{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"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":"51d560bebe17","filters":{"venue":"Statistics Optimization & Information Computing"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.0287477710404654,"gpt":0.2792570264791077,"spread":0.2505092554386423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000387941,0.0002071246,0.0002798344,0.00004565758,0.0005354116,0.0001575529,0.0002972079,0.0001042049,0.0001043878],"category_scores_gemma":[0.002027025,0.0001458736,0.00005850152,0.0004788194,0.0003002845,0.0005278576,0.0002053539,0.0003816951,0.000008115578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000370269,"about_ca_system_score_gemma":0.00009094121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003662538,"about_ca_topic_score_gemma":0.000004527124,"domain_scores_codex":[0.9981198,0.0001303761,0.0009499318,0.0001597845,0.0004151179,0.0002249983],"domain_scores_gemma":[0.9972408,0.001083848,0.0007231099,0.000226203,0.0006172773,0.0001087172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003159083,0.00001116255,0.00000519736,0.00007193243,0.00001550237,3.968836e-8,0.002940915,0.5298098,0.000002352694,0.4642372,0.0003074565,0.002566854],"study_design_scores_gemma":[0.001309213,0.00001506981,0.0001957881,0.00005761814,0.00006429314,0.000004463982,0.000452111,0.9752881,0.00002031913,0.02239614,0.00004083153,0.0001560598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000563765,0.000008279226,0.9957213,0.001442506,0.0001083842,0.0006532948,0.0007349168,0.00009234847,0.0006752035],"genre_scores_gemma":[0.5870252,0.000003701629,0.4120831,0.000627299,0.00003799633,0.00001727077,0.0001849994,0.00001218954,0.000008260531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5864614,"threshold_uncertainty_score":0.5948552,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.02329019575944726,"gpt":0.2514189199685227,"spread":0.2281287242090755,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004961538,0.0001501946,0.0002213766,0.0004101361,0.0002163481,0.0002136157,0.0001849029,0.00006535298,0.00000922121],"category_scores_gemma":[0.0001383804,0.0001642306,0.00004939794,0.000296239,0.00004462142,0.001652931,0.00004048764,0.00008053896,0.000001869911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004520354,"about_ca_system_score_gemma":0.00008275796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005455964,"about_ca_topic_score_gemma":6.192671e-7,"domain_scores_codex":[0.9986487,0.0000466957,0.0007009956,0.000194108,0.0002253952,0.0001840738],"domain_scores_gemma":[0.9986418,0.0002367138,0.0002218608,0.0001659085,0.0006818539,0.00005180819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001434648,0.00003494734,0.00001752419,0.0002908781,0.00002699107,2.854822e-7,0.0006111627,0.160537,0.000008512582,0.008529663,0.0002187777,0.8297099],"study_design_scores_gemma":[0.0004971173,0.00003198663,0.0001126611,0.00008859535,0.00003024051,0.00001673837,0.0001426457,0.9919602,0.002233935,0.004710632,0.00001435224,0.0001608823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001481144,0.00001114817,0.9912814,0.00006024511,0.000199653,0.0006579433,0.0001521647,0.0001129711,0.00604333],"genre_scores_gemma":[0.1304319,0.00003801594,0.8686324,0.0001670724,0.00001680243,0.00001919672,0.0006672379,0.000006376125,0.00002094334],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8314232,"threshold_uncertainty_score":0.6697131,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.04156561893067718,"gpt":0.3439772174284305,"spread":0.3024115984977533,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005932159,0.0002431511,0.0002491927,0.0001490419,0.000679296,0.0002899266,0.0001427283,0.0001423657,0.0003544728],"category_scores_gemma":[0.002644088,0.0002551675,0.00003180133,0.0004281123,0.0002544712,0.0006660608,0.00009562365,0.0002024475,0.00006516895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299599,"about_ca_system_score_gemma":0.0002203057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006897038,"about_ca_topic_score_gemma":0.000007281773,"domain_scores_codex":[0.99796,0.00008839141,0.0009747776,0.00022832,0.0004086556,0.0003398151],"domain_scores_gemma":[0.9964513,0.0007750016,0.0007096241,0.0002059016,0.001674582,0.0001836604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009541328,0.00005783692,0.00004379262,0.00004241085,0.00002474882,1.361266e-7,0.0004418733,0.2210977,0.000006598899,0.7706532