{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":17,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":17,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"1fc6980bc222","filters":{"venue":"Cartography and Geographic Information Science"}},"results":[{"id":"W2069302583","doi":"10.1080/15230406.2014.905756","title":"The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns","year":2014,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":183,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Economic and Social Research Council","keywords":"Population; Geography; Proxy (statistics); Crime rate; Statistic; Criminology; Social media; Cartography; Demography; Computer science; Statistics; Psychology; Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.05293332693314581,"gpt":0.3646340331460903,"spread":0.3117007062129445,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004210652,0.00008374489,0.0001139515,0.000839934,0.001722989,0.0003488024,0.0003268487,0.0000436292,0.00001260955],"category_scores_gemma":[0.0004520654,0.00006457953,0.00007438172,0.001500344,0.001146766,0.002229229,0.0001654216,0.00009604442,3.40719e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001315423,"about_ca_system_score_gemma":0.00006721453,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007945589,"about_ca_topic_score_gemma":0.001975012,"domain_scores_codex":[0.9987464,0.0001235773,0.0003349687,0.0001464143,0.0003219823,0.0003266562],"domain_scores_gemma":[0.9991402,0.0002114129,0.000204937,0.0001851717,0.0001816772,0.00007658206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001191901,0.00001600154,0.892472,0.0000234928,0.00001354291,1.470247e-7,0.04012068,0.00003903746,0.0001080482,0.0122478,0.00000845772,0.05493886],"study_design_scores_gemma":[0.0001705498,0.00001312741,0.9789639,0.00004706754,0.00000951139,7.482512e-7,0.008102667,0.01193909,0.000009369712,0.0005104569,0.0001459326,0.00008755164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949455,0.00006882272,0.003914092,0.0000759387,0.0001454446,0.0001355676,0.00005041889,0.00001280773,0.0006514559],"genre_scores_gemma":[0.9997366,0.0001053602,0.00005220834,0.00002419828,0.00005876862,0.000003612003,0.00001677619,0.000001956,5.08298e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08649191,"threshold_uncertainty_score":0.9995766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052532943","doi":"10.1559/152304010790588089","title":"The Role of Maps in Neighborhood-level Heat Vulnerability Assessment for the City of Toronto","year":2010,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Toronto Public Health; Simon Fraser University; Toronto Metropolitan University","funders":"","keywords":"Vulnerability (computing); Urban heat island; Geography; Hazard; Vulnerability assessment; Natural hazard; Extreme weather; Cartography; Population; Climate change; Environmental resource management; Psychological intervention; Environmental planning; Computer science; Environmental science; Environmental health; Meteorology; Computer security; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01974583967206975,"gpt":0.2997189477365385,"spread":0.2799731080644687,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002882139,0.00008140885,0.0001088922,0.00007005908,0.0005676997,0.00005324685,0.000336166,0.00004480496,0.00005536265],"category_scores_gemma":[0.0001307853,0.00004691273,0.00006601101,0.0006736112,0.001554176,0.001248795,0.0001072669,0.0001257826,4.119522e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001883563,"about_ca_system_score_gemma":0.00003595026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037391,"about_ca_topic_score_gemma":0.006787105,"domain_scores_codex":[0.9988171,0.0000199795,0.0003736914,0.0001164341,0.000387715,0.0002850585],"domain_scores_gemma":[0.9990694,0.0003001994,0.0001412225,0.0003162154,0.00008075532,0.00009219919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002092523,0.00002645946,0.9313784,0.00002011421,0.000002168991,9.49865e-9,0.002090218,0.00001825719,0.002260534,0.007465986,0.00003143998,0.05668547],"study_design_scores_gemma":[0.0002122338,0.00007791775,0.9855025,0.000007616074,0.000004969282,9.594371e-7,0.001836148,0.001890685,0.0006681838,0.004976185,0.00476308,0.00005954291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921593,0.0002175443,0.000290569,0.0005727154,0.0002060514,0.0006267789,0.00007394097,0.000006929882,0.005846183],"genre_scores_gemma":[0.999097,0.0004279688,0.0002396144,0.0001664514,0.000009067424,0.00005409841,0.000003607545,0.000001304593,8.542323e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05662592,"threshold_uncertainty_score":0.5726425,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2343655042","doi":"10.1080/15230406.2016.1176536","title":"Models