{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":9,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":9,"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":"402934be1cde","filters":{"venue":"AGILE GIScience Series"}},"results":[{"id":"W4379646932","doi":"10.5194/agile-giss-4-42-2023","title":"Thinking Geographically about AI Sustainability","year":2023,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Sustainability; Computer science; Transparency (behavior); Artificial intelligence; Metric (unit); Machine learning; Work (physics); Data science; Engineering; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.005618923607351612,"gpt":0.2180025177786206,"spread":0.212383594171269,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003000859,0.0001187366,0.00011962,0.0001629995,0.0002915699,0.0001560623,0.0004642294,0.00007668731,0.00002675614],"category_scores_gemma":[0.0001949742,0.0001054146,0.00006790709,0.001265612,0.0005428654,0.0005189665,0.0001941321,0.0001655485,0.00003765483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003957674,"about_ca_system_score_gemma":0.00002958903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003690583,"about_ca_topic_score_gemma":0.00005357217,"domain_scores_codex":[0.9989293,0.000007834487,0.0001423257,0.0002095902,0.0002512863,0.000459613],"domain_scores_gemma":[0.9994884,0.00004512221,0.00001357885,0.000333978,0.00007692736,0.00004204974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002255025,0.00004708069,0.278293,0.0007313549,0.00009695896,0.0002396538,0.00719753,0.05051483,0.006196197,0.5266049,0.0336399,0.09641608],"study_design_scores_gemma":[0.0002170432,0.0001672966,0.2790447,0.00006526128,0.00001524927,0.00002810308,0.01955782,0.008795028,0.009061764,0.5081646,0.1740309,0.0008521695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860752,0.0002151454,0.0006957835,0.003603867,0.0006199296,0.0001453441,0.000007404679,0.004948069,0.003689226],"genre_scores_gemma":[0.9989707,0.000233983,0.0002973943,0.0001000762,0.00003585204,0.00003776315,0.000003395469,0.00001318176,0.0003075925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.140391,"threshold_uncertainty_score":0.4298681,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3042993452","doi":"10.5194/agile-giss-1-6-2020","title":"Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling","year":2020,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University","funders":"Global Water Futures","keywords":"Computer science; Raster data; Spatial analysis; Cellular automaton; Raster graphics; Grid; Data mining; Data model (GIS); Spatial data infrastructure; Spatial database; Geographic information system; Distributed computing; Remote sensing; Algorithm; Artificial intelligence; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02080407555336356,"gpt":0.2511358208580585,"spread":0.230331745304695,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003025437,0.0002127607,0.0002286352,0.00003419268,0.0007825118,0.0009429589,0.0009277454,0.0000389578,0.000001378824],"category_scores_gemma":[0.00003865413,0.0001811802,0.00004952421,0.0009648726,0.0002290643,0.001696353,0.0007781407,0.0001280872,0.00001617648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003964055,"about_ca_system_score_gemma":0.00009473818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001896488,"about_ca_topic_score_gemma":0.0001218236,"domain_scores_codex":[0.9981239,0.00008045632,0.0003359677,0.0007730488,0.0003650003,0.0003215853],"domain_scores_gemma":[0.9989413,0.00003884636,0.0001210196,0.0006044459,0.00004750389,0.0002468697],"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":[0.00005007302,0.001320469,0.02172034,0.0008524359,0.0002714666,0.01671656,0.3367159,0.06546184,0.03177051,0.4486892,0.00214663,0.07428458],"study_design_scores_gemma":[0.0001698604,0.0001948794,0.00005538276,0.0000264646,0.00001539433,0.0007899291,0.01711776,0.9792492,0.0003034062,0.0006120231,0.001201044,0.0002646878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5132027,0.0003135671,0.4843048,0.001122503,0.0001476113,0.0003665693,0.000009082149,0.0003441398,0.0001890156],"genre_scores_gemma":[0.9784557,0.00001095462,0.02126138,0.0001023144,0.00007534959,0.00005983312,0.000003782113,0.000007848169,0.00002283012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9137873,"threshold_uncertainty_score":0.9092975,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4281923114","doi":"10.5194/agile-giss-3-7-2022","title":"Six GIScience Ideas That Must Die","year":2022,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Slogan; Epistemology; Sociology; Geoinformatics; Engineering ethics; Data science; Computer science; Political