{"meta":{"query_hash":"148a075f8065","filters":{"venue":"Journal of Disaster Research"},"cohort_total":8,"direct_labels_cover":0,"predictions_cover":8,"exported":8,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/148a075f8065","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Disaster+Research"},"results":[{"id":"W2302975098","doi":"10.20965/jdr.2016.p0097","title":"Shaking Table Test of Quarter Scale 20 Story RC Moment Frame Building Subjected to Long Period Ground Motions","year":2016,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":4,"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":"Earthquake shaking table; Structural engineering; Ground motion; Full scale; Frame (networking); Scale (ratio); Moment (physics); Scale model; Nonlinear system; Quarter (Canadian coin); Test (biology); Finite element method; Ground floor; Engineering; Geotechnical engineering; Geology; Physics; Civil engineering; Mechanical engineering","score_opus":0.02728734134065487,"score_gpt":0.3079323197714892,"score_spread":0.28064497843083436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302975098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98032844,0.00022252662,0.018106557,0.0008464573,0.000112776135,0.00007316398,0.000010946316,0.00001299241,0.00028611606],"genre_scores_gemma":[0.99855757,0.000046675465,0.0004974505,0.000016799728,0.00025955323,0.0000059824943,6.3800235e-7,0.000022628341,0.00059270556],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99829483,0.000061808256,0.0004062736,0.000117389296,0.0006969432,0.00042275302],"domain_scores_gemma":[0.99906456,0.0001337771,0.00007052015,0.00022104268,0.00033146204,0.00017863163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000945003,0.000109418914,0.0002631873,0.000639489,0.00012133276,0.0000734945,0.00030135317,0.000055110206,0.00026170502],"category_scores_gemma":[0.00008330237,0.00007666284,0.00011236292,0.0005946091,0.000062009545,0.00049611356,0.00006433493,0.00041015085,0.000039332288],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042150958,0.00021371146,0.06019311,0.00016275693,0.00024795695,0.000035024823,0.005018422,0.0038482356,0.91091394,0.000014740516,0.005693361,0.013616587],"study_design_scores_gemma":[0.010051732,0.005614277,0.5957734,0.012923588,0.00055260106,0.00068573863,0.04489626,0.05674071,0.2293985,0.0009324517,0.039715867,0.0027148589],"about_ca_topic_score_codex":0.000014241311,"about_ca_topic_score_gemma":0.000025733301,"teacher_disagreement_score":0.68151546,"about_ca_system_score_codex":0.00025421657,"about_ca_system_score_gemma":0.000056978708,"threshold_uncertainty_score":0.31262195},"labels":[],"label_agreement":null},{"id":"W2603647761","doi":"10.20965/jdr.2012.p0619","title":"Impact Tests for IRIS_2010 Benchmark Exercise","year":2012,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission","funders":"","keywords":"Punching; Bending moment; Structural engineering; Missile; Perforation; Bending; Engineering; Geotechnical engineering; Mechanical engineering","score_opus":0.144638789138314,"score_gpt":0.4771348221783929,"score_spread":0.3324960330400789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603647761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977517,0.00023026105,0.00009616514,0.00018781475,0.001103488,0.0003234361,0.00005178791,0.000009350275,0.00024595638],"genre_scores_gemma":[0.99687177,0.000020745092,0.0014660591,0.000012739333,0.0010489525,0.000016800568,0.000003141014,0.000025956238,0.0005338182],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974514,0.0001740522,0.00046894886,0.00011699582,0.00085704814,0.0009315333],"domain_scores_gemma":[0.9980833,0.00025830223,0.00019601484,0.00027008765,0.0006139241,0.0005784002],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004149375,0.00014585181,0.00034196614,0.00026289668,0.00018048815,0.00026523482,0.00049555115,0.00010630753,0.0020992635],"category_scores_gemma":[0.0004500281,0.00009582929,0.00019050253,0.0001864301,0.00014507871,0.0009823373,0.00012735258,0.00027751565,0.00033734943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051527366,0.0003650143,0.012688234,0.000049104805,0.000010267827,0.000006372343,0.0021374798,0.0000012832458,0.95443183,0.000039262763,0.027815597,0.0019402853],"study_design_scores_gemma":[0.0042952425,0.0038207364,0.30710086,0.0005896053,0.00017403906,0.0004013592,0.0021899475,0.000009623689,0.651667,0.0009731945,0.02800809,0.0007702886],"about_ca_topic_score_codex":0.00003387684,"about_ca_topic_score_gemma":0.0000036527938,"teacher_disagreement_score":0.30276483,"about_ca_system_score_codex":0.00014269263,"about_ca_system_score_gemma":0.00019571789,"threshold_uncertainty_score":0.998813},"labels":[],"label_agreement":null},{"id":"W2917350861","doi":"10.20965/jdr.2019.p0387","title":"Disaster