{"meta":{"query_hash":"90c5cf1a2766","filters":{"topic":"Fire Detection and Safety Systems"},"cohort_total":284,"direct_labels_cover":0,"predictions_cover":284,"exported":284,"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/90c5cf1a2766","api":"https://metacan.xera.ac/api/v1/cohort?topic=Fire+Detection+and+Safety+Systems"},"results":[{"id":"W1020036629","doi":"10.2478/cttr-2014-0015","title":"Filtration and Retention Characteristics of Smoke Components in Filters","year":2014,"lang":"en","type":"article","venue":"Beiträge zur Tabakforschung international","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":9,"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":"Filtration (mathematics); Smoke; Chemistry; Phenol; Turbidity; Sidestream smoke; Chromatography; Analytical Chemistry (journal); Mathematics; Organic chemistry; Statistics","score_opus":0.017626832145068824,"score_gpt":0.20716798498142905,"score_spread":0.18954115283636022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1020036629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938573,0.0003785834,0.0049376753,0.000008593339,0.000004495343,0.0000090955255,0.000125328,0.000030425152,0.0006484793],"genre_scores_gemma":[0.9949944,0.00032379423,0.002740777,0.000013947953,0.0000032513312,0.000016341932,0.00029908912,0.000015957587,0.0015924167],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998161,0.000011389973,0.000009960029,0.00005571021,0.00007332753,0.00003347428],"domain_scores_gemma":[0.999783,0.000046754998,0.00004218749,0.000012733949,0.000099802615,0.000015567583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023574437,0.00019678552,0.00012907283,0.0003681728,0.0002152552,0.00027841426,0.00015006817,0.0002041282,0.0007611154],"category_scores_gemma":[0.00030252515,0.00011706973,0.0002566986,0.00023037818,0.00014109496,0.00029516302,0.000116276584,0.00016901255,0.00016972232],"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":[0.00006695588,0.000009673351,0.004001513,0.000016581675,0.000007950953,0.000019260098,0.000047177367,0.00013837335,0.9922759,0.000024728024,0.000015770385,0.0033761414],"study_design_scores_gemma":[0.0000020692023,0.00017109507,0.059504397,0.00000456556,0.000021513602,0.000110161775,0.00008660809,0.0014674494,0.9379961,0.000023446768,0.000603374,0.00000924172],"about_ca_topic_score_codex":0.0037576908,"about_ca_topic_score_gemma":0.0019290972,"teacher_disagreement_score":0.0037576908,"about_ca_system_score_codex":0.00033142237,"about_ca_system_score_gemma":0.00017187839,"threshold_uncertainty_score":0.007471621},"labels":[],"label_agreement":null},{"id":"W143044641","doi":"","title":"Fire Detection Systems in Road Tunnels - Lessons Learnt From an International Research Project","year":2009,"lang":"en","type":"article","venue":"NPARC","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministère des Transports; Fire Protection Research Foundation","keywords":"Warning system; Fire detection; Emergency response; Foundation (evidence); Forensic engineering; Engineering; Environmental science; Transport engineering; Civil engineering; Architectural engineering; Geography; Telecommunications","score_opus":0.058174496478120646,"score_gpt":0.3377644078542281,"score_spread":0.27958991137610745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W143044641","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.247229,0.3572586,0.17282416,0.0607982,0.004009322,0.0007587447,0.0005265698,0.0008433444,0.1557521],"genre_scores_gemma":[0.5898758,0.24648435,0.13232201,0.0039054125,0.00278334,0.00024387328,0.00088286184,0.00027562559,0.023226686],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973062,0.0009738937,0.00021305727,0.00036271208,0.0008414325,0.00030266476],"domain_scores_gemma":[0.9927939,0.0026786446,0.00024088069,0.00058465084,0.003158866,0.00054312637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007670693,0.0012848184,0.00084708974,0.0011454998,0.00079602835,0.0026360068,0.0019779736,0.0023791115,0.0034188905],"category_scores_gemma":[0.008149732,0.00032435884,0.00074812566,0.0016390044,0.0018556319,0.0055480804,0.0011284557,0.0024166142,0.0010861197],"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":[0.00039446645,0.002229504,0.0091216555,0.0012618107,0.000106350395,0.00046115756,0.0014665099,0.025299963,0.0064344457,0.030845024,0.015990838,0.9063883],"study_design_scores_gemma":[0.0004918121,0.018550314,0.036771256,0.0068889577,0.0005605057,0.0037180262,0.016519401,0.15293072,0.076186955,0.12108941,0.56571835,0.00057429256],"about_ca_topic_score_codex":0.00437877,"about_ca_topic_score_gemma":0.0029005778,"teacher_disagreement_score":0.007670693,"about_ca_system_score_codex":0.001484769,"about_ca_system_score_gemma":0.0015616837,"threshold_uncertainty_score":0.04056692},"labels":[],"label_agreement":null},{"id":"W1432470681","doi":"10.1049/iet-ipr.2014.0935","title":"Benchmarking of wildland fire colour segmentation algorithms","year":2015,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Benchmarking; Computer science; Segmentation; Artificial intelligence; Algorithm; Business","score_opus":0.016201138557459617,"score_gpt":0.24417272226189768,"score_spread":0.22797158370443807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1432470681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8001841,0.0065909526,0.16273503,0.00036951146,0.0007102307,0.0005221045,0.0048623253,0.011660153,0.012365594],"genre_scores_gemma":[0.7086504,0.0022546658,0.24526718,0.0002059932,0.00015332237,0.00025521786,0.036842223,0.0011342741,0.0052367435],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99629766,0.00055869983,0.00037109532,0.0011637289,0.0012021698,0.00040660196],"domain_scores_gemma":[0.99724776,0.00081053586,0.00022489185,0.0005076326,0.0010687946,0.00014040247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038299737,0.0016726579,0.0014400211,0.0045380373,0.0006948834,0.0019903195,0.002647155,0.00204691,0.0015493333],"category_scores_gemma":[0.004944292,0.0003912911,0.0016465888,0.0032077804,0.0007238149,0.0015746811,0.00094148866,0.00078645354,0.001253224],"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":[0.002434348,0.0010771358,0.016214274,0.0019511769,0.001097322,0.00034755984,0.0004008122,0.24432205,0.049150262,0.002534174,0.018835409,0.6616355],"study_design_scores_gemma":[0.00014219401,0.0009624527,0.033862565,0.00013220057,0.00024659015,0.00062470627,0.00035639765,0.84795624,0.09947895,0.001385748,0.014760422,0.00009161415],"about_ca_topic_score_codex":0.0069540385,"about_ca_topic_score_gemma":0.008083393,"teacher_disagreement_score":0.0069540385,"about_ca_system_score_codex":0.001259954,"about_ca_system_score_gemma":0.00074692565,"threshold_uncertainty_score":0.020255089},"labels":[],"label_agreement":null},{"id":"W1489870890","doi":"10.4271/2004-01-3551","title":"Potential Driver Exposure to Halons and Alternative Agents from On Board Fire Suppression Systems in Stock Cars","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Stock (firearms); On board; Computer science; Automotive engineering; Engineering; Aerospace engineering; Mechanical engineering","score_opus":0.00921593530586011,"score_gpt":0.22311900405969215,"score_spread":0.21390306875383203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489870890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955427,0.0008489169,0.000188002,0.00015237939,0.000023548067,0.000047878846,0.00036196952,0.000006528276,0.002828146],"genre_scores_gemma":[0.9945978,0.0017047988,0.00016138093,0.00014901801,0.000023601044,0.000025733016,0.00078734057,0.000004340103,0.002545944],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931383,0.00014222656,0.000049013066,0.00007798317,0.00030737376,0.000109629684],"domain_scores_gemma":[0.9991455,0.00014701759,0.00028511876,0.000032713335,0.00030109228,0.00008852696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069584494,0.0003366431,0.00023426555,0.00075185567,0.0005284111,0.0010115731,0.00052279735,0.00057464186,0.0032575333],"category_scores_gemma":[0.0013246527,0.0002527806,0.0005296675,0.0003304733,0.00017287854,0.00050967484,0.0006009203,0.00046010432,0.0005248549],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035466813,0.00040318593,0.9754083,0.00012512394,0.0001255484,0.00053488166,0.0014643567,0.00015998971,0.0014921759,0.00016061896,0.001048919,0.01872214],"study_design_scores_gemma":[0.000014602144,0.0017021126,0.9841201,0.0001401217,0.00015572115,0.0009130053,0.0044906135,0.00053016754,0.0023137175,0.00017039487,0.005422652,0.000026778756],"about_ca_topic_score_codex":0.028832348,"about_ca_topic_score_gemma":0.031674773,"teacher_disagreement_score":0.028832348,"about_ca_system_score_codex":0.0007838551,"about_ca_system_score_gemma":0.00054949906,"threshold_uncertainty_score":0.05732906},"labels":[],"label_agreement":null},{"id":"W1546080351","doi":"10.1109/intlec.1990.171261","title":"Equipment powerdown in case of fire","year":2002,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Bell (Canada)","funders":"","keywords":"ALARM; Reliability (semiconductor); Computer science; Reliability engineering; Fire protection; Fire detection; Fire alarm system; Manual fire alarm activation; Computer security; Sampling (signal processing); Embedded system; Engineering; Power (physics); Detector; Architectural engineering; Telecommunications; Electrical engineering","score_opus":0.012417087581731583,"score_gpt":0.19524267580528384,"score_spread":0.18282558822355227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546080351","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8806726,0.0050315056,0.016622202,0.002796662,0.0015558926,0.00032277897,0.00052847574,0.00040052464,0.09206942],"genre_scores_gemma":[0.9862651,0.0012345307,0.0030713112,0.0006750891,0.0001758712,0.000033912173,0.00020770317,0.00002454914,0.008312037],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950993,0.00008157823,0.000059784965,0.000054961583,0.0002026635,0.00009101819],"domain_scores_gemma":[0.99790895,0.00090037985,0.0005085814,0.00015113925,0.0004152239,0.00011585972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004135977,0.00017601323,0.00016971552,0.0009947882,0.00050898304,0.0005469731,0.00050441764,0.0008691152,0.008433189],"category_scores_gemma":[0.004151851,0.000082815124,0.00024868848,0.00040059446,0.00032460137,0.0006138041,0.0004873382,0.00050801534,0.0011615194],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022330687,0.00049671734,0.27932787,0.00095202297,0.00008792107,0.051125947,0.0044224844,0.0015389324,0.017712764,0.0046010995,0.021535305,0.61596596],"study_design_scores_gemma":[0.00018270449,0.0048747016,0.4271258,0.0010969925,0.0002994578,0.12622613,0.018095886,0.0073298984,0.06779957,0.0072000017,0.3396511,0.00011779617],"about_ca_topic_score_codex":0.0009780728,"about_ca_topic_score_gemma":0.0020984744,"teacher_disagreement_score":0.008433189,"about_ca_system_score_codex":0.00032218485,"about_ca_system_score_gemma":0.00034439602,"threshold_uncertainty_score":0.028211832},"labels":[],"label_agreement":null},{"id":"W1551080104","doi":"10.1109/crv.2015.22","title":"Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices","year":2015,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"University of Victoria","funders":"","keywords":"Crowds; Computer science; Computer vision; Mobile device; Artificial intelligence; Adjacency list; Computer security","score_opus":0.012632148550628724,"score_gpt":0.1994197100123475,"score_spread":0.1867875614617188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551080104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66056913,0.0006494506,0.33149582,0.0002461815,0.00013897575,0.00022559456,0.0009045581,0.00094405044,0.004826171],"genre_scores_gemma":[0.8985131,0.00027764335,0.09958691,0.000088055865,0.00007116327,0.000035546513,0.00050458754,0.000030738283,0.0008923079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980146,0.000031586045,0.0000082761435,0.000052382216,0.00006859571,0.000037705035],"domain_scores_gemma":[0.9995764,0.00017073822,0.000096048396,0.0000425269,0.000072951814,0.000041330073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027428987,0.0005452852,0.00041953093,0.0013191635,0.000314812,0.00038819577,0.0006379034,0.0005998548,0.00054495863],"category_scores_gemma":[0.001550356,0.00020223469,0.00035299073,0.00044622965,0.0003006774,0.0005206331,0.00043334515,0.0004850645,0.00024262068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022370715,0.00048773768,0.04608198,0.0006668193,0.0002788332,0.0035674928,0.0006854461,0.10630371,0.29836223,0.0027263195,0.0039633834,0.5346389],"study_design_scores_gemma":[0.00005117233,0.00041625532,0.06462371,0.000086509885,0.000079200356,0.0017538203,0.000517965,0.8179649,0.10911594,0.0024720582,0.0028670675,0.00005142796],"about_ca_topic_score_codex":0.0034885595,"about_ca_topic_score_gemma":0.0074830493,"teacher_disagreement_score":0.0034885595,"about_ca_system_score_codex":0.00028414847,"about_ca_system_score_gemma":0.00022755917,"threshold_uncertainty_score":0.00693655},"labels":[],"label_agreement":null},{"id":"W1852719163","doi":"10.1139/cjfr-2014-0347","title":"A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":622,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"U.S. Forest Service; National Aeronautics and Space Administration","keywords":"Firefighting; Fire detection; Wildfire suppression; Aerial survey; Remote sensing; Fire control; Field (mathematics); Systems engineering; Aeronautics; Computer science; Environmental science; Engineering; Architectural engineering; Geography; Cartography","score_opus":0.07671189151839218,"score_gpt":0.31362997315299007,"score_spread":0.23691808163459788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1852719163","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009624007,0.8687779,0.07784716,0.0013203406,0.00077653764,0.00017018426,0.0002857763,0.00048516502,0.04071299],"genre_scores_gemma":[0.03133676,0.90476906,0.052638516,0.00077720854,0.0005869529,0.00009442147,0.00064162526,0.000061200284,0.009094326],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99944526,0.00006545538,0.00006268926,0.000093174334,0.0002856093,0.000047877766],"domain_scores_gemma":[0.9991953,0.0003519804,0.00009461181,0.00004825446,0.0002719121,0.000038026792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000652319,0.0007510944,0.0006026366,0.0039246688,0.00038366448,0.0011800218,0.000952935,0.0009060947,0.002905642],"category_scores_gemma":[0.0009105526,0.0004645012,0.00057295413,0.00399757,0.00030977328,0.002092904,0.0004390619,0.00071625016,0.0017462382],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055398865,0.00009950934,0.0018811865,0.0063598403,0.000052949003,0.0002351419,0.00012711984,0.0020974497,0.012561088,0.0069583296,0.011980863,0.9575912],"study_design_scores_gemma":[0.0000135398805,0.00042113665,0.009061584,0.004565579,0.00019371924,0.0023289137,0.00039772078,0.007359594,0.0128404405,0.0062236404,0.95649475,0.00009942186],"about_ca_topic_score_codex":0.0014763578,"about_ca_topic_score_gemma":0.00194916,"teacher_disagreement_score":0.0039246688,"about_ca_system_score_codex":0.00043009844,"about_ca_system_score_gemma":0.0008430885,"threshold_uncertainty_score":0.0097203255},"labels":[],"label_agreement":null},{"id":"W1939848959","doi":"","title":"불활성가스계 소화약제의 불꽃소화 특성에 관한 연구","year":2008,"lang":"ko","type":"article","venue":"한국화재소방학회 학술대회 논문집","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Montreal Protocol; Ozone layer; Context (archaeology); Environmental science; Ozone; Waste management; Meteorology; Engineering; Geography","score_opus":0.013365847239617014,"score_gpt":0.1902553686221664,"score_spread":0.1768895213825494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939848959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96135604,0.0010887145,0.01605742,0.000099852754,0.00009337956,0.00010980578,0.0006918502,0.00021293087,0.020289991],"genre_scores_gemma":[0.97210103,0.0005039878,0.011342046,0.0000896677,0.000017615024,0.000042106924,0.0006463356,0.000027050572,0.015230142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967456,0.000020676049,0.000010145085,0.00007390465,0.00018491702,0.000035753877],"domain_scores_gemma":[0.99971074,0.000036537334,0.000054973203,0.000023571813,0.0001483508,0.00002576768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024933758,0.00021289526,0.0001308877,0.00046012033,0.0003559459,0.00028590637,0.00028349063,0.00023101321,0.0027951899],"category_scores_gemma":[0.00033481282,0.00011291716,0.00016982463,0.00029240892,0.00025767603,0.00025152205,0.00013955607,0.00029482387,0.0008032952],"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":[0.00030860156,0.00007422829,0.012701391,0.00011525353,0.000026383417,0.00011185285,0.0003137631,0.00030009236,0.9423395,0.0011679286,0.00059518806,0.041945845],"study_design_scores_gemma":[0.0000063581238,0.00029846487,0.03463804,0.0000054626976,0.000024982031,0.00019878312,0.00012811278,0.0016297067,0.95208806,0.00025812202,0.010700886,0.000023002744],"about_ca_topic_score_codex":0.003368935,"about_ca_topic_score_gemma":0.0038137638,"teacher_disagreement_score":0.003368935,"about_ca_system_score_codex":0.00031354112,"about_ca_system_score_gemma":0.0003134281,"threshold_uncertainty_score":0.009350836},"labels":[],"label_agreement":null},{"id":"W1963632870","doi":"10.1061/(asce)1076-0431(2001)7:4(131)","title":"Measurements of Air Leakage through Revolving Doors of Institutional Building","year":2001,"lang":"en","type":"article","venue":"Journal of Architectural Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Doors; Leakage (economics); Architectural engineering; Engineering; Environmental science; Forensic engineering; Mechanical engineering; Economics","score_opus":0.01765411863007514,"score_gpt":0.218775998284411,"score_spread":0.20112187965433587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963632870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975804,0.000069072164,0.0018639198,0.0000016391868,0.000001948715,0.000012444762,0.00013929003,0.00004884981,0.00028243795],"genre_scores_gemma":[0.99831474,0.00006140052,0.0010896875,0.0000031765271,0.0000021462938,0.000008615958,0.00022574264,0.000011776168,0.00028281682],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994783,0.000049647057,0.000023409288,0.0001118563,0.00022037569,0.0001164158],"domain_scores_gemma":[0.99917775,0.00018017144,0.00024201057,0.000078263314,0.00024518577,0.00007662465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002753059,0.00037837558,0.0002927361,0.0010209982,0.0005884352,0.0005721219,0.00049049535,0.0002752463,0.0006474414],"category_scores_gemma":[0.0008897892,0.00021643176,0.00030674774,0.00068732724,0.00053011905,0.00034293006,0.00034707555,0.00032399045,0.00024229474],"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":[0.00083494873,0.00029467914,0.21089257,0.00018445736,0.00009361557,0.0004789413,0.0020708404,0.0025497451,0.75020134,0.00016873058,0.0001488856,0.032081258],"study_design_scores_gemma":[0.000014274306,0.000896172,0.7608656,0.000014846934,0.000059824073,0.00043321043,0.00079120626,0.0025961453,0.23338707,0.00006201185,0.00084161636,0.000038063212],"about_ca_topic_score_codex":0.00966388,"about_ca_topic_score_gemma":0.011468062,"teacher_disagreement_score":0.00966388,"about_ca_system_score_codex":0.0005209464,"about_ca_system_score_gemma":0.00032981485,"threshold_uncertainty_score":0.019215286},"labels":[],"label_agreement":null},{"id":"W1967116105","doi":"10.1109/igarss.2014.6947267","title":"Forest fires, sunglint, and a solar eclipse: Responsive remote sensing with AeroCube-4","year":2014,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"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":"CubeSat; Remote sensing; Solar eclipse; Spacecraft; Eclipse; Environmental science; Computer science; Meteorology; Geography; Aerospace engineering; Engineering; Satellite; Astronomy; Physics","score_opus":0.004471554463479133,"score_gpt":0.17038204934292264,"score_spread":0.1659104948794435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967116105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98953736,0.0002491502,0.003950752,0.00016151402,0.000024221406,0.000049032566,0.0005448585,0.0001844335,0.0052986895],"genre_scores_gemma":[0.9790182,0.00019392527,0.018124405,0.000108541906,0.0000190717,0.00003108197,0.0011388179,0.000044699555,0.0013212214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997812,0.000023097537,0.0000036305014,0.000047315032,0.00010163798,0.000043120202],"domain_scores_gemma":[0.9999163,0.000014360908,0.000010584768,0.0000111983,0.00002486568,0.000022600765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029728087,0.00027210687,0.00020156862,0.00034290878,0.00036351054,0.0004741514,0.0004710325,0.00022739673,0.0005721549],"category_scores_gemma":[0.00019489543,0.00013094337,0.0001692096,0.0004637555,0.00034230013,0.00041426579,0.00053998583,0.0004609672,0.000096850905],"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":[0.001969635,0.00056513445,0.10358064,0.00017676325,0.0002817645,0.0011162692,0.0011832719,0.032785147,0.72136265,0.0012860314,0.0057716705,0.12992097],"study_design_scores_gemma":[0.00028229787,0.0009706128,0.5447985,0.000056434325,0.00019148595,0.001320479,0.002254304,0.177713,0.2530199,0.0012298871,0.017953092,0.00020998031],"about_ca_topic_score_codex":0.03345376,"about_ca_topic_score_gemma":0.12478368,"teacher_disagreement_score":0.03345376,"about_ca_system_score_codex":0.00069260836,"about_ca_system_score_gemma":0.0004318404,"threshold_uncertainty_score":0.06651807},"labels":[],"label_agreement":null},{"id":"W1969910747","doi":"10.1007/bf03187040","title":"Progress in research and application of water mist fire suppression technology","year":2003,"lang":"en","type":"article","venue":"Chinese Science Bulletin","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Laboratory; National Research Council Canada; Underwriters Laboratories","keywords":"Mist; Environmental science; Meteorology; Geography","score_opus":0.01113789938389656,"score_gpt":0.2833983957415873,"score_spread":0.2722604963576908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969910747","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2200326,0.47162053,0.2319975,0.0034267823,0.0011223505,0.0002612891,0.00034374342,0.0005322454,0.07066302],"genre_scores_gemma":[0.5309112,0.36866063,0.08376793,0.00066536106,0.0010822144,0.00011697083,0.00060474157,0.0000766494,0.014114285],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994029,0.000068059555,0.000047124144,0.00014723383,0.00028357768,0.00005107829],"domain_scores_gemma":[0.9991474,0.00029166922,0.00008839789,0.00004872674,0.00037867983,0.000045159988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001203744,0.000556513,0.0006149075,0.0012728021,0.0003757562,0.0011038576,0.00089058134,0.0007795321,0.0025089371],"category_scores_gemma":[0.0011842495,0.00022943538,0.0005046266,0.0020415364,0.0004924604,0.0015745447,0.00044521652,0.0006436838,0.0004838315],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022831006,0.00030257393,0.008296272,0.0029952193,0.00005408636,0.00015218102,0.00041528838,0.0042223856,0.15311319,0.011529613,0.0023683787,0.81632245],"study_design_scores_gemma":[0.00011778243,0.0020251619,0.028898891,0.00072997523,0.00055579765,0.0013428432,0.0013720905,0.055532735,0.5296426,0.012858131,0.3667625,0.00016156411],"about_ca_topic_score_codex":0.0034519352,"about_ca_topic_score_gemma":0.0023040848,"teacher_disagreement_score":0.0034519352,"about_ca_system_score_codex":0.00073702564,"about_ca_system_score_gemma":0.0024077524,"threshold_uncertainty_score":0.008393228},"labels":[],"label_agreement":null},{"id":"W1992652813","doi":"10.1145/2769493.2769516","title":"Cooking risk analysis to enhance safety of elderly people in smart kitchen","year":2015,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Smoke; Environmental science; Combustor; Relative humidity; Residence; Waste management; Computer science; Combustion; Engineering; Chemistry; Meteorology; Geography","score_opus":0.0074416038793891385,"score_gpt":0.22636712540904297,"score_spread":0.21892552152965383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992652813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9276989,0.00048391978,0.06931882,0.000092156144,0.000016064401,0.000103412945,0.0002260757,0.00019969385,0.001860907],"genre_scores_gemma":[0.98415065,0.00017224428,0.014963095,0.000017086335,0.0000056887657,0.000024724592,0.000118201715,0.000009002056,0.0005392977],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996474,0.00010831981,0.000025554104,0.000049566417,0.00013189507,0.000037354726],"domain_scores_gemma":[0.9993125,0.00023253328,0.00016187015,0.000054139204,0.00020633101,0.000032642132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007105126,0.00067296636,0.00033347518,0.0009886873,0.00016827574,0.00044122178,0.00025743095,0.00029110655,0.0008364461],"category_scores_gemma":[0.0012933237,0.00015136994,0.0004225206,0.00036636807,0.00013077242,0.0005801655,0.0005005126,0.0002159768,0.00017589713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016849578,0.0011301434,0.42150533,0.0008331748,0.00041861573,0.0010387428,0.0019125985,0.07993821,0.15924215,0.002562368,0.0014826554,0.32825103],"study_design_scores_gemma":[0.000056611178,0.0030317132,0.416503,0.00016286326,0.0007386892,0.0012624906,0.003989605,0.38274312,0.17987865,0.007502388,0.0039821756,0.00014869108],"about_ca_topic_score_codex":0.0009999793,"about_ca_topic_score_gemma":0.0011869678,"teacher_disagreement_score":0.0009999793,"about_ca_system_score_codex":0.00024071468,"about_ca_system_score_gemma":0.00022612457,"threshold_uncertainty_score":0.003757596},"labels":[],"label_agreement":null},{"id":"W2000482553","doi":"10.1109/cidue.2011.5948490","title":"iFAST: An Intelligent Fire-Threat Assessment and Size-up Technology for first responders","year":2011,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer security","score_opus":0.03200125326543314,"score_gpt":0.25920305740629207,"score_spread":0.22720180414085894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000482553","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023210948,0.00024601223,0.944005,0.00036673606,0.00013830616,0.000318391,0.00046602564,0.024763262,0.006485378],"genre_scores_gemma":[0.30660883,0.00031807355,0.67932045,0.000500332,0.00009714013,0.00028263716,0.00069716247,0.00037108577,0.01180416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.0000807509,0.000033006552,0.00008467029,0.00018412247,0.000045096112],"domain_scores_gemma":[0.99902713,0.000350212,0.00011173046,0.00011877958,0.00032366742,0.000068435074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078654126,0.0007638653,0.00053519616,0.0010986757,0.0005667917,0.00081878004,0.0011876983,0.0008525488,0.0063562994],"category_scores_gemma":[0.002165386,0.0002353038,0.0004184515,0.00031635034,0.00033349235,0.0014360033,0.00055119395,0.0006460078,0.0020572795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001295685,0.00025786762,0.0031796356,0.00042274964,0.00006272133,0.0005157001,0.0005873449,0.02008047,0.09808097,0.0051627145,0.032224078,0.83813006],"study_design_scores_gemma":[0.0003434979,0.0014632641,0.0066632032,0.00021319654,0.0002819062,0.0022707656,0.0004567342,0.75701874,0.13889997,0.011608134,0.0804662,0.00031437993],"about_ca_topic_score_codex":0.002919243,"about_ca_topic_score_gemma":0.0026102033,"teacher_disagreement_score":0.0063562994,"about_ca_system_score_codex":0.00062883645,"about_ca_system_score_gemma":0.0006280102,"threshold_uncertainty_score":0.021263957},"labels":[],"label_agreement":null},{"id":"W2007754899","doi":"10.1115/ipc2014-33700","title":"Real