science; Philosophy; Engineering; Law; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02813400605547749,"gpt":0.2795192859020048,"spread":0.2513852798465273,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002731182,0.000162947,0.0002070522,0.000239368,0.008688822,0.0003270634,0.001219908,0.00003737373,0.0007124895],"category_scores_gemma":[0.0003649152,0.0001542636,0.0001260103,0.001791865,0.0026101,0.002301023,0.0007051548,0.0002006464,0.0001234152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617531,"about_ca_system_score_gemma":0.0003179957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004055168,"about_ca_topic_score_gemma":0.004015035,"domain_scores_codex":[0.9964162,0.0002009953,0.000316941,0.0003933478,0.00189739,0.0007751733],"domain_scores_gemma":[0.9989135,0.000130835,0.0002466646,0.0003614048,0.0001821219,0.0001654505],"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":[0.00003292203,0.0001139123,0.1008349,0.00004102568,0.0000377924,0.00004618088,0.376913,0.0009594372,0.0004731492,0.4944692,0.01935538,0.006723132],"study_design_scores_gemma":[0.0001142545,0.00008166698,0.01298516,0.00000868155,0.00000663101,0.00001416268,0.2671228,0.00002625399,0.0001020761,0.004566451,0.7146815,0.0002903727],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4719203,0.001172779,0.0002827835,0.01514038,0.005787468,0.001061251,0.00007217226,0.0006532362,0.5039096],"genre_scores_gemma":[0.9857382,0.0001377101,0.0002680383,0.0006383518,0.0001014521,0.0001889934,0.000003154434,0.00000718508,0.01291687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6953261,"threshold_uncertainty_score":0.9926018,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3043060821","doi":"10.5194/agile-giss-1-14-2020","title":"Uncovering spatiotemporal biases in place-based social sensing","year":2020,"lang":"en","type":"article","venue":"AGILE GIScience Series","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":false},"ca_institutions":"McGill University","funders":"","keywords":"Categorization; Recreation; Data science; Computer science; Service (business); Data collection; World Wide Web; Artificial intelligence; Sociology; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.05500225578592471,"gpt":0.3063683086975761,"spread":0.2513660529116514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007656398,0.00009929202,0.0001658733,0.00008768185,0.0009789733,0.0001864167,0.0002360978,0.00006039595,0.0002180758],"category_scores_gemma":[0.001097145,0.0001056778,0.00008613864,0.001262148,0.0008361746,0.0005825498,0.00003426223,0.0001192075,0.00003089814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001343618,"about_ca_system_score_gemma":0.0007193573,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0128589,"about_ca_topic_score_gemma":0.06328352,"domain_scores_codex":[0.9984443,0.0002120443,0.0002230969,0.0003177125,0.0004498026,0.0003530987],"domain_scores_gemma":[0.9994424,0.0001728856,0.00008425064,0.00009694077,0.00007808857,0.0001254528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003711775,0.0003607336,0.1589288,0.0002227121,0.00005337986,0.0001463686,0.4762972,0.2214309,0.008889186,0.01245779,0.003523894,0.1173179],"study_design_scores_gemma":[0.003499116,0.0008627775,0.1067311,0.0005594788,0.0001859167,0.00000257526,0.382011,0.2147866,0.03260965,0.01099073,0.2436234,0.00413767],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974699,0.00002879775,0.001068532,0.02037973,0.00009287498,0.0001414969,0.000007956278,0.0001114151,0.003470265],"genre_scores_gemma":[0.9982939,0.000005878673,0.0003415519,0.001005576,0.0002062956,0.000003716577,0.000008451038,0.000005677431,0.000128981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2400995,"threshold_uncertainty_score":0.9937146,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399156855","doi":"10.5194/agile-giss-5-34-2024","title":"Data Quality of OpenStreetMap for Industrial Sites in the Arctic","year":2024,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Arctic; Permafrost; Completeness (order theory); The arctic; Environmental science; Industrial pollution; Pollution; Climate change; Resource (disambiguation); Geography; Environmental resource management; Physical geography; Environmental planning; Computer science; Ecology; Geology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.3462491723911512,"gpt":0.3658247025866311,"spread":0.01957553019547992,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001529821,0.00007352722,0.0001196559,0.00005170846,0.0001061681,0.0001686954,0.0007819941,0.0000365417,0.0008753111],"category_scores_gemma":[0.000277856,0.00004433997,0.00003457791,0.0004546756,0.0002997013,0.0008405738,0.00004634503,0.0000911836,0.00002704324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002030886,"about_ca_system_score_gemma":0.00007538294,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006336472,"about_ca_topic_score_gemma":0.1086755,"domain_scores_codex":[0.9990265,0.00006967896,0.0002128952,0.0002616519,0.0002229033,0.0002064217],"domain_scores_gemma":[0.