of direct editing of government spatial data: challenges and constraints to the acceptance of contributed data","year":2016,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; University of Waterloo","keywords":"Mirroring; Data curation; Open data; Geospatial analysis; Open government; Data science; Computer science; Government (linguistics); Crowdsourcing; Popularity; World Wide Web; Political science; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.05290230847871949,"gpt":0.2880082860096625,"spread":0.235105977530943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005439376,0.0001105442,0.0002695736,0.0003722442,0.0004661367,0.00004779176,0.001059192,0.00005166652,0.000004649088],"category_scores_gemma":[0.0009120293,0.00006951439,0.0000394162,0.001229896,0.004695675,0.004543838,0.0006497942,0.00004906281,3.920278e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007079439,"about_ca_system_score_gemma":0.00007690439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005808819,"about_ca_topic_score_gemma":0.000870976,"domain_scores_codex":[0.9975128,0.00007313149,0.0006873597,0.0002147886,0.001234875,0.0002770213],"domain_scores_gemma":[0.9974214,0.0003395189,0.0006839915,0.0007286042,0.0007255006,0.0001009266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007376059,0.00004959778,0.08766565,0.0003344593,0.0001955544,2.01422e-7,0.1119928,0.00003830558,0.0003008315,0.2230362,0.0003566344,0.575956],"study_design_scores_gemma":[0.002748101,0.0003933597,0.6610219,0.001305188,0.0001807875,0.000008460343,0.2746708,0.002428275,0.001248777,0.003681871,0.05151059,0.00080192],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8055056,0.008996915,0.05001476,0.01067319,0.002445273,0.00481295,0.01532383,0.0001547367,0.1020728],"genre_scores_gemma":[0.9955188,0.004145971,0.0002304999,0.00005015527,0.00003092446,0.00001156784,0.000009042554,0.000001522396,0.000001516254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5751541,"threshold_uncertainty_score":0.998013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134592304","doi":"10.1080/15230406.2013.799737","title":"Crowdsourcing techniques for augmenting traditional accessibility maps with transitory obstacle information","year":2013,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Engineer Research and Development Center; University of California, Santa Barbara","keywords":"Crowdsourcing; Geospatial analysis; Workflow; Data science; Obstacle; Computer science; Context (archaeology); Citizen science; Volunteered geographic information; Variety (cybernetics); Domain (mathematical analysis); World Wide Web; Geography; Cartography; Artificial intelligence; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.01631166594256315,"gpt":0.2476779054922666,"spread":0.2313662395497034,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002833525,0.000205462,0.0002128641,0.001699556,0.003742234,0.001355404,0.0003793622,0.0001167187,0.00002875375],"category_scores_gemma":[0.0001924219,0.000176896,0.0001383312,0.002798835,0.002300206,0.024649,0.0000426568,0.0001532768,0.00001031003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004296253,"about_ca_system_score_gemma":0.0002010352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005417,"about_ca_topic_score_gemma":0.0001053031,"domain_scores_codex":[0.9973687,0.00004827609,0.000711006,0.000196864,0.001066969,0.0006081717],"domain_scores_gemma":[0.997027,0.0001793672,0.0004379036,0.0002251661,0.001903369,0.000227138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001119294,0.00009270138,0.2921872,0.0008193463,0.0001550522,2.937102e-7,0.2200034,0.00007634005,0.0001752147,0.2711732,0.001979264,0.213226],"study_design_scores_gemma":[0.002059396,0.0003687675,0.6269479,0.0002980012,0.0000845997,0.00002142388,0.1685497,0.001038527,0.0006795333,0.01554475,0.1831037,0.001303747],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8237852,0.000163795,0.06213837,0.00195522,0.0008712201,0.005722908,0.0001765415,0.0009113101,0.1042754],"genre_scores_gemma":[0.9948241,0.00005692445,0.003772998,0.0005597387,0.00006896793,0.0006492063,0.00005423578,0.000003885532,0.00000994988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3347607,"threshold_uncertainty_score":0.9996813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004493951","doi":"10.1080/15230406.2013.762139","title":"A