Time Facility Monitoring: Lessons Learned","year":2014,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":true,"ca_institutions":"IntelliView Technologies (Canada)","funders":"","keywords":"Computer science; Critical infrastructure; Systems engineering; Computer security; Real-time computing; Engineering","score_opus":0.028171538383292193,"score_gpt":0.2508827002613226,"score_spread":0.2227111618780304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007754899","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07334403,0.089034125,0.32655427,0.41688406,0.007786155,0.0003278818,0.0007211533,0.0023305125,0.08301772],"genre_scores_gemma":[0.5727661,0.11522139,0.24653462,0.030196562,0.006755382,0.00021294937,0.00074349536,0.00061472505,0.026954744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963246,0.0013507172,0.00020381734,0.00064654584,0.0012326847,0.0002415682],"domain_scores_gemma":[0.97490793,0.012973215,0.00057005795,0.0021873142,0.007681063,0.0016804053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009980775,0.0012933594,0.0006026007,0.0010746071,0.0008338621,0.003807423,0.004130789,0.004117403,0.004966744],"category_scores_gemma":[0.021814372,0.00044846305,0.0005642225,0.0011286174,0.0027481224,0.010887148,0.002051832,0.005672497,0.0019058471],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013645085,0.00048798195,0.0069589275,0.0015827,0.00006771938,0.00062815176,0.0020991475,0.022273218,0.0027628823,0.058478538,0.048658554,0.85586566],"study_design_scores_gemma":[0.00017436681,0.0014270043,0.0093061775,0.0059649833,0.00010509514,0.002850387,0.017792812,0.06519211,0.013619165,0.2404568,0.6427059,0.00040525725],"about_ca_topic_score_codex":0.008650923,"about_ca_topic_score_gemma":0.0068149576,"teacher_disagreement_score":0.009980775,"about_ca_system_score_codex":0.0023560028,"about_ca_system_score_gemma":0.0024563405,"threshold_uncertainty_score":0.052784085},"labels":[],"label_agreement":null},{"id":"W2008220393","doi":"","title":"2 nd Workshop on Smart Surveillance System Applications","year":2011,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Context (archaeology); Identification (biology); Homeland security; Emergency management; Situation awareness; Computer security; Agency (philosophy); Business; Engineering; Computer science; Political science","score_opus":0.12035174586068095,"score_gpt":0.3493515099513772,"score_spread":0.22899976409069625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008220393","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019329356,0.070098095,0.5118362,0.030453745,0.03413611,0.0013218158,0.0027805497,0.01007766,0.3199666],"genre_scores_gemma":[0.09534513,0.04377354,0.15532269,0.0067120297,0.011353373,0.0007968939,0.008933785,0.0017681383,0.6759944],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846506,0.00026640052,0.0001340663,0.00033949988,0.00062240195,0.00017262787],"domain_scores_gemma":[0.99813485,0.00040951394,0.000053123185,0.00026967426,0.0008758211,0.00025703732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038688635,0.0013693966,0.00078920013,0.0013388379,0.00072709564,0.004118925,0.0022371025,0.0030326047,0.04007073],"category_scores_gemma":[0.0027045733,0.0005636087,0.0009864907,0.0012734436,0.00078375684,0.0042985184,0.0022462565,0.003266634,0.023591598],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029528703,0.0002563554,0.0012395193,0.0005770623,0.00007453909,0.00046503748,0.0006043975,0.0023628785,0.013324235,0.02129627,0.46433815,0.49516615],"study_design_scores_gemma":[0.00003183661,0.00018306312,0.0012549132,0.00032789545,0.00003168036,0.000389103,0.00021574448,0.01015719,0.0048759794,0.007691836,0.9748082,0.0000325482],"about_ca_topic_score_codex":0.0038125138,"about_ca_topic_score_gemma":0.0034534924,"teacher_disagreement_score":0.04007073,"about_ca_system_score_codex":0.0009207236,"about_ca_system_score_gemma":0.0011245854,"threshold_uncertainty_score":0.13405001},"labels":[],"label_agreement":null},{"id":"W2015115674","doi":"10.1016/s1352-2310(99)00341-6","title":"Chemical characterisation of the haze in Brunei Darussalam during the 1998 episode","year":2000,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Smiths Detection (Canada)","funders":"","keywords":"Haze; Environmental chemistry; Benzene; BTEX; Pollution; Toluene; Ethylbenzene; Air pollution; Pollutant; Xylene; Environmental science; Chemistry; Smoke; Human health; Organic chemistry; Environmental health","score_opus":0.001905579752272176,"score_gpt":0.13709517540641575,"score_spread":0.13518959565414357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015115674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974656,0.0002769205,0.00015302852,0.00006003551,0.00001932801,0.000022588974,0.00030719966,0.0000055649434,0.0016896692],"genre_scores_gemma":[0.9974916,0.00024692295,0.00039388073,0.00006546876,0.000017161732,0.000011303203,0.00038624543,0.0000060218185,0.0013813624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999881,0.0000055021087,0.000004313518,0.000019282868,0.000060693743,0.000029235154],"domain_scores_gemma":[0.9998802,0.000010046624,0.000016286765,0.0000029549842,0.00007234944,0.000018137414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015704644,0.00024579538,0.00043930093,0.0010462512,0.0017173772,0.00066740066,0.00044775632,0.0006240342,0.000476053],"category_scores_gemma":[0.00021719727,0.0002234837,0.00017514802,0.0007703111,0.00045355823,0.00024141182,0.0003265553,0.00050809694,0.00011829333],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016560354,0.00028260765,0.52667296,0.00041065385,0.00023734376,0.006298656,0.008143732,0.0020362167,0.42433262,0.00059824454,0.001629894,0.02770105],"study_design_scores_gemma":[0.000011447168,0.000107276785,0.9843494,0.000019887993,0.000026660784,0.0002412752,0.0018015373,0.000641824,0.009403769,0.000036682468,0.0033494118,0.000010689978],"about_ca_topic_score_codex":0.2907236,"about_ca_topic_score_gemma":0.442899,"teacher_disagreement_score":0.2907236,"about_ca_system_score_codex":0.0014764492,"about_ca_system_score_gemma":0.00080392195,"threshold_uncertainty_score":0.5780628},"labels":[],"label_agreement":null},{"id":"W2027250845","doi":"10.1121/1.4777941","title":"Ferret and its applications","year":2005,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Fire Detection and Safety Systems","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":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Muzzle; Software deployment; Supersonic speed; Computer science; Muzzle velocity; Acoustics; Software; Projectile; Aerospace engineering; Aeronautics; Geology; Computer hardware; Telecommunications; Physics; Engineering; Operating system; Mechanical engineering","score_opus":0.00808164460503154,"score_gpt":0.21885244968609321,"score_spread":0.21077080508106166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027250845","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030867595,0.019400734,0.2855924,0.0061230115,0.005820373,0.0012314024,0.005480005,0.061562404,0.58392215],"genre_scores_gemma":[0.29624125,0.016595116,0.1212224,0.0027462384,0.0022308712,0.00086321315,0.014837837,0.0047896937,0.5404734],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841225,0.000156469,0.00007123825,0.00024619035,0.00095487223,0.00015882077],"domain_scores_gemma":[0.9987501,0.00020144923,0.0000659718,0.00017157047,0.00063530164,0.00017559057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012949556,0.0012169089,0.00060648395,0.0020633808,0.001160465,0.0020978544,0.002528066,0.0015204491,0.065432265],"category_scores_gemma":[0.003678058,0.0003022795,0.00050030247,0.0015832569,0.00040489968,0.003077768,0.0020959268,0.0008155234,0.029294794],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006755544,0.00016823129,0.0022551492,0.0007621225,0.000031625677,0.0006432175,0.00022991971,0.004416166,0.010083271,0.020082237,0.29468635,0.66596615],"study_design_scores_gemma":[0.0000393702,0.0001840196,0.0012522334,0.00017894298,0.000031992517,0.0013440232,0.00008824172,0.010829527,0.00846004,0.0045549334,0.9729873,0.00004938963],"about_ca_topic_score_codex":0.003433221,"about_ca_topic_score_gemma":0.0023278787,"teacher_disagreement_score":0.065432265,"about_ca_system_score_codex":0.0008906022,"about_ca_system_score_gemma":0.0009283455,"threshold_uncertainty_score":0.21889275},"labels":[],"label_agreement":null},{"id":"W2035290160","doi":"10.1080/152873901300343470","title":"CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS IN SMOKE AT MUNICIPAL STRUCTURAL FIRES","year":2001,"lang":"en","type":"article","venue":"Journal of Toxicology and Environmental Health","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Propene; Ethylbenzene; Toluene; Benzene; Styrene; Environmental chemistry; Chemistry; Naphthalene; Volatile organic compound; Gas chromatography; Organic chemistry; Chromatography; Catalysis","score_opus":0.010613720699305386,"score_gpt":0.22539644231391373,"score_spread":0.21478272161460835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035290160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99881774,0.0002302989,0.0005392741,0.0000032871685,0.0000023977232,0.000009947162,0.00009654549,0.0000056737626,0.00029478892],"genre_scores_gemma":[0.9980015,0.0002830672,0.0009934172,0.000013912142,0.000005877209,0.000010576596,0.00032277565,0.0000051102556,0.0003638098],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985254,0.000011067118,0.0000069838325,0.000034483786,0.00006610549,0.0000287477],"domain_scores_gemma":[0.999905,0.000015514908,0.000030067284,0.000005876452,0.000032626558,0.000010923641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014248429,0.00020331508,0.00020302586,0.0007157104,0.00033404346,0.00037395564,0.00021244712,0.0002689663,0.0005584911],"category_scores_gemma":[0.00017161982,0.000094686635,0.00020242028,0.00042629725,0.00017048951,0.00015935239,0.00018841709,0.00016100574,0.00011516914],"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":[0.00018760946,0.000053528587,0.10823708,0.000106493404,0.0000405259,0.00031253055,0.0003236476,0.00039882123,0.8787078,0.000060579354,0.000036285357,0.011535116],"study_design_scores_gemma":[0.0000058197666,0.000395247,0.68602043,0.000029712088,0.000060367307,0.0007635824,0.0009153874,0.0015972011,0.30863145,0.0000729822,0.0014917069,0.000016206755],"about_ca_topic_score_codex":0.0026691037,"about_ca_topic_score_gemma":0.004482776,"teacher_disagreement_score":0.0026691037,"about_ca_system_score_codex":0.0001596135,"about_ca_system_score_gemma":0.00020089571,"threshold_uncertainty_score":0.005307138},"labels":[],"label_agreement":null},{"id":"W2036253413","doi":"10.1108/ijes-03-2012-0001","title":"Modeling number of firefighters responding to an incident using artificial neural networks","year":2013,"lang":"en","type":"article","venue":"International Journal of Emergency Services","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University; University of Toronto; Western University","funders":"","keywords":"Artificial neural network; Computer science; Set (abstract data type); A priori and a posteriori; Artificial intelligence; Machine learning","score_opus":0.026220163897528133,"score_gpt":0.30171752247444744,"score_spread":0.2754973585769193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036253413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64051884,0.0003769941,0.35454684,0.00035849906,0.00012218903,0.00010425458,0.000536289,0.00063477637,0.0028013033],"genre_scores_gemma":[0.9762462,0.00011702222,0.021379812,0.000032910193,0.000020802114,0.00008634646,0.00028882513,0.000008001647,0.0018201778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972206,0.00006298508,0.000029630512,0.000078400095,0.00006347974,0.00004339673],"domain_scores_gemma":[0.9991116,0.0004954767,0.00015539255,0.000043213713,0.0001664429,0.000027879776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007986051,0.0005936988,0.0003949834,0.0004955345,0.00024588188,0.0005508867,0.00074349914,0.00076539733,0.0008585639],"category_scores_gemma":[0.002270823,0.0002923891,0.000471764,0.00043296092,0.0002113936,0.000662359,0.00027685438,0.0005877141,0.00020566984],"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":[0.00008368862,0.00008165833,0.011306785,0.00003172353,0.000034847235,0.000061706174,0.000041454397,0.96806294,0.0008295823,0.00027755214,0.00020797987,0.018980222],"study_design_scores_gemma":[0.0000011579621,0.000014517375,0.0009892869,0.0000027686822,0.000004615564,0.0000061771616,0.000004987874,0.99859256,0.00019885704,0.00013857697,0.00004433691,0.000002110243],"about_ca_topic_score_codex":0.015483792,"about_ca_topic_score_gemma":0.0121824965,"teacher_disagreement_score":0.015483792,"about_ca_system_score_codex":0.00070560595,"about_ca_system_score_gemma":0.0005487802,"threshold_uncertainty_score":0.030787349},"labels":[],"label_agreement":null},{"id":"W2040613848","doi":"10.1109/icip.2012.6467069","title":"Wavelet subband-based steam detection by multiple kernel learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Radial basis function kernel; Artificial intelligence; Multiple kernel learning; Wavelet transform; Support vector machine; Kernel (algebra); Wavelet; Kernel method; Mathematics; Wavelet packet decomposition; Stationary wavelet transform; Computer science; Discrete mathematics","score_opus":0.005712665383534301,"score_gpt":0.1733687874437118,"score_spread":0.16765612206017752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040613848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021492645,0.00014833562,0.9767487,0.000067686364,0.000022955563,0.00002258506,0.000043179163,0.0009874986,0.00046639476],"genre_scores_gemma":[0.47426668,0.00039105583,0.52076805,0.0001123404,0.00006718549,0.00007753088,0.0004729723,0.00024584902,0.003598369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928397,0.00014679853,0.0000393585,0.00016504811,0.0002759874,0.00008893461],"domain_scores_gemma":[0.9990553,0.00028643137,0.00014531371,0.0001650283,0.0002910136,0.000056890905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095341133,0.0006883197,0.0015197167,0.0020199758,0.00023573585,0.00084952445,0.001167877,0.00085770246,0.0013798933],"category_scores_gemma":[0.0026520176,0.00040676497,0.0010632095,0.0012024564,0.00036901628,0.0015767622,0.0011028306,0.0010525094,0.0011908426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046215084,0.00022446,0.002167413,0.00012976113,0.000111627276,0.00011568512,0.00009459132,0.09331867,0.057113763,0.004330612,0.0027227858,0.8392085],"study_design_scores_gemma":[0.0000063638286,0.000023364615,0.00048537168,0.0000031869267,0.000011388491,0.00003757498,0.000008163179,0.9909628,0.0067957016,0.0012066605,0.0004508785,0.0000086368],"about_ca_topic_score_codex":0.0016104049,"about_ca_topic_score_gemma":0.0013970176,"teacher_disagreement_score":0.0020199758,"about_ca_system_score_codex":0.0004895154,"about_ca_system_score_gemma":0.00053426623,"threshold_uncertainty_score":0.0050421357},"labels":[],"label_agreement":null},{"id":"W2045332825","doi":"10.1109/icinfa.2012.6246864","title":"Discrete wavelet transform based steam detection with Adaboost","year":2012,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"AdaBoost; Artificial intelligence; Computer science; Feature extraction; Pattern recognition (psychology); Classifier (UML); Object detection; Wavelet transform; Boiler (water heating); Wavelet; Engineering","score_opus":0.005187874589628731,"score_gpt":0.173841811271553,"score_spread":0.16865393668192424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045332825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03083197,0.00022496576,0.9663127,0.00009378138,0.00013394687,0.00007514254,0.000024869934,0.001544593,0.0007579764],"genre_scores_gemma":[0.41628772,0.00021044996,0.57712483,0.00021314187,0.00011697329,0.0001925333,0.0002024806,0.00018443262,0.0054674586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932754,0.00011854208,0.000044944692,0.00016667,0.00023630347,0.00010606017],"domain_scores_gemma":[0.99923337,0.00026392553,0.00008010654,0.000060060232,0.00030837487,0.00005424494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017715152,0.0010027747,0.0015482827,0.0014290866,0.00044746234,0.0009340281,0.0017007175,0.0014168851,0.0015682755],"category_scores_gemma":[0.0018173403,0.00060218794,0.0010690658,0.0009566411,0.0005800682,0.0011599642,0.0006515262,0.0014353729,0.0009225216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073823956,0.00057199516,0.0021294681,0.00014858102,0.00022457137,0.00008534113,0.000080077356,0.19713181,0.032829545,0.0023076097,0.0032999215,0.7604528],"study_design_scores_gemma":[0.000012220497,0.000057515546,0.00038500322,0.0000037766029,0.000013147189,0.000022830065,0.000007671146,0.9937304,0.0048365975,0.00047978532,0.00044292663,0.000008232936],"about_ca_topic_score_codex":0.003890827,"about_ca_topic_score_gemma":0.0032838099,"teacher_disagreement_score":0.003890827,"about_ca_system_score_codex":0.0006285899,"about_ca_system_score_gemma":0.00090076897,"threshold_uncertainty_score":0.009368777},"labels":[],"label_agreement":null},{"id":"W2052654802","doi":"10.1007/s11518-009-5121-2","title":"Intelligent security systems engineering for modeling fire critical incidents: Towards sustainable security","year":2009,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; York University","funders":"","keywords":"Explosive material; Computer security; Security information and event management; Security engineering; Engineering; Fire protection; Risk analysis (engineering); Security service; Computer science; Cloud computing security; Civil engineering; Network security policy; Information security; Business; Geography","score_opus":0.0127443140685847,"score_gpt":0.24342346814330088,"score_spread":0.23067915407471618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052654802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038099613,0.0003953218,0.95035815,0.00092631247,0.000091837835,0.00005590981,0.00014796785,0.0004149022,0.009509931],"genre_scores_gemma":[0.8546832,0.0007010553,0.13839127,0.0001755436,0.0001112738,0.0001571454,0.00020794533,0.00013471748,0.00543782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961853,0.0001418337,0.000022856879,0.000061955696,0.000110211244,0.000044596178],"domain_scores_gemma":[0.99923444,0.0004198102,0.00009461552,0.00006308201,0.00015383428,0.00003431718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008114262,0.00079602,0.0008784424,0.0005871427,0.0006304991,0.0017857908,0.0013181122,0.0017176414,0.0028549223],"category_scores_gemma":[0.0029502653,0.0006135046,0.00089128764,0.000575898,0.0009691142,0.0021780864,0.0008983668,0.0013496913,0.0004285421],"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":[0.000009408497,0.000014728746,0.0004274574,0.000021457543,0.000021021895,0.000018708355,0.00003528357,0.9827753,0.0002784629,0.011935139,0.00027082185,0.00419233],"study_design_scores_gemma":[0.0000026596997,0.0000031278162,0.000041450647,0.0000022828422,0.0000050331587,0.0000034800823,0.000006461114,0.9948731,0.00008298692,0.004616408,0.00036081116,0.0000020465725],"about_ca_topic_score_codex":0.016225172,"about_ca_topic_score_gemma":0.011560301,"teacher_disagreement_score":0.016225172,"about_ca_system_score_codex":0.0012444295,"about_ca_system_score_gemma":0.001906101,"threshold_uncertainty_score":0.03226143},"labels":[],"label_agreement":null},{"id":"W2059702012","doi":"10.1007/s10694-011-0237-6","title":"Study of a Video Image Fire Detection System for Protection of Large Industrial Applications and Atria","year":2011,"lang":"en","type":"article","venue":"Fire Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Fire detection; Smoke; Computer science; Detector; Computer vision; Infrared; Fire protection; Artificial intelligence; Environmental science; Engineering; Telecommunications; Optics; Architectural engineering; Physics","score_opus":0.02506898157301485,"score_gpt":0.22300153226292627,"score_spread":0.19793255068991142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059702012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9450513,0.00033653463,0.05002041,0.00012782468,0.000053603653,0.00017102648,0.00016054808,0.00033232538,0.0037464108],"genre_scores_gemma":[0.9848394,0.00015990411,0.010852047,0.000036514502,0.000018781184,0.000035809888,0.00012999456,0.00003306319,0.0038944161],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998191,0.000026074522,0.000006082264,0.000040697098,0.00008393617,0.000024134892],"domain_scores_gemma":[0.99949193,0.00017780556,0.000033117405,0.000032308802,0.00023349399,0.00003126639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023190433,0.00024183522,0.0003443737,0.00037104395,0.00039070888,0.00028610244,0.0005209459,0.0004895763,0.0025493843],"category_scores_gemma":[0.0007551573,0.00011272125,0.0002952897,0.00024188941,0.0001839171,0.0003386661,0.00009591263,0.00021176923,0.00037441085],"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":[0.001550393,0.0005158672,0.008907541,0.00050979323,0.000100073885,0.0011866287,0.0004387611,0.02222973,0.8594006,0.0012674971,0.001449012,0.102444164],"study_design_scores_gemma":[0.00010805835,0.0054914807,0.038956884,0.000035367924,0.00021355946,0.0012504727,0.0004896104,0.41009903,0.5365121,0.00024849284,0.006552771,0.00004215064],"about_ca_topic_score_codex":0.006016152,"about_ca_topic_score_gemma":0.003224959,"teacher_disagreement_score":0.006016152,"about_ca_system_score_codex":0.00057188096,"about_ca_system_score_gemma":0.00037312787,"threshold_uncertainty_score":0.011962295},"labels":[],"label_agreement":null},{"id":"W2059935773","doi":"10.1115/1.2978995","title":"Further Development of a Smoke Sensor for Diesel Engines","year":2008,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Smoke; Soot; Diesel fuel; Exhaust gas recirculation; Automotive engineering; SIGNAL (programming language); Diesel engine; Spark plug; Materials science; Insulator (electricity); Diesel particulate filter; Electrode; Exhaust gas; Environmental science; Optoelectronics; Waste management; Internal combustion engine; Engineering; Mechanical engineering; Chemistry; Computer science; Combustion","score_opus":0.01463849186660476,"score_gpt":0.20695139822116906,"score_spread":0.1923129063545643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059935773","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.782986,0.0018509094,0.20669502,0.00036892068,0.0002599309,0.0006821503,0.00023492858,0.0007596859,0.0061624586],"genre_scores_gemma":[0.69541585,0.0015734497,0.29298803,0.00026168663,0.00006450408,0.00018225584,0.0005451969,0.00011386054,0.008855146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996624,0.00004172736,0.000016510723,0.000057013596,0.0001915039,0.000030800147],"domain_scores_gemma":[0.99970824,0.00006225903,0.000014123125,0.000025374346,0.00015476061,0.00003512935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076562015,0.0003705058,0.00042407753,0.00024645551,0.00018325541,0.00033579816,0.00067948597,0.0005469426,0.0015825545],"category_scores_gemma":[0.00059268647,0.00019639489,0.0003782416,0.00015081484,0.00018693935,0.0006702342,0.00030757912,0.00039121052,0.00040119773],"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":[0.000053951822,0.00009730046,0.00046405196,0.00011074979,0.000008284329,0.00006614352,0.00003565821,0.00043365321,0.9792418,0.00029234588,0.00006855468,0.019127533],"study_design_scores_gemma":[0.000031699437,0.0010990087,0.0015425237,0.000011248391,0.000020472706,0.00017516721,0.000025120502,0.0078010177,0.9832507,0.000099219156,0.0059316657,0.000012298257],"about_ca_topic_score_codex":0.00060142163,"about_ca_topic_score_gemma":0.000989815,"teacher_disagreement_score":0.0015825545,"about_ca_system_score_codex":0.00022931422,"about_ca_system_score_gemma":0.0004461865,"threshold_uncertainty_score":0.0052942038},"labels":[],"label_agreement":null},{"id":"W2063412217","doi":"10.1097/00005768-200105001-00034","title":"EFFECT OF EXPERIENCE ON RATES AND RISKS OF INJURY IN RODEO","year":2001,"lang":"en","type":"article","venue":"Medicine & Science in Sports & Exercise","topic":"Fire Detection and Safety Systems","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":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine","score_opus":0.013078306140847196,"score_gpt":0.2967511998846632,"score_spread":0.283672893743816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063412217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984666,0.00016406368,0.000034192028,0.000038328293,0.0000073456185,0.000007924077,0.00015281614,0.000002728623,0.0011260307],"genre_scores_gemma":[0.9991429,0.00009178814,0.000029775301,0.000010240493,0.000005910363,0.000004933543,0.00017968248,0.0000020434923,0.0005326406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865544,0.00018698054,0.00009885522,0.0001819159,0.00037961747,0.00049718295],"domain_scores_gemma":[0.993779,0.000689134,0.0024516487,0.0002409919,0.000980889,0.0018581907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008807573,0.0002823801,0.00029294213,0.0011371896,0.0006666013,0.00087740936,0.0007337508,0.0004650971,0.0031138463],"category_scores_gemma":[0.007296679,0.00023820579,0.0005746317,0.0005829842,0.0006720261,0.0006530782,0.0012411665,0.0005843293,0.00037938237],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008021112,0.000035062338,0.997823,0.000006348802,0.000014892157,0.0000898746,0.0003610249,0.000024958921,0.00005484737,0.00001393512,0.000049385966,0.001446467],"study_design_scores_gemma":[8.3168106e-7,0.000107035485,0.9991289,0.0000053995336,0.0000032962866,0.000087490465,0.00050003576,0.000026702712,0.000018007666,0.00000537393,0.000113943024,0.0000029783585],"about_ca_topic_score_codex":0.08367373,"about_ca_topic_score_gemma":0.11047748,"teacher_disagreement_score":0.08367373,"about_ca_system_score_codex":0.0010014314,"about_ca_system_score_gemma":0.00077269576,"threshold_uncertainty_score":0.16637337},"labels":[],"label_agreement":null},{"id":"W2083251578","doi":"10.1109/crv.2010.29","title":"Flame Region Detection Based on Histogram Backprojection","year":2010,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Histogram; Fire detection; Artificial intelligence; Computer vision; Computer science; Detector; Projection (relational algebra); Smoke; False alarm; Pattern recognition (psychology); Algorithm; Engineering; Image (mathematics)","score_opus":0.005867017714848875,"score_gpt":0.17701349057027455,"score_spread":0.17114647285542567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083251578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03151686,0.00020265015,0.96500254,0.000059617916,0.00005172512,0.00006621935,0.000055399836,0.0016073345,0.0014375641],"genre_scores_gemma":[0.18922065,0.0004238413,0.80736476,0.000041286712,0.00004426964,0.00007593166,0.00013167158,0.00011168343,0.0025859121],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995802,0.00006549925,0.000014855347,0.00006196644,0.00024139772,0.000036069225],"domain_scores_gemma":[0.9994405,0.00022166743,0.000046687044,0.00005749975,0.00019925095,0.000034400906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049546815,0.00056965346,0.00051006535,0.0014060283,0.00026625846,0.00066681,0.0007212778,0.00047682825,0.002567917],"category_scores_gemma":[0.0014230338,0.00029480163,0.00032800087,0.0007269214,0.00044739625,0.00093524414,0.0006019455,0.00055762223,0.0009092366],"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":[0.00031913296,0.00012357459,0.0017050602,0.00017297725,0.000048959297,0.00016814303,0.00010134556,0.014838299,0.25029615,0.0036972407,0.0014795396,0.7270496],"study_design_scores_gemma":[0.000058202535,0.0003160631,0.0075363806,0.000029577073,0.000042988613,0.0010912264,0.00006991702,0.58392805,0.39901268,0.0027957754,0.005030904,0.0000881077],"about_ca_topic_score_codex":0.0018645801,"about_ca_topic_score_gemma":0.0019878414,"teacher_disagreement_score":0.002567917,"about_ca_system_score_codex":0.00021099306,"about_ca_system_score_gemma":0.0004789145,"threshold_uncertainty_score":0.008590519},"labels":[],"label_agreement":null},{"id":"W2085413799","doi":"10.1071/wf13042","title":"Use of night vision goggles for aerial forest fire detection","year":2014,"lang":"en","type":"article","venue":"International Journal of Wildland Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Forest Research Institute; National Research Council Canada; Ministry of Natural Resources and Forestry; Natural Resources Canada; York University","funders":"","keywords":"Context (archaeology); Environmental science; Night vision; Terrain; Daytime; Computer science; Remote sensing; Geography; Artificial intelligence; Cartography; Atmospheric sciences; Geology","score_opus":0.011315567597453965,"score_gpt":0.22875922629788173,"score_spread":0.21744365870042776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085413799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908614,0.0003099214,0.0064768847,0.00004141087,0.000021199166,0.00013016131,0.0002124429,0.00027987477,0.0016666797],"genre_scores_gemma":[0.98225594,0.00015226741,0.015749905,0.00009828647,0.0000056337517,0.00012856044,0.0002751963,0.000038476108,0.0012957111],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99955004,0.00019475356,0.000018959325,0.00009642775,0.000083538296,0.000056236473],"domain_scores_gemma":[0.99839586,0.0007653272,0.00026726938,0.00019467995,0.00015305603,0.0002236877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006846912,0.00047470696,0.00038750755,0.00044865653,0.00023363718,0.00042410826,0.00054353854,0.00037585077,0.0019823932],"category_scores_gemma":[0.0021905568,0.00018259438,0.0002643666,0.00016839807,0.0002939053,0.00041813368,0.0005294156,0.00037570836,0.00040696628],"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":[0.013887577,0.0042881286,0.16033162,0.0013651442,0.00033680146,0.00067787967,0.0016426943,0.0035893673,0.43887857,0.00046044434,0.0035416374,0.37100014],"study_design_scores_gemma":[0.00030946438,0.022094194,0.8351951,0.00019352476,0.00022826786,0.00137483,0.0010992013,0.01339063,0.11643896,0.00064272236,0.008798085,0.00023505859],"about_ca_topic_score_codex":0.0027051985,"about_ca_topic_score_gemma":0.009662133,"teacher_disagreement_score":0.0027051985,"about_ca_system_score_codex":0.0003320701,"about_ca_system_score_gemma":0.0003827333,"threshold_uncertainty_score":0.006631732},"labels":[],"label_agreement":null},{"id":"W2089346978","doi":"10.1016/j.firesaf.2011.12.007","title":"Modeling the risk of structural fire incidents using a self-organizing map","year":2012,"lang":"en","type":"article","venue":"Fire Safety Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; York University","funders":"","keywords":"Damages; Risk assessment; Poison control; Sample (material); Forensic engineering; Computer science; Engineering; Computer security; Medical emergency; Medicine","score_opus":0.012207790260906945,"score_gpt":0.21864598335537241,"score_spread":0.20643819309446548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089346978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73843175,0.00028017696,0.25748444,0.00070903596,0.00007304887,0.000069963986,0.00031821168,0.00029119043,0.002342259],"genre_scores_gemma":[0.99148047,0.000085671934,0.007355938,0.000016745627,0.000019320754,0.00002899285,0.00009018443,0.000011416739,0.0009113988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956435,0.00015902054,0.000021091695,0.00010627659,0.00006244348,0.00008681979],"domain_scores_gemma":[0.99683446,0.002387775,0.00027522977,0.0000977535,0.0002608165,0.00014404055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013040296,0.00062951166,0.00067185133,0.0012851108,0.00058551034,0.0013596242,0.0015673356,0.001511004,0.0013112874],"category_scores_gemma":[0.00463448,0.00070970797,0.00096735224,0.00073580915,0.0007663223,0.0017322907,0.0010691788,0.00096132734,0.00015970545],"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":[0.000039454488,0.000035485868,0.0033997563,0.000008452531,0.000031917076,0.00003625965,0.00003850221,0.99242425,0.00011171156,0.0019988117,0.000108860375,0.0017665369],"study_design_scores_gemma":[0.0000021153646,0.000006863633,0.00028425985,8.2243935e-7,0.0000036086149,0.000005251926,0.000009388393,0.99866104,0.000022738672,0.0009822951,0.000019427374,0.0000021443127],"about_ca_topic_score_codex":0.023388408,"about_ca_topic_score_gemma":0.014083881,"teacher_disagreement_score":0.023388408,"about_ca_system_score_codex":0.0012363525,"about_ca_system_score_gemma":0.0009552175,"threshold_uncertainty_score":0.046504557},"labels":[],"label_agreement":null},{"id":"W2099225632","doi":"10.3801/iafss.fss.8-741","title":"Application Of Water Mist To Extinguish Large Oil Pool Fires For Industrial Oil Cooker Protection","year":2005,"lang":"en","type":"article","venue":"Fire Safety Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Cooker; Mist; Environmental science; Waste management; Environmental engineering; Petroleum engineering; Engineering; Meteorology; Mechanical engineering","score_opus":0.01405039264436467,"score_gpt":0.2263822639755932,"score_spread":0.21233187133122852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099225632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96932495,0.00033171728,0.029390458,0.000027817448,0.000024204848,0.0000929889,0.000018079007,0.00014082948,0.0006489459],"genre_scores_gemma":[0.9889565,0.0001211564,0.010467628,0.000012375595,0.0000042447823,0.0000116371775,0.000010794887,0.00000935127,0.0004062012],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985313,0.000016662689,0.000010126672,0.000030617404,0.00006424324,0.000025183435],"domain_scores_gemma":[0.99972206,0.00008799398,0.00006784083,0.000036549947,0.000058897534,0.000026704121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020609522,0.00023582106,0.0002689581,0.00021704614,0.0002480233,0.00020334648,0.00040688564,0.00028299997,0.0006872287],"category_scores_gemma":[0.00050964375,0.00013109254,0.00020307407,0.000086635446,0.00027009338,0.00042395625,0.00029789313,0.00020151345,0.00011563252],"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":[0.00015438443,0.000042127936,0.0012016203,0.00009682991,0.000005584332,0.00011248009,0.00005160963,0.0010351508,0.98573387,0.000102032725,0.0000368715,0.011427342],"study_design_scores_gemma":[0.000018743096,0.00066365494,0.0028903962,0.0000046858586,0.000012929166,0.00013392016,0.000029362714,0.004785617,0.9907774,0.000034951587,0.00064040767,0.0000078845],"about_ca_topic_score_codex":0.0003996691,"about_ca_topic_score_gemma":0.00094229716,"teacher_disagreement_score":0.0006872287,"about_ca_system_score_codex":0.00020399841,"about_ca_system_score_gemma":0.00019588845,"threshold_uncertainty_score":0.0022990704},"labels":[],"label_agreement":null},{"id":"W22218498","doi":"","title":"Performance Metrics for Acoustic Small Arms Localization Systems","year":2006,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"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":"Crew; Small arms; Situation awareness; Computer science; Aeronautics; Computer security; Simulation; Engineering; Aerospace engineering; Business","score_opus":0.013316578660533134,"score_gpt":0.19582234185579117,"score_spread":0.18250576319525805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W22218498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18376578,0.0053500133,0.7814528,0.0012481416,0.0003754605,0.0006611389,0.0016495287,0.002833841,0.022663383],"genre_scores_gemma":[0.9312098,0.0008997049,0.06308215,0.000109249784,0.00018217339,0.0003509211,0.0014900527,0.0002492731,0.0024266886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9868656,0.0037833792,0.0012428721,0.001324908,0.005342976,0.0014401877],"domain_scores_gemma":[0.9559299,0.026506437,0.004182174,0.0026656424,0.009603359,0.001112469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086428905,0.0019557143,0.0015643858,0.0037482667,0.0009338986,0.003076652,0.0015491947,0.0014953477,0.0039776107],"category_scores_gemma":[0.05028867,0.00028396564,0.0006006042,0.0030431917,0.0009111354,0.0036687565,0.0016838505,0.0010322202,0.0011174281],"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":[0.0014733812,0.00035116277,0.015344725,0.00092543557,0.0002230306,0.00027452692,0.0004965667,0.6708292,0.018600216,0.07691829,0.009622994,0.20494044],"study_design_scores_gemma":[0.000033261553,0.0016230497,0.0055396,0.00009378762,0.00008449604,0.0005099276,0.00030065002,0.95316374,0.010545526,0.02147034,0.0065477807,0.000087923836],"about_ca_topic_score_codex":0.00249111,"about_ca_topic_score_gemma":0.0011866881,"teacher_disagreement_score":0.0086428905,"about_ca_system_score_codex":0.002359571,"about_ca_system_score_gemma":0.0010721264,"threshold_uncertainty_score":0.045708537},"labels":[],"label_agreement":null},{"id":"W2225526831","doi":"","title":"Smoke Alarms Work, But Not Forever: posing the challenge of adopting multifaceted, sustained, interagency responses to ensuring the presence of a functioning smoke alarm.","year":2012,"lang":"en","type":"article","venue":"UWA Profiles and Research Repository (University of Western Australia)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Smoke; ALARM; Work (physics); Computer security; Psychology; Computer science; Engineering; Waste management","score_opus":0.08706951855323952,"score_gpt":0.292447857535405,"score_spread":0.20537833898216545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2225526831","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11624874,0.0022699866,0.0723835,0.76231986,0.004079077,0.00042399773,0.00008131197,0.0018558523,0.040337667],"genre_scores_gemma":[0.834799,0.0019018034,0.0676872,0.0825192,0.0012832516,0.0005130554,0.00008719131,0.00033530334,0.010873945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.96574503,0.01640528,0.0016197764,0.0024833137,0.009088172,0.004658423],"domain_scores_gemma":[0.85925084,0.06323454,0.015713837,0.007565612,0.025968067,0.028267277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06256362,0.0009043395,0.0005937042,0.0012327443,0.012707695,0.016277239,0.004530846,0.01601587,0.005876905],"category_scores_gemma":[0.12870716,0.0009258994,0.00051286863,0.0007422921,0.0071337996,0.011868708,0.013325129,0.015441477,0.0027305759],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000382338,0.0009922495,0.04537496,0.00085148984,0.00023012074,0.0030701456,0.06324638,0.0059982263,0.018980406,0.06858975,0.22135168,0.5709322],"study_design_scores_gemma":[0.00015989858,0.0015761404,0.044208337,0.002627665,0.00019370031,0.003032278,0.23443437,0.016406838,0.009117044,0.17578131,0.5116294,0.0008331002],"about_ca_topic_score_codex":0.0083445255,"about_ca_topic_score_gemma":0.017348746,"teacher_disagreement_score":0.06256362,"about_ca_system_score_codex":0.0045272023,"about_ca_system_score_gemma":0.04755826,"threshold_uncertainty_score":0.33087194},"labels":[],"label_agreement":null},{"id":"W2270042541","doi":"","title":"질소가스를 이용한 청정 소화시스템의 개발","year":2006,"lang":"ko","type":"article","venue":"유체기계 연구개발 발표회 논문집","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Piping; Environmental science; Waste management; Inert gas; Nitrogen gas; Engineering; Nitrogen; Environmental engineering; Chemistry; Chemical engineering","score_opus":0.004362826504492002,"score_gpt":0.17280041692471654,"score_spread":0.16843759042022455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270042541","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016080206,0.00088947336,0.007955768,0.006457651,0.0042533055,0.0036147216,0.00904437,0.0016805433,0.9644961],"genre_scores_gemma":[0.00519902,0.00050924945,0.0035369282,0.0022075889,0.0002441959,0.00082591333,0.0044497596,0.0002425725,0.9827848],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946273,0.00071325724,0.0003078193,0.0005760293,0.0027865546,0.0009890521],"domain_scores_gemma":[0.98919696,0.00079611456,0.00029601128,0.0015942705,0.0070640217,0.0010526837],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005823882,0.0008116807,0.00082902226,0.0021238967,0.0034815297,0.0054415893,0.0028759541,0.0037140222,0.39752737],"category_scores_gemma":[0.010496809,0.000702856,0.00068672094,0.0016188318,0.0018501275,0.0019912964,0.002803532,0.0030714672,0.24871005],"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":[0.00009173563,0.00009988939,0.0004100682,0.0001360552,0.000009068871,0.000096232136,0.00017847144,0.00015800583,0.0024604504,0.05252805,0.9008933,0.042938784],"study_design_scores_gemma":[0.000009090186,0.0000224192,0.0008509138,0.000037071815,0.000003275208,0.000033328524,0.00004155891,0.00006865602,0.00041345163,0.0006876333,0.9978198,0.000012836809],"about_ca_topic_score_codex":0.16013163,"about_ca_topic_score_gemma":0.23428054,"teacher_disagreement_score":0.39752737,"about_ca_system_score_codex":0.0061444947,"about_ca_system_score_gemma":0.020889604,"threshold_uncertainty_score":0.8593541},"labels":[],"label_agreement":null},{"id":"W2287543677","doi":"","title":"Industrial Ventilation Statistics Confirm Energy Savings Opportunity","year":2006,"lang":"en","type":"article","venue":"OakTrust (Texas A&M University Libraries)","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Ventilation (architecture); Factory (object-oriented programming); Workstation; Data logger; Operations management; Computer science; Engineering; Environmental science; Meteorology; Geography; Operating system","score_opus":0.011985527660912593,"score_gpt":0.15092148368118125,"score_spread":0.13893595602026865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287543677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7866393,0.0020445774,0.0050385827,0.0030808519,0.0002661054,0.00006806009,0.094319336,0.0011034693,0.10743965],"genre_scores_gemma":[0.9646143,0.00045084333,0.0012105936,0.00028142246,0.00011259019,0.000029798168,0.026630433,0.000088999266,0.0065810024],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99846447,0.00018617867,0.00015069016,0.00028143765,0.00072742876,0.00018990341],"domain_scores_gemma":[0.9919045,0.0018635412,0.001934305,0.00090226537,0.003118092,0.00027723273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009360094,0.0002921152,0.00023692535,0.0024113227,0.00026578392,0.0007425928,0.00048629806,0.000358397,0.011528982],"category_scores_gemma":[0.007296631,0.00012687742,0.00021715774,0.0035234336,0.00019874226,0.0010208028,0.00066547113,0.00037190964,0.003874088],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021646707,0.00012841294,0.8263738,0.00016884135,0.000073929565,0.000107438405,0.00023173483,0.0014407053,0.0016522337,0.0019180644,0.0534624,0.1142261],"study_design_scores_gemma":[0.000011482486,0.00013607126,0.94994706,0.000046306424,0.000029724142,0.00022209795,0.0004506796,0.0012328394,0.0029803633,0.00090700516,0.044016235,0.000020191463],"about_ca_topic_score_codex":0.0074610314,"about_ca_topic_score_gemma":0.012413106,"teacher_disagreement_score":0.011528982,"about_ca_system_score_codex":0.00055542483,"about_ca_system_score_gemma":0.00045305936,"threshold_uncertainty_score":0.03856826},"labels":[],"label_agreement":null},{"id":"W2349068007","doi":"","title":"Brief Talk about New Fire Fighting Technology and Equipment in Foreign Countries","year":2010,"lang":"en","type":"article","venue":"Linye jixie","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Aeronautics; Engineering; Firefighting; Architectural engineering; Forensic engineering; Geography; Cartography","score_opus":0.004898812279437599,"score_gpt":0.19732763365527467,"score_spread":0.19242882137583708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2349068007","genre_codex":"review","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019686174,0.6935542,0.00915062,0.010442381,0.05844667,0.0001037928,0.00063189695,0.00041882205,0.20756541],"genre_scores_gemma":[0.08551397,0.45736763,0.0073360377,0.013702222,0.030980308,0.00013702294,0.0011986516,0.0001461856,0.40361795],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998398,0.00003209334,0.000016256561,0.000032837408,0.00005057203,0.000028548357],"domain_scores_gemma":[0.9999163,0.00003676255,0.000010851601,0.0000044569338,0.000018097395,0.000013545757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029510426,0.00069479697,0.00042071193,0.0008487304,0.00081439345,0.001441669,0.00028793886,0.0013643977,0.018575834],"category_scores_gemma":[0.00025990428,0.000200315,0.0005326132,0.000995947,0.00026499093,0.001767973,0.0004121725,0.0011476802,0.005914844],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008190362,0.00022651082,0.0027425867,0.0070751607,0.00011292146,0.0047497824,0.0015663626,0.0021040058,0.030558812,0.07026412,0.34961605,0.53016466],"study_design_scores_gemma":[0.000004162316,0.00015820413,0.0011191366,0.00026282287,0.000019905687,0.0013263632,0.00022046544,0.0001054116,0.0011279793,0.0022194164,0.99341834,0.000017788921],"about_ca_topic_score_codex":0.0005023946,"about_ca_topic_score_gemma":0.00068736525,"teacher_disagreement_score":0.018575834,"about_ca_system_score_codex":0.00041462923,"about_ca_system_score_gemma":0.00026661283,"threshold_uncertainty_score":0.062142372},"labels":[],"label_agreement":null},{"id":"W2350641808","doi":"","title":"Study on the Sensitivity of Viscosity to Temperature of Liquid Detergent","year":2001,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"CAE (Canada)","funders":"","keywords":"Viscosity; Sensitivity (control systems); Chemistry; Chromatography; Salt (chemistry); Materials science; Organic chemistry; Composite material","score_opus":0.01508205695822507,"score_gpt":0.22371129783723778,"score_spread":0.20862924087901272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2350641808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903148,0.001804123,0.0060550547,0.00008287751,0.000058648966,0.000043589407,0.00013697513,0.000049476053,0.001454384],"genre_scores_gemma":[0.99575967,0.0009850285,0.0023037298,0.000036391404,0.000018603618,0.00002151701,0.00013061291,0.000021749107,0.00072264776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954176,0.00006351437,0.000039571994,0.00010423416,0.00019286816,0.00005803879],"domain_scores_gemma":[0.998618,0.00062193535,0.00029037843,0.00010152205,0.00030909918,0.000059060723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003807792,0.00032720476,0.00035966464,0.00042282479,0.00025425406,0.0005661532,0.00022487347,0.00033829553,0.0009241874],"category_scores_gemma":[0.0019578368,0.00023291967,0.00045315074,0.00042538368,0.00031579647,0.0005700345,0.00019554414,0.00062581385,0.00022376109],"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":[0.00021688218,0.000055157445,0.0038524582,0.00012754553,0.000023410703,0.00016167594,0.00013937843,0.00049471494,0.9895107,0.00009638086,0.0000588118,0.0052628894],"study_design_scores_gemma":[0.000003268765,0.00038338697,0.006860044,0.000008764888,0.000029855932,0.00018132741,0.00006629332,0.0018367377,0.98992455,0.00004693178,0.0006453767,0.000013358244],"about_ca_topic_score_codex":0.0007160952,"about_ca_topic_score_gemma":0.0004102171,"teacher_disagreement_score":0.0009241874,"about_ca_system_score_codex":0.00023597083,"about_ca_system_score_gemma":0.00015902225,"threshold_uncertainty_score":0.0030917525},"labels":[],"label_agreement":null},{"id":"W2361484631","doi":"","title":"Design of Wireless Thermal Image Video Surveillance Device","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"General Packet Radio Service; Computer science; Fire detection; Wireless; Real-time computing; Computer security; Embedded system; Telecommunications; Architectural engineering","score_opus":0.00823134832676323,"score_gpt":0.2139796450961724,"score_spread":0.20574829676940917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2361484631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048993263,0.0012459364,0.9292192,0.00047890958,0.00051338493,0.00034351985,0.00021637772,0.002384571,0.016604932],"genre_scores_gemma":[0.7467591,0.0011234271,0.21310772,0.0007216467,0.00028041037,0.0006686716,0.00034917792,0.00012676792,0.036862977],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967325,0.000043723907,0.000022040658,0.00012151415,0.00010152936,0.000037909165],"domain_scores_gemma":[0.99981123,0.00003426045,0.00002228358,0.000014317555,0.00010229266,0.000015653299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022423179,0.00044875726,0.00042382153,0.0004529367,0.00030954715,0.0006487027,0.0011205449,0.0007183539,0.0027800023],"category_scores_gemma":[0.0003410563,0.00027511225,0.00024465233,0.0002076359,0.00017619072,0.00050265715,0.0002938762,0.0002573085,0.0012174567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085933594,0.00014415034,0.005490587,0.0010361271,0.00015274454,0.001274478,0.0007232811,0.011765764,0.63522565,0.01324482,0.016293941,0.3137892],"study_design_scores_gemma":[0.00035380296,0.0033560484,0.010043671,0.0002547052,0.0005326038,0.0075354227,0.00033727678,0.31445074,0.4976452,0.0025589021,0.16270748,0.00022420623],"about_ca_topic_score_codex":0.0005360702,"about_ca_topic_score_gemma":0.0003464088,"teacher_disagreement_score":0.0027800023,"about_ca_system_score_codex":0.0003747222,"about_ca_system_score_gemma":0.00024209154,"threshold_uncertainty_score":0.0092999935},"labels":[],"label_agreement":null},{"id":"W2364639211","doi":"","title":"Evaluation System of Fire Fighting Ability Based on VB","year":2003,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"CAE (Canada)","funders":"","keywords":"Firefighting; Engineering; Set (abstract data type); Active fire protection; Marine safety; Aeronautics; Computer security; Forensic engineering; Fire protection; Operations research; Architectural engineering; Computer science; Marine engineering; Civil engineering; Geography; Cartography","score_opus":0.015331107522194635,"score_gpt":0.21929767820945859,"score_spread":0.20396657068726395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364639211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12177591,0.00022457569,0.82136405,0.00018024731,0.00007593513,0.0006771537,0.001272166,0.038919244,0.015510764],"genre_scores_gemma":[0.7689984,0.00019029279,0.22012079,0.000085182866,0.00004384027,0.00072128733,0.0021392468,0.00056334934,0.007137661],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882716,0.00030257698,0.00015768246,0.00027293587,0.00036139737,0.0000783096],"domain_scores_gemma":[0.9976891,0.00067899283,0.00014320457,0.00026599632,0.0011159993,0.00010673467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016653507,0.0006948188,0.0006317231,0.0027709538,0.00049929396,0.0016032662,0.0007923443,0.00045124913,0.0051632393],"category_scores_gemma":[0.004587623,0.0003008509,0.0003796013,0.0010069185,0.0003192532,0.0015302423,0.0005378031,0.00032454444,0.0012195911],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016424644,0.00045089034,0.037730753,0.0006866698,0.00021289197,0.00038043433,0.0011856136,0.051478565,0.059363283,0.01543072,0.017060865,0.81437683],"study_design_scores_gemma":[0.0001768602,0.0005790661,0.020502936,0.00011307562,0.00027967672,0.00056179526,0.0003851795,0.8787271,0.07301741,0.0064149247,0.01907559,0.00016639342],"about_ca_topic_score_codex":0.004715759,"about_ca_topic_score_gemma":0.001757392,"teacher_disagreement_score":0.0051632393,"about_ca_system_score_codex":0.00067419215,"about_ca_system_score_gemma":0.00067989377,"threshold_uncertainty_score":0.01727277},"labels":[],"label_agreement":null},{"id":"W2366064846","doi":"","title":"The influence of environment and time on the flammable liquid smoke sediment","year":2012,"lang":"en","type":"article","venue":"Fire Science and Technology","topic":"Fire Detection and Safety Systems","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":"Apotex Pharmachem (Canada)","funders":"","keywords":"Flammable liquid; Combustion; Smoke; Component (thermodynamics); Environmental science; Sediment; Gasoline; Duration (music); Waste management; Environmental engineering; Petroleum engineering; Engineering; Geology; Chemistry","score_opus":0.004696720152566793,"score_gpt":0.17505871869451126,"score_spread":0.17036199854194448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366064846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99687755,0.00019799331,0.0022529599,0.000012321812,0.000011706603,0.000009171372,0.000085544285,0.000024808465,0.0005278518],"genre_scores_gemma":[0.9978257,0.0001438075,0.001205105,0.000012257056,0.0000033862336,0.000007280331,0.00008368202,0.000019481635,0.00069926056],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997497,0.000041395153,0.000012289971,0.00006233581,0.00009419737,0.000040157887],"domain_scores_gemma":[0.9995617,0.00017620432,0.00007705553,0.000025887259,0.00012184255,0.000037193986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001928754,0.0003085898,0.00022946908,0.0002295682,0.0002571878,0.0005045345,0.0001725533,0.00021170542,0.0008640194],"category_scores_gemma":[0.0007254291,0.00016040917,0.00028508843,0.000251642,0.00025565372,0.00038918311,0.000250063,0.00015184714,0.00017552],"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":[0.0025169563,0.00015996383,0.040861405,0.00016351069,0.00006611256,0.000656207,0.0001970672,0.004930133,0.93103784,0.00013780962,0.00008265963,0.019190269],"study_design_scores_gemma":[0.00002068192,0.001604974,0.15445864,0.000010211613,0.00012280804,0.00028525226,0.0003035998,0.01138255,0.8302891,0.00014766338,0.0013214748,0.000052917487],"about_ca_topic_score_codex":0.0021358787,"about_ca_topic_score_gemma":0.0032249375,"teacher_disagreement_score":0.0021358787,"about_ca_system_score_codex":0.0002174955,"about_ca_system_score_gemma":0.00022688843,"threshold_uncertainty_score":0.0042468905},"labels":[],"label_agreement":null},{"id":"W2375262514","doi":"","title":"Yield