{"meta":{"query_hash":"317279374c8c","filters":{"venue":"SIGSPATIAL Special"},"cohort_total":11,"direct_labels_cover":0,"predictions_cover":11,"exported":11,"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/317279374c8c","api":"https://metacan.xera.ac/api/v1/cohort?venue=SIGSPATIAL+Special"},"results":[{"id":"W1971004146","doi":"10.1145/1966478.1966480","title":"CTS 2010 workshop report","year":2011,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data Management and Algorithms","field":"Computer 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":"University of Calgary","funders":"","keywords":"Computer science; Science and engineering; Library science; Operations research; Engineering management; Data science; Engineering ethics; Engineering","score_opus":0.04689999787022253,"score_gpt":0.23958626505780756,"score_spread":0.19268626718758503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971004146","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":[0.0025874241,0.000018912695,0.5708382,0.0007822785,0.012614924,0.00029161124,0.000007488562,0.00036604592,0.41249308],"genre_scores_gemma":[0.36952266,0.00006883266,0.46002373,0.0025276786,0.072906144,0.0001051352,0.00026495586,0.00010979529,0.09447108],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987565,0.000024379979,0.00022789498,0.00039204373,0.00031108625,0.0002880731],"domain_scores_gemma":[0.99906546,0.000019061732,0.00010307268,0.00067426666,0.000039838123,0.0000983173],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022554428,0.00012328953,0.00012573617,0.000081428996,0.0001026544,0.00015347109,0.0010128695,0.000053058382,0.0013127817],"category_scores_gemma":[0.00006657471,0.0001155197,0.00006091785,0.00027187265,0.000041798365,0.0007577988,0.0005401536,0.000116627554,0.000724845],"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":[0.000022444801,0.00021508498,0.0014122152,0.0000064124765,0.00003894086,0.0019044058,0.0011700921,0.0000017249026,0.000051195864,0.12342872,0.3307285,0.5410203],"study_design_scores_gemma":[0.0010065947,0.00018666704,0.047175556,0.000029240602,0.000029558414,0.00007983528,0.000065526816,0.0032805074,0.002571976,0.029225871,0.9153582,0.0009905035],"about_ca_topic_score_codex":0.00016902736,"about_ca_topic_score_gemma":0.00014937315,"teacher_disagreement_score":0.58462965,"about_ca_system_score_codex":0.000018610943,"about_ca_system_score_gemma":0.000034405646,"threshold_uncertainty_score":0.9996002},"labels":[],"label_agreement":null},{"id":"W1984043155","doi":"10.1145/2189403.2189405","title":"CTS 2011 Workshop Report: the Fourth International Workshop on Computational Transportation Science","year":2012,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"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 Calgary","funders":"","keywords":"Library science; Computer science; Operations research; Engineering","score_opus":0.02560025034052508,"score_gpt":0.2774527309373553,"score_spread":0.2518524805968302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984043155","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":[0.011072741,0.000025281375,0.93489647,0.010133753,0.014526882,0.00043821847,0.000034939567,0.00017499301,0.028696733],"genre_scores_gemma":[0.96229637,0.000010112387,0.022252858,0.0009931045,0.0127661135,0.00002840902,0.00024247321,0.000015065103,0.0013954976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760276,0.000036879715,0.00032394024,0.00041617578,0.0012026156,0.0004176411],"domain_scores_gemma":[0.99892217,0.00016335877,0.0001998321,0.00044492405,0.00013635089,0.00013337946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083755556,0.00015956037,0.00011244821,0.00017972571,0.0004139377,0.0004528717,0.0015685936,0.0000425993,0.00047027037],"category_scores_gemma":[0.00013278298,0.00012313924,0.000072052804,0.000502792,0.00020027297,0.0019182668,0.00015982559,0.00019326188,0.00034612278],"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":[0.000059825725,0.00041864289,0.003324227,0.0000046196865,0.000056199293,0.00009383669,0.0027953456,0.0066105775,0.00003634449,0.47952378,0.057255715,0.44982088],"study_design_scores_gemma":[0.0018172116,0.00012271562,0.40680954,0.00009154802,0.000052332165,0.000053078307,0.00041972185,0.111361675,0.00045441283,0.019386834,0.45821002,0.0012208851],"about_ca_topic_score_codex":0.000028744746,"about_ca_topic_score_gemma":0.00006151643,"teacher_disagreement_score":0.9512236,"about_ca_system_score_codex":0.000084644205,"about_ca_system_score_gemma":0.00009752367,"threshold_uncertainty_score":0.5149131},"labels":[],"label_agreement":null},{"id":"W2054097007","doi":"10.1145/2684380.2684382","title":"ISA 