{"meta":{"query_hash":"4cb542b4c8bc","filters":{"venue":"Series in computer vision"},"cohort_total":9,"direct_labels_cover":0,"predictions_cover":9,"exported":9,"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/4cb542b4c8bc","api":"https://metacan.xera.ac/api/v1/cohort?venue=Series+in+computer+vision"},"results":[{"id":"W2479735988","doi":"10.1142/9789814343008_0023","title":"A PERFORMANCE EVALUATION OF ROBOT LOCALIZATION METHODS IN OUTDOOR TERRAINS","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Robotics and Sensor-Based Localization","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":"York University","funders":"","keywords":"Terrain; Computer science; Robot; Computer vision; Artificial intelligence; Geography; Cartography","score_opus":0.04277938776654567,"score_gpt":0.30289727950532935,"score_spread":0.2601178917387837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479735988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012867132,0.0003110753,0.9668042,0.0000109545645,0.0010151913,0.0005253088,0.000005360969,0.00008332535,0.029957836],"genre_scores_gemma":[0.66176426,0.0028252779,0.3284695,0.00011440554,0.0005622746,0.000054711625,0.0007802996,0.00045809319,0.00497116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984536,0.00008867248,0.0006688579,0.0002731322,0.00034664,0.00016910123],"domain_scores_gemma":[0.99928313,0.000035913956,0.00013230773,0.00033698502,0.00017887643,0.000032767177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007886912,0.00027970326,0.00040697004,0.00047209742,0.000022922859,0.000018577644,0.00015398451,0.0003390467,0.000102843296],"category_scores_gemma":[0.000013070861,0.00029713582,0.00006114054,0.00011598165,0.00005816654,0.00020931003,0.00006517098,0.00022533782,0.00000829393],"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":[0.000018326082,0.0000136264525,0.00007555763,0.00016686173,0.000011281544,0.0000016591035,0.0004398384,0.7893573,0.00006789604,0.0012351242,0.00005496004,0.20855755],"study_design_scores_gemma":[0.00038359425,0.00017347472,0.00086336024,0.0008591436,0.000026726966,0.0000030361641,0.0000039333668,0.99283946,0.00047790725,0.0024499737,0.0016384822,0.00028093238],"about_ca_topic_score_codex":0.000012522811,"about_ca_topic_score_gemma":0.0000653808,"teacher_disagreement_score":0.6604775,"about_ca_system_score_codex":0.00021398143,"about_ca_system_score_gemma":0.000042109306,"threshold_uncertainty_score":0.9999481},"labels":[],"label_agreement":null},{"id":"W2483122351","doi":"10.1142/9789814343008_0017","title":"THE USE OF CONSTRAINTS FOR CALIBRATION-FREE 3D METRIC RECONSTRUCTION: FROM THEORY TO APPLICATIONS","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary 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 Windsor","funders":"","keywords":"Metric (unit); Calibration; Computer science; Mathematical optimization; Mathematics; Engineering; Statistics; Operations management","score_opus":0.044061325397647476,"score_gpt":0.22566198878799437,"score_spread":0.1816006633903469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483122351","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016648206,0.0029045145,0.927181,0.0006379402,0.006666136,0.0041286144,0.007586002,0.000207339,0.04902363],"genre_scores_gemma":[0.038180273,0.0019804854,0.82949907,0.0009797307,0.0022803082,0.00008621259,0.0037097584,0.00008027813,0.12320388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902344,0.00006103851,0.00035784458,0.0002826798,0.00014228371,0.00013269697],"domain_scores_gemma":[0.9984687,0.00081102026,0.00015255413,0.0004195949,0.000089208945,0.000058933503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002627052,0.00016406296,0.00022230169,0.000087895074,0.00018841651,0.00008700371,0.00033977992,0.00014738803,0.0006723308],"category_scores_gemma":[0.000035748475,0.000111993104,0.00007547561,0.00008101113,0.00025501038,0.00022761563,0.000044071847,0.0001311529,0.000