{"meta":{"query_hash":"d3fa214faf59","filters":{"venue":"Series on language processing, pattern recognition, and intelligent systems"},"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/d3fa214faf59","api":"https://metacan.xera.ac/api/v1/cohort?venue=Series+on+language+processing%2C+pattern+recognition%2C+and+intelligent+systems"},"results":[{"id":"W3217430695","doi":"10.1142/9789811239014_0007","title":"Gender Detection from Handwritten Documents Using Transfer Learning Method","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Transfer of learning; Transfer (computing); Artificial intelligence; Natural language processing; Speech recognition","score_opus":0.042310516388248585,"score_gpt":0.2810272819976789,"score_spread":0.2387167656094303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217430695","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":[0.20168355,0.00240081,0.77420217,0.00024112788,0.0006772532,0.00025520602,0.0016048318,0.007177585,0.0117575275],"genre_scores_gemma":[0.58466953,0.0016713721,0.34653187,0.00023835045,0.00030114103,0.00020237792,0.004655984,0.0005211457,0.061208196],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996854,0.000033623113,0.000018144277,0.00010628713,0.000101026126,0.000055474175],"domain_scores_gemma":[0.9996809,0.00006505257,0.000024576573,0.000048855472,0.00015731985,0.000023407023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041246595,0.00069699617,0.0007753773,0.0014014216,0.000402775,0.00053984585,0.00069997326,0.0005306658,0.0058170804],"category_scores_gemma":[0.0005240536,0.00017121837,0.0005386026,0.0011793509,0.00023201446,0.0007209576,0.00052556256,0.00050329574,0.0051211044],"study_design_candidate":"bench_or_experimental","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":[0.00021541507,0.00012867096,0.0016293246,0.00007931664,0.00003521871,0.00015455011,0.000040177783,0.0028329743,0.087740764,0.00041981495,0.00490536,0.90181834],"study_design_scores_gemma":[0.00004535209,0.0006306502,0.027323099,0.00006266029,0.0001726872,0.0020458503,0.00033454294,0.67947036,0.26866204,0.0030858954,0.018040435,0.00012648942],"about_ca_topic_score_codex":0.0015931726,"about_ca_topic_score_gemma":0.0022623893,"teacher_disagreement_score":0.0058170804,"about_ca_system_score_codex":0.00024906293,"about_ca_system_score_gemma":0.0004326012,"threshold_uncertainty_score":0.019460082},"labels":[],"label_agreement":null},{"id":"W3217733771","doi":"10.1142/9789811239014_0002","title":"Intensive Survey on Peripheral Blood Smear Analysis Using Deep Learning","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Peripheral blood; Medicine; Peripheral; Internal medicine","score_opus":0.042900420511525934,"score_gpt":0.26307256702431625,"score_spread":0.22017214651279032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217733771","genre_codex":"review","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":[0.016915385,0.8313239,0.11151034,0.006898518,0.0019530316,0.00010506501,0.0023106702,0.0011549984,0.027828114],"genre_scores_gemma":[0.059781123,0.8150524,0.06931142,0.005899584,0.0035163327,0.00011401988,0.009232071,0.0004688745,0.03662412],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99940753,0.00010623266,0.00006395438,0.00014498115,0.00024485608,0.000032479813],"domain_scores_gemma":[0.997131,0.0016522645,0.00008108564,0.00016587392,0.00088452635,0.000085256666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014297584,0.000523469,0.00060180237,0.0021666917,0.00015265694,0.0007805739,0.001035471,0.00055283395,0.005645671],"category_scores_gemma":[0.0036238611,0.00039624472,0.0006304342,0.0022641777,0.00022499012,0.0018579182,0.0006558254,0.0008431134,0.0026435442],"study_design_candidate":"observational","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":[0.000058173817,0.000065776585,0.0018647632,0.0011161894,0.000066880246,0.00003640279,0.000016919606,0.0016083338,0.002088923,0.0019132517,0.043524116,0.94764024],"study_design_scores_gemma":[0.000031764557,0.00043486807,0.014581148,0.0026313409,0.0003743017,0.0015615684,0.00015054531,0.036913462,0.016458089,0.011784577,0.914969,0.00010925531],"about_ca_topic_score_codex":0.0024872762,"about_ca_topic_score_gemma":0.004440925,"teacher_disagreement_score":0.005645671,"about_ca_system_score_codex":0.0005018746,"about_ca_system_score_gemma":0.0009841532,"threshold_uncertainty_score":0.018886626},"labels":[],"label_agreement":null},{"id":"W4200523749","doi":"10.1142/9789811239014_bmatter","title":"BACK