{"meta":{"query_hash":"fdae88af7433","filters":{"venue":"The Journal of Prediction Markets"},"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/fdae88af7433","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+Journal+of+Prediction+Markets"},"results":[{"id":"W1486580665","doi":"10.5750/jpm.v7i3.824","title":"Liquidity Provision and Cross Arbitrage in Continuous Double-Auction Prediction Markets","year":2014,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","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 British Columbia","funders":"","keywords":"Market liquidity; Arbitrage; Market maker; Bid price; Double auction; Economics; Stock (firearms); Market impact; Financial economics; Walrasian auction; Order (exchange); Business; Econometrics; Microeconomics; Monetary economics; Stock market; Revenue equivalence; Auction theory; Market microstructure; Finance; Common value auction","score_opus":0.014794196533049003,"score_gpt":0.22146425029089709,"score_spread":0.20667005375784808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486580665","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":[0.9876792,0.00070314086,0.005643897,0.0005195657,0.0013488565,0.00018109451,0.000042818618,0.000017020737,0.003864418],"genre_scores_gemma":[0.996718,0.0021376978,0.00004620061,0.00009031639,0.0005111273,0.0000032253477,0.0000059155536,0.000015241077,0.00047230063],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99844915,0.00006327211,0.0009965991,0.00017878441,0.000110679306,0.00020149787],"domain_scores_gemma":[0.9986592,0.00008320322,0.0008571999,0.0002146112,0.00009976573,0.00008604695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054577924,0.00013623289,0.00031903374,0.000292026,0.00015747882,0.0000940633,0.00016096918,0.00011963922,0.0001761624],"category_scores_gemma":[0.000108981025,0.000111231246,0.00008102796,0.00018413021,0.00008783264,0.0005719614,0.00004098749,0.00042179637,0.000009462217],"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":[0.0037759864,0.00025497208,0.9745093,0.00012507512,0.000119671684,0.000008559894,0.0010407936,0.0049258727,0.00018217835,0.0032270267,0.005228593,0.006601962],"study_design_scores_gemma":[0.0018945865,0.00026178407,0.88245624,0.000077735254,0.000020889505,0.00012809437,0.000047840993,0.089677095,0.000074642216,0.0031528831,0.022097666,0.00011056215],"about_ca_topic_score_codex":0.000063771804,"about_ca_topic_score_gemma":0.000013448655,"teacher_disagreement_score":0.092053086,"about_ca_system_score_codex":0.000087399065,"about_ca_system_score_gemma":0.00002096758,"threshold_uncertainty_score":0.45358786},"labels":[],"label_agreement":null},{"id":"W1498192041","doi":"10.5750/jpm.v3i1.455","title":"THE DESIGN OF IDEA MARKETS: AN ECONOMIST’S PERSPECTIVE","year":2012,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Incentive; Process (computing); Perspective (graphical); Economics; Management science; Computer science; Microeconomics; Marketing; Business; Artificial intelligence","score_opus":0.07816162452673893,"score_gpt":0.35981476123987605,"score_spread":0.28165313671313713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498192041","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":[0.3073779,0.002303088,0.6475166,0.009301624,0.0034945158,0.0007270354,0.00007200706,0.00004704988,0.02916014],"genre_scores_gemma":[0.99706894,0.00027228147,0.00043383203,0.00008567754,0.00054754544,0.000004674116,2.4759595e-7,0.000007868887,0.0015789338],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962238,0.0020648858,0.00083522574,0.00009658424,0.00060233683,0.00017713396],"domain_scores_gemma":[0.9931152,0.0044144206,0.001148137,0.0005001246,0.00068129064,0.00014083876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025799325,0.00009474792,0.00017596701,0.00012033654,0.00052550994,0.00007052861,0.0008560795,0.0000509428,0.0005435311],"category_scores_gemma":[0.0020524,0.00004587584,0.00012209323,0.00030316252,0.00030736698,0.00084051053,0.000053490632,0.00023520929,0.000042309133],"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":[0.021171095,0.0016066672,0.021224229,0.000012608687,0.0011384268,0.0000023874388,0.029082919,0.010686541,0.010582089,0.273099,0.42762858,0.20376545],"study_design_scores_gemma":[0.0012713763,0.0005981837,0.30081642,0.00003697518,0.00029472713,0.000655252,0.063317746,0.0068018124,0.0034026604,0.48468998,0.13785532,0.00025955233],"about_ca_topic_score_codex":0.0000035720363,"about_ca_topic_score_gemma":0.000001123691,"teacher_disagreement_score":0.689691,"about_ca_system_score_codex":0.000084733525,"about_ca_system_score_gemma":0.000089595414,"threshold_uncertainty_score":0.8941583},"labels":[],"label_agreement":null},{"id":"W1504426303","doi":"10.5750/jpm.v6i3.592","title":"LONG-TERM