of Chemical Components in the Mainstream Smoke under Two Smoking Methods","year":2012,"lang":"en","type":"article","venue":"Zhongguo yancao kexue","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"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":"tar (computing); Sidestream smoke; Smoke; Carbon monoxide; Phenol; Yield (engineering); Nicotine; Chemistry; Ammonia; Environmental science; Pulp and paper industry; Organic chemistry; Materials science; Medicine; Catalysis; Engineering; Metallurgy","score_opus":0.059863207841194865,"score_gpt":0.298761098405251,"score_spread":0.23889789056405614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375262514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99673754,0.00039370346,0.0013862362,0.000014975127,0.000008535646,0.0000132719715,0.00015917553,0.000015078766,0.0012715547],"genre_scores_gemma":[0.9960846,0.00041759797,0.0007866285,0.000022514028,0.000008328864,0.000020839356,0.0004292229,0.000014417417,0.002215972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951744,0.000029275683,0.000021604988,0.00008224173,0.0002792862,0.000070238355],"domain_scores_gemma":[0.9996309,0.0000477997,0.000057982445,0.000022470003,0.00019833654,0.000042460404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025034734,0.00026735407,0.00023299342,0.0005607546,0.00029282263,0.00032483853,0.00015098132,0.00024457418,0.0010316909],"category_scores_gemma":[0.00049213285,0.00014310353,0.00044808083,0.00027539278,0.00020224742,0.00042038463,0.00036777163,0.00022523201,0.00013004224],"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":[0.001071208,0.00009369713,0.082366966,0.00023351,0.00011737265,0.00029972644,0.00068984955,0.00051993306,0.88455546,0.00018463109,0.00013631342,0.029731406],"study_design_scores_gemma":[0.000013724402,0.001110753,0.35192236,0.000016719194,0.00021100674,0.00035221173,0.0007910867,0.0023285293,0.64118624,0.00017792305,0.001850941,0.000038436345],"about_ca_topic_score_codex":0.005199007,"about_ca_topic_score_gemma":0.00553252,"teacher_disagreement_score":0.005199007,"about_ca_system_score_codex":0.00028259662,"about_ca_system_score_gemma":0.00023239505,"threshold_uncertainty_score":0.010337532},"labels":[],"label_agreement":null},{"id":"W2378448435","doi":"","title":"Design and Realization of the Fireproof and Theft-proof Alarm System Based on Phone Network","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"ALARM; Computer science; Phone; Ringing; Manual fire alarm activation; Realization (probability); Computer security; Phone call; Telecommunications; Real-time computing; Embedded system; Electrical engineering","score_opus":0.00840932793564667,"score_gpt":0.16911492140766488,"score_spread":0.1607055934720182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2378448435","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106931224,0.00059750484,0.8595358,0.0005314339,0.00032313901,0.00041898192,0.00012312632,0.0028667848,0.028671963],"genre_scores_gemma":[0.799522,0.0004222639,0.18189347,0.0002157476,0.00009548385,0.00034561532,0.00016818874,0.00006941062,0.017267913],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.999699,0.000055526212,0.00001491431,0.000066724235,0.00011522275,0.000048643244],"domain_scores_gemma":[0.99982977,0.000024930388,0.00001623742,0.000019356128,0.0000894015,0.000020269346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020523202,0.00030124525,0.00026294985,0.00030773485,0.0004699171,0.0007182799,0.00062038715,0.0006113164,0.0025667702],"category_scores_gemma":[0.00031121727,0.0002319406,0.00018687056,0.00013888555,0.00018197527,0.0006612855,0.00025204924,0.00030227232,0.0009353796],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067092193,0.0002330838,0.0067450344,0.00056353345,0.00007954857,0.0013162072,0.0014382498,0.02371221,0.55363744,0.041862283,0.009693823,0.36004773],"study_design_scores_gemma":[0.00024987487,0.0022041264,0.010673385,0.00013467068,0.0002349684,0.0034525755,0.00044470982,0.38662517,0.41476098,0.0055612833,0.1754705,0.0001877037],"about_ca_topic_score_codex":0.001082707,"about_ca_topic_score_gemma":0.0009858279,"teacher_disagreement_score":0.0025667702,"about_ca_system_score_codex":0.000426061,"about_ca_system_score_gemma":0.0005005875,"threshold_uncertainty_score":0.008586764},"labels":[],"label_agreement":null},{"id":"W2385654693","doi":"","title":"Confirmation of Metis200 Automatic Hematology Analyzer","year":2013,"lang":"en","type":"article","venue":"China Medical Devices","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Hematology analyzer; Hematology; Medicine; Internal medicine; Medical physics; Computer science","score_opus":0.0044444419429541925,"score_gpt":0.20707055445241287,"score_spread":0.2026261125094587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2385654693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40705922,0.014381773,0.48908466,0.0072515802,0.006164904,0.0020550531,0.004911393,0.022857541,0.046233926],"genre_scores_gemma":[0.684118,0.0041419757,0.26429293,0.004668102,0.0017600508,0.0014062074,0.0062777945,0.0013620555,0.03197288],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99253005,0.0014321336,0.00063196494,0.00093490287,0.004120322,0.00035066568],"domain_scores_gemma":[0.99531555,0.0008122226,0.00043637952,0.00039913738,0.0028422459,0.00019447115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028409585,0.0012583511,0.0007306816,0.0033356245,0.001000624,0.0010793636,0.0017247013,0.0011715619,0.004491714],"category_scores_gemma":[0.008271969,0.0004984121,0.00060026743,0.0010717334,0.00045919165,0.00082343246,0.00080623693,0.0014919278,0.003689921],"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":[0.0032421139,0.0005610071,0.1251366,0.0012036313,0.0002012126,0.004529713,0.0014125848,0.00088636536,0.48328167,0.004656493,0.06729406,0.30759463],"study_design_scores_gemma":[0.00019348312,0.0016249713,0.090596296,0.0004647922,0.0003095054,0.01374141,0.0008851172,0.015442013,0.7160462,0.0024136591,0.15801264,0.00026986405],"about_ca_topic_score_codex":0.0013176786,"about_ca_topic_score_gemma":0.0012127294,"teacher_disagreement_score":0.004491714,"about_ca_system_score_codex":0.00044468063,"about_ca_system_score_gemma":0.0011048318,"threshold_uncertainty_score":0.015026271},"labels":[],"label_agreement":null},{"id":"W2475020744","doi":"10.1109/icuas.2016.7502546","title":"Vision-based forest fire detection in aerial images for firefighting using UAVs","year":2016,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Artificial intelligence; Computer science; Optical flow; Computer vision; Feature (linguistics); Fire detection; Thresholding; Pixel; Remote sensing; Image (mathematics); Geography; Engineering; Cartography","score_opus":0.010595842656967846,"score_gpt":0.22859637810906325,"score_spread":0.2180005354520954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475020744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39321184,0.00086414267,0.6002552,0.000097009615,0.0000839401,0.00013414574,0.00015286256,0.0014667208,0.0037341984],"genre_scores_gemma":[0.7130115,0.00056069426,0.2847023,0.00004785038,0.000026546804,0.000039321254,0.00017234788,0.00003322081,0.0014062853],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989045,0.000014141321,0.0000051672146,0.00002433564,0.00004953707,0.000016400778],"domain_scores_gemma":[0.99987566,0.000029317529,0.000025535088,0.000012511388,0.00004406294,0.000012929845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017466121,0.00037494444,0.0003499093,0.0012104558,0.00022532695,0.00031783953,0.0002752261,0.000279794,0.0006980431],"category_scores_gemma":[0.0004154708,0.00019729184,0.00028617991,0.00039592266,0.00020807187,0.0003893191,0.00018514894,0.00025224613,0.0002560604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052915746,0.00013961765,0.005018125,0.00020104808,0.00005548038,0.00027536604,0.00008737041,0.01766825,0.49470052,0.00087737263,0.0012250289,0.47922269],"study_design_scores_gemma":[0.000046859433,0.0003485103,0.023772173,0.000046574965,0.00007955194,0.0009207067,0.00011644724,0.69198644,0.27875596,0.0007393918,0.003142902,0.000044508393],"about_ca_topic_score_codex":0.002790473,"about_ca_topic_score_gemma":0.0038434367,"teacher_disagreement_score":0.002790473,"about_ca_system_score_codex":0.00025591272,"about_ca_system_score_gemma":0.00029114846,"threshold_uncertainty_score":0.0055484176},"labels":[],"label_agreement":null},{"id":"W2492628054","doi":"10.1142/9789812791177_0003","title":"Estimation of Live Fuel Moisture Content","year":2003,"lang":"en","type":"book-chapter","venue":"Series in remote sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Water content; Environmental science; Estimation; Content (measure theory); Mathematics; Geology; Engineering; Geotechnical engineering; Systems engineering","score_opus":0.0177687054407611,"score_gpt":0.201811774569028,"score_spread":0.1840430691282669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492628054","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004461284,0.00822572,0.95221364,0.00015863846,0.0006929598,0.000040911833,0.00059012097,0.0033440934,0.030272596],"genre_scores_gemma":[0.124759085,0.017204922,0.5001508,0.00033846294,0.00089713564,0.00011281652,0.0030810174,0.0020902927,0.35136557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998252,0.000013111848,0.0000049768,0.00005233203,0.00009914691,0.0000051982174],"domain_scores_gemma":[0.99975544,0.00011139639,0.000014190854,0.00004427756,0.000067202374,0.0000074906166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023912876,0.0008576342,0.00064669404,0.0009947958,0.00011941567,0.00051671633,0.0010285146,0.00048752426,0.010521587],"category_scores_gemma":[0.00064112915,0.00033195803,0.0002483847,0.0008079711,0.00022679195,0.00078962103,0.00032041618,0.0004223329,0.008516708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004560263,0.00003225262,0.0008279325,0.00022069548,0.000022552236,0.00006345836,0.000034180503,0.0056117903,0.03485604,0.0029561864,0.027729623,0.9275997],"study_design_scores_gemma":[0.000015598343,0.00019676573,0.015378418,0.00027822706,0.00012698144,0.0020918227,0.00014464027,0.23976904,0.33909553,0.028353974,0.37441334,0.0001357147],"about_ca_topic_score_codex":0.00084330415,"about_ca_topic_score_gemma":0.0018960437,"teacher_disagreement_score":0.010521587,"about_ca_system_score_codex":0.00017283505,"about_ca_system_score_gemma":0.00013069587,"threshold_uncertainty_score":0.03519821},"labels":[],"label_agreement":null},{"id":"W2505661532","doi":"10.2495/safe-v6-n2-254-269","title":"Firefighting robot with video full-closed loop control","year":2016,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Firefighting; Robot; Computer science; Filter (signal processing); Simulation; Debugging; Engineering; Computer vision; Artificial intelligence","score_opus":0.003123077957975578,"score_gpt":0.17466531039136335,"score_spread":0.17154223243338776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505661532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05294384,0.00040442016,0.9307744,0.0000968757,0.00017050018,0.00020860892,0.00010323483,0.008420805,0.0068773064],"genre_scores_gemma":[0.7262495,0.00027627914,0.2570382,0.00021237714,0.00008946404,0.000492573,0.00031261318,0.00018250509,0.015146517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996768,0.000022685777,0.0000149754205,0.00010145277,0.00014342573,0.00004051187],"domain_scores_gemma":[0.99974257,0.000040398212,0.000039694176,0.00003602049,0.00011349327,0.000027893924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029779517,0.0007115229,0.00043976828,0.00035813334,0.00035958455,0.00040109505,0.0011476775,0.0005881187,0.0028424219],"category_scores_gemma":[0.0004619745,0.00024041421,0.0002990048,0.00014790728,0.00030408578,0.00050033897,0.00047815777,0.00039588622,0.00069784455],"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":[0.000951914,0.0002823737,0.0019487354,0.0006559527,0.000057943875,0.00054248405,0.00048555274,0.04463343,0.44165298,0.0033662985,0.006635074,0.4987873],"study_design_scores_gemma":[0.00052582606,0.0028256408,0.006841293,0.00009605428,0.0001236618,0.0015680477,0.00009203288,0.76192814,0.18557906,0.001648467,0.038601156,0.0001706632],"about_ca_topic_score_codex":0.0029547939,"about_ca_topic_score_gemma":0.00184828,"teacher_disagreement_score":0.0029547939,"about_ca_system_score_codex":0.0003086613,"about_ca_system_score_gemma":0.0006072272,"threshold_uncertainty_score":0.009508848},"labels":[],"label_agreement":null},{"id":"W2508764147","doi":"10.3390/s16081310","title":"Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":298,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Canadian Forest Service; York University","funders":"Ontario Centres of Excellence","keywords":"Drone; Remote sensing; Fire detection; Hyperspectral imaging; Context (archaeology); Computer science; Systems engineering; Environmental science; Environmental monitoring; Engineering; Architectural engineering; Geography","score_opus":0.022143959722690582,"score_gpt":0.2603073152471543,"score_spread":0.2381633555244637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508764147","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074023375,0.98819494,0.0027608732,0.0003377686,0.0002852741,0.000019672822,0.000052939973,0.000022360191,0.0075858906],"genre_scores_gemma":[0.0075803953,0.9858159,0.0025614912,0.00021336856,0.0002327255,0.000017424492,0.00008255609,0.00000723859,0.0034890275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995981,0.00005124842,0.000028501308,0.000082492814,0.0002088031,0.000030917494],"domain_scores_gemma":[0.9995542,0.00019775567,0.00006880725,0.000020256513,0.00013962314,0.000019323556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076685817,0.00090601086,0.00076668756,0.0024812946,0.0002888743,0.0009432197,0.0007673823,0.00121045,0.0053311307],"category_scores_gemma":[0.00070539286,0.00031435644,0.00069058524,0.00257231,0.00047849835,0.0013541412,0.0006972605,0.0010766576,0.0028668083],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029903189,0.00009649094,0.00048696675,0.012727814,0.000078857694,0.00012695341,0.00006119776,0.0011294396,0.008632372,0.009132486,0.01335736,0.9541402],"study_design_scores_gemma":[0.0000048461784,0.00014042376,0.0016533871,0.0034828025,0.00010378002,0.0009906552,0.00010338647,0.0007648703,0.005177899,0.0049291397,0.98261285,0.000035933856],"about_ca_topic_score_codex":0.0010198812,"about_ca_topic_score_gemma":0.0018248109,"teacher_disagreement_score":0.0053311307,"about_ca_system_score_codex":0.00045420096,"about_ca_system_score_gemma":0.00075846654,"threshold_uncertainty_score":0.017834425},"labels":[],"label_agreement":null},{"id":"W2551889016","doi":"10.1115/ipc2016-64118","title":"Robust Direct Hydrocarbon Sensor Based on Novel Carbon Nanotube Nanocomposites for Leakage Detection","year":2016,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring and Maintenance; Materials and Joining","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Leakage (economics); Environmental science; Pipeline transport; Leak; Materials science; Carbon nanotube; Petroleum engineering; Computer science; Process engineering; Nanotechnology; Environmental engineering; Engineering","score_opus":0.014572023249705789,"score_gpt":0.19773787514733746,"score_spread":0.18316585189763165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551889016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9567487,0.0030166246,0.03589292,0.0002316141,0.00016960075,0.00008595529,0.00033595623,0.00048084074,0.0030377335],"genre_scores_gemma":[0.9683802,0.0012011175,0.025991788,0.00009757015,0.000027430315,0.000060837436,0.0001895996,0.000023739933,0.0040275296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997503,0.00002011001,0.00001423649,0.0000659373,0.00013086825,0.000018632913],"domain_scores_gemma":[0.9997961,0.00003983814,0.000056755165,0.000010156579,0.00008282127,0.000014380586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001609098,0.0003340056,0.00027449575,0.00030002624,0.00013748673,0.00023539626,0.0003426575,0.0005537653,0.000540836],"category_scores_gemma":[0.0002935421,0.00019089415,0.00021850377,0.0002649101,0.00018068029,0.00058321515,0.00019071039,0.00029165577,0.00018971036],"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":[0.000025828364,0.000014075352,0.00009998902,0.00006921619,0.0000030104845,0.000036014244,0.000009753134,0.00015491167,0.99748933,0.0000310142,0.00004906076,0.0020179364],"study_design_scores_gemma":[0.0000037085158,0.00018754348,0.0009238303,0.000004717869,0.000009070148,0.00013242076,0.000018639741,0.004295979,0.99332124,0.000017649216,0.0010726047,0.000012596472],"about_ca_topic_score_codex":0.00056354183,"about_ca_topic_score_gemma":0.0020285684,"teacher_disagreement_score":0.00056354183,"about_ca_system_score_codex":0.00021422819,"about_ca_system_score_gemma":0.00014780238,"threshold_uncertainty_score":0.0018092394},"labels":[],"label_agreement":null},{"id":"W2569436968","doi":"10.1007/s10846-016-0464-7","title":"Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":211,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Ağrı İbrahim Çeçen Üniversitesi","keywords":"Optical flow; Firefighting; Fire detection; Artificial intelligence; Computer vision; Computer science; Pixel; Feature (linguistics); Remote sensing; Engineering; Geography; Image (mathematics); Cartography","score_opus":0.029034327835592667,"score_gpt":0.269197493163856,"score_spread":0.24016316532826332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569436968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78862536,0.0011636139,0.19819467,0.00020165942,0.00022423547,0.00013050133,0.0005586378,0.0010990378,0.009802247],"genre_scores_gemma":[0.91094804,0.00050069817,0.085341305,0.00008227013,0.00006847853,0.000034795125,0.000380841,0.000032676584,0.0026108567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998548,0.000018027142,0.0000052716305,0.000029156203,0.00006708239,0.000025667323],"domain_scores_gemma":[0.9998683,0.000021431746,0.000025229563,0.000015312518,0.000057006528,0.000012765203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012873161,0.00046377274,0.00022207668,0.0013332468,0.00018891413,0.00035468378,0.00028944525,0.00031977502,0.0009585836],"category_scores_gemma":[0.00024337761,0.00018723332,0.0003389237,0.00049168273,0.00015483155,0.00046814885,0.0002225447,0.00025815924,0.00034683524],"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":[0.00046935515,0.00031505481,0.017504638,0.00027575798,0.00014175716,0.00039332366,0.00017340646,0.018424794,0.4057334,0.00084750063,0.0034106867,0.55231035],"study_design_scores_gemma":[0.000059485646,0.00040374737,0.11916244,0.00006599379,0.00024370942,0.000845399,0.0004903164,0.69765526,0.17395711,0.0010141002,0.006040723,0.00006177134],"about_ca_topic_score_codex":0.0027292052,"about_ca_topic_score_gemma":0.0060364394,"teacher_disagreement_score":0.0027292052,"about_ca_system_score_codex":0.000154893,"about_ca_system_score_gemma":0.0002655686,"threshold_uncertainty_score":0.0054265857},"labels":[],"label_agreement":null},{"id":"W2609483211","doi":"10.1093/sleepj/zsx050.745","title":"0746 REVIEW OF A MULTISENSOR, LOW COST, AND UNOBTRUSIVE APPROACH TO DETECT MOVEMENTS IN SIT AND SLEEP","year":2017,"lang":"en","type":"review","venue":"SLEEP","topic":"Fire Detection and Safety Systems","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":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"sync; Computer science; Polysomnography; Videography; Feature (linguistics); Electromyography; Movement (music); Simulation; Artificial intelligence; Computer vision; Physical medicine and rehabilitation; Medicine; Electroencephalography","score_opus":0.042654033658987886,"score_gpt":0.2980948524793919,"score_spread":0.255440818820404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609483211","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005622786,0.9844301,0.0030218994,0.0011078507,0.0032299245,0.00012438545,0.00022759213,0.00008453577,0.0072113946],"genre_scores_gemma":[0.0027988402,0.98167485,0.0040541226,0.0013785317,0.0021781886,0.00015371997,0.0005215629,0.000051448165,0.0071887737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863017,0.00029244216,0.00029838303,0.0002496207,0.00046885686,0.000060656097],"domain_scores_gemma":[0.9962288,0.001148269,0.0003255412,0.00017795977,0.0019126039,0.00020685315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022227808,0.0010044556,0.0013778553,0.005081423,0.00045094514,0.001756753,0.00190792,0.0019094496,0.020723872],"category_scores_gemma":[0.0046207397,0.00048625332,0.0012273057,0.0031832552,0.0006359888,0.0020908813,0.0011210723,0.0013662382,0.011001035],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015333561,0.00011158095,0.00035983927,0.025323022,0.00016452896,0.00023828581,0.00008268626,0.00016379813,0.00315837,0.001791999,0.054469764,0.9139828],"study_design_scores_gemma":[0.000014566698,0.00024283434,0.0018198825,0.0095387045,0.00028085423,0.0014271976,0.000080152,0.00013822976,0.0013909155,0.00046514958,0.98456556,0.00003585665],"about_ca_topic_score_codex":0.0019499683,"about_ca_topic_score_gemma":0.0029892027,"teacher_disagreement_score":0.020723872,"about_ca_system_score_codex":0.000971484,"about_ca_system_score_gemma":0.0025321878,"threshold_uncertainty_score":0.06932831},"labels":[],"label_agreement":null},{"id":"W2653459521","doi":"10.1109/ccece.2017.7946722","title":"Black Ice detection system using Kinect","year":2017,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Remote sensing; Computer vision; Geology; Visibility; Computer science; Artificial intelligence; Computer graphics (images); Optics; Physics","score_opus":0.01971869589906026,"score_gpt":0.22270350380310722,"score_spread":0.20298480790404697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2653459521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15800941,0.0023593935,0.75083566,0.0003861041,0.00089934445,0.0013894824,0.02626258,0.033406306,0.026451677],"genre_scores_gemma":[0.51850325,0.001864849,0.4168195,0.0006457311,0.00013751739,0.0017177197,0.033584103,0.0012883113,0.025438981],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992829,0.000041677242,0.000054427917,0.00017405361,0.0003807209,0.00006625655],"domain_scores_gemma":[0.9997851,0.000013467613,0.000033912253,0.000017105705,0.00011149971,0.000038849754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046176056,0.0012071743,0.0013142157,0.001844365,0.0004687461,0.00078134524,0.0010358746,0.00069627643,0.0065637114],"category_scores_gemma":[0.0004045723,0.00058479485,0.0006101706,0.0009655433,0.00020425649,0.0009421943,0.001055828,0.00071023474,0.0031059978],"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":[0.0023874019,0.0007849492,0.016006788,0.00219504,0.00028401605,0.00094624545,0.00059987255,0.021500092,0.48739567,0.0035222324,0.044556387,0.41982126],"study_design_scores_gemma":[0.00042633677,0.0007424862,0.083594695,0.00044451538,0.00023598874,0.0017397717,0.0005350667,0.4033062,0.41116512,0.0033054415,0.09397769,0.0005266982],"about_ca_topic_score_codex":0.0029763242,"about_ca_topic_score_gemma":0.004260664,"teacher_disagreement_score":0.0065637114,"about_ca_system_score_codex":0.00042445608,"about_ca_system_score_gemma":0.00091442326,"threshold_uncertainty_score":0.021957815},"labels":[],"label_agreement":null},{"id":"W2733381601","doi":"10.1016/j.firesaf.2017.06.012","title":"Computer vision for wildfire research: An evolving image dataset for processing and analysis","year":2017,"lang":"en","type":"article","venue":"Fire Safety Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":188,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Frame (networking); Computer science; Ground truth; Field (mathematics); Image processing; Artificial intelligence; Computer vision; Pixel; Image (mathematics)","score_opus":0.04839038859194959,"score_gpt":0.3559245179726619,"score_spread":0.3075341293807123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733381601","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13257152,0.004287028,0.04552416,0.0019682667,0.0014368062,0.0016252811,0.78304315,0.021133611,0.008410169],"genre_scores_gemma":[0.053471617,0.0008348154,0.0501866,0.00038860823,0.00013229916,0.0007826488,0.890014,0.00085210917,0.003337345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986897,0.00013019901,0.00013251662,0.00038394867,0.00047624344,0.00018742721],"domain_scores_gemma":[0.9980903,0.0001551304,0.00012106158,0.0005015763,0.0007929246,0.00033902892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014193328,0.0019218014,0.001151013,0.003998153,0.0009542084,0.0013389545,0.0028222746,0.0021748657,0.0034408122],"category_scores_gemma":[0.0031752717,0.00046710647,0.0015988324,0.003261572,0.00062124006,0.001347597,0.0023754588,0.0018893786,0.0061773234],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008807779,0.0017404862,0.022022717,0.0016136836,0.00051760266,0.00047449747,0.00032124174,0.008933494,0.034346957,0.0019298563,0.6964449,0.23077379],"study_design_scores_gemma":[0.00059359666,0.0007709682,0.2196005,0.00074441393,0.0006556289,0.0033274477,0.0016025506,0.10000361,0.068049975,0.007868121,0.5962535,0.00052963616],"about_ca_topic_score_codex":0.030139912,"about_ca_topic_score_gemma":0.06818335,"teacher_disagreement_score":0.030139912,"about_ca_system_score_codex":0.0013372494,"about_ca_system_score_gemma":0.0021010712,"threshold_uncertainty_score":0.059928954},"labels":[],"label_agreement":null},{"id":"W2742600586","doi":"10.1016/j.firesaf.2017.06.001","title":"Fire Safety Science: Proceedings of the 12th International Symposium","year":2017,"lang":"en","type":"article","venue":"Fire Safety Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fire safety; Engineering; Forensic engineering; Poison control; Aeronautics; Medical emergency; Medicine; Civil