{"meta":{"query_hash":"52aa5ad3b5d4","filters":{"venue":"Science, technology and social development proceedings series."},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/52aa5ad3b5d4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Science%2C+technology+and+social+development+proceedings+series."},"results":[{"id":"W4403359846","doi":"10.70088/gyxfz858","title":"Prediction of Canadian Federal Election Results Based on Multilevel Regression and Post-Stratification","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":0,"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":"Stratification (seeds); Regression; Multilevel model; Statistics; Regression analysis; Environmental science; Econometrics; Mathematics; Biology","score_opus":0.03369866507648832,"score_gpt":0.2935642430255804,"score_spread":0.25986557794909204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403359846","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":[0.9555796,0.00059224473,0.009846898,0.0035929563,0.00013631531,0.00022569833,0.017284507,0.0003109959,0.012430705],"genre_scores_gemma":[0.98552865,0.0001474982,0.0032901645,0.00010004356,0.000019533452,0.00007961652,0.007055773,0.00003758889,0.0037411794],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976374,0.0006185999,0.000096391675,0.0005085871,0.00049609906,0.0006429455],"domain_scores_gemma":[0.992642,0.0021448312,0.0010299742,0.0007615477,0.0027438134,0.00067784573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059228293,0.0005594544,0.00071407703,0.0019958008,0.0018859826,0.0016641081,0.0013088563,0.0004711681,0.0071008583],"category_scores_gemma":[0.020279298,0.0002912273,0.0015130313,0.0029456124,0.0005940967,0.00070998055,0.0013040799,0.0016577905,0.0010970206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029716053,0.000063556814,0.9505716,0.000066688706,0.0003421504,0.00006745892,0.00059452286,0.0070149424,0.00014283968,0.0047834944,0.011878768,0.024176843],"study_design_scores_gemma":[0.00006562038,0.000072464165,0.91063625,0.00010721711,0.00029572443,0.000025210733,0.0013159676,0.07260385,0.00041892094,0.0020646616,0.01232734,0.00006675623],"about_ca_topic_score_codex":0.9517166,"about_ca_topic_score_gemma":0.95826757,"teacher_disagreement_score":0.0482834,"about_ca_system_score_codex":0.010396235,"about_ca_system_score_gemma":0.0149936415,"threshold_uncertainty_score":0.097135425},"labels":[],"label_agreement":null},{"id":"W4404414979","doi":"10.70088/8f92z263","title":"An Empirical Study on the Determinants of Housing Prices in Beijing and Model Optimization","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Korean Urban and Social Studies","field":"Environmental 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":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Beijing; Econometrics; Computer science; Business; Economics; Geography; China; Archaeology","score_opus":0.02085861611085667,"score_gpt":0.2819385987532715,"score_spread":0.26107998264241483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404414979","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":[0.99434847,0.00027930998,0.003585479,0.00026694525,0.000006721834,0.00002107704,0.00039275794,0.00003897873,0.0010601238],"genre_scores_gemma":[0.99691725,0.000211954,0.0015365015,0.000016264852,0.0000063350108,0.00002615537,0.00088059716,0.000008210256,0.00039674062],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993686,0.00027633263,0.000042245192,0.00013486657,0.00008431054,0.000093535404],"domain_scores_gemma":[0.9973362,0.0020223637,0.00026072864,0.00013058675,0.00017298086,0.00007712109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016767499,0.00061832933,0.0006332307,0.0007406203,0.000356432,0.0011041295,0.000769484,0.0005131422,0.0020644118],"category_scores_gemma":[0.0056254277,0.00042355317,0.0007199463,0.0019737808,0.00047080265,0.0013874632,0.00070083066,0.0008698384,0.0001852321],"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":[0.00016941024,0.00030215117,0.63320416,0.0002469159,0.00030978335,0.0009814919,0.00047957126,0.3221006,0.0008579265,0.0055627264,0.002098067,0.03368717],"study_design_scores_gemma":[0.000029184761,0.000106788044,0.2581559,0.000034246572,0.000081964026,0.00009809137,0.0009833663,0.736255,0.00062322075,0.0021588015,0.0014465069,0.000026925469],"about_ca_topic_score_codex":0.05374039,"about_ca_topic_score_gemma":0.044162806,"teacher_disagreement_score":0.05374039,"about_ca_system_score_codex":0.0019091453,"about_ca_system_score_gemma":0.0013941128,"threshold_uncertainty_score":0.106855154},"labels":[],"label_agreement":null},{"id":"W4404617864","doi":"10.70088/nfqn2e82","title":"The Application of Machine Learning in Finance: Situation and Challenges","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Finance; Computer science; Business; Artificial