2013 workshop report: a report on the Fifth International Workshop on Indoor Spatial Awareness","year":2014,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Context-Aware Activity Recognition Systems","field":"Computer 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":"University of Saskatchewan","funders":"","keywords":"Computer science; The Internet; Population; Human–computer interaction; Multimedia; Data science; World Wide Web","score_opus":0.03639649305360779,"score_gpt":0.28469514470301865,"score_spread":0.24829865164941087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054097007","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":[0.11989427,0.000010337203,0.7480951,0.03887512,0.03130105,0.0014274237,0.000041554464,0.00057285326,0.059782267],"genre_scores_gemma":[0.97575194,0.0000033909498,0.00027614404,0.0013402022,0.020212807,0.00015194273,0.000071072216,0.000037457325,0.0021550518],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954584,0.00045396166,0.0009082398,0.0011280196,0.0015419343,0.00050939416],"domain_scores_gemma":[0.99529773,0.0017456084,0.0008131361,0.0015738102,0.00036988917,0.00019982718],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014493765,0.0004124537,0.00045948583,0.00024886028,0.00041764544,0.00068419037,0.0016459583,0.00024344838,0.0012369874],"category_scores_gemma":[0.002705392,0.00032378352,0.0002753514,0.0004615893,0.00012139604,0.00048617457,0.0004993034,0.00064098625,0.0009947196],"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":[0.00050065183,0.00086297013,0.008330008,0.000014975986,0.00024959276,0.002173531,0.0023863176,0.00042017022,0.00018664029,0.017115386,0.13152654,0.8362332],"study_design_scores_gemma":[0.0018331027,0.000315809,0.021914128,0.00044662133,0.000030742853,0.0010158372,0.00013504758,0.0198262,0.0016809453,0.0036076913,0.9479395,0.0012543473],"about_ca_topic_score_codex":0.0010117949,"about_ca_topic_score_gemma":0.0040443297,"teacher_disagreement_score":0.8558577,"about_ca_system_score_codex":0.0002288676,"about_ca_system_score_gemma":0.00026473287,"threshold_uncertainty_score":0.99992144},"labels":[],"label_agreement":null},{"id":"W2078313061","doi":"10.1145/2505403.2505411","title":"SWE 2012 workshop report","year":2013,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer 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":"University of Calgary","funders":"","keywords":"Sensor web; World Wide Web; Computer science; Web site; Web application; State (computer science); Data science; The Internet; Telecommunications; Key distribution in wireless sensor networks; Wireless","score_opus":0.009933006286088968,"score_gpt":0.21942367313712216,"score_spread":0.20949066685103318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078313061","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":[0.055248823,0.00014485723,0.7705797,0.005310061,0.012530607,0.00062842056,0.000001963379,0.0008283962,0.15472718],"genre_scores_gemma":[0.9233657,0.000018664045,0.03679036,0.0008773514,0.026111407,0.00007821463,0.000026688207,0.000050413764,0.01268117],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781835,0.00007621169,0.00040946028,0.00056944427,0.0005509934,0.00057555723],"domain_scores_gemma":[0.9984391,0.00014070273,0.00017775415,0.0008730981,0.00014964963,0.00021971672],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018627905,0.00021481114,0.000230341,0.00010403241,0.00017729323,0.00032963272,0.0009801183,0.00015964897,0.0