MATTER","year":2021,"lang":"en","type":"paratext","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"","field":"","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":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.03230856072463521,"score_gpt":0.27516790911438616,"score_spread":0.24285934838975093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200523749","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030590474,0.0010709922,0.0009983843,0.0046706605,0.007370213,0.00006753906,0.0038290934,0.001058119,0.9806291],"genre_scores_gemma":[0.0004314917,0.00039302307,0.00016526875,0.0002726659,0.00032515786,0.00001429553,0.000678067,0.00018914179,0.9975309],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940526,0.00005962077,0.000026372167,0.00023487913,0.0001955852,0.000078281504],"domain_scores_gemma":[0.9989365,0.0001701901,0.00006154826,0.00016572844,0.00033989866,0.00032618947],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006031963,0.0017247102,0.001413826,0.0026551827,0.0018272819,0.0087953815,0.0013016689,0.0024175006,0.9048691],"category_scores_gemma":[0.0020549176,0.0004918651,0.0007820275,0.002315427,0.0011890125,0.003513269,0.0020056951,0.0023013493,0.90735954],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028539194,0.000029350394,0.00008534788,0.00015244345,0.000006881811,0.00003386196,0.000019887975,0.000063877145,0.00026456045,0.0071009034,0.9190936,0.07312061],"study_design_scores_gemma":[0.0000067352094,0.000010754226,0.00015048707,0.00006826827,0.00000317988,0.000027404347,0.000017738705,0.000096133605,0.00014156148,0.0011937835,0.99828064,0.000003308388],"about_ca_topic_score_codex":0.0024466177,"about_ca_topic_score_gemma":0.0047288183,"teacher_disagreement_score":0.09513092,"about_ca_system_score_codex":0.0016098231,"about_ca_system_score_gemma":0.0017249384,"threshold_uncertainty_score":0.13569266},"labels":[],"label_agreement":null},{"id":"W4200571108","doi":"10.1142/9789811239014_fmatter","title":"FRONT MATTER","year":2021,"lang":"en","type":"paratext","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"","field":"","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":"Concordia University","funders":"","keywords":"Front (military); Geology; Oceanography","score_opus":0.028583859819508894,"score_gpt":0.2716969246950449,"score_spread":0.24311306487553602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200571108","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004984146,0.0013404014,0.0015926475,0.0060941568,0.008213621,0.00015535326,0.003051627,0.0009667642,0.978087],"genre_scores_gemma":[0.00066899776,0.0008193055,0.000462771,0.0007709018,0.00089492893,0.00005347204,0.000883387,0.0002281381,0.9952181],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911016,0.000079171135,0.00003852955,0.00039497556,0.00024052628,0.00013678007],"domain_scores_gemma":[0.99808717,0.00048562294,0.00011055794,0.00016323206,0.00052930386,0.0006241043],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010447974,0.0037763712,0.0021269033,0.0032796895,0.0019298916,0.008183408,0.0018766499,0.0047317613,0.90629095],"category_scores_gemma":[0.0019946832,0.00080959,0.0012247943,0.0020278774,0.0016749152,0.004358058,0.0020445853,0.0029206644,0.9012477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007651569,0.000090154936,0.00021284884,0.0003768803,0.00001778584,0.00009689421,0.00004966626,0.00018268853,0.0009185627,0.008811523,0.8148146,0.17435181],"study_design_scores_gemma":[0.000026059693,0.0000435146,0.00028947182,0.0001759735,0.0000074652658,0.00007527431,0.00003737506,0.00020761478,0.0002229815,0.0012207943,0.9976851,0.000008347564],"about_ca_topic_score_codex":0.0026273208,"about_ca_topic_score_gemma":0.0038283714,"teacher_disagreement_score":0.09370905,"about_ca_system_score_codex":0.001531224,"about_ca_system_score_gemma":0.002179511,"threshold_uncertainty_score":0.13366461},"labels":[],"label_agreement":null},{"id":"W4205908004","doi":"10.1142/9789811239014_0011","title":"A