,0.005228426,0.002393755],"study_design_scores_gemma":[0.0005672642,0.00004476031,0.0008579462,0.00004665843,0.0000424037,0.000002515925,0.0001249346,0.8672152,0.0000266095,0.1306353,0.0001864578,0.0002499982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009119548,0.000003020862,0.9939696,0.0005511105,0.0001127209,0.0004598988,0.001313986,0.0002738325,0.002403859],"genre_scores_gemma":[0.5864872,0.0000117489,0.4091304,0.0005794375,0.00004791855,0.00001987988,0.003635935,0.00001729981,0.00007015948],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6461174,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.04746319776124497,"gpt":0.4133984807299549,"spread":0.3659352829687099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007925451,0.000208841,0.0002363071,0.0002439714,0.002634241,0.0001045982,0.0001704748,0.0001559852,0.0002671333],"category_scores_gemma":[0.005460855,0.0002269245,0.00003991356,0.0006124977,0.00005887488,0.001426915,0.000198252,0.0008261508,0.0001809742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003626691,"about_ca_system_score_gemma":0.0006601325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009354718,"about_ca_topic_score_gemma":0.00002856647,"domain_scores_codex":[0.9969664,0.0004106682,0.001537909,0.0002299477,0.0003584914,0.0004965391],"domain_scores_gemma":[0.9960985,0.001536535,0.0007501401,0.0002565061,0.001221215,0.0001371063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000481575,0.00001667394,0.02133418,0.0005694373,0.000011862,4.800522e-7,0.003875511,0.9233823,0.000001981789,0.01589118,0.003226596,0.03164168],"study_design_scores_gemma":[0.0002585125,0.00003024461,0.001209236,0.0002789755,0.00002525449,3.752801e-7,0.002367049,0.9841276,0.00001096703,0.001310231,0.01022254,0.0001589675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005010981,0.00006223044,0.9923745,0.00110971,0.001900152,0.001096258,0.0004011975,0.0004908724,0.00206393],"genre_scores_gemma":[0.2939771,0.0001314648,0.6930562,0.006759694,0.0004978273,0.0001202748,0.004843036,0.0000470502,0.0005673496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2993183,"threshold_uncertainty_score":0.9986642,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.005838467422967215,"gpt":0.234335996638628,"spread":0.2284975292156608,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005336359,0.0001962037,0.0002508691,0.0003121389,0.0002143551,0.0001577149,0.0001070004,0.00009672704,0.00003141789],"category_scores_gemma":[0.0002337253,0.0002147691,0.00001737623,0.0006795154,0.0001380533,0.0006883657,0.0000585674,0.0001645072,0.00000266052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008315784,"about_ca_system_score_gemma":0.0001235281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005288488,"about_ca_topic_score_gemma":9.657424e-7,"domain_scores_codex":[0.9984977,0.00006022694,0.0008282713,0.0001408982,0.000227694,0.0002452357],"domain_scores_gemma":[0.9986668,0.0002576769,0.0003016854,0.0001492614,0.0005654962,0.00005913613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007536713,0.000007420001,0.002321854,0.0003410544,0.00004135264,2.749719e-7,0.0007761109,0.9313717,0.00002810971,0.01113818,0.00005696865,0.05390945],"study_design_scores_gemma":[0.0007263146,0.00003344054,0.0005418902,0.0002293186,0.00003744545,0.000004832342,0.0004366675,0.9971906,0.0004445433,0.0001190702,0.00003972145,0.000196132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003984097,0.0000196804,0.9910806,0.00003484738,0.0001169296,0.0003308722,0.00005375648,0.0003232591,0.004055982],"genre_scores_gemma":[0.4630386,0.00001825996,0.5367206,0.00004410934,0.000009580361,0.000003047534,0.0001453524,0.00001344935,0.000006999216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4590545,"threshold_uncertainty_score":0.8758029,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Computer