019263999],"category_scores_gemma":[0.00011024004,0.00020257392,0.00011628928,0.0004918153,0.000076852695,0.0007574632,0.0003702626,0.00025912916,0.0015487581],"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":[0.000016956912,0.00033881093,0.0014842148,0.0000084405865,0.000053179916,0.0007436647,0.000771843,0.025212068,0.00096203154,0.09738499,0.5120809,0.3609429],"study_design_scores_gemma":[0.0015100607,0.00020886658,0.023315703,0.000092222006,0.000025185345,0.00042126823,0.00006689456,0.17640005,0.0035090623,0.0075753382,0.784825,0.002050315],"about_ca_topic_score_codex":0.00024134415,"about_ca_topic_score_gemma":0.00023683808,"teacher_disagreement_score":0.8681169,"about_ca_system_score_codex":0.00006996301,"about_ca_system_score_gemma":0.000067914836,"threshold_uncertainty_score":0.99922866},"labels":[],"label_agreement":null},{"id":"W2339356764","doi":"10.1145/1862413.1862416","title":"Intrinsic dimensionality","year":2010,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data Management and Algorithms","field":"Computer Science","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":"University of Ottawa","funders":"","keywords":"Curse of dimensionality; Search engine indexing; Similarity (geometry); Computer science; Information retrieval; Workload; Gauge (firearms); Data mining; Theoretical computer science; Artificial intelligence; Geography","score_opus":0.0074448023591275085,"score_gpt":0.22220236922687658,"score_spread":0.21475756686774908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339356764","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":[0.28657827,0.000021560818,0.41290486,0.013201074,0.053303033,0.0009895292,0.000074838135,0.0012393526,0.23168749],"genre_scores_gemma":[0.92667836,0.000003507214,0.048998088,0.001069423,0.022085872,0.00001200306,0.000047775986,0.000012883756,0.0010920892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990669,0.000020791591,0.00013535445,0.00028294855,0.0002919707,0.00020202204],"domain_scores_gemma":[0.99933964,0.000033895536,0.000044604774,0.0004570974,0.00004331245,0.00008146877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001933373,0.00009129613,0.000093039176,0.000053431926,0.0001139227,0.00018441718,0.0007642197,0.000041372637,0.0006536287],"category_scores_gemma":[0.00006886343,0.000082668106,0.00004459326,0.00020096748,0.000045808298,0.0005080769,0.0005147601,0.00018464295,0.00063410687],"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":[0.0000042802485,0.00005842354,0.00037268325,0.0000018311246,0.000006550824,0.000019465637,0.000055113287,6.8563065e-7,0.000623413,0.48819143,0.02317745,0.4874887],"study_design_scores_gemma":[0.0006722296,0.00007382186,0.05267055,0.0000039384495,0.0000073938336,0.000005621093,0.0000046376467,0.0028632537,0.0020276154,0.052187324,0.88908124,0.00040234815],"about_ca_topic_score_codex":0.000076052944,"about_ca_topic_score_gemma":0.00026257994,"teacher_disagreement_score":0.8659038,"about_ca_system_score_codex":0.000009187024,"about_ca_system_score_gemma":0.000025662732,"threshold_uncertainty_score":0.81503725},"labels":[],"label_agreement":null},{"id":"W2471780513","doi":"10.1145/2961028.2961034","title":"A fully GIS-integrated simulation approach for analyzing the spread of epidemics in urban areas","year":2016,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Human Mobility and Location-Based Analysis","field":"Social 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":"Université Laval","funders":"","keywords":"Geographic information system; Public health; Context (archaeology); Communicable disease; Variety (cybernetics); Environmental planning; Geography; Population; GIS and public health; Computer science; Data science; Environmental