Comprehensive Unconstrained, License Plate Database","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Vehicle License Plate Recognition","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":"Concordia University","funders":"","keywords":"License; Database; Computer science; MIT License; Operating system","score_opus":0.033099675279345236,"score_gpt":0.22988537030068054,"score_spread":0.1967856950213353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205908004","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019580921,0.0073481603,0.12823339,0.00037210868,0.00066055346,0.0010773094,0.716203,0.062389735,0.06413492],"genre_scores_gemma":[0.011601,0.0019673132,0.05624673,0.0001488789,0.000075032054,0.00032852666,0.8876616,0.0014301394,0.040540814],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993954,0.000036584133,0.00006975758,0.00013033226,0.00033446553,0.00003351183],"domain_scores_gemma":[0.9989147,0.000104312625,0.00004042644,0.00032593485,0.0005248593,0.00008973395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056038034,0.0016303299,0.0011375904,0.0061426987,0.00068112696,0.0014983503,0.0026108748,0.0006246426,0.055215504],"category_scores_gemma":[0.001785219,0.00061397464,0.0005167885,0.0069083218,0.00022643112,0.0024648635,0.0012691547,0.00067930465,0.06852131],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021200468,0.00023299188,0.001041641,0.0006080017,0.00004973857,0.00018550787,0.00003311935,0.0016402674,0.008754799,0.00112653,0.6006076,0.38550776],"study_design_scores_gemma":[0.00016132124,0.0001784996,0.013217049,0.0002235524,0.00013414558,0.0015991228,0.0001822055,0.024221232,0.041166708,0.002591282,0.9161984,0.00012644511],"about_ca_topic_score_codex":0.009163763,"about_ca_topic_score_gemma":0.018344028,"teacher_disagreement_score":0.055215504,"about_ca_system_score_codex":0.0004209645,"about_ca_system_score_gemma":0.0018082915,"threshold_uncertainty_score":0.18471426},"labels":[],"label_agreement":null},{"id":"W4226360438","doi":"10.1142/9789811239014_0013","title":"Predicting US Elections with Social Media and Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Computational and Text Analysis Methods","field":"Social Sciences","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":"Concordia University","funders":"","keywords":"Social media; Computer science; Artificial neural network; Political science; Artificial intelligence; World Wide Web","score_opus":0.04137972767914375,"score_gpt":0.29116265423457327,"score_spread":0.24978292655542952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226360438","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":[0.7090069,0.013174913,0.20111102,0.008451272,0.0014082665,0.0001435649,0.0077189608,0.0015459962,0.0574391],"genre_scores_gemma":[0.95655996,0.0016301047,0.026797146,0.00019928909,0.0009062324,0.00005543032,0.0020570592,0.00006877487,0.011726047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997844,0.00008309895,0.000012353312,0.00004463404,0.00004249747,0.00003286798],"domain_scores_gemma":[0.99859494,0.0011039521,0.000110845554,0.000055164994,0.00009677531,0.00003834104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008147357,0.0006993368,0.0004689233,0.0015250883,0.00028617526,0.0012116861,0.00057510287,0.00060243416,0.004346602],"category_scores_gemma":[0.0041804593,0.00035890716,0.00055562373,0.0013646797,0.00027989643,0.0015690849,0.0004038633,0.0010996222,0.0012685057],"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":[0.00046476012,0.00042518467,0.09112109,0.00015607175,0.0005064142,0.00016934676,0.00009623402,0.3705174,0.00088328467,0.015453122,0.04441746,0.47578964],"study_design_scores_gemma":[0.000009078486,0.000021454569,0.008305508,0.000024253482,0.000026422385,0.000026263308,0.000035480414,0.974796,0.00037541174,0.014577299,0.0017933181,0.000009401051],"about_ca_topic_score_codex":0.009600199,"about_ca_topic_score_gemma":0.02762329,"teacher_disagreement_score":0.009600199,"about_ca_system_score_codex":0.00067139365,"about_ca_system_score_gemma":0.00028068852,"threshold_uncertainty_score":0.019088626},"labels":[],"label_agreement":null},{"id":"W4402426001","doi":"10.1142/9789811289125_0008","title":"An