026221725],"study_design_candidate":"design_other","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":[0.00010901981,0.0000034255183,0.00053375115,0.00001318039,0.000024674433,9.257331e-7,0.00011895667,0.00028089713,9.756181e-7,0.00831692,0.0014256146,0.9891717],"study_design_scores_gemma":[0.00045662042,0.0007339287,0.011345001,0.0004261011,0.000049289054,0.00003002289,0.00006292376,0.0047228658,0.000031533607,0.30482644,0.676654,0.0006613254],"about_ca_topic_score_codex":0.00016463586,"about_ca_topic_score_gemma":0.0016904082,"teacher_disagreement_score":0.9885103,"about_ca_system_score_codex":0.0000056928725,"about_ca_system_score_gemma":0.000036591508,"threshold_uncertainty_score":0.7361551},"labels":[],"label_agreement":null},{"id":"W2495563369","doi":"10.1142/9789814343008_0007","title":"NONPARAMETRIC SAMPLE-BASED METHODS FOR IMAGE UNDERSTANDING","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Medical Image Segmentation 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":"University of Waterloo","funders":"","keywords":"Nonparametric statistics; Sample (material); Image (mathematics); Computer science; Artificial intelligence; Statistics; Mathematics; Pattern recognition (psychology); Chemistry; Chromatography","score_opus":0.08641229262845973,"score_gpt":0.3803471155718827,"score_spread":0.293934822943423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2495563369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6756803e-7,0.00010417034,0.98681885,0.00021797433,0.0010999149,0.00088658085,0.00001761571,0.00045189034,0.010402829],"genre_scores_gemma":[0.000027614074,0.00008371059,0.99579304,0.0007179408,0.00014331877,0.000047040132,0.000053242577,0.00006285728,0.0030712467],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745595,0.00012895149,0.000721079,0.00091616786,0.00038376523,0.00039410134],"domain_scores_gemma":[0.99668914,0.001662646,0.00035248158,0.0009871108,0.00015631152,0.00015231947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012072648,0.00045184296,0.00060098484,0.0010234739,0.00012446086,0.00030130838,0.001359641,0.00037499663,0.0001587633],"category_scores_gemma":[0.00014706967,0.00044342654,0.00022546416,0.00025444603,0.00023607578,0.0007662279,0.00064481515,0.00042548546,0.000019298779],"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":[0.000043612323,0.000039983963,0.0000012469616,0.00018354811,0.000021750622,0.000029660021,0.00017395795,0.0000070345727,0.000095015836,0.26046732,0.0033822013,0.7355547],"study_design_scores_gemma":[0.00088580046,0.00146174,0.000006772024,0.0007935597,0.000024813051,0.000020986081,0.0000043831737,0.08935669,0.006710771,0.8750213,0.024779886,0.0009333441],"about_ca_topic_score_codex":0.000013611414,"about_ca_topic_score_gemma":0.0000044347207,"teacher_disagreement_score":0.73462135,"about_ca_system_score_codex":0.00042801062,"about_ca_system_score_gemma":0.00014627283,"threshold_uncertainty_score":0.99980175},"labels":[],"label_agreement":null},{"id":"W2500125890","doi":"10.1142/9789814343008_0008","title":"A PROBABILISTIC FORMULATION FOR THE CORRESPONDENCE PROBLEM","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Advanced Vision and Imaging","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":"York University","funders":"","keywords":"Probabilistic logic; Computer science; Mathematics; Artificial