702155","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":[0.22855897,0.0008604611,0.7472763,0.0017393981,0.00026255526,0.00051473273,0.0018794289,0.0007130263,0.018195095],"genre_scores_gemma":[0.83137584,0.00047225,0.15477681,0.00022194388,0.0001299421,0.0004949657,0.001466293,0.00016914334,0.010892824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987,0.00041772213,0.000058160495,0.00037590865,0.00015701177,0.0002910497],"domain_scores_gemma":[0.9987326,0.00078753027,0.00021597465,0.0000640304,0.000103199134,0.00009661969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010695109,0.0016227454,0.0014187976,0.00067755894,0.00070362835,0.001650391,0.0016230427,0.0020906266,0.0044986787],"category_scores_gemma":[0.0024371953,0.00088184513,0.0011941184,0.0012691475,0.00080937595,0.0018767901,0.000858721,0.0012214081,0.00042684682],"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":[0.00009313356,0.000050018574,0.0003264477,0.000079326644,0.0000305765,0.00015744545,0.000037363883,0.9808492,0.00080726197,0.008503944,0.0013288738,0.0077363825],"study_design_scores_gemma":[0.000065729546,0.00005171487,0.00018917973,0.000006332091,0.000016110023,0.00005517165,0.00004540478,0.9905021,0.00052601076,0.0071870987,0.0013456529,0.000009479016],"about_ca_topic_score_codex":0.010640035,"about_ca_topic_score_gemma":0.00621443,"teacher_disagreement_score":0.010640035,"about_ca_system_score_codex":0.0016047057,"about_ca_system_score_gemma":0.0022106122,"threshold_uncertainty_score":0.021156192},"labels":[],"label_agreement":null}]}