health; Cartography; Medicine","score_opus":0.030686729954210738,"score_gpt":0.3078830644961611,"score_spread":0.27719633454195036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471780513","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":[0.21460436,0.000057447298,0.76897216,0.0027796396,0.00034610162,0.0014630212,0.00012809882,0.00005990885,0.011589279],"genre_scores_gemma":[0.99673057,0.00000836297,0.00028294098,0.000037790385,0.0025806052,0.000037182977,0.00003271017,0.0000074324885,0.00028238294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984786,0.00038180084,0.0004049193,0.00023121643,0.00026117245,0.00024232776],"domain_scores_gemma":[0.99804294,0.0012977194,0.00019387869,0.00018442742,0.00022449886,0.000056564222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016070297,0.00009769696,0.00022966012,0.00011573614,0.00028436386,0.000030083145,0.00025527412,0.00011537034,0.00041421887],"category_scores_gemma":[0.0026221946,0.000062254105,0.00015591628,0.00051439763,0.0002988275,0.00011954643,0.00001544976,0.00008175039,0.0000054121697],"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":[0.0008625095,0.00070692913,0.3323116,0.0000615493,0.0001861372,0.000001116024,0.041565225,0.052223306,0.00084174227,0.07052856,0.004039595,0.4966717],"study_design_scores_gemma":[0.0081800865,0.0007384256,0.11597282,0.0004949525,0.00080002606,3.1635253e-7,0.023467036,0.52975696,0.0020592352,0.062340852,0.2539709,0.0022183778],"about_ca_topic_score_codex":0.011726635,"about_ca_topic_score_gemma":0.06322202,"teacher_disagreement_score":0.78212625,"about_ca_system_score_codex":0.00020297302,"about_ca_system_score_gemma":0.0002703108,"threshold_uncertainty_score":0.9948544},"labels":[],"label_agreement":null},{"id":"W3005901815","doi":"10.1145/3383653.3383667","title":"The Eighth ACM SIGSPATIAL International Workshop on Analysis for Big Spatial Data","year":2020,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Big data; Data science; Realm; Computer science; White paper; Geography; Data mining","score_opus":0.13769180854241006,"score_gpt":0.3515859207313645,"score_spread":0.21389411218895446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005901815","genre_codex":"commentary","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":[0.019917445,0.00026937947,0.18534926,0.3763064,0.067601904,0.008272376,0.006074196,0.0010790695,0.33512998],"genre_scores_gemma":[0.9303745,0.00011454821,0.00028460633,0.0012472988,0.06676237,0.00010431217,0.0005961044,0.000020770176,0.0004955073],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967503,0.00019609426,0.0006642685,0.0005076552,0.0013473585,0.00053431856],"domain_scores_gemma":[0.996794,0.0014137767,0.00040959727,0.00075370964,0.00041049358,0.00021845671],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011418924,0.00023542566,0.00036938986,0.00018711943,0.0021495197,0.00051104324,0.0026591832,0.00015946955,0.000517729],"category_scores_gemma":[0.0065438426,0.00018416198,0.0002971172,0.0010834144,0.00037713125,0.0003124309,0.00066972023,0.00023275438,0.00016269638],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002595198,0.00024995528,0.045910243,0.00003278906,0.0053080833,0.000012408574,0.07775734,0.0011396946,0.000022406392,0.08611151,0.2950728,0.48578757],"study_design_scores_gemma":[0.00074990967,0.000082846556,0.0074691596,0.00000952152,0.0001971127,1.0866245e-7,0.004823413,0.0019310869,0.000012096951,0.0006985117,0.9837437,0.00028251854],"about_ca_topic_score_codex":0.0036916686,"about_ca_topic_score_gemma":0.070138976,"teacher_disagreement_score":0.910457,"about_ca_system_score_codex":0.00010675684,"about_ca_system_score_gemma":0.00022497818,"threshold_uncertainty_score":0.99914956},"labels":[],"label_agreement":null},{"id":"W3034073691","doi":"10.1145/3404111.3404112","title":"Introduction to this special issue: Modeling and understanding the spread of COVID-19","year":2020,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"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); Globe; German; 2019-20 coronavirus outbreak; Data science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Position (finance); Geography; Computer science; Regional science; Business; Infectious disease (medical