9989162,0.0006384766,0.00003861929,0.000357692,0.00002081595,0.00002817113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001064744,0.0000276804,0.9576701,0.0002330202,0.00001100921,0.00001398865,0.004894269,0.00007659592,0.00177774,0.001891464,0.010535,0.02276269],"study_design_scores_gemma":[0.0005229667,0.000529137,0.8074849,0.0002461741,0.00003670366,0.0000413887,0.01688737,0.007226925,0.0007311989,0.009156226,0.1566765,0.0004604601],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819096,0.001186222,0.00001667773,0.003463972,0.0006365362,0.0003388089,0.01054011,0.00001723421,0.001890781],"genre_scores_gemma":[0.9977005,0.00008777063,0.0000797206,0.0001517377,0.0001614008,0.000002749808,0.001678383,0.000001432893,0.0001363403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1501851,"threshold_uncertainty_score":0.9584042,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411154871","doi":"10.5194/agile-giss-6-9-2025","title":"Mobility Vitality in Active and Micro-Mobility Modes: Measuring Urban Vitality Through Spatiotemporal Similarity","year":2025,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Human Mobility and Location-Based Analysis","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":"McGill University","funders":"China Scholarship Council","keywords":"Vitality; Similarity (geometry); Economic geography; Geography; Computer science; Artificial intelligence; Philosophy","retraction":null,"screen_n_in":null,"score":{"opus":0.029368997570734,"gpt":0.3148625343065243,"spread":0.2854935367357904,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.003627578,0.0002319745,0.000420335,0.0001222238,0.001494362,0.0002581194,0.0005242724,0.0001856886,0.00009582819],"category_scores_gemma":[0.001711223,0.0002370294,0.0001543322,0.00151351,0.003740623,0.002052127,0.000204204,0.0003259013,0.000004060088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005797812,"about_ca_system_score_gemma":0.0008475879,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07572722,"about_ca_topic_score_gemma":0.1700908,"domain_scores_codex":[0.9965518,0.0008314627,0.0005380705,0.0009629473,0.0005636583,0.0005521048],"domain_scores_gemma":[0.998411,0.0003494894,0.0001504151,0.0006520307,0.0003010419,0.0001360439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002268118,0.001361607,0.868315,0.0003158633,0.00007273528,0.000005887598,0.07441238,0.0005426835,0.003500391,0.04022155,0.0001511189,0.01087395],"study_design_scores_gemma":[0.0005057715,0.00007654347,0.8123298,0.0001235118,0.00006876232,5.011013e-7,0.05748593,0.0009279628,0.01822757,0.1061666,0.003426908,0.000660181],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845011,0.0001670549,0.001305043,0.002529215,0.0001267581,0.0006029943,0.0000576384,0.0001053761,0.01060478],"genre_scores_gemma":[0.9988169,0.00005341534,0.0003829149,0.0002455972,0.00004015604,0.00007257448,0.00001190249,0.000005080412,0.0003714289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09436361,"threshold_uncertainty_score":0.9998056,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283579283","doi":"10.5194/agile-giss-3-25-2022","title":"Violent crime in Lithuania: trends and patterns in 2015–2020","year":2022,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Banking, Crisis Management, COVID-19 Impact","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Vancouver Island University","funders":"","keywords":"Violent crime; Harm; Coronavirus disease 2019 (COVID-19); Criminology; Geography; Distribution (mathematics); Pandemic; 2019-20 coronavirus outbreak; Demography; Demographic economics; Political science; Psychology; Sociology; Medicine; Economics; Law","retraction":null,"screen_n_in":null,"score":{"opus":0.0131867547684494,"gpt":0.247850577806324,"spread":0.2346638230378746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000580176,0.0001446498,0.0001743353,0.00007235774,0.0002872141,0.0001326735,0.0004602624,0.00002842179,0.0009979309],"category_scores_gemma":[0.00004858207,0.00006860888,0.00004505316,0.001275743,0.0001323793,0.0005306363,0.0006233767,0.0001811479,0.000007166129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000931762,"about_ca_system_score_gemma":0.0000114728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003181802,"about_ca_topic_score_gemma":0.006947401,"domain_scores_codex":[0.9984003,0.0001277843,0.0002280141,0.00047516,0.0003403318,0.000428416],"domain_scores_gemma":[0.9996765,0.00006438697,0.00008320513,0.0000895342,0.00001160344,0.00007479302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007392381,0.0002987013,0.7769921,0.0000204743,0.000007528859,0.0001510953,0.002827586,0.0001712538,0.02944557,0.001365907,0.004102121,0.1845437],"study_design_scores_gemma":[0.0001265795,0.0003200298,0.9707258,0.00001276647,0.000003039339,0.00001171079,0.002749004,0.00008253207,0.0001940801,0.0006430319,0.02494765,0.000183712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890388,0.0001986881,0.000001399234,0.00948035,0.0001563848,0.0001253628,0.00004636892,0.00004128417,0.0009113266],"genre_scores_gemma":[0.9983177,0.0001060976,0.00001951967,0.0009047072,0.00004208877,0.00005357171,0.00001697366,0.000001091916,0.0005382195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1937338,"threshold_uncertainty_score":0.9999153,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3170222427","doi":"10.5194/agile-giss-2-9-2021","title":"Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors","year":2021,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Cisco