geoinformatic approach to the collection of archaeological survey data","year":2013,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"British Institute at Ankara; Trent University; Society of Antiquaries of London; College of Charleston","keywords":"Archaeology; Field (mathematics); Geography; History","retraction":null,"screen_n_in":null,"score":{"opus":0.04412099421120724,"gpt":0.2500233654834835,"spread":0.2059023712722763,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003593694,0.0000958541,0.0001210198,0.0006564821,0.0006968681,0.0001305909,0.0008311761,0.00004811336,0.0001340935],"category_scores_gemma":[0.0008492363,0.00005319468,0.00003513325,0.003837292,0.001702405,0.002902602,0.0001902689,0.0001785317,0.00005532153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001412237,"about_ca_system_score_gemma":0.00006432973,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007737464,"about_ca_topic_score_gemma":0.0006433658,"domain_scores_codex":[0.9983708,0.0001269656,0.0003199951,0.0001842362,0.0006399751,0.0003580295],"domain_scores_gemma":[0.9986706,0.000249427,0.0001076868,0.0004090351,0.0003340316,0.0002291522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005395089,0.000007858343,0.8068808,0.00003623197,0.00001079213,5.950772e-8,0.002212287,0.0006913741,0.000005632643,0.000635285,0.0004295189,0.1890363],"study_design_scores_gemma":[0.00008666908,0.0001859483,0.9574777,0.00000442525,0.000002309772,0.000009447921,0.000594892,0.03896027,0.000009071486,0.001490842,0.001102381,0.00007600692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457837,0.000168695,0.03762773,0.0005164488,0.0001477206,0.001428927,0.0001635918,0.00004597241,0.01411717],"genre_scores_gemma":[0.996264,0.0001571691,0.003066524,0.0003337823,0.00001074411,0.00001662242,0.0001467663,5.063515e-7,0.000003882903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1889603,"threshold_uncertainty_score":0.9988701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4290081412","doi":"10.1080/15230406.2022.2056510","title":"An exploratory assessment of the effectiveness of geomasking methods on privacy protection and analytical accuracy for individual-level geospatial data","year":2022,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Geospatial analysis; Computer science; Usability; Data sharing; Confidentiality; Data science; Spatial analysis; Set (abstract data type); Data mining; Computer security; Geography; Cartography; Human–computer interaction; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.0913995966678913,"gpt":0.3727777015378933,"spread":0.281378104870002,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.01095951,0.0001281514,0.0001893245,0.001018244,0.0008274109,0.0001896497,0.01104155,0.00004896793,8.973086e-7],"category_scores_gemma":[0.005970849,0.0001015089,0.00005637436,0.003311474,0.000878721,0.005124213,0.02528764,0.0002588247,2.188529e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001813703,"about_ca_system_score_gemma":0.0001912228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002926983,"about_ca_topic_score_gemma":0.000001698509,"domain_scores_codex":[0.9977052,0.0004703708,0.0003945642,0.0004126777,0.0007819911,0.0002351727],"domain_scores_gemma":[0.9945618,0.0006751352,0.0004257803,0.004021554,0.0002586199,0.0000570899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002649143,0.000447324,0.1328716,0.001022266,0.0001901999,6.631036e-7,0.004006521,0.001440532,0.009474649,0.1668771,0.000242197,0.683162],"study_design_scores_gemma":[0.0005783192,0.0005545163,0.5565333,0.00006012211,0.00002799867,0.000008684015,0.0005442463,0.4057953,0.005017057,0.03035889,0.0003274515,0.0001941204],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3736282,0.00002806582,0.6248288,0.0002216449,0.0002119439,0.0007653461,0.0002313544,0.00005642248,0.00002822623],"genre_scores_gemma":[0.8913391,0.00001578307,0.1084121,0.00005016999,0.000004387082,0.0001480791,0.0000279237,0.000002426857,3.390781e-8],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6829679,"threshold_uncertainty_score":0.9943092,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2485367107","doi":"10.1080/15230406.2016.1212673","title":"Searching for social justice in GIScience publications","year":2016,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Crowdsourcing; Data science; Geospatial analysis; Representation (politics); Politics; Sociology; Knowledge management; Geography; Political science; World Wide Web; Computer science; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.03313100030497017,"gpt":0.3323476197384053,"spread":0.2992166194334351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.005546106,0.0001278023,0.0001561908,0.002942445,0.004079679,0.0004995504,0.0005165591,0.00008173287,0.00001157679],"category_scores_gemma":[0.001039327,0.00009864481,0.0001011819,0.006208784,0.003786763,0.008746648,0.00009534057,0.00009164662,0.000009954208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004372124,"about_ca_system_score_gemma":0.0002577795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002950045,"about_ca_topic_score_gemma":0.0004003598,"domain_scores_codex":[0.9975988,0.00005914268,0.0005138926,0.000233202,0.0008847226,0.0007102821],"domain_scores_gemma":[0.9980075,0.0003361409,0.0002345148,0.0001739571,0.001067697,0.0001802363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000007769198,0.00001042678,0.04168997,0.00003502951,0.000004947949,4.736656e-8,0.02822267,0.000001750803,0.00006195778,0.8966603,0.0002222476,0.0330829],"study_design_scores_gemma":[0.001324479,0.00006560133,0.4883643,0.00009087474,0.00002611067,0.0000038735,0.05612549,0.0001768865,0.00004067846,0.01945485,0.4338115,0.000515407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6219305,0.0002589657,0.04695724,0.0351569,0.002451326,0.003882956,0.0001602861,0.000657135,0.2885447],"genre_scores_gemma":[0.9982697,0.0002361508,0.000684082,0.000437259,0.00007824063,0.0002263989,0.000003155751,0.000002707517,0.00006224992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8772054,"threshold_uncertainty_score":0.9989244,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2340578172","doi":"10.1080/15230406.2015.1129648","title":"An ontology-driven multi-agent system for nautical chart generalization","year":2016,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Generalization; Cartographic generalization; Nautical chart; Computer science; Ontology; Artificial intelligence; Process (computing); Data mining; Set (abstract data type); Relevance (law); Automation; Chart; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02363683975319325,"gpt":0.3020271020019804,"spread":0.2783902622487872,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00280782,0.0001885356,0.0002332857,0.001477848,0.002896899,0.0003582652,0.0004356395,0.0001537351,0.000009476424],"category_scores_gemma":[0.0002249058,0.0001390813,0.0001428004,0.002032382,0.002589728,0.005830121,0.00005430168,0.00006293187,0.00001783929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005881199,"about_ca_system_score_gemma":0.0001489838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002543707,"about_ca_topic_score_gemma":0.0003045607,"domain_scores_codex":[0.9975659,0.00009517565,0.0006308615,0.0002836132,0.0007703324,0.0006541016],"domain_scores_gemma":[0.9974939,0.0001071157,0.0003178799,0.0003130949,0.001425434,0.0003425661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000294964,0.00003726755,0.1499915,0.0001129298,0.00004112588,3.401611e-7,0.02941886,0.00002107077,0.000392036,0.8029965,0.0001403067,0.01681861],"study_design_scores_gemma":[0.006789661,0.000784283,0.5310498,0.0004894513,0.0001784657,0.00004360933,0.133738,0.01052611,0.0007527047,0.002633904,0.3109393,0.002074796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4797852,0.0002798701,0.4972654,0.002852109,0.003633452,0.003605187,0.000251913,0.001080718,0.01124615],"genre_scores_gemma":[0.9959485,0.0002297587,0.003143136,0.0003199909,0.00009132158,0.0002262183,0.00001730095,0.000004911416,0.00001889292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8003626,"threshold_uncertainty_score":0.9984012,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3109225104","doi":"10.1080/15230406.2020.1845981","title":"Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development","year":2020,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Urban sprawl; Urban planning; Context (archaeology); Environmental planning; Geography; Geographic information system; Suitability analysis; Transport engineering; Metropolitan area; Computer science; Environmental resource management; Civil engineering; Environmental