specialty); Disease; Psychology; Medicine; Virology","score_opus":0.04824369830473133,"score_gpt":0.29820721068652745,"score_spread":0.24996351238179612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034073691","genre_codex":"commentary","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":[0.15815838,0.00036073502,0.20611651,0.54595083,0.008754192,0.0052442937,0.0018796532,0.00058075663,0.07295466],"genre_scores_gemma":[0.8042752,0.00004426622,0.00047014255,0.0045558475,0.19031039,0.000009634106,0.00015010529,0.000033282893,0.00015113107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985752,0.00008059196,0.00031265253,0.00038001573,0.0004396487,0.00021186608],"domain_scores_gemma":[0.9990957,0.00007849483,0.00008761754,0.0002490088,0.000059149846,0.00043007074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023605135,0.00014973617,0.00032605356,0.0000637294,0.00014280884,0.000031973817,0.00012848679,0.00006121639,0.007322436],"category_scores_gemma":[0.0014436477,0.00012132355,0.00006505517,0.00029314958,0.00011529063,0.00008358437,0.00012659874,0.0001748491,0.00012129964],"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":[0.0048678447,0.00011486357,0.0027748395,0.0002856478,0.00014935169,0.000055746175,0.011105618,0.003730515,0.003136215,0.0057577286,0.9532257,0.014795974],"study_design_scores_gemma":[0.0028288364,0.00070471887,0.0008938944,0.000045156172,0.00020511368,0.000019043344,0.0026452972,0.015005293,0.0005628202,0.0013438411,0.9753566,0.00038943364],"about_ca_topic_score_codex":0.0002260103,"about_ca_topic_score_gemma":0.0002928228,"teacher_disagreement_score":0.6461168,"about_ca_system_score_codex":0.0002054862,"about_ca_system_score_gemma":0.0001994329,"threshold_uncertainty_score":0.993585},"labels":[],"label_agreement":null},{"id":"W3134939827","doi":"10.1145/3447994.3448001","title":"The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2020)","year":2021,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Big data; Data science; Realm; Analytics; Computer science; White paper; Geography; Data mining","score_opus":0.06302794444142454,"score_gpt":0.2929766588186056,"score_spread":0.22994871437718103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134939827","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":[0.0009008368,0.000117333075,0.8271041,0.08015174,0.054878384,0.00089665735,0.00080834253,0.0003007509,0.034841847],"genre_scores_gemma":[0.72555345,0.00035535518,0.031981967,0.003594438,0.21128282,0.00024214541,0.004629395,0.00007694381,0.02228348],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961559,0.00017651515,0.0006827684,0.0012721486,0.0009646694,0.00074800913],"domain_scores_gemma":[0.994212,0.0017242142,0.00034309833,0.0029555834,0.0005499731,0.00021513841],"candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007504728,0.0003953707,0.0003718265,0.00006439387,0.00085764664,0.0008320605,0.005763263,0.00025661231,0.00045566776],"category_scores_gemma":[0.008356164,0.0003301176,0.00022566003,0.00047000404,0.00016794472,0.0002913279,0.0032715022,0.0004926863,0.00009059744],"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":[0.0004884661,0.00040424333,0.00051233603,0.000027559125,0.00028934935,0.00024250001,0.00040609372,0.0021144247,0.00081431743,0.017497629,0.24184236,0.73536074],"study_design_scores_gemma":[0.0013849646,0.00012244815,0.0006569467,0.00004211274,0.000038210925,0.000033378008,0.000082851475,0.07869227,0.0027314767,0.009801414,0.905905,0.0005089499],"about_ca_topic_score_codex":0.00013815032,"about_ca_topic_score_gemma":0.0028751236,"teacher_disagreement_score":0.79512215,"about_ca_system_score_codex":0.000115253584,"about_ca_system_score_gemma":0.00064929907,"threshold_uncertainty_score":0.9999969},"labels":[],"label_agreement":null},{"id":"W3162149128","doi":"10.1145/3404820.3404821","title":"Introduction to this special issue: Modeling and understanding the spread of COVID-19","year":2020,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"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); Globe; 2019-20 coronavirus outbreak; German; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Position (finance); Pandemic; Geography; Computer science; Public relations; Regional science; Political