intelligence","score_opus":0.02719599370339358,"score_gpt":0.2768547168200793,"score_spread":0.2496587231166857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500125890","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020332168,0.0003237839,0.98257726,0.00053072267,0.0011585968,0.0012845567,0.0000052707223,0.00015452791,0.013963268],"genre_scores_gemma":[0.003631658,0.00031283317,0.8835302,0.00089564355,0.00049270777,0.00015774347,0.000029472254,0.00010235421,0.110847406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981506,0.000025059011,0.00050217216,0.00069461897,0.0003139849,0.00031355085],"domain_scores_gemma":[0.99809426,0.00041747914,0.0002869757,0.00094635366,0.00019680141,0.00005810311],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004000234,0.0003463991,0.00031651286,0.0001922962,0.00022250021,0.00021114087,0.0012725136,0.00016104578,0.000034371427],"category_scores_gemma":[0.000027806294,0.0002500036,0.00013557174,0.00009777746,0.00008279862,0.0010159387,0.00080340507,0.00033060764,0.000047211415],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006312476,0.000013620455,0.0000013280182,0.00005750574,0.000006308529,0.0000069495622,0.00037923138,0.00049582025,0.000005669963,0.50815254,0.00105365,0.48976424],"study_design_scores_gemma":[0.00026977545,0.0004061613,0.000056162495,0.00052192423,0.0000073998654,0.00003297853,0.0000015998853,0.35188556,0.000015684116,0.4527857,0.19370309,0.00031398132],"about_ca_topic_score_codex":0.000003916408,"about_ca_topic_score_gemma":0.000015895837,"teacher_disagreement_score":0.48945028,"about_ca_system_score_codex":0.00010171338,"about_ca_system_score_gemma":0.000093602335,"threshold_uncertainty_score":0.99999523},"labels":[],"label_agreement":null},{"id":"W2502392682","doi":"10.1142/9789814460941_0002","title":"DISTRIBUTION MATCHING APPROACHES TO MEDICAL IMAGE SEGMENTATION","year":2013,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Western University; CARE Canada","funders":"","keywords":"Matching (statistics); Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Distribution (mathematics); Image segmentation; Computer vision; Mathematics; Statistics","score_opus":0.024156559010349,"score_gpt":0.2830106850650491,"score_spread":0.2588541260547001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502392682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010794534,0.00033081917,0.8717259,0.032024864,0.0022176714,0.001962981,0.00003584453,0.00033333054,0.08057404],"genre_scores_gemma":[0.061221987,0.0014492268,0.68645406,0.013918543,0.009347638,0.00018922296,0.009983451,0.0006959021,0.21673998],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979199,0.000037929283,0.0005184396,0.0005115787,0.0007415492,0.00027057467],"domain_scores_gemma":[0.9990253,0.000078337,0.00013036086,0.00032469188,0.00005139945,0.00038994712],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047214964,0.000315919,0.0005169089,0.00017484748,0.00007789654,0.00009776311,0.00018606479,0.00032931333,0.00088770146],"category_scores_gemma":[0.00008326259,0.00027481644,0.000112957016,0.00005605443,0.00012773852,0.000193302,0.00030634005,0.0009738429,0.00025022298],"study_design_candidate":"design_other","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":[0.00019119187,0.00010482601,0.00007576465,0.00057030795,0.000080116966,0.00047938255,0.0006931707,0.0002551458,0.00022881513,0.029057259,0.02810286,0.94016117],"study_design_scores_gemma":[0.006625633,0.0040337658,0.006426728,0.020118326,0.00034539972,0.0024536536,0.00016161968,0.24156365,0.00039982575,0.06777378,0.6471172,0.002980411],"about_ca_topic_score_codex":0.000034269877,"about_ca_topic_score_gemma":0.0000049531413,"teacher_disagreement_score":0.93718076,"about_ca_system_score_codex":0.00022142398,"about_ca_system_score_gemma":0.00010877314,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W2507041834","doi":"10.1142/9789814460941_0011","title":"ULTRASOUND VOLUME RECONSTRUCTION BASED ON DIRECT FRAME INTERPOLATION","year":2013,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Flow Measurement and Analysis","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":"Mitel (Canada)","funders":"","keywords":"Interpolation (computer graphics); Volume (thermodynamics); Ultrasound; Frame (networking); Computer science; Computer