intelligence","score_opus":0.04917615059396748,"score_gpt":0.3397595198631979,"score_spread":0.29058336926923045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404617864","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007663805,0.73857814,0.037833944,0.19293264,0.0023698944,0.000036222937,0.00015451649,0.00013484611,0.020296074],"genre_scores_gemma":[0.19615673,0.7292305,0.03220097,0.0173908,0.01793997,0.00013234661,0.00023064343,0.00008976866,0.0066283178],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961935,0.0017105745,0.00018416857,0.00048045834,0.0012243418,0.00020688174],"domain_scores_gemma":[0.9838616,0.012665053,0.00039645156,0.00051439373,0.0021319462,0.0004306025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008119427,0.0007355854,0.0012357469,0.0021300754,0.0011202855,0.004737855,0.0018087674,0.0060861288,0.0032495786],"category_scores_gemma":[0.012915876,0.0005083111,0.0006109917,0.0026268298,0.0043599554,0.009784804,0.0022018787,0.00664033,0.0019460956],"study_design_candidate":"not_applicable","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":[0.00012274184,0.00012416452,0.0036307166,0.002035068,0.00005750709,0.00028288955,0.00046436623,0.0060711373,0.00068162347,0.40033117,0.053382345,0.53281635],"study_design_scores_gemma":[0.000027601114,0.00015141822,0.0022584768,0.0021982698,0.00003084994,0.0007953353,0.001093417,0.028444659,0.0008987087,0.60473883,0.35925847,0.00010409273],"about_ca_topic_score_codex":0.0021873252,"about_ca_topic_score_gemma":0.0013962135,"teacher_disagreement_score":0.008119427,"about_ca_system_score_codex":0.0021157537,"about_ca_system_score_gemma":0.0019880496,"threshold_uncertainty_score":0.04294008},"labels":[],"label_agreement":null},{"id":"W4404617869","doi":"10.70088/t3ar0344","title":"Forecasting Models for Apple Inc. Stock Price Using Regression Smoothing and Box Jenkins Time Series Analysis","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Box–Jenkins; Econometrics; Time series; Regression analysis; Stock (firearms); Series (stratigraphy); Stock price; Regression; Smoothing; Exponential smoothing; Mathematics; Statistics; Computer science; Economics; Autoregressive integrated moving average; Engineering","score_opus":0.08503060164789271,"score_gpt":0.3478653884950637,"score_spread":0.262834786847171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404617869","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":[0.74631673,0.0014145627,0.24532317,0.0006779053,0.00008461852,0.00009456754,0.0015826871,0.0011378026,0.0033680713],"genre_scores_gemma":[0.91646343,0.0005910124,0.07746015,0.00006787027,0.00004191707,0.000111142974,0.002364486,0.000055422635,0.00284451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996184,0.00013311279,0.00002833478,0.0000958588,0.00009330342,0.000030911047],"domain_scores_gemma":[0.99855,0.00092448574,0.00020373057,0.00007273901,0.00021853535,0.000030572483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024473555,0.0005470351,0.000568094,0.0010990556,0.00020754145,0.00060889573,0.00048323278,0.00046809734,0.0010595077],"category_scores_gemma":[0.00450749,0.0002119672,0.0008857597,0.0011043067,0.0001666828,0.0006865433,0.00027541668,0.00067233265,0.00031594947],"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":[0.00007834592,0.000069587266,0.019503837,0.00006453243,0.00012009563,0.000083076346,0.00007411017,0.921524,0.001099464,0.004192999,0.0018453578,0.05134458],"study_design_scores_gemma":[0.0000025904974,0.000011179615,0.0024504722,0.0000047747703,0.0000083984005,0.0000063525717,0.0000075384505,0.9963201,0.00016570948,0.0007402249,0.00027676477,0.000005877147],"about_ca_topic_score_codex":0.022074398,"about_ca_topic_score_gemma":0.016888948,"teacher_disagreement_score":0.022074398,"about_ca_system_score_codex":0.00056817714,"about_ca_system_score_gemma":0.00068241375,"threshold_uncertainty_score":0.043891847},"labels":[],"label_agreement":null},{"id":"W4404617895","doi":"10.70088/n3mbj650","title":"Application of LSTM-Based Seq2Seq Models in Natural Language to SQL Conversion in Financial Domain","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Computer science; SQL; Domain (mathematical analysis); Programming language; Natural language; Natural language processing; Artificial intelligence; Mathematics","score_opus":0.023181368501331998,"score_gpt":0.3304489928041144,"score_spread":0.30726762430278237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404617895","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":[0.23400564,0.0010889731,0.748268,0.0017047316,0.00040726055,0.00018136746,0.001595031,0.0058027934,0.0069462126],"genre_scores_gemma":[0.8690865,0.00046034026,0.123061314,0.00062815653,0.00007318663,0.00021219083,0.0016934545,0.00013842499,0.0046463883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969137,0.0001070109,0.000028602542,0.00008819097,0.000048201586,0.00003675193],"domain_scores_gemma":[0.99925023,0.00046230506,0.000050573308,0.000048735237,0.00016158783,0.000026462443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010278764,0.0006710114,0.0003936024,0.00033925683,0.00025741305,0.00076144736,0.0009171937,0.0007644766,0.0023699857],"category_scores_gemma":[0.0026791326,0.00020594968,0.0005160991,0.00049680844,0.00028679884,0.0013658297,0.00051871885,0.0011622516,0.0008524034],"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":[0.0004125558,0.00030429038,0.0050235745,0.00020917461,0.00015849284,0.00048537785,0.00034385078,0.6904838,0.019286502,0.0073595284,0.007093574,0.26883924],"study_design_scores_gemma":[0.0000051716856,0.000038843315,0.0002613826,0.000006939156,0.000010702937,0.00002568694,0.00002068392,0.99351317,0.0034718555,0.002093046,0.0005447027,0.000007854311],"about_ca_topic_score_codex":0.008182444,"about_ca_topic_score_gemma":0.009692462,"teacher_disagreement_score":0.008182444,"about_ca_system_score_codex":0.00068410515,"about_ca_system_score_gemma":0.00095597556,"threshold_uncertainty_score":0.016269624},"labels":[],"label_agreement":null}]}