Response and Mitigation Support Technology for All-Hazards in Tokyo Metropolitan Area","year":2019,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Earthquake and Disaster Impact 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":"Vector Institute","funders":"","keywords":"Emergency management; Natural hazard; Flood myth; Natural disaster; Metropolitan area; Business; Hazard; Scope (computer science); Environmental planning; Disaster response; Computer science; Computer security; Geography","score_opus":0.07791586194626085,"score_gpt":0.4461032217378744,"score_spread":0.36818735979161354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917350861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97841287,0.00031067393,0.000041809537,0.01314587,0.00015479114,0.00043854467,0.000010117133,0.000007353748,0.0074779633],"genre_scores_gemma":[0.99651426,0.000095914766,0.00021411732,0.00014264653,0.0001339971,0.000013829062,0.0000010423956,0.000013390397,0.002870813],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9973012,0.0005955339,0.00045133356,0.00018775759,0.0008216856,0.0006424791],"domain_scores_gemma":[0.9980708,0.0009340012,0.0001444062,0.00017951048,0.00047591244,0.00019538368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059772455,0.00011116459,0.0003196892,0.0008841387,0.00019206651,0.00014388206,0.00029835035,0.00013529568,0.00016239523],"category_scores_gemma":[0.0017311419,0.000089224006,0.00009092945,0.0005492868,0.00053145224,0.000527276,0.00012572088,0.00037249416,0.000028479395],"study_design_candidate":"qualitative","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":[0.0070632636,0.0005742742,0.7124742,0.00018402426,0.00023227639,0.000098737524,0.21412055,0.000003671156,0.012860301,0.018749872,0.012626349,0.021012457],"study_design_scores_gemma":[0.0049834326,0.004985245,0.08384733,0.00035437112,0.000046456884,0.00005834594,0.7718398,0.00002694481,0.0010887495,0.033547718,0.09879166,0.00042994408],"about_ca_topic_score_codex":0.000091401234,"about_ca_topic_score_gemma":0.0007917007,"teacher_disagreement_score":0.6286269,"about_ca_system_score_codex":0.00028734363,"about_ca_system_score_gemma":0.00036130176,"threshold_uncertainty_score":0.36384496},"labels":[],"label_agreement":null},{"id":"W3011524335","doi":"10.20965/jdr.2020.p0212","title":"Questionnaire Survey on the Difficulty of Attending Work for Commuters After the 2018 Osaka Earthquake","year":2020,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Disaster Management and Resilience","field":"Social Sciences","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":"Work (physics); Attendance; Questionnaire; Quarter (Canadian coin); Downtown; Transport engineering; Traffic congestion; Engineering; Psychology; Forensic engineering; Geography; Economic growth; Mathematics; Statistics; Economics","score_opus":0.20019824082763427,"score_gpt":0.41018834920848424,"score_spread":0.20999010838084997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011524335","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95099884,0.00013231546,0.00025355263,0.046023615,0.0002473679,0.00042687193,0.000008943177,0.000004362639,0.0019041277],"genre_scores_gemma":[0.99818575,0.000040968745,0.000021134201,0.000413121,0.00033681508,0.000010960283,6.7956967e-7,0.000008045277,0.0009825175],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9966146,0.0014682343,0.00031455787,0.00011564684,0.0011549038,0.00033209863],"domain_scores_gemma":[0.9978215,0.0013672999,0.00016915117,0.00018872046,0.0003310763,0.00012227138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005387041,0.00007888918,0.00015541403,0.00006810892,0.00050744135,0.00022081035,0.0009504224,0.000038837403,0.00008772322],"category_scores_gemma":[0.0012139915,0.000037913913,0.000138865,0.00056301174,0.00076771906,0.00018103044,0.00014805558,0.00037722004,0.000022492184],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004576112,0.0005239924,0.3505963,0.00015990276,0.0003136157,0.00002877868,0.23523343,0.00017138252,0.00019876643,0.008358562,0.36832038,0.031518802],"study_design_scores_gemma":[0.0005340277,0.00062900706,0.92138517,0.00048184596,0.000023432664,7.384232e-7,0.04399169,0.000043715074,0.000034263612,0.00059750804,0.03214582,0.000132802],"about_ca_topic_score_codex":0.00010169806,"about_ca_topic_score_gemma":0.0005145248,"teacher_disagreement_score":0.57078886,"about_ca_system_score_codex":0.000037983722,"about_ca_system_score_gemma":0.00007335414,"threshold_uncertainty_score":0.390288},"labels":[],"label_agreement":null},{"id":"W3110154526","doi":"10.20965/jdr.2020.p0833","title":"Social, Economic