PREDICTION MARKETS","year":2013,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":9,"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":"Portfolio; Economics; Financial market; Outcome (game theory); Term (time); Prediction market; Time horizon; Market liquidity; Horizon; Financial economics; Constraint (computer-aided design); Market impact; Cash; Econometrics; Microeconomics; Monetary economics; Market microstructure; Finance","score_opus":0.016505381386007014,"score_gpt":0.2030008885685492,"score_spread":0.18649550718254218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504426303","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":[0.9724538,0.0018349198,0.0058404226,0.0010458791,0.0022163563,0.00021149348,0.00008210345,0.000025480369,0.016289571],"genre_scores_gemma":[0.993109,0.0031444207,0.00005625765,0.00018351508,0.00072084845,0.000004699829,0.000007498629,0.000018918443,0.002754857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985256,0.000036803962,0.00097080995,0.00012852642,0.00011668921,0.00022155078],"domain_scores_gemma":[0.99846494,0.00006871449,0.00091174396,0.00028548646,0.00015149669,0.00011760535],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002049305,0.00013485165,0.00027067473,0.00024684818,0.00016279386,0.00009087402,0.00030143932,0.00008954012,0.005106468],"category_scores_gemma":[0.000073790005,0.00010321201,0.00014830205,0.00019335534,0.000064897846,0.00067057955,0.00004132245,0.00030831734,0.00026676938],"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":[0.00016218916,0.00010428325,0.9284833,0.000033974553,0.00020547795,0.0000057270872,0.0002797427,0.0003080991,0.000060827635,0.00077194517,0.06472972,0.0048547294],"study_design_scores_gemma":[0.00048539083,0.00010371951,0.9715872,0.000043454398,0.00003225307,0.00013985448,0.000033875178,0.012888016,0.000022013044,0.0020453846,0.012527141,0.00009172864],"about_ca_topic_score_codex":0.000026989646,"about_ca_topic_score_gemma":0.0000027270576,"teacher_disagreement_score":0.05220258,"about_ca_system_score_codex":0.00009030284,"about_ca_system_score_gemma":0.00002370757,"threshold_uncertainty_score":0.995803},"labels":[],"label_agreement":null},{"id":"W1930997193","doi":"10.5750/jpm.v2i1.433","title":"THE IMPACT OF SENTIMENT ON POINT SPREADS IN THE COLLEGE FOOTBALL WAGERING MARKET","year":2012,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":4,"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":"Football; Arbitrage; Exploit; Financial economics; Efficient-market hypothesis; Point (geometry); Economics; Stock market; Computer science; History; Political science; Law; Computer security","score_opus":0.019360404621718827,"score_gpt":0.23974995423358014,"score_spread":0.2203895496118613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1930997193","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":[0.96822464,0.0013975304,0.00020721172,0.0007871787,0.00063361286,0.0001499923,0.00006973684,0.0000026060925,0.028527468],"genre_scores_gemma":[0.99742955,0.0017862003,0.000010401143,0.000058028265,0.0002464544,0.0000019018464,6.9109166e-7,0.000008191146,0.0004585625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885285,0.0000747657,0.00069362833,0.000054927757,0.0001048965,0.00021890456],"domain_scores_gemma":[0.9986834,0.00026099023,0.0007161699,0.00025627256,0.000037077793,0.000046124165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007685595,0.00009476077,0.00019760714,0.00013060159,0.00012821308,0.000027088761,0.0002999827,0.000036600966,0.00039747168],"category_scores_gemma":[0.000061514475,0.000046315643,0.00017499499,0.00017965837,0.000047080906,0.0001808823,0.000029988401,0.00024339756,0.000009059706],"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":[0.0017056247,0.00056921545,0.859817,0.000030904615,0.00056470244,0.000007387982,0.003760077,0.005537425,0.00006254635,0.009541995,0.116215326,0.0021877903],"study_design_scores_gemma":[0.00041215282,0.00020190263,0.97122884,0.000039448867,0.000018259328,0.000059434224,0.0003550722,0.015819998,0.000025773053,0.0011937702,0.010585945,0.00005941693],"about_ca_topic_score_codex":0.000057552596,"about_ca_topic_score_gemma":0.0000055036508,"teacher_disagreement_score":0.111411825,"about_ca_system_score_codex":0.00012072512,"about_ca_system_score_gemma":0.000018615936,"threshold_uncertainty_score":0.4352036},"labels":[],"label_agreement":null},{"id":"W3211341696","doi":"10.5750/jpm.v15i3.1964","title":"Holiday