science; Cartography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01900916898496227,"gpt":0.2549327313697136,"spread":0.2359235623847513,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002179042,0.0001134485,0.0002509577,0.0006575554,0.0001882351,0.0000641704,0.0002190649,0.00005274699,0.0004344674],"category_scores_gemma":[0.0003427614,0.00009715401,0.0001260041,0.006565969,0.001011963,0.001322492,0.0001193963,0.00008894891,0.000007557154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003419398,"about_ca_system_score_gemma":0.00004765532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003938,"about_ca_topic_score_gemma":0.001559037,"domain_scores_codex":[0.9980217,0.00008569792,0.0005668449,0.0002789443,0.0008050212,0.0002418525],"domain_scores_gemma":[0.9993769,0.00005606328,0.00008525002,0.0001888491,0.0001139413,0.0001789945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002141164,0.00003731097,0.9566594,0.000009870577,0.00003211246,9.770014e-8,0.008659984,0.0005717866,0.0001540147,0.00003074977,0.000002989395,0.03382028],"study_design_scores_gemma":[0.000309288,0.00002647293,0.7799538,0.000002818224,0.0001077216,1.299317e-7,0.0008055909,0.2182427,0.0001609815,0.00001394393,0.000281569,0.00009498592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952083,0.00003791384,0.003928022,0.0001147306,0.00003280284,0.0002058398,0.000009302271,0.00001437222,0.0004487393],"genre_scores_gemma":[0.9991568,0.000009103073,0.0004840612,0.0002961269,0.00000338176,0.00002186943,0.00002734893,0.000001130073,2.241103e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2176709,"threshold_uncertainty_score":0.4757113,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2901146955","doi":"10.1080/15230406.2019.1524665","title":"Introduction to special issue on frontiers of geospatial data science from the joint UCGIS symposium / Autocarto 2018 conference","year":2018,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Association for Girls in Science","funders":"","keywords":"Geospatial analysis; Joint (building); Data science; Computer science; Library science; Geography; Cartography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0236281476977389,"gpt":0.2726131529760935,"spread":0.2489850052783545,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.006117292,0.0002289025,0.000294025,0.001792369,0.004480257,0.0007821815,0.001911707,0.00009388541,0.0001130326],"category_scores_gemma":[0.0008571857,0.0001774598,0.00008621631,0.007773331,0.01422905,0.007580953,0.0005902931,0.0001928842,0.00009449757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004677644,"about_ca_system_score_gemma":0.000475513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004095139,"about_ca_topic_score_gemma":0.001546616,"domain_scores_codex":[0.9956421,0.00009894427,0.0007878855,0.0005448883,0.002245934,0.0006803042],"domain_scores_gemma":[0.9955802,0.0000924184,0.0005133752,0.001187632,0.00232103,0.0003053693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002642222,0.0001033559,0.08871419,0.00004765173,0.0001470119,4.964294e-7,0.4358863,0.0001295431,0.000826888,0.1755251,0.2443339,0.05402128],"study_design_scores_gemma":[0.0003707963,0.0003273536,0.1156522,0.00006116166,0.00003983587,0.00000191648,0.06774867,0.0007326584,0.0005575343,0.0005671971,0.8135493,0.0003913743],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6899046,0.0002239451,0.01114575,0.0411672,0.03308975,0.005042,0.001157341,0.0004705761,0.2177988],"genre_scores_gemma":[0.9924904,0.0002902364,0.00113913,0.0009678753,0.005006748,0.00003629299,0.00003468521,0.000004592858,0.00003001893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5692154,"threshold_uncertainty_score":0.9968158,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407339825","doi":"10.1080/15230406.2024.2446556","title":"Advancing replicable and reproducible GIScience: an approach with KNIME","year":2025,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Computer science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.02155855089517029,"gpt":0.3217020130570342,"spread":0.3001434621618639,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01562869,0.0001585769,0.0002032499,0.003299641,0.001717715,0.002679477,0.001028261,0.00003822273,0.000009711092],"category_scores_gemma":[0.001112537,0.0001082723,0.00004398791,0.0136456,0.002230623,0.007784802,0.000504853,0.000132407,0.000005441505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009056864,"about_ca_system_score_gemma":0.0001395524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008053573,"about_ca_topic_score_gemma":0.000009450191,"domain_scores_codex":[0.9961801,0.00006164192,0.0005673378,0.00119186,0.001526196,0.0004728649],"domain_scores_gemma":[0.9968849,0.0001585313,0.0002313993,0.001770854,0.0007113112,0.0002429658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00008365178,0.000130513,0.4051678,0.00007927656,0.0000245209,0.000001676669,0.004965104,0.002351868,0.0004545868,0.1273115,0.003843834,0.4555857],"study_design_scores_gemma":[0.001089453,0.0003511984,0.6838143,0.0001043815,0.00004639821,0.00006470321,0.0240852,0.09811354,0.0004709455,0.02343535,0.1676827,0.0007418289],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8573436,0.0002609366,0.08184094,0.0006475582,0.0004384673,0.0004737551,0.00001248759,0.0001505692,0.05883173],"genre_scores_gemma":[0.987088,0.00004339634,0.0119802,0.0006453147,0.00001287,0.00002054278,0.0000082004,0.000001923672,0.000199507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4548438,"threshold_uncertainty_score":0.9995819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091334245","doi":"10.1559/152304003100010947","title":"Representation of Generalized Map Series Using Semi-Structured Data Models","year":2003,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"McMaster University","keywords":"Computer science; Representation (politics); Schema (genetic algorithms); Information retrieval; Original equipment manufacturer; Data mining; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.0408222443581195,"gpt":0.2735583114638165,"spread":0.232736067105697,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009377978,0.000116214,0.0001454432,0.0009646339,0.000355598,0.0004838826,0.001144395,0.00003396363,0.000003832989],"category_scores_gemma":[0.00005227767,0.0001047507,0.00004674925,0.00381568,0.0005360338,0.02411621,0.0004263448,0.0000601275,7.589906e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003831378,"about_ca_system_score_gemma":0.00005688091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004714647,"about_ca_topic_score_gemma":0.000003275642,"domain_scores_codex":[0.9985157,0.0000436907,0.0003549173,0.0003068454,0.0005373646,0.0002414501],"domain_scores_gemma":[0.9984044,0.00001847532,0.0002280132,0.001015395,0.0002502767,0.00008347536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008888703,0.00001746444,0.007002817,0.00007982807,0.0000350215,0.000001104089,0.001818482,0.00302176,0.001232588,0.9666669,0.0002449701,0.01987011],"study_design_scores_gemma":[0.001139304,0.0000808955,0.007966436,0.00005121547,0.00005042045,0.00004870006,0.001348749,0.8812324,0.007472486,0.08895524,0.0110389,0.0006152153],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07044651,0.0002511266,0.9259545,0.0001051145,0.0005825064,0.0002918969,0.00006477691,0.00008708048,0.002216485],"genre_scores_gemma":[0.8414299,0.0002651164,0.1580235,0.0001697798,0.00001221234,0.000005540358,0.00008450629,0.000002587237,0.000006893531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8782107,"threshold_uncertainty_score":0.9895329,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410008706","doi":"10.1080/15230406.2025.2492670","title":"Multi-scale dynamic population estimation: an Adaptive Inverse Distance Weighting (AIDW) model incorporating spatial characteristics","year":2025,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Carleton University","funders":"","keywords":"Scale (ratio); Weighting; Population; Computer science; Spatial ecology; Estimation; Spatial analysis; Inverse distance weighting; Data mining; Geography; Statistics; Mathematics; Cartography; Ecology; Multivariate interpolation; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01074510989304097,"gpt":0.282387029684779,"spread":0.2716419197917381,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001944731,0.0001474904,0.0001776985,0.001090886,0.003013655,0.0004764161,0.0002936439,0.0001017751,0.00000889967],"category_scores_gemma":[0.0002435648,0.000154691,0.00008772709,0.003195372,0.001504831,0.004469477,0.00004776676,0.000168611,0.000002706681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007482568,"about_ca_system_score_gemma":0.0002986727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590165,"about_ca_topic_score_gemma":0.01435715,"domain_scores_codex":[0.998237,0.000110521,0.0005164066,0.0002921704,0.0005447911,0.0002990876],"domain_scores_gemma":[0.9985495,0.00006615357,0.00033229,0.0002611653,0.0006243764,0.0001665197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008848804,0.0002021017,0.4533133,0.0001626632,0.00004874158,8.707968e-7,0.05159823,0.04068212,0.0002648933,0.1150013,0.00001248632,0.3386248],"study_