vision; Medicine; Radiology; Physics; Telecommunications","score_opus":0.007786804180982046,"score_gpt":0.18895679441275162,"score_spread":0.18116999023176958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507041834","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050385837,0.0004331665,0.15801659,0.00018523118,0.00880921,0.00090389675,0.000049528342,0.0013059449,0.82525784],"genre_scores_gemma":[0.78125083,0.0008977008,0.07386003,0.00039606803,0.0039397995,0.00006196993,0.0010202144,0.00061680644,0.1379566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988434,0.000020594884,0.00037484695,0.00032057794,0.00026914623,0.00017148141],"domain_scores_gemma":[0.9994158,0.000059460875,0.00007722257,0.00034326283,0.000058014673,0.000046200705],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013046362,0.000334719,0.00036477737,0.00041086914,0.000044677905,0.000110066016,0.00013920365,0.00031012815,0.0015264261],"category_scores_gemma":[0.000007956545,0.00033997325,0.00014983317,0.00006330512,0.000044294764,0.0002700626,0.00002440149,0.00037775736,0.00033292294],"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":[0.00012820371,0.000056035125,0.0007358635,0.0004888635,0.0003044545,0.000025444579,0.00026133005,0.232219,0.0018003732,0.0015936949,0.03673207,0.72565466],"study_design_scores_gemma":[0.00021575537,0.0002091917,0.00036696266,0.0012634555,0.000035175246,0.000007559227,0.0000027145,0.9620611,0.00010867094,0.0015129414,0.03369949,0.0005169518],"about_ca_topic_score_codex":0.000009594808,"about_ca_topic_score_gemma":0.000029655142,"teacher_disagreement_score":0.7762122,"about_ca_system_score_codex":0.00016700008,"about_ca_system_score_gemma":0.000010277985,"threshold_uncertainty_score":0.9999052},"labels":[],"label_agreement":null},{"id":"W4283652538","doi":"10.1142/9789811257452_0007","title":"Peripheral Blood Smear Analysis Using Deep Learning: Current Challenges and Future Directions","year":2022,"lang":"en","type":"book-chapter","venue":"Series in computer vision","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; Current (fluid); Peripheral; Medicine; Computer science; Internal medicine; Engineering; Electrical engineering","score_opus":0.019888009327195484,"score_gpt":0.2631684633860333,"score_spread":0.2432804540588378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283652538","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030250677,0.32839414,0.5991275,0.0030045551,0.028085947,0.0016042509,0.000029745564,0.0019945826,0.034734245],"genre_scores_gemma":[0.021429623,0.6677545,0.2804098,0.00027658592,0.014202561,0.0001673785,0.00018215844,0.0005194379,0.015057975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99746364,0.00015177907,0.00043104382,0.0011214938,0.0005249962,0.0003070313],"domain_scores_gemma":[0.99874985,0.000065189226,0.00029643285,0.0006985946,0.00009285231,0.00009709711],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028773802,0.00044130284,0.00058325223,0.0008337757,0.00044540374,0.00021376707,0.0005773556,0.0002377787,0.0001482263],"category_scores_gemma":[0.0000032729668,0.00048019603,0.00023673482,0.00037318232,0.00009963604,0.00071067066,0.001312975,0.0011238384,0.0000026746943],"study_design_candidate":"design_other","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":[0.00001898155,0.000037622933,0.000026879194,0.00007227062,0.00025159438,0.000038693495,0.0026312675,0.005817683,0.0000036684796,0.018885165,0.000012474355,0.9722037],"study_design_scores_gemma":[0.0002971156,0.00068978115,0.00069741043,0.00011778867,0.0002698642,0.00013944485,0.000048947804,0.1879946,0.000004446433,0.004121465,0.80493397,0.0006851969],"about_ca_topic_score_codex":0.000018138166,"about_ca_topic_score_gemma":0.00015998879,"teacher_disagreement_score":0.9715185,"about_ca_system_score_codex":0.00032893452,"about_ca_system_score_gemma":0.000058097175,"threshold_uncertainty_score":0.999765},"labels":[],"label_agreement":null},{"id":"W4412732308","doi":"10.1142/9789819807154_0002","title":"2D and 3D Detection of Counterfeit Coins","year":2025,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Currency Recognition and