and Health Effects of the 2016 Alberta Wildfires: Pediatric Resilience","year":2020,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mount Royal University; University of Alberta; University of Calgary","funders":"","keywords":"Snowball sampling; Influencer marketing; Mental health; Community resilience; Government (linguistics); Psychological resilience; Disaster recovery; Focus group; Nonprobability sampling; Suicide prevention; Poison control; Psychology; Public relations; Environmental health; Political science; Medicine; Sociology; Business; Social psychology; Psychiatry; Engineering","score_opus":0.07011933919696103,"score_gpt":0.40786720716916025,"score_spread":0.33774786797219924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110154526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.936817,0.0011648119,0.000012288922,0.05303205,0.00021231643,0.00033312128,8.030127e-7,0.0000029854768,0.008424627],"genre_scores_gemma":[0.99645364,0.001136265,0.00001712828,0.00028030455,0.00060428353,0.0000018622549,5.352568e-8,0.0000067680703,0.0014996842],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99762976,0.00069799396,0.00036125322,0.00014977627,0.0007980063,0.00036322424],"domain_scores_gemma":[0.99883795,0.00044530636,0.00030288717,0.00011035506,0.00008462255,0.00021886178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018755944,0.00007296445,0.00021345727,0.00010734189,0.0005090825,0.00011347785,0.00078752724,0.000040634928,0.000031155123],"category_scores_gemma":[0.00039806784,0.000047013295,0.00009086928,0.00039902862,0.0006371865,0.00033667526,0.00027503376,0.0002894669,0.000017323246],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025601074,0.0003185012,0.33827916,0.0014254188,0.000111769565,0.000019865769,0.34365055,0.000033528977,0.00038003715,0.021290883,0.2623512,0.031883065],"study_design_scores_gemma":[0.004691444,0.00338967,0.6995571,0.0010319026,0.00015166719,0.000010095295,0.1342214,0.00021376637,0.00038139796,0.0097424565,0.14585008,0.0007589582],"about_ca_topic_score_codex":0.0010469841,"about_ca_topic_score_gemma":0.00056310993,"teacher_disagreement_score":0.361278,"about_ca_system_score_codex":0.000079969126,"about_ca_system_score_gemma":0.0006086759,"threshold_uncertainty_score":0.39155024},"labels":[],"label_agreement":null},{"id":"W3203289779","doi":"10.20965/jdr.2021.p1030","title":"Inter-Model Comparison for Tsunami Debris Simulation","year":2021,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Earthquake and Tsunami Effects","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Debris; Debris flow; Parametric statistics; Computer simulation; Geology; Simulation modeling; Marine engineering; Computer science; Meteorology; Simulation; Engineering; Statistics; Geography; Mathematics","score_opus":0.12554675774327098,"score_gpt":0.42696955936065156,"score_spread":0.30142280161738055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203289779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7944261,0.0005704415,0.20308939,0.00012782618,0.00019037962,0.00011207907,0.000004177539,0.000014397323,0.0014652167],"genre_scores_gemma":[0.9976501,0.000017670302,0.0017354958,0.000017520539,0.00019980867,0.0000041761045,0.0000035827068,0.000024804473,0.0003468726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884886,0.0000911099,0.00032844846,0.00008223529,0.0003808741,0.0002684594],"domain_scores_gemma":[0.9987107,0.00050326,0.00004003178,0.00014432376,0.000494902,0.000106789215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076218595,0.0000780626,0.00020654463,0.00016341035,0.000054388704,0.000092874485,0.0001338602,0.000059892853,0.000033185428],"category_scores_gemma":[0.00023181128,0.000069336704,0.00012091802,0.0001851964,0.000024079824,0.00020573892,0.000037085305,0.00038517607,0.000019602845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010154102,0.00012000105,0.0024583628,0.00027033032,0.00011701194,0.000036925685,0.0019910561,0.9366237,0.010365035,0.00016920634,0.006387749,0.041359086],"study_design_scores_gemma":[0.00061568635,0.0001719074,0.00062805513,0.00012734983,0.000012281278,0.00001467058,0.00039363984,0.98694605,0.0059964806,0.001197059,0.003816907,0.00007993453],"about_ca_topic_score_codex":8.208762e-7,"about_ca_topic_score_gemma":0.000019526608,"teacher_disagreement_score":0.20322397,"about_ca_system_score_codex":0.00006561784,"about_ca_system_score_gemma":0.00005505562,"threshold_uncertainty_score":0.28274685},"labels":[],"label_agreement":null},{"id":"W4414654492","doi":"10.20965/jdr.2025.p0598","title":"Multi-Sensing