science; Engineering; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.009189233084896655,"gpt":0.2591130718710099,"spread":0.2499238387861132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004200883,0.0001641878,0.0001767716,0.0005351033,0.0003699342,0.000394594,0.0003164831,0.00007169244,0.000003010549],"category_scores_gemma":[0.00004494628,0.0001906122,0.00003477056,0.001005204,0.0000320381,0.001115103,0.0000740504,0.0001727986,0.000005885719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223668,"about_ca_system_score_gemma":0.0001384562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009509388,"about_ca_topic_score_gemma":0.000008249163,"domain_scores_codex":[0.9984306,0.00005035512,0.0007960968,0.0002134371,0.0002403864,0.0002691286],"domain_scores_gemma":[0.9989075,0.00007462235,0.0003007469,0.0002790444,0.0003897292,0.00004833618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003281403,0.00002322514,0.000156311,0.00003319381,0.00000354484,3.124761e-7,0.0001299565,0.9601209,0.00003717232,0.02230472,0.00004226304,0.01714509],"study_design_scores_gemma":[0.0002925701,0.00001648793,0.0003388578,0.00003234467,0.000006104367,0.000009418934,0.0000416742,0.9983327,0.0004521784,0.0001421728,0.0001559537,0.0001795749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003226801,0.00001250606,0.9946058,0.0000247384,0.0003060968,0.0006378014,0.00005993514,0.0003137132,0.000812559],"genre_scores_gemma":[0.637804,0.000009337044,0.361989,0.00007021706,0.00001000661,0.00003747252,0.00005787815,0.000005161804,0.0000169365],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6345772,"threshold_uncertainty_score":0.7772942,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Cellular manufacturing; Computer science; Engineering; Manufacturing engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.003772466434183708,"gpt":0.2071512032245166,"spread":0.2033787367903329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004006015,0.000300194,0.000292182,0.0004742144,0.0002237376,0.000267873,0.0001831456,0.0001664569,0.00002310116],"category_scores_gemma":[0.0001137209,0.0003661397,0.00003587667,0.0003937862,0.00004056675,0.0008798222,0.0000671665,0.00030853,0.00001817106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008691481,"about_ca_system_score_gemma":0.00009156165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004999044,"about_ca_topic_score_gemma":0.000009965567,"domain_scores_codex":[0.9979746,0.0000577672,0.001052689,0.0002263692,0.0002986773,0.0003898389],"domain_scores_gemma":[0.9988598,0.0002340306,0.0002624553,0.0003054292,0.0002719355,0.0000663795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009674433,0.00001813313,0.00003831998,0.0006263746,0.00000731114,5.652227e-7,0.0006501555,0.9943515,0.00001334376,0.0006746207,0.0001868045,0.003423203],"study_design_scores_gemma":[0.0007270908,0.0000116582,0.0003679164,0.0001053434,0.00001742454,8.916899e-7,0.0003428509,0.9964218,0.001053757,0.0003273322,0.0003055453,0.0003183462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001825935,0.0001030559,0.9844452,0.00001292114,0.0009548499,0.0005286576,0.00007212994,0.0005006467,0.01155661],"genre_scores_gemma":[0.620044,0.00006996111,0.3792562,0.000026884,0.00001619485,0.0000125896,0.0004801001,0.00001986091,0.00007419458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6182181,"threshold_uncertainty_score":0.9998791,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.06453785820404344,"gpt":0.3825543744520991,"spread":0.3180165162480557,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007529182,0.0001360956,0.0004277293,0.0003775037,0.0006700024,0.00003150327,0.00007575152,0.0001150117,0.00008862678],"category_scores_gemma":[0.0006335511,0.0001588244,0.00002131409,0.0005469465,0.00004740311,0.000416156,0.0001068039,0.0003619442,0.000007597261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108807,"about_ca_system_score_gemma":0.0002442866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007621495,"about_ca_topic_score_gemma":0.00008257822,"domain_scores_codex":[0.997873,0.0003195402,0.001210465,0.0001828603,0.0001416362,0.0002724711],"domain_scores_gemma":[0.9975631,0.0009873824,0.0009566445,0.0001090214,0.0001914966,0.0001924186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005702573,0.00001607491,0.01050299,0.0005880209,0.00007496357,1.507199e-7,0.04757837,0.7690877,2.765615e-7,0.1704699,0.0001570911,0.001467514],"study_design_scores_gemma":[0.0009146364,0.00006568142,0.01097155,0.00009537631,0.00004613726,9.882628e-8,0.004121511,0.981992,2.199172e-7,0.001153269,0.0005195104,0.0001200049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01643541,0.00003437588,0.9785764,0.001941931,0.0001717116,0.00116394,0.0008104488,0.00005991524,0.0008059309],"genre_scores_gemma":[0.8346466,0.0001461593,0.1595625,0.004663444,0.0000407927,0.00002522769,0.0009020958,0.00001048423,0.000002736097],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8190139,"threshold_uncertainty_score":0.6476671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}