Detection","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":"Counterfeit; Computer science; Computer security; History; Archaeology","score_opus":0.011672642875274034,"score_gpt":0.24300387474694948,"score_spread":0.23133123187167545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412732308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042630854,0.00061556126,0.92879593,0.00011092817,0.0034083507,0.00034783888,0.000020565327,0.00017559757,0.06609893],"genre_scores_gemma":[0.5824009,0.011107876,0.14781012,0.0017909969,0.0022768308,0.0000980125,0.00020393479,0.00024053646,0.25407076],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986858,0.000029656501,0.00042595319,0.0004908845,0.00023190764,0.0001358013],"domain_scores_gemma":[0.99915075,0.00008075657,0.00019814784,0.0003961418,0.00013261389,0.000041594085],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016467723,0.000239446,0.00033934167,0.00047060548,0.0000683259,0.00009658268,0.00027224942,0.00024704137,0.000023689494],"category_scores_gemma":[0.0000070559504,0.00024887142,0.00007511905,0.000107408836,0.00009162613,0.0004152151,0.00044452,0.00029035067,0.000014213791],"study_design_candidate":"design_other","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":[0.000050769144,0.00002427995,0.00000527629,0.00018924847,0.000017455051,0.000011695292,0.00015010482,0.000042840413,0.00013830906,0.012624635,0.0003968623,0.9863485],"study_design_scores_gemma":[0.0021243973,0.0037307008,0.0008350675,0.0051306733,0.000067366316,0.00037828437,0.000012525292,0.4438918,0.0045241234,0.08276637,0.454915,0.0016236926],"about_ca_topic_score_codex":0.00001376812,"about_ca_topic_score_gemma":0.00010669912,"teacher_disagreement_score":0.9847248,"about_ca_system_score_codex":0.000062243635,"about_ca_system_score_gemma":0.00005240968,"threshold_uncertainty_score":0.99999636},"labels":[],"label_agreement":null},{"id":"W4412732328","doi":"10.1142/9789819807154_0021","title":"ChatGPT vs. Google Search on Drones","year":2025,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"UAV Applications and Optimization","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":"Drone; Computer science; World Wide Web; Biology; Botany","score_opus":0.006524329912236971,"score_gpt":0.22405407782531395,"score_spread":0.21752974791307697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412732328","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002587878,0.0007835304,0.35926807,0.0014342156,0.002190843,0.0010406638,0.00007468419,0.0008580697,0.63409114],"genre_scores_gemma":[0.020060997,0.012686911,0.13135025,0.0017781735,0.0020083422,0.00016973415,0.001718976,0.00057780626,0.8296488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991527,0.000006815194,0.00024127732,0.00028086395,0.00016268305,0.00015564534],"domain_scores_gemma":[0.999457,0.00003993145,0.000023692235,0.0004057425,0.000041229057,0.000032413296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006815191,0.00023267424,0.00023597154,0.0002684004,0.000053040974,0.000059234408,0.00019469074,0.00021451723,0.0001359658],"category_scores_gemma":[0.0000010320401,0.0002450055,0.000053103937,0.00007237752,0.00003401231,0.00012315226,0.00012118205,0.00030150777,0.00009969846],"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":[0.00005905509,0.00003664713,0.000007947473,0.00030909744,0.0000460039,0.000017533514,0.00022820987,0.3891639,0.000018199798,0.19629475,0.05171443,0.3621042],"study_design_scores_gemma":[0.00019440078,0.00018585079,0.00006922325,0.0006779039,0.000007069661,0.0000031623176,0.0000017259725,0.28127325,0.000113249596,0.003969167,0.7131551,0.00034987228],"about_ca_topic_score_codex":0.0000042514075,"about_ca_topic_score_gemma":0.000012233691,"teacher_disagreement_score":0.6614407,"about_ca_system_score_codex":0.0001137577,"about_ca_system_score_gemma":0.000018367497,"threshold_uncertainty_score":0.9991034},"labels":[],"label_agreement":null}]}