design_scores_gemma":[0.0001706694,0.00002159157,0.1264198,0.00004480208,0.00002812861,1.706322e-7,0.004723576,0.8652203,0.000008440203,0.003132533,0.00007264048,0.0001574098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4184088,0.00001766182,0.5798224,0.0001539898,0.0001350949,0.0002634042,0.00002920526,0.00009214006,0.00107733],"genre_scores_gemma":[0.9855825,0.00003803862,0.01391182,0.000268704,0.0000193122,0.00003454528,0.0001251314,0.000002644289,0.00001730669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8245382,"threshold_uncertainty_score":0.9982843,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2944432865","doi":"10.1080/15230406.2020.1737575","title":"The gerrymandering jumble: map projections permute districts’ compactness scores","year":2020,"lang":"en","type":"preprint","venue":"Cartography and Geographic Information Science","topic":"Local Government Finance and Decentralization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Division of Mathematical Sciences; National Science Foundation","keywords":"Compact space; Convex hull; Proxy (statistics); Projection (relational algebra); Gerrymandering; Regular polygon; Hull; Mathematics; Computer science; Geography; Cartography; Politics; Statistics; Algorithm; Political science; Pure mathematics; Geometry; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02229595041262826,"gpt":0.2877892529446827,"spread":0.2654933025320544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.001240439,0.0002062187,0.0001861558,0.0003947028,0.004793391,0.002104899,0.0006819123,0.0001519978,0.000007631902],"category_scores_gemma":[0.0002605958,0.0001612232,0.0001532322,0.002742747,0.002844163,0.002309737,0.0003542056,0.0003905779,0.000007606565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005996857,"about_ca_system_score_gemma":0.0005216353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001062373,"about_ca_topic_score_gemma":0.0003300921,"domain_scores_codex":[0.9973586,0.00007642208,0.0004289904,0.0003184121,0.001288771,0.0005287839],"domain_scores_gemma":[0.9985411,0.00009445518,0.0003723935,0.0003037219,0.0004448786,0.0002434118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007388193,0.00006976513,0.2421006,0.0004350442,0.00009996203,0.000002852626,0.06270291,0.0009758055,0.00002962514,0.6486846,0.005004388,0.03982062],"study_design_scores_gemma":[0.0006285121,0.00009179881,0.3664594,0.0003022154,0.0001128936,0.00000388685,0.02489027,0.006109971,0.00007781182,0.02063492,0.5796847,0.001003643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7472599,0.005684469,0.087269,0.03008625,0.01589806,0.00767287,0.0005479872,0.001628296,0.1039532],"genre_scores_gemma":[0.9940622,0.005225499,0.00008448698,0.0003387022,0.0001402898,0.00008025197,0.00003983159,0.000004509744,0.00002420573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6280497,"threshold_uncertainty_score":0.9998695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407878462","doi":"10.1080/15230406.2025.2464661","title":"A framework to evaluate the effectiveness of web-based geo-participation tools as a public participation technique","year":2025,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Public participation; Computer science; Data science; World Wide Web; Geography; Political science; Public relations","retraction":null,"screen_n_in":null,"score":{"opus":0.02702291370916414,"gpt":0.3532917964993226,"spread":0.3262688827901585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01460279,0.0001807095,0.0002578057,0.002549334,0.002257974,0.0006059654,0.0005359561,0.0001423477,0.00001517885],"category_scores_gemma":[0.004497937,0.0001410724,0.0001526474,0.01193077,0.002282505,0.003317241,0.000118535,0.0001848219,0.00001277121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004447934,"about_ca_system_score_gemma":0.0006117743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004733434,"about_ca_topic_score_gemma":0.0001377633,"domain_scores_codex":[0.9967015,0.0005730677,0.0007279014,0.0002299463,0.00121494,0.000552682],"domain_scores_gemma":[0.9956674,0.001214621,0.0002647573,0.0004326056,0.002243511,0.0001771274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000885328,0.00003939446,0.1365525,0.0001794028,0.00004285316,9.763701e-8,0.01103666,0.000252288,0.0003411873,0.8422402,0.00003940174,0.009187565],"study_design_scores_gemma":[0.0007959312,0.0002110995,0.9343448,0.000574169,0.00008721268,8.385656e-7,0.009735914,0.0007425303,0.002461857,0.0275655,0.02310172,0.0003784625],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8930236,0.0001196139,0.05586004,0.002351267,0.0005906117,0.003393457,0.00002070994,0.0001892874,0.04445147],"genre_scores_gemma":[0.9970876,0.00005265007,0.0005027571,0.0008402505,0.00001444025,0.001487976,0.000007279487,0.000002837673,0.000004209614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8146746,"threshold_uncertainty_score":0.999041,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406196887","doi":"10.1080/15230406.2024.2439311","title":"Automated