Data-Based Estimation of Isolated Settlements During Disasters: A Case Study Using the 2024 Noto Peninsula Earthquake","year":2025,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Council for Science, Technology and Innovation; Swine Innovation Porc","keywords":"Estimation; Human settlement; Peninsula; Process (computing); Emergency management; Settlement (finance); Event (particle physics)","score_opus":0.18155618683587824,"score_gpt":0.48533175092529535,"score_spread":0.3037755640894171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414654492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922726,0.000072171555,0.0054547475,0.0005490225,0.00034259527,0.0007716156,0.0000090909725,0.0000068176114,0.0005213426],"genre_scores_gemma":[0.99741155,0.0000045749903,0.0012630994,0.000020623233,0.000078669946,0.0000017743492,0.0000015860999,0.000010447161,0.0012076645],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9959545,0.001312904,0.00067542994,0.0002561777,0.0013702604,0.00043068934],"domain_scores_gemma":[0.99820966,0.00036879795,0.00030939613,0.0005481827,0.00046282212,0.000101122925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046653403,0.00012612864,0.00024785413,0.00053417997,0.0008646837,0.00040731218,0.00093097537,0.00004802388,0.000051781928],"category_scores_gemma":[0.00050270744,0.000087795495,0.00007974866,0.0012144549,0.0004762742,0.000729962,0.00054569857,0.00042223264,0.0000045700785],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003294944,0.008493791,0.2002547,0.0015987096,0.002322483,0.01014483,0.6055404,0.050958574,0.011581579,0.00022841894,0.0033869275,0.10219466],"study_design_scores_gemma":[0.0036390813,0.00038682172,0.007868414,0.0009723946,0.00024690738,0.0000779469,0.72859967,0.2572389,0.00017232554,0.000073516276,0.0004934176,0.0002306366],"about_ca_topic_score_codex":0.0012319996,"about_ca_topic_score_gemma":0.00187251,"teacher_disagreement_score":0.20628032,"about_ca_system_score_codex":0.00013256745,"about_ca_system_score_gemma":0.0003550475,"threshold_uncertainty_score":0.66505355},"labels":[],"label_agreement":null},{"id":"W4416833602","doi":"10.20965/jdr.2025.p1103","title":"Impacts of the December 2022 Heavy Snowfall on Tree Fall and Power Outages in Sado City, Japan","year":2025,"lang":"en","type":"article","venue":"Journal of Disaster Research","topic":"Tree Root and Stability Studies","field":"Engineering","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":"Students on Ice","funders":"","keywords":"Snow; Bamboo; Breakage; Field survey; Snow removal; Wind speed; Flooding (psychology); Precipitation","score_opus":0.03592483874382862,"score_gpt":0.33988486083700037,"score_spread":0.30396002209317174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416833602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98956764,0.0014214803,0.000008299978,0.0017127756,0.00015700405,0.00011739525,0.0000048802144,0.0000040753525,0.0070064445],"genre_scores_gemma":[0.9995013,0.00009618969,0.00001428474,0.00001567242,0.000025457517,0.0000019687118,9.482199e-8,0.000008741942,0.00033628967],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986936,0.00013502553,0.0003395213,0.00008872728,0.000472351,0.00027081792],"domain_scores_gemma":[0.99922657,0.00035220315,0.00003482278,0.00020220406,0.00012173135,0.00006246761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000931519,0.00009784144,0.00025261551,0.0003015821,0.000051856685,0.000039341954,0.00024161904,0.00005083695,0.000021730471],"category_scores_gemma":[0.00029667615,0.000061071696,0.00008555441,0.00037230237,0.00012742665,0.0000959391,0.0001830755,0.0006045926,0.0000016878125],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020023296,0.00014323616,0.9817372,0.00015501527,0.00011138283,0.000007650367,0.0046284194,0.00015792775,0.0012917224,0.00012531565,0.006273589,0.005168282],"study_design_scores_gemma":[0.00068728474,0.00020901789,0.99306047,0.00031760609,0.000007998272,0.000004735128,0.0022361167,0.000056698063,0.0009540125,0.00079649745,0.0016071892,0.00006238494],"about_ca_topic_score_codex":0.000053137577,"about_ca_topic_score_gemma":0.019287415,"teacher_disagreement_score":0.019234277,"about_ca_system_score_codex":0.000098094744,"about_ca_system_score_gemma":0.000043155564,"threshold_uncertainty_score":0.99860805},"labels":[],"label_agreement":null}]}