Effects in the US Equity Futures Markets","year":2021,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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 British Columbia","funders":"","keywords":"Futures contract; Equity (law); Economics; Coronavirus disease 2019 (COVID-19); Cash; Monetary economics; Financial economics; Finance; Internal medicine","score_opus":0.017123949773508323,"score_gpt":0.24233278414981768,"score_spread":0.22520883437630937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211341696","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":[0.94227254,0.0038104195,0.003289224,0.002270383,0.0017852103,0.00019100262,0.00007424448,0.000009525514,0.046297442],"genre_scores_gemma":[0.9974505,0.0011432914,0.00013032163,0.00054725836,0.00035294885,0.0000037475813,0.0000040505984,0.000011304094,0.00035659847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981161,0.00055312173,0.00079306745,0.00015394032,0.00014328651,0.00024049562],"domain_scores_gemma":[0.9981822,0.00068624533,0.00057202054,0.00040705776,0.000089917194,0.000062559404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009018216,0.00013253161,0.0003171628,0.0001390228,0.0001679066,0.000096571224,0.00047624126,0.000097667216,0.0004408359],"category_scores_gemma":[0.0008140651,0.0000894063,0.00017446403,0.00033984132,0.00005820249,0.0002185628,0.00012293956,0.0005323922,0.000009038711],"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":[0.001353565,0.0006686889,0.92095673,0.00027118833,0.00036425897,0.00021677987,0.002762272,0.00023591978,0.00018807703,0.016659066,0.03742813,0.018895335],"study_design_scores_gemma":[0.00068682176,0.000056673143,0.9335486,0.00004288293,0.000024209252,0.00018536967,0.00013693717,0.014494598,0.00001640644,0.03393286,0.016785624,0.000088979694],"about_ca_topic_score_codex":0.000024524565,"about_ca_topic_score_gemma":0.000053072905,"teacher_disagreement_score":0.05517793,"about_ca_system_score_codex":0.000104275714,"about_ca_system_score_gemma":0.00005189443,"threshold_uncertainty_score":0.48268437},"labels":[],"label_agreement":null},{"id":"W3213220878","doi":"10.5750/jpm.v15i3.1963","title":"Who are the most important players in team sports","year":2021,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","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":"Dalhousie University; University of British Columbia","funders":"","keywords":"Basketball; Football; Football players; Advertising; American football; Team sport; Psychology; Applied psychology; Business; Athletes; Political science; Physical therapy; Medicine; History","score_opus":0.012910516217975709,"score_gpt":0.20015413603280444,"score_spread":0.18724361981482873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213220878","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":[0.9779361,0.0055359337,0.00035274826,0.0022280307,0.0011184288,0.000079529156,0.00006640225,0.0000056245426,0.012677187],"genre_scores_gemma":[0.98942626,0.008596643,0.000015436886,0.00039974897,0.00026114442,9.2830135e-7,0.0000023445464,0.000009967433,0.0012875387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987524,0.000024232146,0.00087593816,0.00009574908,0.00009812894,0.00015351512],"domain_scores_gemma":[0.9986172,0.00005949504,0.0009505672,0.000244541,0.00007899743,0.000049212365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026970129,0.00008643786,0.00025285524,0.00012182313,0.000107871456,0.00004216139,0.00019518269,0.000049831408,0.0006194165],"category_scores_gemma":[0.00011014281,0.000056499906,0.00009129051,0.00030827065,0.000040973904,0.00017376497,0.00003329478,0.00031936064,0.000007279201],"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":[0.00007816481,0.000048544374,0.9777726,0.00000898583,0.000052423653,0.00005152976,0.0004256138,0.001646567,0.0000073176543,0.0015046474,0.017875917,0.0005276774],"study_design_scores_gemma":[0.0003751046,0.000027454877,0.9080815,0.00008063946,0.000017651033,0.00021649576,0.0008024108,0.018372422,0.00002960027,0.0021087616,0.06981164,0.000076339216],"about_ca_topic_score_codex":0.00002698794,"about_ca_topic_score_gemma":0.000045184242,"teacher_disagreement_score":0.06969114,"about_ca_system_score_codex":0.000065175744,"about_ca_system_score_gemma":0.000054922606,"threshold_uncertainty_score":0.6782176},"labels":[],"label_agreement":null},{"id":"W4321374074","doi":"10.5750/jpm.v16i3.1957","title":"Health risk, stimulus packages, and subordinated bank