selection of urban road network by fusion of PageRank algorithm and attribute importance metrics","year":2025,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Computer science; Weighting; Data mining; Automation; Selection (genetic algorithm); Focus (optics); Process (computing); Artificial intelligence; Machine learning; Feature selection; Generalization; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.006538195198231108,"gpt":0.2547668730364471,"spread":0.248228677838216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003453256,0.0001559468,0.0003153398,0.002063768,0.001424619,0.0001387356,0.0002444061,0.0001337724,0.00000621135],"category_scores_gemma":[0.0002300518,0.0001479811,0.0001008857,0.01379465,0.002224707,0.002524141,0.00009810931,0.0001227132,4.712455e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001803458,"about_ca_system_score_gemma":0.0001427736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009394885,"about_ca_topic_score_gemma":0.00006750003,"domain_scores_codex":[0.9976773,0.0000770265,0.0008475095,0.0001862773,0.000805037,0.0004068397],"domain_scores_gemma":[0.9975359,0.0001231752,0.0006499107,0.0001693472,0.001409042,0.0001125706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001180121,0.00002187723,0.9373677,0.0001461759,0.00006579966,8.201011e-8,0.009174135,0.00003408117,0.00009157921,0.02538984,0.002534674,0.02516229],"study_design_scores_gemma":[0.001117143,0.0001745391,0.9328004,0.0002386424,0.0000984822,0.000003708622,0.01825472,0.009351055,0.0004845416,0.001264183,0.03575669,0.0004558965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669174,0.004679809,0.01188732,0.0003571104,0.0009035805,0.001275413,0.000130413,0.0003938378,0.01345512],"genre_scores_gemma":[0.9964662,0.002195575,0.001128903,0.0001327981,0.00001637746,0.00002417239,0.00001787904,0.0000020165,0.0000161171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03322201,"threshold_uncertainty_score":0.9998754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415953715","doi":"10.1080/15230406.2025.2576496","title":"Deepfake geography: a novel detection method for identifying manipulated satellite images","year":2025,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Satellite; Pattern recognition (psychology); Satellite imagery; Visualization; Feature (linguistics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01654757700934191,"gpt":0.318270455849615,"spread":0.3017228788402731,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001706848,0.0002200659,0.0002100061,0.003313352,0.001047173,0.0009773874,0.0007509288,0.0000995153,7.558278e-7],"category_scores_gemma":[0.0001643586,0.0002075914,0.000210413,0.007246546,0.0004677102,0.008630044,0.0002354635,0.0001726893,0.00000116103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000168826,"about_ca_system_score_gemma":0.00006160707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004445043,"about_ca_topic_score_gemma":0.000005501909,"domain_scores_codex":[0.9981406,0.00002808654,0.0004949783,0.0004559266,0.0003966845,0.0004837277],"domain_scores_gemma":[0.9982189,0.0001438647,0.0002222137,0.0004900848,0.0008046136,0.000120302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003058035,0.00002781062,0.003719519,0.0001429259,0.00003598525,5.099485e-7,0.0004470038,0.00002451808,0.0227363,0.07769719,0.0000172302,0.8951204],"study_design_scores_gemma":[0.002486419,0.0004573414,0.2604651,0.0003341804,0.0001249526,0.00007790835,0.0007415627,0.06509913,0.4925795,0.1133395,0.0629025,0.001391953],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002920232,0.0007346159,0.9937057,0.0001403771,0.0003558887,0.0006217459,0.00001018564,0.0004357706,0.001075406],"genre_scores_gemma":[0.6147388,0.000795427,0.3833765,0.000916769,0.00001526832,0.0001338915,0.000009707831,0.000004903849,0.000008709243],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8937285,"threshold_uncertainty_score":0.9424969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}