engineering","score_opus":0.007072957785251548,"score_gpt":0.21902027918979777,"score_spread":0.21194732140454622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742600586","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011187768,0.36951798,0.05224653,0.0636194,0.1869372,0.00023005478,0.00079822796,0.0010247837,0.31443807],"genre_scores_gemma":[0.09484667,0.25038022,0.03384134,0.005736661,0.09149863,0.00023273827,0.0022803165,0.0010489497,0.5201344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861395,0.00025685524,0.00007264661,0.00018257435,0.0006795212,0.00019449586],"domain_scores_gemma":[0.9947242,0.0007259506,0.0001894,0.00034988916,0.0028827316,0.001127805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005934545,0.0015111852,0.0011844791,0.002845448,0.0012897932,0.006167159,0.0013968206,0.0025750864,0.038901642],"category_scores_gemma":[0.0031269763,0.00047010102,0.00080974126,0.001677368,0.0017554506,0.003845995,0.0030284321,0.003510142,0.013622272],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018573449,0.000267731,0.0011348098,0.0007571607,0.000064017804,0.0001441228,0.0003199114,0.0009402469,0.0038036045,0.019654581,0.5574093,0.41531885],"study_design_scores_gemma":[0.000010991457,0.00007242783,0.0011764172,0.00038925625,0.000028249953,0.00019932371,0.00020197585,0.0012410011,0.0016286498,0.0075298594,0.98750174,0.00002020295],"about_ca_topic_score_codex":0.002552632,"about_ca_topic_score_gemma":0.005360738,"teacher_disagreement_score":0.038901642,"about_ca_system_score_codex":0.0018599994,"about_ca_system_score_gemma":0.0036600148,"threshold_uncertainty_score":0.130139},"labels":[],"label_agreement":null},{"id":"W2744502852","doi":"10.6028/nist.gcr.02-843-1","title":"Analysis of needs and existing capabilities for full-scale fire resistance testing","year":2008,"lang":"en","type":"report","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":46,"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":"National Institute of Standards and Technology; Victoria University; Commonwealth Scientific and Industrial Research Organisation; National Research Council Canada; Canadian Institute of Steel Construction","keywords":"Forensic engineering; Fireproofing; Environmental science; Engineering; Civil engineering","score_opus":0.04486944966902952,"score_gpt":0.25446511655471327,"score_spread":0.20959566688568376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744502852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51467407,0.0023330883,0.2802876,0.0061983448,0.00027163437,0.0026508665,0.0065109115,0.009033181,0.17804027],"genre_scores_gemma":[0.8237946,0.00093548297,0.15158004,0.00040032438,0.00009765454,0.00095826073,0.00570385,0.0006313297,0.015898567],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.993895,0.0010319767,0.00033849687,0.0005045369,0.0036198043,0.00061017304],"domain_scores_gemma":[0.96982163,0.010370736,0.0012956097,0.0035346774,0.013730577,0.0012466115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009992845,0.00098706,0.0006891566,0.0024986083,0.00075088616,0.0024337627,0.003959219,0.0010387057,0.021119893],"category_scores_gemma":[0.022300212,0.00047128825,0.0008881955,0.0013886846,0.00065833,0.0064460593,0.001880031,0.0010284147,0.0070910584],"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":[0.002085632,0.0019125504,0.09856966,0.0025854162,0.00012200703,0.0014326726,0.0015032842,0.031141441,0.1011794,0.021615831,0.014619449,0.7232327],"study_design_scores_gemma":[0.00037660706,0.007874622,0.16007626,0.0018177544,0.0004784701,0.005567476,0.006900129,0.3453169,0.19234094,0.039910983,0.23891638,0.00042344988],"about_ca_topic_score_codex":0.003125098,"about_ca_topic_score_gemma":0.0048869406,"teacher_disagreement_score":0.021119893,"about_ca_system_score_codex":0.0016628815,"about_ca_system_score_gemma":0.0031371803,"threshold_uncertainty_score":0.07065308},"labels":[],"label_agreement":null},{"id":"W2768546781","doi":"10.1088/1361-6501/aa9cf3","title":"A multimodal 3D framework for fire characteristics estimation","year":2017,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Moncton","funders":"","keywords":"Estimation; Computer science; Artificial intelligence; Engineering; Systems engineering","score_opus":0.026817348460884956,"score_gpt":0.2559132071275321,"score_spread":0.22909585866664717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768546781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061409,0.00015489836,0.99223024,0.000042970958,0.00001726419,0.000037056918,0.00014716113,0.0006080914,0.0006214264],"genre_scores_gemma":[0.29841304,0.000602432,0.6977835,0.00010774897,0.000079936435,0.00018610823,0.0010755623,0.0002019914,0.0015496186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995493,0.00010857963,0.00002052827,0.00010464222,0.00015779783,0.000059188125],"domain_scores_gemma":[0.9997284,0.000050340775,0.000040460753,0.00005100267,0.00010377184,0.000025975589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069235,0.0009609935,0.00086812454,0.002733381,0.0003726455,0.0014569632,0.0010862817,0.0010187654,0.003015818],"category_scores_gemma":[0.0011219622,0.00055693043,0.0016245478,0.0016605265,0.000457912,0.0007093864,0.0015163255,0.000968511,0.0010629856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026400457,0.00018160972,0.002384041,0.00021109771,0.00019053456,0.0002558993,0.00016674839,0.45991912,0.051709764,0.008767319,0.0024599284,0.47348994],"study_design_scores_gemma":[0.0000046326886,0.000026986423,0.00065058994,0.000014348943,0.000014584848,0.00006136048,0.000026304377,0.9930352,0.0030274394,0.0016849708,0.0014378218,0.000015852553],"about_ca_topic_score_codex":0.006992655,"about_ca_topic_score_gemma":0.0071452954,"teacher_disagreement_score":0.006992655,"about_ca_system_score_codex":0.0004865106,"about_ca_system_score_gemma":0.0007400319,"threshold_uncertainty_score":0.013903916},"labels":[],"label_agreement":null},{"id":"W2794499044","doi":"10.1007/s10846-018-0803-y","title":"Learning-Based Smoke Detection for Unmanned Aerial Vehicles Applied to Forest Fire Surveillance","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Fire detection; Smoke; Computer science; Hue; Artificial intelligence; Fuzzy logic; RGB color model; Remote sensing; Segmentation; Environmental science; Computer vision; Engineering; Geography; Meteorology","score_opus":0.016549229623864712,"score_gpt":0.23379533411016995,"score_spread":0.21724610448630524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794499044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35436115,0.0005548679,0.6408715,0.00013995955,0.00015875915,0.0000873753,0.00007462466,0.0012218013,0.0025298924],"genre_scores_gemma":[0.95331407,0.00010599122,0.04490695,0.000041027684,0.00002258606,0.000022556074,0.000074772324,0.00002173404,0.0014902706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983203,0.000019917292,0.000008730202,0.00004487553,0.000056318902,0.000038066268],"domain_scores_gemma":[0.999691,0.00011276459,0.00002971729,0.00002113831,0.0001266842,0.000018752635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003583996,0.00038808442,0.00043420607,0.00046286272,0.00023200053,0.00036056273,0.00055813644,0.00042622615,0.0008215002],"category_scores_gemma":[0.0008796465,0.0001786179,0.00032262038,0.00028250826,0.00018324563,0.00035258193,0.0004055508,0.00039686658,0.00021146475],"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":[0.00042688605,0.00038210934,0.007294841,0.000090559726,0.000072379895,0.00012117478,0.000079137186,0.23774114,0.06530044,0.00074607716,0.0010189976,0.68672633],"study_design_scores_gemma":[0.0000043410005,0.000057966954,0.0014574468,0.000002357999,0.000007023878,0.00001782261,0.000009688958,0.9912095,0.006908573,0.00014877159,0.00017247886,0.000004024041],"about_ca_topic_score_codex":0.006826476,"about_ca_topic_score_gemma":0.007945316,"teacher_disagreement_score":0.006826476,"about_ca_system_score_codex":0.00028923724,"about_ca_system_score_gemma":0.0005293372,"threshold_uncertainty_score":0.013573527},"labels":[],"label_agreement":null},{"id":"W2799675047","doi":"10.1117/12.2304936","title":"Wildland fires detection and segmentation using deep learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Convolutional neural network; Segmentation; Computer science; Deep learning; Pixel; Fire detection; Artificial intelligence; Image segmentation; Artificial neural network; Environmental science; Remote sensing; Machine learning; Geography; Architectural engineering; Engineering","score_opus":0.008805987703908915,"score_gpt":0.21218278055144119,"score_spread":0.20337679284753227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799675047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45632324,0.0014692976,0.5305515,0.00028397326,0.00012480348,0.00012061522,0.0016367955,0.0051048207,0.004384975],"genre_scores_gemma":[0.850879,0.00043191478,0.14186037,0.000117308235,0.00004631856,0.000047012985,0.003114703,0.000094967574,0.0034083312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997482,0.000022794837,0.000014266577,0.00008201529,0.000058554357,0.0000742039],"domain_scores_gemma":[0.9998092,0.00005158098,0.00003499479,0.000026137825,0.00005423564,0.00002388416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003952555,0.0007766922,0.00060025626,0.001386627,0.00030603498,0.00064970995,0.0008161908,0.0007371082,0.0011270591],"category_scores_gemma":[0.000552981,0.00032776612,0.0007557603,0.00063569937,0.0002633086,0.00071676157,0.0005492689,0.0006656573,0.0005039385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007311546,0.00054637523,0.011215945,0.00014048633,0.0002055784,0.0002922934,0.00010948539,0.23075132,0.052201364,0.0012236042,0.0052602603,0.69732213],"study_design_scores_gemma":[0.000009062164,0.000040961513,0.0036306924,0.0000141728315,0.000020974412,0.000070060414,0.00002334618,0.97946566,0.015040867,0.00092098955,0.00075278955,0.000010346908],"about_ca_topic_score_codex":0.010228332,"about_ca_topic_score_gemma":0.015347929,"teacher_disagreement_score":0.010228332,"about_ca_system_score_codex":0.0007605753,"about_ca_system_score_gemma":0.00058035133,"threshold_uncertainty_score":0.020337582},"labels":[],"label_agreement":null},{"id":"W2898642934","doi":"","title":"Smoke detection in full-scale house experiments","year":2009,"lang":"en","type":"article","venue":"NPARC","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Full scale; Scale (ratio); Smoke; Computer science; Environmental science; Cartography; Geography; Meteorology; Computer vision","score_opus":0.011598232484600077,"score_gpt":0.22131698487108536,"score_spread":0.2097187523864853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898642934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9729705,0.00014063624,0.024538321,0.000025373265,0.00005558943,0.00015459234,0.00029060937,0.00027143137,0.0015530119],"genre_scores_gemma":[0.98677063,0.00020762856,0.009779099,0.000048980222,0.000015228279,0.00019772247,0.00050112244,0.000056814264,0.0024228632],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993087,0.000101655656,0.000043038704,0.00013754521,0.00030846664,0.000100623794],"domain_scores_gemma":[0.998248,0.0009051733,0.00014989018,0.00016580292,0.0004442508,0.00008691971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009852477,0.000483539,0.00049684726,0.0005161559,0.0005359928,0.00028808767,0.0007090164,0.0005264861,0.001837772],"category_scores_gemma":[0.002563047,0.0002512007,0.0004304001,0.00037073667,0.00046879193,0.00039763137,0.0005003924,0.00047411813,0.0005049163],"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":[0.00259899,0.0020396847,0.016423875,0.0005707468,0.00008934987,0.00039040565,0.0007048444,0.023426663,0.9108158,0.00058971933,0.0005909798,0.04175895],"study_design_scores_gemma":[0.00010487547,0.007581919,0.04555631,0.000034632085,0.00010172531,0.0003211804,0.0006101409,0.07741669,0.86566323,0.000626866,0.0018767487,0.0001057069],"about_ca_topic_score_codex":0.0018233218,"about_ca_topic_score_gemma":0.0035448591,"teacher_disagreement_score":0.001837772,"about_ca_system_score_codex":0.00043880602,"about_ca_system_score_gemma":0.00030630818,"threshold_uncertainty_score":0.006147921},"labels":[],"label_agreement":null},{"id":"W2899895176","doi":"10.3390/s18113780","title":"A Video Based Fire Smoke Detection Using Robust AdaBoost","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; AdaBoost; Smoke; Computer vision; Computer science; Histogram; Fire detection; Pattern recognition (psychology); Classifier (UML); Histogram of oriented gradients; Luminance; Engineering; Image (mathematics)","score_opus":0.024556624884898265,"score_gpt":0.2167378825340213,"score_spread":0.19218125764912303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899895176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103747904,0.00082878134,0.88954926,0.00014277558,0.00029693404,0.00017338734,0.00009507133,0.0027289588,0.0024367755],"genre_scores_gemma":[0.68854505,0.0003883612,0.3040916,0.0002342302,0.000106014304,0.00017286293,0.00040416964,0.00011470414,0.0059430264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994351,0.00006118023,0.0000221443,0.00016119966,0.00021492766,0.00010535177],"domain_scores_gemma":[0.999676,0.000057063437,0.000035398123,0.000021479142,0.00017961058,0.000030491994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080112723,0.00096785737,0.0014299558,0.0012929881,0.00047224163,0.00064328173,0.0016356332,0.0011174264,0.0011686705],"category_scores_gemma":[0.0007442757,0.00042976794,0.0010214521,0.0006566397,0.0002856222,0.0006662056,0.00049607374,0.0009249517,0.0007208934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008307202,0.0009488195,0.0038746234,0.00020777376,0.00023453314,0.00018287264,0.00004967025,0.11817944,0.08637652,0.0009678996,0.0027667142,0.7853803],"study_design_scores_gemma":[0.00001699577,0.0001417531,0.0012320868,0.000008611649,0.000024335866,0.00006136443,0.000011071356,0.9824338,0.015259594,0.00022775399,0.00056820194,0.000014505192],"about_ca_topic_score_codex":0.005568604,"about_ca_topic_score_gemma":0.005244893,"teacher_disagreement_score":0.005568604,"about_ca_system_score_codex":0.0005680702,"about_ca_system_score_gemma":0.0008166887,"threshold_uncertainty_score":0.011072338},"labels":[],"label_agreement":null},{"id":"W2901133594","doi":"10.1139/juvs-2018-0022","title":"Wildfire early warning system based on wireless sensors and unmanned aerial vehicle","year":2018,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Technology Sydney","keywords":"Warning system; Environmental science; Bluetooth; Early warning system; Bluetooth Low Energy; Computer science; Remote sensing; Meteorology; Wireless; Geography; Telecommunications","score_opus":0.007093146641072555,"score_gpt":0.19574700842917198,"score_spread":0.18865386178809943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901133594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41773513,0.0024776575,0.5457097,0.0006050836,0.0005972581,0.0004241945,0.00040517515,0.008624106,0.023421679],"genre_scores_gemma":[0.96531713,0.00044995945,0.026630446,0.00022897072,0.000060638868,0.00013142075,0.0002777978,0.000020686304,0.0068828994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997491,0.000032209427,0.000016784346,0.00006593496,0.00010189054,0.000034008124],"domain_scores_gemma":[0.9998883,0.000016459784,0.000018709545,0.000014394262,0.00004711193,0.00001504328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017699912,0.00041115118,0.00038600306,0.00047607502,0.00029498947,0.0003036029,0.00054062676,0.00039354857,0.0013503953],"category_scores_gemma":[0.0002264661,0.00018367457,0.00019755821,0.00019298156,0.00010186687,0.0007614372,0.00044537356,0.0002694283,0.00033610885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011780452,0.0006421208,0.019414145,0.0005585363,0.00019182282,0.0020318741,0.0005702987,0.028559938,0.31218582,0.0060369694,0.019186877,0.6094435],"study_design_scores_gemma":[0.00030191411,0.0028119907,0.030633215,0.000112978676,0.0002984636,0.002884999,0.00047467192,0.76261055,0.14850955,0.0036142468,0.04757832,0.00016915186],"about_ca_topic_score_codex":0.00089824613,"about_ca_topic_score_gemma":0.0012589181,"teacher_disagreement_score":0.0013503953,"about_ca_system_score_codex":0.0001414827,"about_ca_system_score_gemma":0.00024037067,"threshold_uncertainty_score":0.0045175552},"labels":[],"label_agreement":null},{"id":"W2901961791","doi":"10.1109/icmlc.2018.8527021","title":"Investigating The Influential Factors On Firefighter Injuries Using Statistical Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"National Foundation for Infectious Diseases","keywords":"Computer science; Big data; Principal component analysis; Artificial neural network; Component (thermodynamics); Decision support system; Principal (computer security); Statistical analysis; Artificial intelligence; Machine learning; Data science; Computer security; Data mining","score_opus":0.019504703192490244,"score_gpt":0.24493143436240528,"score_spread":0.22542673116991502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901961791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92169327,0.0007127609,0.072688095,0.0009970256,0.00008187769,0.00011707005,0.000678833,0.00027650147,0.00275468],"genre_scores_gemma":[0.99132115,0.0002579969,0.0075409934,0.000041629413,0.000040464256,0.000020690943,0.0003899698,0.000012481323,0.00037476319],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989379,0.00040209346,0.00006431268,0.00018083854,0.00025923646,0.00015564122],"domain_scores_gemma":[0.9928468,0.004625771,0.0010272857,0.00035964543,0.0008849702,0.00025548832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022728995,0.000848717,0.00042284696,0.0033468157,0.00060259673,0.0011881788,0.00051975745,0.00050515286,0.0011164444],"category_scores_gemma":[0.010470075,0.00024623238,0.0010201256,0.001799729,0.00051037734,0.00089612656,0.00057078636,0.00097442954,0.00026148208],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018843495,0.0003406342,0.84819204,0.00008632267,0.00056789507,0.0002399769,0.00026152175,0.050455697,0.0016739233,0.0022523748,0.0016313309,0.094109975],"study_design_scores_gemma":[0.000011530345,0.00012354404,0.4503123,0.000054130385,0.0002713969,0.00011476278,0.00047716513,0.5398131,0.0018620531,0.0056767073,0.001230533,0.00005282081],"about_ca_topic_score_codex":0.024278283,"about_ca_topic_score_gemma":0.033481713,"teacher_disagreement_score":0.024278283,"about_ca_system_score_codex":0.00085630326,"about_ca_system_score_gemma":0.0017830534,"threshold_uncertainty_score":0.04827392},"labels":[],"label_agreement":null},{"id":"W2906161342","doi":"10.1109/tcsvt.2018.2889193","title":"3D Parallel Fully Convolutional Networks for Real-Time Video Wildfire Smoke Detection","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Key Laboratory of System Control and Information Processing; Fundamental Research Fund of Shandong University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Smoke; Computer science; Convolutional neural network; Fire detection; Artificial intelligence; Segmentation; Image segmentation; Object detection; Convolution (computer science); Deep learning; Computer vision; Pattern recognition (psychology); Remote sensing; Artificial neural network; Geography; Meteorology; Engineering","score_opus":0.01417253048745199,"score_gpt":0.22148638672498794,"score_spread":0.20731385623753595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906161342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08653453,0.0011533964,0.9023553,0.00026983445,0.00009697249,0.000078770965,0.00042408126,0.0059010154,0.0031861651],"genre_scores_gemma":[0.75959235,0.00076773687,0.23182583,0.00020702966,0.00005403649,0.0001239801,0.0011184418,0.00016265154,0.0061480426],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982184,0.000016330156,0.00000866135,0.000049161412,0.000065727174,0.00003829867],"domain_scores_gemma":[0.9998115,0.000051290313,0.000027198987,0.00002894765,0.00006637415,0.000014732008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033676607,0.00076550123,0.00038447234,0.00061060017,0.00024731233,0.00038796032,0.0010696623,0.00066829304,0.0019480018],"category_scores_gemma":[0.00073265383,0.00048877945,0.0005286016,0.00042524017,0.00022548791,0.00065627776,0.00045816984,0.0006991772,0.0005486795],"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":[0.00044039893,0.00021048382,0.0022039781,0.0001050847,0.00011096299,0.00018094743,0.0000563166,0.41089794,0.05719788,0.002238759,0.0047810185,0.52157617],"study_design_scores_gemma":[0.0000029946361,0.000015496393,0.00030878466,0.0000023078978,0.000007348298,0.000018130548,0.0000023644673,0.99448425,0.0044280724,0.0003537903,0.00037236628,0.0000041167496],"about_ca_topic_score_codex":0.021100027,"about_ca_topic_score_gemma":0.029129112,"teacher_disagreement_score":0.021100027,"about_ca_system_score_codex":0.00085368194,"about_ca_system_score_gemma":0.0008722795,"threshold_uncertainty_score":0.0419544},"labels":[],"label_agreement":null},{"id":"W2909273349","doi":"10.1109/epec.2018.8598382","title":"Can Linear Heat Sensors be a Good and Practical Replacement of Traditional Protective Fuses?","year":2018,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Overcurrent; Recloser; Electric power system; Power-system protection; Reliability engineering; Computer science; Protective relay; Power (physics); Electrical engineering; Engineering; Circuit breaker; Voltage","score_opus":0.0325027603330959,"score_gpt":0.2498399350021567,"score_spread":0.2173371746690608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909273349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19714074,0.20177001,0.45621786,0.048770774,0.010821195,0.00035833556,0.00062717893,0.0039251875,0.08036877],"genre_scores_gemma":[0.798238,0.032253224,0.13613147,0.0028966367,0.0012900832,0.000098859266,0.00024634867,0.00018028979,0.028665097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999323,0.00014801972,0.000018655026,0.00013880125,0.00030397988,0.000067501205],"domain_scores_gemma":[0.9991628,0.0003011446,0.00014406697,0.00014485486,0.00020049633,0.000046646168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010905926,0.0008491623,0.00061827514,0.0005707121,0.0002620556,0.001118043,0.0014115471,0.0016543957,0.0034763473],"category_scores_gemma":[0.0027082209,0.000289781,0.0004296039,0.0003537551,0.001374686,0.003929421,0.00042940388,0.0009960145,0.0018238644],"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":[0.0021895475,0.00046297512,0.008311289,0.004027098,0.00024800355,0.0009710963,0.000571244,0.005843322,0.2995697,0.09133935,0.021661218,0.56480515],"study_design_scores_gemma":[0.00021107291,0.00549219,0.007382984,0.0008890944,0.00036957185,0.004814422,0.0011488543,0.016274555,0.40921304,0.039002277,0.5149,0.0003018867],"about_ca_topic_score_codex":0.00033864935,"about_ca_topic_score_gemma":0.0005102397,"teacher_disagreement_score":0.0034763473,"about_ca_system_score_codex":0.00042084514,"about_ca_system_score_gemma":0.00029093315,"threshold_uncertainty_score":0.011629581},"labels":[],"label_agreement":null},{"id":"W2921949718","doi":"10.1115/1.4043160","title":"Analytical Solution of Water Vapor Condensation in Flow Channel of Battery Pack","year":2019,"lang":"en","type":"article","venue":"Journal of Heat Transfer","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"General Motors (Canada)","funders":"","keywords":"Laminar flow; Condensation; Evaporation; Mechanics; Aspect ratio (aeronautics); Flow (mathematics); Channel (broadcasting); Thermodynamics; Hydraulic diameter; Volumetric flow rate; Materials science; Reynolds number; Physics; Engineering; Turbulence; Composite material; Electrical engineering","score_opus":0.010470585562049496,"score_gpt":0.19708100079554983,"score_spread":0.18661041523350033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921949718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32487667,0.0021139146,0.62023395,0.00044237703,0.00017866182,0.0003454998,0.0011824801,0.0008658605,0.0497606],"genre_scores_gemma":[0.9560747,0.0011469673,0.028102148,0.000071700764,0.000040279367,0.00019194452,0.00025112956,0.00009547861,0.0140257375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998247,0.000017299957,0.0000069589996,0.000033997792,0.000066619956,0.000050493054],"domain_scores_gemma":[0.999765,0.00009407061,0.000025218273,0.000010831422,0.0000962456,0.000008543269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029850382,0.00047886543,0.000516576,0.0005491567,0.00041908122,0.00048408157,0.000590965,0.0007386615,0.0015839655],"category_scores_gemma":[0.00084170786,0.00026633564,0.0005819275,0.00041686968,0.0005824807,0.0007537241,0.000420803,0.00042145475,0.000282026],"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":[0.000054726344,0.00004779594,0.0019455083,0.00032931266,0.000020036581,0.0005412888,0.00032059662,0.8778521,0.06695612,0.03747381,0.0012033948,0.0132552255],"study_design_scores_gemma":[0.000012185095,0.000017778579,0.0003262809,0.000010559248,0.000004039411,0.000053349602,0.000041548952,0.98853546,0.008287156,0.001910205,0.0007903234,0.0000111813515],"about_ca_topic_score_codex":0.007736893,"about_ca_topic_score_gemma":0.0051320996,"teacher_disagreement_score":0.007736893,"about_ca_system_score_codex":0.0009553054,"about_ca_system_score_gemma":0.0014481202,"threshold_uncertainty_score":0.01538372},"labels":[],"label_agreement":null},{"id":"W2940937694","doi":"10.1504/ijem.2019.099374","title":"Canada's 2016 Fort McMurray wildfire evacuation: experiences of the Muslim community","year":2019,"lang":"en","type":"article","venue":"International Journal of Emergency Management","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Emergency management; Geography; Poison control; Medical emergency; Political science; Medicine","score_opus":0.009715073693580847,"score_gpt":0.22569504221359074,"score_spread":0.2159799685200099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940937694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98301226,0.0004622722,0.00018834481,0.0042684036,0.00010721886,0.00003085907,0.000060245136,0.00000976608,0.011860681],"genre_scores_gemma":[0.9953479,0.0005276068,0.00015058211,0.00080702826,0.000015398371,0.000011972289,0.000026654303,0.0000083270315,0.0031044919],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9985083,0.0004629987,0.000021110644,0.00007822415,0.0002818965,0.0006475246],"domain_scores_gemma":[0.9975069,0.0004399112,0.00017085153,0.00006538877,0.000451393,0.0013655205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001545537,0.0004067941,0.00038292768,0.00060853834,0.02828667,0.004018993,0.0014320585,0.0015031699,0.0029706624],"category_scores_gemma":[0.0030578747,0.00024509826,0.00022930941,0.0010928274,0.009051218,0.0013413676,0.0045070513,0.0030292156,0.000212223],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009806264,0.00007122509,0.014122206,0.00009324278,0.000011111459,0.0024518128,0.9614523,0.00017262028,0.00070360093,0.0022003052,0.0063463957,0.012277228],"study_design_scores_gemma":[0.0000016745319,0.0000171852,0.003185211,0.0000417791,0.0000023892044,0.00012762855,0.9880315,0.00004194222,0.00006289911,0.000067400695,0.0084089935,0.000011332604],"about_ca_topic_score_codex":0.8913933,"about_ca_topic_score_gemma":0.9736126,"teacher_disagreement_score":0.108606696,"about_ca_system_score_codex":0.022917042,"about_ca_system_score_gemma":0.038540654,"threshold_uncertainty_score":0.21849257},"labels":[],"label_agreement":null},{"id":"W2961252543","doi":"10.1109/isass.2019.8757707","title":"A Survey on Forest Fire