yields: evidence from the COVID-19 outbreak.","year":2023,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":3,"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":"Coronavirus disease 2019 (COVID-19); Stimulus (psychology); Bond; Government bond; Pandemic; Stock (firearms); German government; Empirical evidence; German; Outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Monetary economics; Business; 2019-20 coronavirus outbreak; Economics; Actuarial science; Financial economics; Econometrics; Finance; Psychology; Medicine; Virology; Geography","score_opus":0.06805207004284212,"score_gpt":0.298875691943759,"score_spread":0.23082362190091688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321374074","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":[0.85426,0.01597409,0.015480741,0.11063165,0.001748229,0.00046743872,0.0010424639,0.00012103129,0.00027429868],"genre_scores_gemma":[0.97147626,0.023632495,0.000050838476,0.0041818735,0.00036003685,0.0000030443057,0.0000071882764,0.000020141017,0.0002681056],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979418,0.000495895,0.00090078736,0.00020362502,0.00014474372,0.00031313137],"domain_scores_gemma":[0.99325305,0.0046784435,0.0013212725,0.00040367967,0.000062464176,0.00028108954],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011093737,0.00015577133,0.00038438677,0.00022443602,0.00045089968,0.00009034294,0.00044842076,0.00009776572,0.00026372386],"category_scores_gemma":[0.009540145,0.00010633043,0.0000931685,0.00053456303,0.00012344166,0.00038255585,0.0001361278,0.00061155716,0.00007727444],"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":[0.00055113784,0.000029765237,0.66060084,0.00004766586,0.00023065135,0.0000140901775,0.0056679216,0.001651726,0.000035475863,0.00014767166,0.3262611,0.004761938],"study_design_scores_gemma":[0.0010538483,0.00020854759,0.9337825,0.00012380425,0.00005246821,0.000081725906,0.00042995132,0.011395964,0.000005822742,0.010270713,0.042463575,0.0001310504],"about_ca_topic_score_codex":0.0035735704,"about_ca_topic_score_gemma":0.00017641648,"teacher_disagreement_score":0.28379753,"about_ca_system_score_codex":0.00036775842,"about_ca_system_score_gemma":0.00024097615,"threshold_uncertainty_score":0.9988029},"labels":[],"label_agreement":null},{"id":"W4389298444","doi":"10.5750/jpm.v17i1.2039","title":"Crash Prediction Using Fundamental Variables: Evidence from Mainland China","year":2023,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","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 British Columbia","funders":"","keywords":"Equity (law); Crash; Econometrics; Earnings yield; Mainland China; Earnings; Economics; China; Stock (firearms); Financial economics; Portfolio; China mainland; Price–earnings ratio; Earnings per share; Finance; Computer science; Geography","score_opus":0.04930843489987076,"score_gpt":0.23901320807734894,"score_spread":0.1897047731774782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389298444","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":[0.9840674,0.001527139,0.0056098374,0.0005403934,0.0027928334,0.00016699085,0.00045101432,0.000051919014,0.004792465],"genre_scores_gemma":[0.99412966,0.0040982952,0.0003619897,0.000083514125,0.00084578776,0.000002976737,0.000013862593,0.000020649459,0.0004432445],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984717,0.00011508433,0.00086423237,0.00017103029,0.0001362998,0.00024166483],"domain_scores_gemma":[0.99865484,0.00023247565,0.0007594573,0.00022348289,0.00005009229,0.000079644524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028747318,0.00014754689,0.0002934035,0.00026262322,0.00027920445,0.000110511784,0.00028640425,0.000094387426,0.00066308834],"category_scores_gemma":[0.00030334623,0.00012047794,0.00011946161,0.00040580938,0.000091496324,0.0009197316,0.00007113818,0.00026673762,0.00007053286],"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":[0.0011957502,0.00018989175,0.88017607,0.0001006463,0.00059026957,0.000036477504,0.003112562,0.006172195,0.003902672,0.007327451,0.09579607,0.0013999712],"study_design_scores_gemma":[0.000563936,0.00018081922,0.9262266,0.00023596472,0.000053702937,0.000042210642,0.00029457742,0.030855224,0.00004598531,0.035227053,0.0061569964,0.000116910145],"about_ca_topic_score_codex":0.00020069991,"about_ca_topic_score_gemma":0.0000037802015,"teacher_disagreement_score":0.089639075,"about_ca_system_score_codex":0.00017182271,"about_ca_system_score_gemma":0.000057389916,"threshold_uncertainty_score":0.72603524},"labels":[],"label_agreement":null}]}