science; Business; Infectious disease (medical specialty); Medicine; Disease; Virology","score_opus":0.04824369830473133,"score_gpt":0.29820721068652745,"score_spread":0.24996351238179612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162149128","genre_codex":"commentary","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":[0.15815838,0.00036073502,0.20611651,0.54595083,0.008754192,0.0052442937,0.0018796532,0.00058075663,0.07295466],"genre_scores_gemma":[0.8042752,0.00004426622,0.00047014255,0.0045558475,0.19031039,0.000009634106,0.00015010529,0.000033282893,0.00015113107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985752,0.00008059196,0.00031265253,0.00038001573,0.0004396487,0.00021186608],"domain_scores_gemma":[0.9990957,0.00007849483,0.00008761754,0.0002490088,0.000059149846,0.00043007074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023605135,0.00014973617,0.00032605356,0.0000637294,0.00014280884,0.000031973817,0.00012848679,0.00006121639,0.007322436],"category_scores_gemma":[0.0014436477,0.00012132355,0.00006505517,0.00029314958,0.00011529063,0.00008358437,0.00012659874,0.0001748491,0.00012129964],"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":[0.0048678447,0.00011486357,0.0027748395,0.0002856478,0.00014935169,0.000055746175,0.011105618,0.003730515,0.003136215,0.0057577286,0.9532257,0.014795974],"study_design_scores_gemma":[0.0028288364,0.00070471887,0.0008938944,0.000045156172,0.00020511368,0.000019043344,0.0026452972,0.015005293,0.0005628202,0.0013438411,0.9753566,0.00038943364],"about_ca_topic_score_codex":0.0002260103,"about_ca_topic_score_gemma":0.0002928228,"teacher_disagreement_score":0.6461168,"about_ca_system_score_codex":0.0002054862,"about_ca_system_score_gemma":0.0001994329,"threshold_uncertainty_score":0.993585},"labels":[],"label_agreement":null},{"id":"W4388470413","doi":"10.1145/3632268.3632270","title":"Conference Report: The 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022) Seattle, Washington, USA November 1--4, 2022","year":2022,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Geographic Information Systems Studies","field":"Social 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 Alberta","funders":"","keywords":"Library science; Geography; Cartography; Operations research; Engineering; Computer science","score_opus":0.026387266507370194,"score_gpt":0.29482431182658453,"score_spread":0.2684370453192143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388470413","genre_codex":"other","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":[0.25548682,0.0005946396,0.0022318964,0.026042294,0.07378265,0.009449802,0.0027936997,0.00070385385,0.62891436],"genre_scores_gemma":[0.990993,0.0004939324,0.00006077672,0.0005430353,0.0054375012,0.0010002612,0.0005598444,0.000028019347,0.0008835871],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99140173,0.0010115873,0.0019104541,0.00064530433,0.0040302156,0.0010006782],"domain_scores_gemma":[0.99508154,0.0009279163,0.0016394674,0.001097341,0.0010335796,0.00022017362],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036307236,0.00053395703,0.00067274715,0.0008666277,0.0029768054,0.0008679974,0.0029147142,0.00025037574,0.007702063],"category_scores_gemma":[0.0041761203,0.00048423687,0.0002854964,0.0016369609,0.0007511698,0.002505563,0.0014297039,0.0012373433,0.0002523796],"study_design_candidate":"not_applicable","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":[0.0015909788,0.00075582514,0.27126896,0.00018440088,0.00047315547,0.00022871904,0.12649438,0.011421039,0.00008660434,0.49768245,0.040951237,0.048862245],"study_design_scores_gemma":[0.002498621,0.0004357419,0.038412444,0.00014658958,0.000055383844,0.000049960054,0.054764964,0.002508665,0.000021190164,0.004946569,0.89493763,0.0012222562],"about_ca_topic_score_codex":0.024820615,"about_ca_topic_score_gemma":0.06552211,"teacher_disagreement_score":0.8539864,"about_ca_system_score_codex":0.0006637497,"about_ca_system_score_gemma":0.00079701375,"threshold_uncertainty_score":0.9997609},"labels":[],"label_agreement":null}]}