Monitoring Using Unmanned Aerial Vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Damages; Flexibility (engineering); Fire detection; Computer science; Remote sensing; Environmental science; Firefighting; Environmental resource management; Engineering; Architectural engineering; Geography; Cartography","score_opus":0.02622813269794623,"score_gpt":0.232985215962171,"score_spread":0.20675708326422476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961252543","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58039796,0.26995975,0.05611426,0.0036510543,0.00053364574,0.00024709015,0.0020124577,0.00066636706,0.086417384],"genre_scores_gemma":[0.81101555,0.16362281,0.0126334075,0.00069142587,0.00019790007,0.000048964834,0.0021147807,0.00004156182,0.009633669],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994099,0.00010963312,0.00006227553,0.00009890406,0.00025905392,0.000060267605],"domain_scores_gemma":[0.99876094,0.00030866088,0.00023129363,0.00007808874,0.0005432046,0.00007783276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006098845,0.00031890336,0.00017910257,0.0018364227,0.00024155524,0.00056942843,0.0002638431,0.0002346872,0.000960261],"category_scores_gemma":[0.0010365439,0.00011009261,0.00025769026,0.0021067306,0.000142418,0.0010424908,0.00023583678,0.0001957415,0.00045529896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009042458,0.00007958395,0.046372466,0.0011497535,0.000053102085,0.00016806489,0.00028003938,0.002134861,0.008500407,0.0012262088,0.0061629433,0.93378216],"study_design_scores_gemma":[0.000014261419,0.0011148223,0.38456827,0.0021508262,0.00028231376,0.0019407602,0.004279809,0.012396198,0.021566218,0.0016059834,0.569999,0.00008164919],"about_ca_topic_score_codex":0.0031740358,"about_ca_topic_score_gemma":0.0032912488,"teacher_disagreement_score":0.0031740358,"about_ca_system_score_codex":0.00018409114,"about_ca_system_score_gemma":0.0003781601,"threshold_uncertainty_score":0.0063111186},"labels":[],"label_agreement":null},{"id":"W2964097815","doi":"","title":"Evaluation of Automatic Incident Detection Systems Using the Automatic Incident Detection Comparison and Analysis Tool","year":2004,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Computer science; Data mining","score_opus":0.01826490100678451,"score_gpt":0.232952415265178,"score_spread":0.21468751425839347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964097815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8397383,0.00024601095,0.14887284,0.00015200416,0.0001339153,0.0011206304,0.0006437544,0.0037521194,0.0053402595],"genre_scores_gemma":[0.8338712,0.000109441586,0.16292045,0.00005130505,0.000022905793,0.0004570921,0.0011493079,0.0000776462,0.0013408227],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9953542,0.0016784495,0.00035495943,0.0005347575,0.0019040031,0.00017366353],"domain_scores_gemma":[0.9897502,0.0055864695,0.0006165251,0.0007902328,0.0030013397,0.00025522665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058366787,0.0007171165,0.0006356515,0.0016159327,0.00043149496,0.001279876,0.0014458171,0.0005538746,0.0016774918],"category_scores_gemma":[0.012818685,0.00026716397,0.00033225343,0.0010547703,0.00043721125,0.0013991864,0.00072070374,0.00060517207,0.0003892827],"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":[0.0043626204,0.003752769,0.038994685,0.00068621675,0.00037874538,0.00022546996,0.00082137104,0.33684504,0.05881631,0.009171729,0.0071953833,0.5387496],"study_design_scores_gemma":[0.0005113835,0.004815175,0.014711672,0.00004578601,0.00009685275,0.00021821319,0.00035947535,0.9033993,0.06849615,0.0009129055,0.0063407165,0.000092406444],"about_ca_topic_score_codex":0.0038228778,"about_ca_topic_score_gemma":0.0027898068,"teacher_disagreement_score":0.0058366787,"about_ca_system_score_codex":0.0017759681,"about_ca_system_score_gemma":0.0013198779,"threshold_uncertainty_score":0.030867696},"labels":[],"label_agreement":null},{"id":"W2967154765","doi":"10.1007/s11042-019-08047-5","title":"A motion and lightness saliency approach for forest smoke segmentation and detection","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Background subtraction; Smoke; Pattern recognition (psychology); Motion detection; Segmentation; Feature (linguistics); Motion (physics); Pixel","score_opus":0.013540297704522098,"score_gpt":0.21721106017236347,"score_spread":0.20367076246784138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967154765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049224004,0.00080106535,0.9458913,0.00013778027,0.00010909923,0.00017615609,0.00015367508,0.0010675378,0.0024393017],"genre_scores_gemma":[0.49917245,0.0007385663,0.49363232,0.00019621318,0.00025651843,0.000118916396,0.0005353025,0.00024911057,0.005100522],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980706,0.000016384342,0.000007723517,0.000050078404,0.00007726601,0.000041464384],"domain_scores_gemma":[0.99976975,0.000043322172,0.000022054961,0.000022235065,0.00010876692,0.000033795637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031686862,0.00073787355,0.00096535526,0.0023246142,0.00046338365,0.00066655653,0.0009805262,0.00062481407,0.0022923278],"category_scores_gemma":[0.00059607765,0.00038601816,0.0010014309,0.0009821628,0.00029364816,0.0006296356,0.0008572469,0.00047720133,0.00048871146],"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":[0.00053598196,0.0003470893,0.001791382,0.00022725767,0.00016776678,0.00024523216,0.00014223171,0.030671416,0.268948,0.004270188,0.002806971,0.6898465],"study_design_scores_gemma":[0.000035798683,0.00024790913,0.0057340143,0.000020858002,0.00013131255,0.00030310568,0.000056846922,0.9440095,0.041840386,0.0033015406,0.004284356,0.000034386474],"about_ca_topic_score_codex":0.00792638,"about_ca_topic_score_gemma":0.015540839,"teacher_disagreement_score":0.00792638,"about_ca_system_score_codex":0.0004906043,"about_ca_system_score_gemma":0.000816393,"threshold_uncertainty_score":0.015760481},"labels":[],"label_agreement":null},{"id":"W2968072966","doi":"10.18280/ria.330107","title":"Design of a Fire Detection System Based on Four-rotor Aircraft","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rotor (electric); Aerospace engineering; Aeronautics; Helicopter rotor; Computer science; Automotive engineering; Engineering; Mechanical engineering","score_opus":0.026470938548797823,"score_gpt":0.21206721765192862,"score_spread":0.1855962791031308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968072966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07278899,0.0006018485,0.9163902,0.00021796447,0.00035750208,0.00036536172,0.00007420741,0.0035043028,0.0056997226],"genre_scores_gemma":[0.8064706,0.00034349682,0.18661886,0.00017779146,0.000099338606,0.00036232383,0.00013629212,0.00004569983,0.0057456526],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952877,0.00006147718,0.000039527687,0.00015907055,0.00015141648,0.000059696304],"domain_scores_gemma":[0.9996927,0.000033878667,0.000045917815,0.0000395811,0.00014846248,0.000039536168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036571044,0.0007375424,0.001000217,0.0006432373,0.0007525632,0.0005631724,0.0014803889,0.00084955036,0.0021949408],"category_scores_gemma":[0.0003283713,0.0004993929,0.0005681181,0.00022175928,0.0003038099,0.0006645119,0.0006235369,0.00047118132,0.0010016856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009472386,0.00038881824,0.0076344446,0.0010096071,0.00030791073,0.0018410587,0.0006659027,0.07482661,0.5934091,0.0074820095,0.004022433,0.3074649],"study_design_scores_gemma":[0.00030390715,0.0022863671,0.006803705,0.00009848014,0.00034356918,0.0017373496,0.00015487324,0.8128632,0.14408799,0.0015848359,0.029559711,0.00017609708],"about_ca_topic_score_codex":0.0019008341,"about_ca_topic_score_gemma":0.0012652454,"teacher_disagreement_score":0.0021949408,"about_ca_system_score_codex":0.0003713881,"about_ca_system_score_gemma":0.000529093,"threshold_uncertainty_score":0.0073428154},"labels":[],"label_agreement":null},{"id":"W2978477598","doi":"10.1109/iciai.2019.8850811","title":"Wildfire Flame and Smoke Detection Using Static Image Features and Artificial Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Fire detection; Smoke; Computer science; Artificial neural network; Signature (topology); Block (permutation group theory); Artificial intelligence; Damages; Remote sensing; Environmental science; Computer vision; Engineering; Geology; Mathematics","score_opus":0.008868607028045022,"score_gpt":0.20481865795546467,"score_spread":0.19595005092741966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978477598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35916758,0.0006813113,0.6351711,0.00011822288,0.00010728149,0.000085144544,0.00011924526,0.0013352372,0.003214839],"genre_scores_gemma":[0.860732,0.00032561747,0.1351544,0.000042141346,0.000042914107,0.00004242952,0.00018595124,0.000028577691,0.0034460216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984765,0.0000122833935,0.000011007655,0.00004542289,0.000058446753,0.000025252923],"domain_scores_gemma":[0.99983025,0.00004130778,0.00003385975,0.000010863257,0.00007034595,0.000013386523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027812214,0.00048707717,0.0004281707,0.0010571259,0.000204434,0.00040333736,0.00048573705,0.00056458404,0.0006288727],"category_scores_gemma":[0.0005902368,0.00027379266,0.00047767622,0.0004915822,0.00020160244,0.00063945743,0.00028142353,0.00032349816,0.00017005911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005202601,0.00038363345,0.009930076,0.00011996289,0.00012759538,0.00031627872,0.000068092864,0.16199765,0.08134633,0.0012711491,0.0011816777,0.74273723],"study_design_scores_gemma":[0.000004605564,0.00006388095,0.0031455015,0.0000048358875,0.000017673327,0.00006424109,0.000011941299,0.98787177,0.008319689,0.00024928772,0.00023745766,0.00000909809],"about_ca_topic_score_codex":0.005202229,"about_ca_topic_score_gemma":0.0053174384,"teacher_disagreement_score":0.005202229,"about_ca_system_score_codex":0.0003448264,"about_ca_system_score_gemma":0.0003274029,"threshold_uncertainty_score":0.010343909},"labels":[],"label_agreement":null},{"id":"W2978858971","doi":"10.1109/iciai.2019.8850815","title":"A Deep Learning Based Forest Fire Detection Approach Using UAV and YOLOv3","year":2019,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":238,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Fire detection; Artificial intelligence; Convolutional neural network; Deep learning; Frame rate; RGB color model; Frame (networking); Convolution (computer science); Artificial neural network; Remote sensing; Computer vision; Engineering","score_opus":0.007592004548448513,"score_gpt":0.17469117300909773,"score_spread":0.1670991684606492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978858971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14638755,0.0005271002,0.84649414,0.00014207073,0.00009759285,0.000081415696,0.00019268143,0.0026217136,0.0034557807],"genre_scores_gemma":[0.66819584,0.00042029913,0.32403138,0.00018771744,0.000038281043,0.000073297546,0.00070816535,0.00011685413,0.006228136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998877,0.000007659413,0.000005128309,0.000034869146,0.00003417287,0.000030406563],"domain_scores_gemma":[0.9999342,0.000010577793,0.000010888367,0.0000077241675,0.000027928261,0.000008762208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017048806,0.0005911661,0.00047468458,0.00062625145,0.00025472528,0.00038028363,0.00068467134,0.00044129623,0.0012802082],"category_scores_gemma":[0.0002487449,0.00026623535,0.00059268286,0.00038045624,0.000170069,0.0004470194,0.00040155603,0.000424786,0.00033892208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043526673,0.00019956063,0.007055987,0.0001402932,0.0001435365,0.0003110634,0.00008483923,0.13190524,0.09435801,0.0024625524,0.0040599075,0.7588438],"study_design_scores_gemma":[0.000010632421,0.000067838235,0.0020644458,0.000008328104,0.00002476416,0.000111535526,0.000018190543,0.9811638,0.014947881,0.0004304318,0.001141741,0.000010429408],"about_ca_topic_score_codex":0.009559896,"about_ca_topic_score_gemma":0.013190632,"teacher_disagreement_score":0.009559896,"about_ca_system_score_codex":0.0004479179,"about_ca_system_score_gemma":0.0005847435,"threshold_uncertainty_score":0.019008517},"labels":[],"label_agreement":null},{"id":"W2982350908","doi":"","title":"A Low Cost Automated Accident Notification System: Design, Simulation and Experimental Results","year":2018,"lang":"en","type":"article","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Computer science; Accident (philosophy); Computer security","score_opus":0.030420750799091248,"score_gpt":0.275301491415635,"score_spread":0.24488074061654375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982350908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8607913,0.0006896585,0.11763889,0.0003880188,0.000338085,0.0012121096,0.0010411526,0.005909948,0.011990745],"genre_scores_gemma":[0.96991783,0.00022331509,0.024302902,0.00004359134,0.000016277403,0.00047655497,0.00041438974,0.00006784891,0.004537326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995363,0.00012651454,0.000029828214,0.00006636196,0.00016539678,0.000075754644],"domain_scores_gemma":[0.9989182,0.00035608752,0.00009445144,0.00008593622,0.00044896192,0.0000964719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083600864,0.00093993987,0.00077231706,0.0007510003,0.0004194483,0.0005784006,0.0013125563,0.0008666984,0.0070954883],"category_scores_gemma":[0.0010111699,0.00026951308,0.0005637017,0.00035696264,0.00034502006,0.00053379155,0.00035439007,0.00039457262,0.0009464109],"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":[0.00276587,0.0034226389,0.019402422,0.00181096,0.00024929654,0.00096277,0.0005925127,0.76084185,0.05810636,0.002135962,0.008616864,0.14109254],"study_design_scores_gemma":[0.00024777793,0.0029476418,0.005300311,0.0000310376,0.00010463151,0.0001328281,0.00017827468,0.9678542,0.02005905,0.00024700855,0.0028463418,0.000050870098],"about_ca_topic_score_codex":0.0066408957,"about_ca_topic_score_gemma":0.0030736348,"teacher_disagreement_score":0.0070954883,"about_ca_system_score_codex":0.0008308689,"about_ca_system_score_gemma":0.0006739214,"threshold_uncertainty_score":0.023736775},"labels":[],"label_agreement":null},{"id":"W2991642850","doi":"10.1115/detc2019-97895","title":"Fire Detection Using Both Infrared and Visual Images With Application to Unmanned Aerial Vehicle Forest Fire Surveillance","year":2019,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Fire detection; Computer science; Remote sensing; Fire protection; Environmental science; Real-time computing; Engineering; Architectural engineering; Geography","score_opus":0.0037536043013727035,"score_gpt":0.1997998323711964,"score_spread":0.1960462280698237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991642850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8332324,0.00061205094,0.16297507,0.000119282406,0.000069526,0.00006391337,0.0000929068,0.00048086033,0.0023540105],"genre_scores_gemma":[0.94130963,0.00015495771,0.057786528,0.00002674751,0.000015630829,0.000011019026,0.000052287534,0.0000069610082,0.00063626067],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998697,0.000023546441,0.00000577468,0.000027170963,0.000056484118,0.00001730501],"domain_scores_gemma":[0.99985504,0.00003348532,0.000024580715,0.000015017394,0.000057396523,0.000014440448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021758529,0.00024795925,0.0001706375,0.0007865622,0.000113178576,0.00021034929,0.00018329822,0.00028445935,0.00042753952],"category_scores_gemma":[0.00030772225,0.00009982537,0.00019718953,0.0003190226,0.000108658445,0.00019528551,0.00012437766,0.00014766658,0.000077374054],"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":[0.0007481212,0.0003512034,0.013655935,0.0002224949,0.000098727935,0.0006478,0.00019434112,0.035023782,0.5070306,0.00080359564,0.0010501714,0.44017333],"study_design_scores_gemma":[0.000034285087,0.00070553785,0.040006083,0.00002298896,0.00007108548,0.0007412341,0.00022528421,0.79116005,0.16455936,0.00042658817,0.0020115876,0.00003599137],"about_ca_topic_score_codex":0.0016896512,"about_ca_topic_score_gemma":0.0018923673,"teacher_disagreement_score":0.0016896512,"about_ca_system_score_codex":0.0001155838,"about_ca_system_score_gemma":0.000116452684,"threshold_uncertainty_score":0.0033596754},"labels":[],"label_agreement":null},{"id":"W2999706450","doi":"","title":"The Non Random Nature of Fire Inspection Compliance: A Platform for Predicting Fire Risk","year":2013,"lang":"en","type":"article","venue":"UWA Profiles and Research Repository (University of Western Australia)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Forensic engineering; Risk analysis (engineering); Environmental science; Engineering; Business","score_opus":0.03820008943053147,"score_gpt":0.27547312806770013,"score_spread":0.23727303863716867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999706450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8202604,0.00013136592,0.16967264,0.00057070836,0.000047577083,0.00018382957,0.003561256,0.0012802965,0.0042918455],"genre_scores_gemma":[0.97741413,0.000041522897,0.020050863,0.000028441655,0.000021070331,0.00008836264,0.0013946244,0.00004476929,0.00091621117],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978179,0.00079160446,0.00014664969,0.00063400867,0.000488235,0.00012157478],"domain_scores_gemma":[0.9644399,0.023756437,0.0053847386,0.003585227,0.0022232307,0.0006105203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004986692,0.0005361624,0.0006339291,0.0034921286,0.00049624924,0.0012373657,0.0013159523,0.0012055007,0.0020032874],"category_scores_gemma":[0.035065863,0.00046380277,0.0005973303,0.0023425405,0.0005100505,0.0023819278,0.0011000899,0.0011853589,0.0006957687],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003812905,0.00068312645,0.68854386,0.00009686558,0.00027818582,0.00023668905,0.00040041338,0.20975935,0.0024470617,0.008543293,0.0035720293,0.085057914],"study_design_scores_gemma":[0.000007500175,0.00010055471,0.07077837,0.0000110769315,0.000022156768,0.000049364775,0.00006874692,0.9214441,0.00060752284,0.006523662,0.00036684732,0.000020045321],"about_ca_topic_score_codex":0.011377285,"about_ca_topic_score_gemma":0.014035502,"teacher_disagreement_score":0.011377285,"about_ca_system_score_codex":0.0007333132,"about_ca_system_score_gemma":0.0009424049,"threshold_uncertainty_score":0.026372433},"labels":[],"label_agreement":null},{"id":"W3001404686","doi":"10.4271/2020-01-0138","title":"A Forward Collision Warning System Using Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Collision; Computer science; Warning system; Collision avoidance; Artificial intelligence; Computer security; Telecommunications","score_opus":0.013531966780554329,"score_gpt":0.22106737899590123,"score_spread":0.2075354122153469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001404686","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08377951,0.00045723518,0.9010471,0.0005399408,0.00022344575,0.00015591993,0.00011185759,0.006811121,0.0068738796],"genre_scores_gemma":[0.9346975,0.00009583287,0.060657095,0.00018951816,0.000021216172,0.0001519447,0.00011456707,0.00006553859,0.004006809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997954,0.00002809285,0.0000135552955,0.000062328916,0.000054359527,0.000046244586],"domain_scores_gemma":[0.9996712,0.00009568758,0.000042745756,0.000027101525,0.00011319927,0.000050066923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004937318,0.0007826351,0.0006661663,0.0002636834,0.0004181644,0.0005451371,0.001317205,0.00080619694,0.002777505],"category_scores_gemma":[0.00085778255,0.00036710716,0.00047156142,0.00013151698,0.00036604007,0.0005634908,0.0010073358,0.001174543,0.0005418476],"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":[0.0003942727,0.00036683815,0.0021731209,0.00013567494,0.00008833691,0.00041745926,0.0001511848,0.79959035,0.015734546,0.0032833912,0.003969804,0.17369498],"study_design_scores_gemma":[0.000016068541,0.00004806252,0.000099906814,0.0000034360085,0.000006180099,0.000013379639,0.0000035595785,0.9978581,0.0010725544,0.00056990894,0.00030354064,0.000005304475],"about_ca_topic_score_codex":0.008179112,"about_ca_topic_score_gemma":0.0058386293,"teacher_disagreement_score":0.008179112,"about_ca_system_score_codex":0.0007061229,"about_ca_system_score_gemma":0.0009125176,"threshold_uncertainty_score":0.016263008},"labels":[],"label_agreement":null},{"id":"W3005134855","doi":"10.35143/elementer.v5i2.3122","title":"Sistem Monitoring Nilai FFMC untuk Menentukan Potensi Penyulutan Api Menjadi Kebakaran","year":2019,"lang":"id","type":"article","venue":"Jurnal Elektro dan Mesin Terapan","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Physics; Humanities; Forestry; Geography; Art","score_opus":0.007539490055977154,"score_gpt":0.2054758480582143,"score_spread":0.19793635800223716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005134855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8181457,0.002135604,0.07591872,0.0006839406,0.0002625186,0.00080167915,0.018314235,0.010135192,0.07360242],"genre_scores_gemma":[0.93743783,0.00067691214,0.02517041,0.0001907193,0.000038597973,0.00022833055,0.008623217,0.00021769696,0.027416382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937916,0.00002545959,0.000016923035,0.000108856926,0.00038249965,0.00008703381],"domain_scores_gemma":[0.99941623,0.000052783435,0.00005191428,0.00003056465,0.0004004242,0.000048114573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005382092,0.0006019177,0.0005216229,0.0010346264,0.0008555333,0.0013576432,0.0006247775,0.0004885859,0.005113545],"category_scores_gemma":[0.00069528475,0.00020520481,0.00025885896,0.0009528647,0.00019545411,0.00055804016,0.0004484203,0.00053872296,0.001740417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017298366,0.00039777346,0.21888997,0.00073123333,0.00017398129,0.0006578659,0.00095599145,0.0126118325,0.27005938,0.0015444959,0.034495935,0.45775178],"study_design_scores_gemma":[0.000116177755,0.0005930436,0.53519684,0.0001480294,0.0003265437,0.00046430837,0.0015436967,0.13527243,0.22819811,0.0010908182,0.096836,0.00021393705],"about_ca_topic_score_codex":0.104973376,"about_ca_topic_score_gemma":0.15677077,"teacher_disagreement_score":0.104973376,"about_ca_system_score_codex":0.0014087302,"about_ca_system_score_gemma":0.0015732829,"threshold_uncertainty_score":0.20872474},"labels":[],"label_agreement":null},{"id":"W3031763273","doi":"10.4018/ijdst.2020070101","title":"Using Wireless Multimedia Sensor Networks to Enhance Early Forest Fire Detection","year":2020,"lang":"en","type":"article","venue":"International Journal of Distributed Systems and Technologies","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Fire detection; Wireless sensor network; Real-time computing; Wireless; Modal; Energy consumption; Field (mathematics); Multimedia; Telecommunications; Computer network; Architectural engineering","score_opus":0.014387568089046725,"score_gpt":0.24098998310153635,"score_spread":0.22660241501248962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031763273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41258043,0.0018659102,0.5767582,0.00023813972,0.00017573638,0.00020739839,0.00018987831,0.0011959308,0.0067883446],"genre_scores_gemma":[0.8799872,0.00066447427,0.116641976,0.000079650235,0.000046402136,0.000060778104,0.00011412732,0.000021289106,0.0023840806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986196,0.000029317825,0.0000066594826,0.00003479092,0.000049788756,0.000017402186],"domain_scores_gemma":[0.99984336,0.000060651357,0.000021142741,0.0000132055275,0.0000524556,0.000009222831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024737246,0.000538733,0.00022822557,0.0005096765,0.00014252518,0.00029001842,0.0004741955,0.0003856088,0.0007676273],"category_scores_gemma":[0.0004068478,0.000113203285,0.00018299036,0.00032943458,0.00009724996,0.00055934425,0.0002645209,0.00019634039,0.00018520703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070456736,0.00035612573,0.007812901,0.00049300474,0.00009849685,0.00043411754,0.00016746813,0.03976345,0.38894153,0.0016195208,0.0010496146,0.55855924],"study_design_scores_gemma":[0.000048656926,0.0014065367,0.015603553,0.00005695335,0.0002469356,0.0008502274,0.0002117406,0.5896447,0.37757704,0.0018911266,0.012409487,0.00005310449],"about_ca_topic_score_codex":0.00076925737,"about_ca_topic_score_gemma":0.0017745873,"teacher_disagreement_score":0.00076925737,"about_ca_system_score_codex":0.00018682744,"about_ca_system_score_gemma":0.00015049076,"threshold_uncertainty_score":0.002567947},"labels":[],"label_agreement":null},{"id":"W3038510778","doi":"10.1139/juvs-2020-0009","title":"Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern","year":2020,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fire detection; Smoke; Computer science; Artificial intelligence; Remote sensing; Range (aeronautics); Environmental science; Computer vision; Pattern recognition (psychology); Geography; Meteorology; Engineering","score_opus":0.02027515789426762,"score_gpt":0.21239849095155916,"score_spread":0.19212333305729154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038510778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5057548,0.00096367195,0.48578084,0.00011730184,0.00009356035,0.00015138004,0.00047247787,0.0025484064,0.004117578],"genre_scores_gemma":[0.8118575,0.0005815597,0.18475714,0.00005922921,0.000025876656,0.000047808448,0.00061269035,0.000055483735,0.0020028185],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99979025,0.00002072017,0.0000078604335,0.000046766178,0.000102953265,0.000031442243],"domain_scores_gemma":[0.99978584,0.00002963003,0.000042568965,0.00002198023,0.00010226733,0.000017716977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023310652,0.00039063997,0.00031850743,0.0016984433,0.00018865458,0.00037663267,0.00036832548,0.00034228872,0.00060662586],"category_scores_gemma":[0.000512677,0.00018211726,0.00044051243,0.00062875106,0.0001726165,0.00045828146,0.00027560344,0.00029329455,0.00040304693],"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":[0.00044991358,0.00023825746,0.017535154,0.000282887,0.00011134798,0.00038718336,0.00013094774,0.01966935,0.22682464,0.0005880306,0.0015071022,0.73227507],"study_design_scores_gemma":[0.00002460018,0.00025014862,0.068077244,0.000056647277,0.00009676657,0.0010596067,0.00018441146,0.7753932,0.15120442,0.0007869449,0.0028102838,0.000055881963],"about_ca_topic_score_codex":0.0039034642,"about_ca_topic_score_gemma":0.0075567905,"teacher_disagreement_score":0.0039034642,"about_ca_system_score_codex":0.00022530633,"about_ca_system_score_gemma":0.00024133193,"threshold_uncertainty_score":0.007761538},"labels":[],"label_agreement":null},{"id":"W3081908113","doi":"10.1007/s42835-020-00529-z","title":"IoT-Based Intelligent Residential Kitchen Fire Prevention