Systems","keywords":"Occupancy; Event (particle physics); Computer science; Gyroscope; Workflow; Real-time computing; Accelerometer; Task (project management); Internet of Things; Simulation; Database; Engineering; Embedded system; Systems engineering; Aerospace engineering; Architectural engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03068985332120091,"gpt":0.2399161141575706,"spread":0.2092262608363697,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001005487,0.0001188649,0.0001338732,0.0000676984,0.0001470652,0.00008468985,0.0001286356,0.00005714008,0.000005409067],"category_scores_gemma":[0.0001000522,0.00012,0.00003713999,0.0004557551,0.00007775961,0.0007032984,0.00005603604,0.00008048054,3.06593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004271691,"about_ca_system_score_gemma":0.00007809768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001911903,"about_ca_topic_score_gemma":0.0001299464,"domain_scores_codex":[0.9991786,0.000007092755,0.0001623874,0.0002280578,0.0001238596,0.0003000299],"domain_scores_gemma":[0.9996579,0.00003686227,0.00003583601,0.0001496101,0.00008067812,0.00003909449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007451829,0.000006761435,0.0009308454,0.00004500211,0.000003593004,0.000007062537,0.0009942611,0.9889408,0.00799464,0.0006282766,0.00002281769,0.0004184829],"study_design_scores_gemma":[0.0001139418,0.0000121309,0.00005859699,0.00007676068,0.000004515406,0.00001858403,0.0004279741,0.9574468,0.04064757,0.001002059,0.00003990148,0.0001511538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8283379,0.000106403,0.1707968,0.00004504964,0.0002481008,0.000145457,0.000009131977,0.00006482709,0.000246324],"genre_scores_gemma":[0.9350759,0.00002699402,0.06445453,0.00001831948,0.00003689966,0.00003799645,0.000005806273,0.0000166055,0.0003269894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.106738,"threshold_uncertainty_score":0.4893459,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3172073922","doi":"10.5194/agile-giss-2-21-2021","title":"Using eigen decomposition and sequence-based representation to extract movement patterns from contextualized tracking data","year":2021,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Categorical variable; Dimensionality reduction; Computer science; Principal component analysis; Set (abstract data type); Curse of dimensionality; Artificial intelligence; Representation (politics); Data mining; Biome; Data set; Sequence (biology); Pattern recognition (psychology); Geography; Ecology; Machine learning; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.1282803332838962,"gpt":0.3555813841266788,"spread":0.2273010508427825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002527934,0.00009660084,0.0001082365,0.00002250691,0.0003313304,0.0001246426,0.0002393016,0.00004160216,0.000808501],"category_scores_gemma":[0.0001267089,0.0000997352,0.00001836622,0.0002135825,0.0001712254,0.001433542,0.0002783325,0.00005720744,0.00002650266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008313403,"about_ca_system_score_gemma":0.00004622248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001997267,"about_ca_topic_score_gemma":0.002705892,"domain_scores_codex":[0.9987315,0.0001056205,0.0001958669,0.0005422604,0.0002350735,0.0001897002],"domain_scores_gemma":[0.9993253,0.00009079026,0.00007972212,0.0004044561,0.00002129843,0.00007843401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002823227,0.00004707013,0.7588591,0.000002940772,0.000006614362,0.00002780855,0.0006694059,0.002104384,0.229769,0.00006192234,0.000139025,0.008284509],"study_design_scores_gemma":[0.0002707723,0.00004153256,0.923781,0.00003723967,0.00002139631,0.000009399618,0.0009667798,0.01737397,0.0556495,0.001244397,0.0004170733,0.0001869266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977305,0.00002410493,0.02048048,0.001611903,0.0001443699,0.0001603999,0.00008581716,0.00002617088,0.0001617529],"genre_scores_gemma":[0.9840916,0.00001179817,0.01297619,0.002622803,0.00002738023,0.00001202007,0.0001893896,0.000005626139,0.00006319354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1741195,"threshold_uncertainty_score":0.8852519,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}