Encoder–Decoder Approach to Offline Handwritten Mathematical Expression Recognition with Residual Attention","year":2024,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Speech recognition; Encoder; Residual; Artificial intelligence; Pattern recognition (psychology); Expression (computer science); Algorithm; Programming language","score_opus":0.03051297855439314,"score_gpt":0.26037560981552166,"score_spread":0.22986263126112852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402426001","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":[0.00303382,0.0006575892,0.9871891,0.00012830544,0.00018742721,0.00005740019,0.00012026405,0.0033303031,0.005295791],"genre_scores_gemma":[0.09368816,0.0008601743,0.8643671,0.0003826285,0.000309569,0.0001589514,0.0007576207,0.0007186025,0.03875705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996351,0.000067167115,0.00003171413,0.00010076558,0.00012058531,0.000044616525],"domain_scores_gemma":[0.9996822,0.00015316714,0.0000117062555,0.000051224146,0.00008668052,0.00001511978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043091984,0.0011720586,0.0009305155,0.00048272338,0.00044063607,0.0012768102,0.0020679838,0.0012536334,0.013019639],"category_scores_gemma":[0.0008171351,0.0006244947,0.0007687477,0.00072631415,0.00045335572,0.0012356291,0.0010857669,0.0016156897,0.0058543966],"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":[0.0002119164,0.00014631766,0.0001226863,0.00016316159,0.00012035901,0.0002398009,0.000093081915,0.039915536,0.056939192,0.023846561,0.012805987,0.8653953],"study_design_scores_gemma":[0.00003335608,0.00014598017,0.00022754805,0.000028777098,0.00007969897,0.00036461058,0.000036602327,0.9048389,0.06124412,0.017039375,0.015928656,0.000032295928],"about_ca_topic_score_codex":0.006107204,"about_ca_topic_score_gemma":0.013058679,"teacher_disagreement_score":0.013019639,"about_ca_system_score_codex":0.00050846225,"about_ca_system_score_gemma":0.0010235645,"threshold_uncertainty_score":0.04355508},"labels":[],"label_agreement":null},{"id":"W4402426016","doi":"10.1142/9789811289125_0006","title":"Shop Signboard Detection Using the ShoS Dataset","year":2024,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Vehicle License Plate Recognition","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":"Concordia University","funders":"","keywords":"Computer science; Geography","score_opus":0.03510642452363035,"score_gpt":0.2412932678993078,"score_spread":0.20618684337567744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402426016","genre_codex":"dataset","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":[0.3777553,0.0033674242,0.023895107,0.00081378786,0.001702079,0.0009891809,0.48251683,0.0707923,0.038168],"genre_scores_gemma":[0.115405984,0.00047600997,0.024507534,0.00018879931,0.00016063997,0.00020151128,0.84096766,0.00090460817,0.017187294],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931,0.000049646656,0.00005423669,0.00019223626,0.0002462043,0.00014770024],"domain_scores_gemma":[0.99951744,0.000040799285,0.000030319685,0.00013771295,0.00018708697,0.000086753724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003842796,0.0021875831,0.001275811,0.003902967,0.00058971136,0.0012785711,0.0012509628,0.0012839638,0.009950196],"category_scores_gemma":[0.0009490801,0.00030296043,0.0012037853,0.0021256134,0.00027233214,0.00085150957,0.0011108863,0.00077951164,0.019126283],"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":[0.00088856556,0.00088168186,0.018739643,0.0006837933,0.0002681656,0.0009359783,0.000085046784,0.0036663036,0.01198051,0.00040908743,0.68411493,0.27734628],"study_design_scores_gemma":[0.0010108146,0.0012967277,0.15564267,0.00054021814,0.00046777763,0.0038824386,0.002076021,0.24742408,0.058212638,0.002713889,0.5263501,0.00038270451],"about_ca_topic_score_codex":0.017100692,"about_ca_topic_score_gemma":0.051306587,"teacher_disagreement_score":0.017100692,"about_ca_system_score_codex":0.00038519673,"about_ca_system_score_gemma":0.0009475305,"threshold_uncertainty_score":0.034002304},"labels":[],"label_agreement":null}]}