System","year":2020,"lang":"en","type":"article","venue":"Journal of Electrical Engineering and Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Stove; Computer science; Computer security; Electricity; Android (operating system); Embedded system; Computer network; Telecommunications; Engineering; Operating system; Electrical engineering","score_opus":0.00623088338927568,"score_gpt":0.18556880218778027,"score_spread":0.17933791879850458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081908113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47163406,0.0019598627,0.43450382,0.0008629542,0.0014138796,0.00054696936,0.0022656433,0.030293686,0.056519073],"genre_scores_gemma":[0.97545415,0.00020382777,0.015237044,0.00032931214,0.000086491746,0.00013259114,0.0007460604,0.00007101555,0.0077394405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975234,0.00002646337,0.000021729827,0.000064661785,0.00008888673,0.000045930105],"domain_scores_gemma":[0.99979824,0.000026140655,0.000027988719,0.000031867086,0.000082546576,0.00003319947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001688811,0.00058254146,0.00077398063,0.0006184339,0.00047563005,0.00046063354,0.0010455343,0.00054217724,0.0044306363],"category_scores_gemma":[0.00023325281,0.00023344479,0.00037658258,0.00040891598,0.00013335183,0.0006541107,0.000697119,0.00032182474,0.0014345674],"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":[0.0043615587,0.0018961619,0.03354327,0.001051114,0.00040589212,0.0027009512,0.00041340097,0.06246969,0.2335767,0.0042984933,0.061982848,0.5932999],"study_design_scores_gemma":[0.00031216227,0.0011840173,0.033469472,0.0001158481,0.00043921883,0.0017165865,0.0003239755,0.8516572,0.07883492,0.0032211994,0.02855213,0.00017330544],"about_ca_topic_score_codex":0.0010943329,"about_ca_topic_score_gemma":0.0015528115,"teacher_disagreement_score":0.0044306363,"about_ca_system_score_codex":0.00026827303,"about_ca_system_score_gemma":0.00032882873,"threshold_uncertainty_score":0.014821887},"labels":[],"label_agreement":null},{"id":"W3101582481","doi":"10.11159/ffhmt20.190","title":"Application of Deep Learning Convolutional Neural Network for Spray Characterization","year":2020,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Convolutional neural network; Computer science; Characterization (materials science); Artificial intelligence; Deep learning; Materials science; Nanotechnology","score_opus":0.014924710619523092,"score_gpt":0.20446608223165147,"score_spread":0.1895413716121284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101582481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20143484,0.0014324526,0.78613263,0.0007516901,0.00017090888,0.000108844324,0.00049870857,0.004223971,0.005245919],"genre_scores_gemma":[0.8743125,0.0004409154,0.12047893,0.0002077079,0.000027555834,0.00003713082,0.00046703464,0.00008225821,0.0039460417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998472,0.00001884794,0.000007647395,0.000039617167,0.00005501621,0.00003169334],"domain_scores_gemma":[0.99973696,0.00008019195,0.000037323534,0.000027904678,0.00010310283,0.000014542608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038875997,0.0006170956,0.0003354734,0.00066039176,0.00017071937,0.000480474,0.0005194613,0.0006815744,0.0011644141],"category_scores_gemma":[0.00094202295,0.00025266447,0.0004227998,0.0004410065,0.00025888288,0.00065888924,0.0003636128,0.00065136503,0.0002971607],"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":[0.00030479903,0.00017018817,0.004726622,0.00016044443,0.00009613459,0.00033252974,0.00008264794,0.52843714,0.12776262,0.002559174,0.0025865047,0.33278123],"study_design_scores_gemma":[0.0000018122541,0.000018870278,0.0006437649,0.0000047006383,0.000005907752,0.000021833986,0.000005689661,0.9868809,0.011450015,0.00051347347,0.00044868438,0.0000043197974],"about_ca_topic_score_codex":0.010753114,"about_ca_topic_score_gemma":0.010295349,"teacher_disagreement_score":0.010753114,"about_ca_system_score_codex":0.0009625312,"about_ca_system_score_gemma":0.0006853062,"threshold_uncertainty_score":0.02138108},"labels":[],"label_agreement":null},{"id":"W3105393511","doi":"10.1007/s11042-021-11766-3","title":"STCNet: spatiotemporal cross network for industrial smoke detection","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Jiangsu Provincial Key Research and Development Program; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Smoke; Artificial intelligence; Motion (physics); Path (computing); Code (set theory); Task (project management); Computer network","score_opus":0.04146220603052695,"score_gpt":0.2531490601909085,"score_spread":0.21168685416038155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105393511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055286054,0.0009184013,0.84633213,0.00028529388,0.0005592219,0.00039257386,0.017507134,0.07076695,0.007952222],"genre_scores_gemma":[0.4497408,0.0008294215,0.48803356,0.00042238575,0.0002236912,0.00066426024,0.03999446,0.0020670826,0.018024486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998067,0.000024001078,0.000009235237,0.000052262287,0.00008060746,0.000027353373],"domain_scores_gemma":[0.9997062,0.00008612549,0.000025735073,0.000067277935,0.00007928805,0.000035401492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004964875,0.0009126881,0.00053310103,0.0014715217,0.00036097268,0.00061057956,0.0009648476,0.00057418906,0.0076086265],"category_scores_gemma":[0.001178718,0.00027871464,0.00037317484,0.0009108815,0.00015077846,0.000994151,0.0011566932,0.0004519941,0.0017692181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022104816,0.0004615258,0.010498545,0.0004720434,0.0003323545,0.0005054183,0.00017591615,0.078574665,0.05502933,0.007791455,0.14151359,0.7024348],"study_design_scores_gemma":[0.000065805616,0.00017550153,0.003887778,0.000027255268,0.000051645606,0.00023035455,0.000055853987,0.94168043,0.025372455,0.0044987327,0.023909684,0.00004450143],"about_ca_topic_score_codex":0.0072540366,"about_ca_topic_score_gemma":0.009448118,"teacher_disagreement_score":0.0076086265,"about_ca_system_score_codex":0.00041474582,"about_ca_system_score_gemma":0.0005405436,"threshold_uncertainty_score":0.025453389},"labels":[],"label_agreement":null},{"id":"W3106723402","doi":"10.1145/3408308.3427978","title":"Can Future Wireless Networks Detect Fires?","year":2020,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless; Computer science; Wireless network; Computer network; Environmental science; Telecommunications","score_opus":0.0063359007299130885,"score_gpt":0.16607695286030472,"score_spread":0.15974105213039164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106723402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22322856,0.0328415,0.46196994,0.17467669,0.010725258,0.0001782315,0.0037104853,0.005506696,0.08716263],"genre_scores_gemma":[0.9329929,0.010725743,0.03689137,0.005177873,0.0011994393,0.00007365343,0.001090561,0.0002360889,0.011612517],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994111,0.00012136324,0.000025558396,0.00014844105,0.00016823462,0.00012531753],"domain_scores_gemma":[0.9962219,0.001706915,0.00048071585,0.00041002093,0.0009396556,0.0002407593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021732904,0.0009181209,0.0005400059,0.00061801,0.00061716535,0.0022505054,0.0013429526,0.001916668,0.0043544243],"category_scores_gemma":[0.014506456,0.00047067498,0.00031784407,0.00065766246,0.0009820758,0.011124326,0.0008309527,0.0030271413,0.0027905565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063332915,0.00019271988,0.05703017,0.0007320446,0.0002076041,0.0006190257,0.0005571096,0.0653603,0.008621818,0.07354281,0.08599102,0.706512],"study_design_scores_gemma":[0.000111132256,0.0005562196,0.035932,0.0007373337,0.00025691817,0.0021714238,0.0034398385,0.30715558,0.021825165,0.38780856,0.23978773,0.00021810828],"about_ca_topic_score_codex":0.0033305616,"about_ca_topic_score_gemma":0.0051168255,"teacher_disagreement_score":0.0043544243,"about_ca_system_score_codex":0.00078638614,"about_ca_system_score_gemma":0.0005545605,"threshold_uncertainty_score":0.014566958},"labels":[],"label_agreement":null},{"id":"W3118926271","doi":"10.22215/etd/2010-08874","title":"Quantitative fire risk analysis case study using CUrisk","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.018250676843206873,"score_gpt":0.29729103571126153,"score_spread":0.27904035886805467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118926271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73569155,0.0005388628,0.17728017,0.00082050194,0.000090389476,0.00076056644,0.0022210355,0.0014446075,0.081152216],"genre_scores_gemma":[0.9107966,0.00026557848,0.068629004,0.000044539673,0.00001542824,0.00015208131,0.00085441256,0.00015216734,0.01909023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880195,0.00037681102,0.00006731992,0.0001576472,0.00046886413,0.00012748665],"domain_scores_gemma":[0.9969908,0.0019353285,0.0001548775,0.00025760892,0.0005135639,0.00014782419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020710938,0.0007126096,0.0005966972,0.0025491852,0.0012382765,0.001571324,0.001127718,0.0012925626,0.009235015],"category_scores_gemma":[0.0030262864,0.00031970916,0.0010522527,0.0016310775,0.0005273676,0.0010005635,0.0008356005,0.0008041671,0.00085683045],"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":[0.0018462982,0.0019529867,0.028905392,0.00076140626,0.00028908573,0.011184527,0.0027158414,0.66828436,0.026534338,0.03939999,0.013356619,0.2047691],"study_design_scores_gemma":[0.00024946628,0.0011423571,0.019424556,0.00015723532,0.00022488556,0.002402793,0.003412103,0.88609177,0.036046363,0.013814433,0.036884587,0.0001494613],"about_ca_topic_score_codex":0.019767435,"about_ca_topic_score_gemma":0.021725833,"teacher_disagreement_score":0.019767435,"about_ca_system_score_codex":0.0017980695,"about_ca_system_score_gemma":0.001313371,"threshold_uncertainty_score":0.039304733},"labels":[],"label_agreement":null},{"id":"W3120076703","doi":"10.1007/s00521-020-05541-y","title":"Patchwise dictionary learning for video forest fire smoke detection in wavelet domain","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Block (permutation group theory); Pattern recognition (psychology); Pixel; Feature (linguistics); Wavelet; Computer vision; Smoke; Process (computing); Mathematics; Geography","score_opus":0.00967830815352519,"score_gpt":0.22478432995057024,"score_spread":0.21510602179704505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120076703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040383596,0.00046394783,0.9575938,0.00011622165,0.00007900347,0.000032855904,0.00014339664,0.00046129365,0.0007258868],"genre_scores_gemma":[0.5207917,0.0010398969,0.47194782,0.00018727045,0.00014469345,0.00009679145,0.0011466882,0.00014744795,0.0044976166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998197,0.00003401598,0.000012270265,0.000050193597,0.000052671763,0.000031192496],"domain_scores_gemma":[0.9995389,0.00017729544,0.000038775197,0.000073269664,0.00014331692,0.000028468436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043344323,0.000403677,0.00080328295,0.0005333689,0.00017264464,0.0004190079,0.00069781597,0.0006002091,0.0014085008],"category_scores_gemma":[0.0014521077,0.00021002922,0.00052040565,0.000782694,0.0002585129,0.00057504536,0.00057369075,0.0008574406,0.000572647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044786322,0.00021281911,0.0016780935,0.0001942588,0.0000827689,0.00008907236,0.0000634639,0.1088534,0.050110064,0.0045637107,0.005489447,0.828215],"study_design_scores_gemma":[0.000008772281,0.00004311729,0.00045218127,0.0000048957518,0.000010854462,0.000035607725,0.000012405941,0.9933083,0.004676228,0.0009120942,0.00053076434,0.000004706789],"about_ca_topic_score_codex":0.003862006,"about_ca_topic_score_gemma":0.0040154904,"teacher_disagreement_score":0.003862006,"about_ca_system_score_codex":0.0002382728,"about_ca_system_score_gemma":0.000588053,"threshold_uncertainty_score":0.007679045},"labels":[],"label_agreement":null},{"id":"W3121579122","doi":"10.1155/2021/8704924","title":"Multisensor‐Weighted Fusion Algorithm Based on Improved AHP for Aircraft Fire Detection","year":2021,"lang":"en","type":"article","venue":"Complexity","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"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":"Natural Science Foundation of Tianjin City; Concordia University","keywords":"Fire detection; Node (physics); Computer science; Constant false alarm rate; Algorithm; Analytic hierarchy process; Sensor fusion; False alarm; Wireless sensor network; ALARM; Fusion; Real-time computing; Data mining; Artificial intelligence; Mathematics; Engineering; Operations research","score_opus":0.02459445849573809,"score_gpt":0.2332297041607145,"score_spread":0.20863524566497643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121579122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01034057,0.0001956882,0.9884093,0.00008391095,0.000039493494,0.00007345905,0.000029754647,0.00020309114,0.0006246193],"genre_scores_gemma":[0.57702756,0.0003857476,0.4206691,0.000110736284,0.000078562894,0.00044578873,0.00021916881,0.0000631583,0.0010001486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790263,0.00055496447,0.00023860803,0.0003645783,0.0007152287,0.00022398912],"domain_scores_gemma":[0.998453,0.0007043263,0.00014475155,0.00005351131,0.00058499863,0.000059343285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028107946,0.0011457506,0.0015876852,0.0017009419,0.0010596925,0.0011984097,0.0015180613,0.00086255476,0.0015905201],"category_scores_gemma":[0.004233129,0.00058517,0.0016323935,0.001663191,0.000535074,0.0016070441,0.0015184828,0.0015639911,0.0001914189],"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":[0.00024391167,0.00013657124,0.0019260449,0.0003283208,0.00024096268,0.00013286491,0.00033463226,0.79085344,0.005365441,0.0062444545,0.0009750696,0.19321825],"study_design_scores_gemma":[0.0000136413355,0.000037294067,0.00017836294,0.000007801948,0.00001604111,0.000012697819,0.000025130152,0.9969453,0.000660819,0.0018780922,0.00021546791,0.000009436992],"about_ca_topic_score_codex":0.0124958465,"about_ca_topic_score_gemma":0.00603016,"teacher_disagreement_score":0.0124958465,"about_ca_system_score_codex":0.0010776412,"about_ca_system_score_gemma":0.0021576744,"threshold_uncertainty_score":0.024846196},"labels":[],"label_agreement":null},{"id":"W3127122945","doi":"10.1109/jiot.2021.3056675","title":"Optimizing the Egress Route Using a New Smoke Emulator IoT System","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Firefighting; Computer science; Smoke; Routing (electronic design automation); Process (computing); Internet of Things; Computational fluid dynamics; Real-time computing; Simulation; Computer network; Distributed computing; Embedded system; Engineering; Aerospace engineering","score_opus":0.02558088652207433,"score_gpt":0.23695053630172103,"score_spread":0.2113696497796467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127122945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27017963,0.00022416303,0.7201811,0.00017109493,0.000113332026,0.00012748012,0.000060088954,0.0020585044,0.0068845926],"genre_scores_gemma":[0.9131184,0.00008190151,0.08447541,0.00006723415,0.000012137035,0.00006314944,0.0000541032,0.000043056785,0.0020845297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990666,0.000014955212,0.0000067146007,0.000021481887,0.000028830882,0.000021381387],"domain_scores_gemma":[0.99988127,0.000027364205,0.000022078046,0.000020793483,0.00003159423,0.00001696836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028958736,0.0004296639,0.00043087982,0.00028094984,0.000313603,0.0004979935,0.0008518846,0.0004553684,0.0011385205],"category_scores_gemma":[0.00030818288,0.00017495423,0.0003388765,0.00014924465,0.00015646082,0.00053093367,0.0005245113,0.00033517816,0.00023269927],"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":[0.00041966303,0.00039170816,0.0055735568,0.000113873735,0.000081084785,0.00035548268,0.00013361956,0.73442185,0.11441119,0.004186701,0.0012570054,0.1386543],"study_design_scores_gemma":[0.000022408503,0.00016069629,0.0006082611,0.000004191315,0.000028707609,0.00005498355,0.000017286762,0.98455185,0.012948527,0.0003756421,0.0012166793,0.000010843311],"about_ca_topic_score_codex":0.0011222102,"about_ca_topic_score_gemma":0.0014862141,"teacher_disagreement_score":0.0011385205,"about_ca_system_score_codex":0.00032363698,"about_ca_system_score_gemma":0.00044393813,"threshold_uncertainty_score":0.0038086772},"labels":[],"label_agreement":null},{"id":"W3130567206","doi":"10.3390/drones5010015","title":"Unmanned Aerial Vehicles for Wildland Fires: Sensing, Perception, Cooperation and Assistance","year":2021,"lang":"en","type":"article","venue":"Drones","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Firefighting; Fire detection; Task (project management); Cover (algebra); Scale (ratio); Aerial survey","score_opus":0.008225007616847607,"score_gpt":0.20539604712781354,"score_spread":0.19717103951096593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130567206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08757562,0.031526692,0.85257906,0.00075903704,0.00042579536,0.00014267475,0.00012889663,0.00078416464,0.026078075],"genre_scores_gemma":[0.8830306,0.017010951,0.089104876,0.00024663805,0.00024372837,0.00006891255,0.00029668654,0.000033752873,0.009963919],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999879,0.000027035,0.000005883533,0.000022082775,0.00004665254,0.000019472247],"domain_scores_gemma":[0.99991274,0.000029608873,0.000017746706,0.000010879602,0.000020196101,0.000008913542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016339161,0.00034143808,0.0002212868,0.00021985652,0.0001717534,0.00049118337,0.0003365534,0.00045275348,0.0007278173],"category_scores_gemma":[0.00025585026,0.00008718616,0.0002006633,0.00023095991,0.00017955844,0.00061753194,0.00040509098,0.0004111526,0.00026003888],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010839708,0.000110560315,0.0024073368,0.0008474453,0.000079914185,0.00047321361,0.00030162476,0.094241574,0.13074192,0.028695798,0.0055478876,0.7364443],"study_design_scores_gemma":[0.000036972313,0.00090651657,0.007892906,0.00031477006,0.00010781534,0.0011438578,0.0009173883,0.7313528,0.06344597,0.027589817,0.16620508,0.0000861302],"about_ca_topic_score_codex":0.0014139136,"about_ca_topic_score_gemma":0.0014393671,"teacher_disagreement_score":0.0014139136,"about_ca_system_score_codex":0.00016695926,"about_ca_system_score_gemma":0.00028602115,"threshold_uncertainty_score":0.002811432},"labels":[],"label_agreement":null},{"id":"W3133239213","doi":"10.1155/2021/6634944","title":"Model-Based Predictive Detector of a Fire inside the Road Tunnel for Intelligent Vehicles","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agentúra na Podporu Výskumu a Vývoja","keywords":"Smoke; Detector; Fire detection; Automotive engineering; Transport engineering; Computer science; Engineering; Simulation; Telecommunications; Architectural engineering","score_opus":0.013223231769998029,"score_gpt":0.22846320378713517,"score_spread":0.21523997201713713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133239213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13582389,0.00014112146,0.8597337,0.00005840896,0.000044228287,0.00004492795,0.00008422995,0.0018374535,0.0022319907],"genre_scores_gemma":[0.96842545,0.000060475486,0.030579695,0.000012579046,0.0000069787616,0.000034333836,0.00009601029,0.00003538815,0.0007491004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987185,0.00001898023,0.0000043684427,0.00003128762,0.000048045422,0.000025447402],"domain_scores_gemma":[0.99984264,0.000050880866,0.000021439415,0.000019934565,0.0000519669,0.00001304123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025489897,0.00054910773,0.0005485051,0.00044364203,0.0002222135,0.0005376706,0.00069228996,0.00046056832,0.0009464005],"category_scores_gemma":[0.00058488065,0.0002999962,0.0005059692,0.00019135374,0.00021240034,0.00044421415,0.0004125719,0.00051565556,0.00028624758],"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":[0.0002061369,0.000106914675,0.00436016,0.000068756315,0.00004706972,0.00013869832,0.000054115928,0.9134883,0.022646863,0.0013131774,0.00062289846,0.056946967],"study_design_scores_gemma":[0.0000027422523,0.000022851176,0.0003995124,0.0000022232484,0.0000055054843,0.000015723683,0.0000069652683,0.9968867,0.0023218882,0.00022759278,0.000104950035,0.00000334585],"about_ca_topic_score_codex":0.005079784,"about_ca_topic_score_gemma":0.0043571806,"teacher_disagreement_score":0.005079784,"about_ca_system_score_codex":0.00036173966,"about_ca_system_score_gemma":0.000553708,"threshold_uncertainty_score":0.010100424},"labels":[],"label_agreement":null},{"id":"W3133806306","doi":"10.1109/tii.2021.3064845","title":"Tensor-Based Approach for Liquefied Natural Gas Leakage Detection From Surveillance Thermal Cameras: A Feasibility Study in Rural Areas","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IntelliView Technologies (Canada); Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leakage (economics); Liquefied natural gas; Background subtraction; Computer science; Residual; Thermal; Artificial intelligence; Natural gas; Environmental science; Engineering; Waste management; Algorithm; Pixel; Physics","score_opus":0.03633067353197567,"score_gpt":0.2415406569504676,"score_spread":0.20520998341849195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133806306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37211034,0.0011418982,0.6219769,0.00056753575,0.000079576545,0.00012749265,0.00025144807,0.0012717419,0.0024730691],"genre_scores_gemma":[0.871115,0.0006838003,0.12598974,0.0000926436,0.000041706797,0.000043464228,0.00028072746,0.00004353483,0.0017092377],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999691,0.00007512314,0.00001584165,0.00007585248,0.00007819583,0.00006403594],"domain_scores_gemma":[0.9995834,0.00008369435,0.000052661875,0.000041584986,0.00019439173,0.00004433213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005647498,0.00096666766,0.0006093802,0.0008298493,0.0003233104,0.0006275833,0.0005860039,0.00057075836,0.0007793304],"category_scores_gemma":[0.0010481826,0.00020891157,0.00056527543,0.00061463594,0.00033138602,0.0011340291,0.0005031473,0.0005461393,0.00027797002],"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":[0.0013277194,0.0004922145,0.025294174,0.0005201212,0.00018262533,0.0010738458,0.0005023962,0.2500327,0.15565322,0.004452691,0.0038184968,0.55664986],"study_design_scores_gemma":[0.000008557016,0.000087329856,0.0024737362,0.000008902558,0.000022889917,0.00011855733,0.00008664769,0.9849045,0.011250981,0.0005302447,0.0004924376,0.000015129716],"about_ca_topic_score_codex":0.010013888,"about_ca_topic_score_gemma":0.009370088,"teacher_disagreement_score":0.010013888,"about_ca_system_score_codex":0.0005072414,"about_ca_system_score_gemma":0.00078442856,"threshold_uncertainty_score":0.01991117},"labels":[],"label_agreement":null},{"id":"W3139146945","doi":"10.18280/i2m.200108","title":"Forest Fire Detection Using Wireless Multimedia Sensor Networks and Image Compression","year":2021,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Wireless sensor network; Image compression; Real-time computing; Data transmission; Energy consumption; Data compression; Wireless; Image sensor; Base station; Fire detection; Image processing; Computer network; Artificial intelligence; Telecommunications; Electrical engineering; Engineering; Image (mathematics)","score_opus":0.015623782163023653,"score_gpt":0.24493310366512838,"score_spread":0.22930932150210473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139146945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16565797,0.00839859,0.80390877,0.00077316,0.00033083366,0.00021505442,0.00023238407,0.0014352332,0.01904789],"genre_scores_gemma":[0.8258711,0.004056863,0.16234662,0.00022182413,0.00027426388,0.000121378835,0.00027328634,0.000035253626,0.006799321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972564,0.00005064724,0.000013402444,0.00005725609,0.0001319167,0.000021209315],"domain_scores_gemma":[0.9998617,0.000041936164,0.000022849405,0.000014374976,0.00005343234,0.0000057272155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022349712,0.00032964133,0.00021481584,0.0008642174,0.0001798439,0.0003855088,0.00035943446,0.00040162305,0.0007002889],"category_scores_gemma":[0.00044838848,0.000105207015,0.00022023919,0.0007086445,0.00021366864,0.00070344267,0.00025792894,0.00022774562,0.00019177348],"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":[0.00039505103,0.00017921561,0.004514265,0.00040601383,0.00009286418,0.0004976384,0.00012863218,0.042246565,0.20124803,0.008191055,0.0032195747,0.7388812],"study_design_scores_gemma":[0.000039816856,0.0005639363,0.009033602,0.00009282929,0.00011735247,0.0013866344,0.0001994016,0.76014614,0.19912393,0.0068435655,0.022389691,0.00006298845],"about_ca_topic_score_codex":0.0007509771,"about_ca_topic_score_gemma":0.00079062843,"teacher_disagreement_score":0.0008642174,"about_ca_system_score_codex":0.00026145167,"about_ca_system_score_gemma":0.0001746036,"threshold_uncertainty_score":0.002342701},"labels":[],"label_agreement":null},{"id":"W3177714301","doi":"10.21203/rs.3.rs-684055/v1","title":"WITHDRAWN: Reliability Framework for Characterizing Heat Wave and Cold Spell Events","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spell; Reliability (semiconductor); Heat wave; Computer science; Reliability engineering; Physics; Geology; Engineering; Thermodynamics; Philosophy","score_opus":0.051875241939235296,"score_gpt":0.33445687398479534,"score_spread":0.28258163204556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177714301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004464326,0.00055240793,0.97361416,0.00062601565,0.00041840784,0.00013126383,0.0040842965,0.0045153415,0.011593862],"genre_scores_gemma":[0.3241625,0.0015223426,0.57770514,0.0009870483,0.0016182456,0.00068384624,0.016768694,0.006603724,0.06994845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982728,0.00039261364,0.00012076357,0.00027478993,0.0007858599,0.00015314402],"domain_scores_gemma":[0.9951786,0.00104542,0.00024794246,0.0009548188,0.0024281885,0.00014498636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002690108,0.0019388647,0.000991564,0.0020897505,0.0008224769,0.0024651578,0.0030594305,0.0017761997,0.021192515],"category_scores_gemma":[0.010535949,0.00065365824,0.0013824301,0.001152605,0.00091024255,0.0029144264,0.0013767246,0.0025482506,0.010298488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007271817,0.00014999739,0.004512081,0.001097934,0.00020617535,0.0006327789,0.00037969215,0.15055178,0.0241999,0.35682204,0.16839129,0.2923292],"study_design_scores_gemma":[0.00008278484,0.00016309894,0.0024649047,0.0001840746,0.00011406976,0.0005704116,0.00009685205,0.64116484,0.017237369,0.23924364,0.098574795,0.00010319762],"about_ca_topic_score_codex":0.008018959,"about_ca_topic_score_gemma":0.0056522763,"teacher_disagreement_score":0.021192515,"about_ca_system_score_codex":0.00093049917,"about_ca_system_score_gemma":0.0017066192,"threshold_uncertainty_score":0.07089603},"labels":[],"label_agreement":null},{"id":"W3179564294","doi":"10.55417/fr.2021007","title":"Design and Deployment of an Autonomous Unmanned Ground Vehicle for Urban Firefighting Scenarios","year":2021,"lang":"en","type":"article","venue":"Field Robotics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":8,"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":"York University; Khalifa University of Science, Technology and Research; České Vysoké Učení Technické v Praze; University of Pennsylvania","keywords":"Software deployment; Firefighting; Identification (biology); Robot; Unmanned ground vehicle; Robotics; Aeronautics; Simulation; Computer science; Engineering; Real-time computing; Systems engineering; Artificial intelligence","score_opus":0.018709337935723797,"score_gpt":0.22123938023333106,"score_spread":0.20253004229760727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179564294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12451669,0.000185885,0.8643937,0.0002344005,0.00010668706,0.00032339158,0.0000858508,0.0019139487,0.008239536],"genre_scores_gemma":[0.7808026,0.00012574639,0.2136001,0.000077761746,0.0000132511705,0.00022009909,0.000121480254,0.00006238953,0.004976647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998616,0.000018287663,0.0000063467924,0.00003356579,0.000048978844,0.00003124829],"domain_scores_gemma":[0.99989724,0.000009199136,0.000017261182,0.000013987826,0.00003946665,0.000022834705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018154563,0.00037279032,0.00020001117,0.00023252628,0.00023533027,0.00029756216,0.00072263664,0.00042626762,0.0009833468],"category_scores_gemma":[0.00017131456,0.00019390728,0.00018064135,0.00006424635,0.00021512636,0.00032841827,0.00043289902,0.00027556613,0.0005771388],"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":[0.0002075384,0.00016703046,0.0065035056,0.00036223966,0.0000833512,0.0009988776,0.00042870268,0.30065802,0.41483864,0.0076237125,0.00466711,0.26346123],"study_design_scores_gemma":[0.000085154454,0.0014672813,0.005653185,0.00003800941,0.00006394791,0.00053865236,0.00027479074,0.8670342,0.083444856,0.0020774018,0.03927641,0.000045970035],"about_ca_topic_score_codex":0.001617338,"about_ca_topic_score_gemma":0.0019769808,"teacher_disagreement_score":0.001617338,"about_ca_system_score_codex":0.00023835097,"about_ca_system_score_gemma":0.00059724005,"threshold_uncertainty_score":0.00328964},"labels":[],"label_agreement":null},{"id":"W3186499362","doi":"10.18280/ts.380324","title":"Forest Fire Recognition Based on Feature Extraction from Multi-View Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Pattern recognition (psychology); Feature (linguistics); Feature extraction; Hue; Segmentation; Similarity (geometry); Computer vision; Image (mathematics)","score_opus":0.019554203453353434,"score_gpt":0.22376538517959646,"score_spread":0.20421118172624303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186499362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11434616,0.00066646724,0.87995636,0.000088479304,0.00009253426,0.0000947573,0.0002796468,0.0018244213,0.0026511915],"genre_scores_gemma":[0.7354389,0.00080927624,0.26080388,0.00010032842,0.00005648072,0.000068384186,0.00057412224,0.00009161966,0.0020570122],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996555,0.000026935344,0.00001804873,0.00013006642,0.00012287483,0.000046631932],"domain_scores_gemma":[0.9997799,0.000037017435,0.000042733853,0.00003902876,0.00008083655,0.000020478288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023880636,0.00080011104,0.00066242996,0.0018073852,0.00022152577,0.0005326934,0.0005818282,0.00054011814,0.00096043665],"category_scores_gemma":[0.00061706564,0.0002983732,0.0008907566,0.0010126878,0.00026229958,0.0010702539,0.00044492417,0.00058161526,0.00048396832],"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":[0.00033539243,0.00015550728,0.00578195,0.00016873398,0.00011960672,0.00038738464,0.00008505346,0.031524975,0.18931505,0.0011369184,0.0017726943,0.7692168],"study_design_scores_gemma":[0.000020266132,0.00016646119,0.018935144,0.00002706596,0.00013036576,0.000931227,0.00009591984,0.86224073,0.11301747,0.0019025602,0.002471506,0.00006132328],"about_ca_topic_score_codex":0.0036231468,"about_ca_topic_score_gemma":0.0048552146,"teacher_disagreement_score":0.0036231468,"about_ca_system_score_codex":0.0003419125,"about_ca_system_score_gemma":0.00033411226,"threshold_uncertainty_score":0.0072041154},"labels":[],"label_agreement":null},{"id":"W3186960229","doi":"10.18280/ts.380336","title":"Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Fire detection; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Image (mathematics); Artificial neural network; Process (computing); Contextual image classification; Set (abstract data type); Computer vision; Engineering","score_opus":0.02181327368013957,"score_gpt":0.23197114896773732,"score_spread":0.21015787528759775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186960229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18104775,0.0007652922,0.81269133,0.00015137036,0.00011527385,0.0001468683,0.0002525739,0.0018132415,0.0030163294],"genre_scores_gemma":[0.79554766,0.00071533554,0.1977938,0.00008895437,0.00004387577,0.0001014815,0.0007102322,0.000062339466,0.0049362103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997904,0.000014007969,0.000015256054,0.0000570383,0.00007074897,0.00005251899],"domain_scores_gemma":[0.9998542,0.000028608294,0.000024518136,0.000022229711,0.000060498813,0.000009875963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031857486,0.00072943413,0.0004325587,0.0010137995,0.00023728094,0.00042885408,0.0005480768,0.00050814636,0.00077000074],"category_scores_gemma":[0.000553968,0.0002895501,0.00063681207,0.0006801692,0.000263553,0.0006789015,0.00033994223,0.000489219,0.00029586826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033461247,0.00029764755,0.008233041,0.00014400366,0.00012743632,0.00022811136,0.00006960707,0.10688731,0.15782969,0.00245585,0.002158845,0.72123384],"study_design_scores_gemma":[0.0000079249685,0.00008416578,0.005942574,0.00001255485,0.00005029681,0.00010164315,0.000015961163,0.9337475,0.05815183,0.00064858625,0.0012198092,0.00001711648],"about_ca_topic_score_codex":0.010851197,"about_ca_topic_score_gemma":0.010904385,"teacher_disagreement_score":0.010851197,"about_ca_system_score_codex":0.00060019246,"about_ca_system_score_gemma":0.00057254086,"threshold_uncertainty_score":0.021576107},"labels":[],"label_agreement":null},{"id":"W3187189595","doi":"10.3390/s21165402","title":"Sensors for Fire and Smoke Monitoring","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"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 Forest Service; York University","funders":"","keywords":"Smoke; Civilization; Engineering; Architectural engineering; Environmental science; Computer science; Forensic engineering; Computer security; Waste management; History; Archaeology","score_opus":0.01736827008493812,"score_gpt":0.22712565780175037,"score_spread":0.20975738771681224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187189595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048133776,0.25600567,0.46460253,0.020181792,0.011943455,0.00048635688,0.0037673393,0.006426224,0.18845281],"genre_scores_gemma":[0.5486876,0.13305569,0.19719313,0.0077857,0.0034007072,0.00048026722,0.002943586,0.0004202093,0.10603303],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990128,0.00010733471,0.000024107152,0.00018034488,0.0006032746,0.00007213248],"domain_scores_gemma":[0.99955994,0.00010581868,0.00006763063,0.000047577556,0.0001865542,0.000032328204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057494343,0.00064632925,0.0005628652,0.0008947371,0.0005444815,0.0014630717,0.0010297514,0.0018102127,0.0066597043],"category_scores_gemma":[0.0013168287,0.00041641766,0.00046098232,0.0009682001,0.0006506351,0.0018257126,0.0012133024,0.0018436532,0.003546844],"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":[0.00042542646,0.00017463206,0.0030493152,0.0021733083,0.00016272128,0.0002950773,0.000326225,0.004142554,0.26361963,0.06091904,0.08861357,0.57609856],"study_design_scores_gemma":[0.00004569234,0.00056604383,0.004892653,0.0006747776,0.00015461692,0.0014887464,0.00030984564,0.023585467,0.2444828,0.031992186,0.6916323,0.00017485008],"about_ca_topic_score_codex":0.00068063574,"about_ca_topic_score_gemma":0.0013081552,"teacher_disagreement_score":0.0066597043,"about_ca_system_score_codex":0.00052022265,"about_ca_system_score_gemma":0.0005084596,"threshold_uncertainty_score":0.022278965},"labels":[],"label_agreement":null},{"id":"W3188766157","doi":"10.1109/icuas51884.2021.9476865","title":"Forest Fire Detection and Localization Using Thermal and Visual Cameras","year":2021,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Homography; Artificial intelligence; Computer science; Process (computing); Frame (networking); Pixel; Visual servoing; Fire detection; Tracking (education); Frame rate; Image (mathematics); Engineering","score_opus":0.007682104742669145,"score_gpt":0.20785154010904647,"score_spread":0.20016943536637732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188766157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23706907,0.0013261539,0.7492443,0.00012018349,0.00014816751,0.000105159925,0.00012910405,0.0015974753,0.010260506],"genre_scores_gemma":[0.82016736,0.0004929801,0.17515282,0.00010376313,0.000058949263,0.000056206187,0.00012921069,0.00002753548,0.0038111666],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966395,0.000029786084,0.000009869752,0.00009859917,0.00015849178,0.00003924934],"domain_scores_gemma":[0.9998124,0.000026343338,0.00004107645,0.0000197958,0.00008210939,0.00001821283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000171164,0.00034237944,0.00029928546,0.0007806586,0.00023757841,0.00046819728,0.00046072822,0.00047185732,0.00089428114],"category_scores_gemma":[0.00034040597,0.00024166005,0.00027199223,0.0003434722,0.00021922559,0.0005879404,0.00031530458,0.0002439647,0.00030034763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003567118,0.000088046836,0.0060746497,0.00028907324,0.000052452713,0.00024828225,0.00017295413,0.008294305,0.60778433,0.0018585271,0.0011993621,0.37358123],"study_design_scores_gemma":[0.00008942379,0.00089986104,0.04236085,0.00012287202,0.00020745641,0.0022754264,0.00029156916,0.29468468,0.6392495,0.0012510662,0.018425541,0.00014181812],"about_ca_topic_score_codex":0.0016431505,"about_ca_topic_score_gemma":0.0024857067,"teacher_disagreement_score":0.0016431505,"about_ca_system_score_codex":0.00027458867,"about_ca_system_score_gemma":0.0003038377,"threshold_uncertainty_score":0.0032671094},"labels":[],"label_agreement":null},{"id":"W3192727496","doi":"","title":"Geolocating and Mosaicking Airborne Infrared Video for Wildfire Risk Analysis over Time without IMU Information","year":2019,"lang":"en","type":"article","venue":"NPARC","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Inertial measurement unit; Environmental science; Meteorology; Computer science; Computer vision; Geography","score_opus":0.002445658204890201,"score_gpt":0.17919150883594018,"score_spread":0.17674585063104997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192727496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16025339,0.00014410361,0.83055836,0.000097617405,0.00007161635,0.00023910825,0.00094253436,0.0038960848,0.003797061],"genre_scores_gemma":[0.26244387,0.00018744665,0.7333416,0.00003158309,0.000027017773,0.00014267603,0.0012139368,0.0003718955,0.0022398948],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99985516,0.00001183093,0.000005518172,0.000032441785,0.000070332615,0.000024716492],"domain_scores_gemma":[0.9998363,0.000026257336,0.000022418642,0.0000250187,0.000074582356,0.0000153205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029507835,0.00036006945,0.00021614265,0.0010949434,0.00021317476,0.00046652596,0.00038885712,0.0002385904,0.0023195823],"category_scores_gemma":[0.000792709,0.00017774294,0.0003805096,0.00061665295,0.00016632908,0.00034540446,0.0003869153,0.00025476998,0.00076053],"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":[0.0002684637,0.00013116145,0.008357636,0.00020673772,0.00007249602,0.0002412601,0.00046338968,0.03892158,0.2371708,0.0021787295,0.0044563813,0.7075314],"study_design_scores_gemma":[0.000034976874,0.00019999489,0.0449878,0.00006504659,0.00007499844,0.00057563407,0.0005601709,0.7734456,0.16150722,0.0020372444,0.016445488,0.000065763015],"about_ca_topic_score_codex":0.00878786,"about_ca_topic_score_gemma":0.021010151,"teacher_disagreement_score":0.00878786,"about_ca_system_score_codex":0.00029792264,"about_ca_system_score_gemma":0.00061810575,"threshold_uncertainty_score":0.01747346},"labels":[],"label_agreement":null},{"id":"W3198090248","doi":"10.3390/rs13173527","title":"Wildfire Segmentation Using Deep Vision Transformers","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Transformer; Artificial intelligence; Architecture; Image segmentation; Pixel; Computer vision; Locality; Pattern recognition (psychology); Geography; Engineering","score_opus":0.010375772932874927,"score_gpt":0.23279360838413576,"score_spread":0.22241783545126084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198090248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06550967,0.00054512627,0.9269075,0.00014569801,0.00007116733,0.00008349173,0.000234414,0.004334185,0.0021688808],"genre_scores_gemma":[0.79455996,0.00053002464,0.20071667,0.00018563893,0.00003981794,0.000055022436,0.0008357091,0.00022943075,0.0028477206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998,0.000017975599,0.000009179383,0.000084942505,0.00004580618,0.000042074742],"domain_scores_gemma":[0.9998155,0.000052090727,0.00003150598,0.000029687591,0.00004745186,0.000023789495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004988715,0.0010091629,0.00065743714,0.0011490636,0.00024347138,0.0012479459,0.0011172594,0.0007964115,0.0014638982],"category_scores_gemma":[0.00094846543,0.00040863754,0.00092391047,0.00057391083,0.0004897549,0.0017093779,0.0008438102,0.00090012205,0.000583361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005306175,0.0002490481,0.0033164923,0.00020826269,0.00014693585,0.00024669312,0.00009564965,0.4336668,0.051359434,0.011072624,0.0032155798,0.49589187],"study_design_scores_gemma":[0.0000068527775,0.000047256577,0.00037488726,0.000008315238,0.000018442057,0.00006602724,0.000011003584,0.9849231,0.01125026,0.002674394,0.0006129387,0.0000064856054],"about_ca_topic_score_codex":0.0052624643,"about_ca_topic_score_gemma":0.00694792,"teacher_disagreement_score":0.0052624643,"about_ca_system_score_codex":0.0011074456,"about_ca_system_score_gemma":0.001120174,"threshold_uncertainty_score":0.010463655},"labels":[],"label_agreement":null},{"id":"W3204384816","doi":"10.18280/ts.380420","title":"Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fault (geology); Electrical equipment; Artificial intelligence; Convolutional neural network; Computer science; Image (mathematics); Infrared; Fusion; Thermal; Artificial neural network; Segmentation; Computer vision; Image fusion; Pattern recognition (psychology); Engineering; Electrical engineering","score_opus":0.011037500408268827,"score_gpt":0.21813081744524848,"score_spread":0.20709331703697964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204384816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19447379,0.00035548248,0.80201226,0.00014485825,0.000043858166,0.000052478692,0.000049451686,0.0010051043,0.0018627122],"genre_scores_gemma":[0.9421516,0.0001651677,0.05713013,0.000029430976,0.000015633997,0.00001592035,0.000042396325,0.0000118672515,0.00043786835],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996784,0.000041339612,0.000018651126,0.00008843306,0.00012678666,0.00004641457],"domain_scores_gemma":[0.9997483,0.00006303348,0.000057473368,0.00003123325,0.000085211606,0.000014795983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037944567,0.00045249914,0.00049617566,0.0007835855,0.00020390545,0.0004056544,0.0004111914,0.0005787528,0.00038184263],"category_scores_gemma":[0.0008863186,0.00017561844,0.0005007636,0.00042049782,0.0003190971,0.0009252288,0.00043100963,0.0003904395,0.00014660117],"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":[0.0006253584,0.00017055229,0.01038266,0.00019882683,0.00011542215,0.00053306465,0.0003578581,0.25551498,0.2035127,0.0031684386,0.0012932494,0.5241269],"study_design_scores_gemma":[0.0000062203308,0.00008933615,0.003966244,0.00000859309,0.000033425746,0.00015303282,0.000036240242,0.96598566,0.028410845,0.00096278265,0.0003352858,0.000012219162],"about_ca_topic_score_codex":0.0017879094,"about_ca_topic_score_gemma":0.0015337119,"teacher_disagreement_score":0.0017879094,"about_ca_system_score_codex":0.00038638906,"about_ca_system_score_gemma":0.00025434233,"threshold_uncertainty_score":0.0035549998},"labels":[],"label_agreement":null},{"id":"W3208573629","doi":"10.9798/kosham.2021.21.5.165","title":"A Prediction Model of Casualties Based on Machine Learning for Selection of Fire Scenario","year":2021,"lang":"en","type":"article","venue":"Korean Society of Hazard Mitigation","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Tellabs (Canada)","funders":"","keywords":"Cart; Computer science; Selection (genetic algorithm); Decision tree; Model selection; Firefighting; Machine learning; Artificial intelligence; Engineering; Geography; Cartography","score_opus":0.015651608621588268,"score_gpt":0.21001929443007822,"score_spread":0.19436768580848995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208573629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29031685,0.0006258093,0.6998527,0.0006243777,0.00018140543,0.00018907573,0.0014081884,0.0018044565,0.0049971486],"genre_scores_gemma":[0.9369436,0.0003820183,0.057876017,0.00009906141,0.000072791845,0.00022429189,0.0013375778,0.000048820482,0.0030157706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967194,0.00008748912,0.000020704132,0.000083490966,0.00007772824,0.000058718088],"domain_scores_gemma":[0.99894124,0.0006206347,0.00009421836,0.00003855509,0.00026295384,0.000042424268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092300604,0.0007508072,0.00069724425,0.001433152,0.00040505736,0.0006542413,0.0009602577,0.00069053355,0.002069494],"category_scores_gemma":[0.0024347955,0.00029201913,0.0007924168,0.00096203375,0.00019865471,0.00077112194,0.0003451214,0.0007745956,0.00051005394],"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":[0.00008416751,0.0001260548,0.009806452,0.00004292104,0.00006288963,0.00013239171,0.000037612033,0.9417132,0.00067451905,0.0012168242,0.0015282035,0.04457474],"study_design_scores_gemma":[0.0000019682911,0.000010939108,0.0004207008,0.000002078569,0.000005457734,0.000011612714,0.000002534154,0.999124,0.00007423937,0.00025685612,0.00008675688,0.0000028121588],"about_ca_topic_score_codex":0.022592155,"about_ca_topic_score_gemma":0.012837446,"teacher_disagreement_score":0.022592155,"about_ca_system_score_codex":0.00066227285,"about_ca_system_score_gemma":0.0009915942,"threshold_uncertainty_score":0.04492134},"labels":[],"label_agreement":null},{"id":"W3208995432","doi":"10.3390/electronics10212675","title":"A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Energy, Northern Development and Mines","funders":"National Natural Science Foundation of China","keywords":"Smoke; Weighting; Segmentation; Pixel; Computer science; Artificial intelligence; Fire detection; Ambiguity; Pattern recognition (psychology); Image segmentation; Environmental science; Engineering","score_opus":0.01010927841231603,"score_gpt":0.24515937276929012,"score_spread":0.23505009435697408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208995432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04802808,0.00041917167,0.94771904,0.00010112168,0.00009631308,0.00010224266,0.0001014412,0.0014209276,0.002011745],"genre_scores_gemma":[0.5215046,0.00059054396,0.4703299,0.00024285296,0.0000894482,0.0001637898,0.00067116105,0.00033244945,0.006075202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956745,0.000031367555,0.000035431563,0.00014745847,0.00015628207,0.00006200668],"domain_scores_gemma":[0.9996921,0.000047500238,0.0000338614,0.000034143806,0.00016634706,0.000026136931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054865936,0.00093532255,0.00082559616,0.0015734094,0.00047671355,0.00087566796,0.0010532847,0.0009777327,0.001982133],"category_scores_gemma":[0.0009655134,0.00040293572,0.00093318755,0.0008935171,0.00044053973,0.0018792945,0.00087488064,0.0008130179,0.0006199967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044328687,0.00022626267,0.0041411608,0.00024798812,0.000102702536,0.00015208646,0.00018306384,0.06740599,0.1316375,0.0059599373,0.0027759136,0.7867242],"study_design_scores_gemma":[0.00002329576,0.00013108896,0.0024114724,0.00002085307,0.00007471996,0.00017213907,0.000058346268,0.93521005,0.0556482,0.0033624463,0.002851783,0.00003560111],"about_ca_topic_score_codex":0.004806864,"about_ca_topic_score_gemma":0.0062474995,"teacher_disagreement_score":0.004806864,"about_ca_system_score_codex":0.0006712836,"about_ca_system_score_gemma":0.0010029662,"threshold_uncertainty_score":0.009557784},"labels":[],"label_agreement":null},{"id":"W3217380447","doi":"10.3390/s21237785","title":"Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Energy, Northern Development and Mines","funders":"State Key Laboratory of Fire Science; University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Smoke; Classifier (UML); RGB color model; Feature (linguistics); Deep learning; Pixel; Pattern recognition (psychology); Test data; Computer vision; Engineering","score_opus":0.02282959069153991,"score_gpt":0.21033028816167462,"score_spread":0.1875006974701347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217380447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.631494,0.00032847203,0.3621667,0.00017451892,0.0001454975,0.00010988311,0.0005830333,0.002096896,0.0029010526],"genre_scores_gemma":[0.9079789,0.00015308858,0.0886402,0.000121712634,0.00002127516,0.00006356638,0.001415904,0.00006856398,0.0015366715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998394,0.000020161042,0.000007870203,0.00005748453,0.00004224813,0.00003283282],"domain_scores_gemma":[0.99978846,0.000055010256,0.000020675485,0.000046116726,0.00007456521,0.000015173637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004492195,0.0005791006,0.00031412605,0.0004977149,0.00014206117,0.00038965364,0.0004902743,0.00047109375,0.00066657393],"category_scores_gemma":[0.00086532155,0.00016856482,0.0005912847,0.00033487412,0.00031921698,0.0005448607,0.0003676905,0.00059306284,0.00027909383],"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":[0.00053786644,0.00043834242,0.0126705775,0.00017966887,0.00013994188,0.0003146681,0.00012974268,0.46605614,0.12748219,0.0021111064,0.0036903333,0.38624936],"study_design_scores_gemma":[0.0000065281592,0.000048895356,0.0024373382,0.00000641044,0.000010731352,0.00005338517,0.000024852206,0.97377485,0.022700137,0.00042122827,0.00050557486,0.000010020039],"about_ca_topic_score_codex":0.0034448712,"about_ca_topic_score_gemma":0.004835714,"teacher_disagreement_score":0.0034448712,"about_ca_system_score_codex":0.00034110525,"about_ca_system_score_gemma":0.00033268385,"threshold_uncertainty_score":0.0068496466},"labels":[],"label_agreement":null},{"id":"W3217793227","doi":"","title":"A Machine Learning Approach for Fire-Fighting Detection in the Power Industry","year":2021,"lang":"en","type":"article","venue":"Pure (Coventry University)","topic":"Fire Detection and Safety Systems","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":"University of Victoria","funders":"","keywords":"Firefighting; Power (physics); Computer science; Artificial intelligence; Aeronautics; Engineering; Geography; Cartography","score_opus":0.010513440456215334,"score_gpt":0.17593499229713308,"score_spread":0.16542155184091775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217793227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08977516,0.0016140552,0.90288746,0.0006569011,0.000116213974,0.00009507794,0.00009783024,0.0004412224,0.0043160715],"genre_scores_gemma":[0.8789042,0.0006272537,0.11524276,0.00013400664,0.000078553676,0.00013904166,0.0001357107,0.00001667322,0.0047217687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997471,0.00007763378,0.000020802929,0.000057797046,0.00006491502,0.00003163687],"domain_scores_gemma":[0.99962926,0.00022172791,0.000027092558,0.000011772001,0.00009749447,0.000012696948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061802863,0.0004145249,0.000471191,0.00054441614,0.00036381077,0.00060914585,0.0006084275,0.0008029994,0.001290777],"category_scores_gemma":[0.0011954813,0.00019835206,0.00038274264,0.00049339427,0.00022035724,0.00047468298,0.00034100478,0.0007385503,0.00024315425],"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":[0.00009737781,0.00024154162,0.0028598814,0.00009481168,0.00008417162,0.00013160813,0.000056332054,0.72170705,0.0034331083,0.0034798782,0.0010612599,0.26675296],"study_design_scores_gemma":[0.0000013973147,0.000017225244,0.00026995587,0.0000030163435,0.0000024803935,0.000005880788,0.000004901863,0.99877125,0.0002496912,0.00054878293,0.00012345225,0.0000020095997],"about_ca_topic_score_codex":0.009621395,"about_ca_topic_score_gemma":0.006138732,"teacher_disagreement_score":0.009621395,"about_ca_system_score_codex":0.0006484394,"about_ca_system_score_gemma":0.0007419481,"threshold_uncertainty_score":0.019130766},"labels":[],"label_agreement":null},{"id":"W4200077715","doi":"10.1088/1757-899x/1208/1/012033","title":"Fire prediction with logistic regression on territory of Bosnia and Herzegovina","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"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":"Logistic regression; Meteorology; Precipitation; Environmental science; Wind speed; Logistic model tree; Index (typography); Relative humidity; Regression analysis; Climatology; Computer science; Geography; Machine learning; Geology","score_opus":0.012832774616300786,"score_gpt":0.19301430088150331,"score_spread":0.18018152626520254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200077715","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9850932,0.00037756996,0.003495645,0.0002704807,0.000040857984,0.000020957803,0.008166883,0.00027056094,0.0022638747],"genre_scores_gemma":[0.98914844,0.0001109301,0.0019801427,0.000011510329,0.000017819048,0.000013232422,0.007894477,0.000020127105,0.0008034172],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997861,0.000044535474,0.000010452709,0.00007362494,0.000035507215,0.000049795184],"domain_scores_gemma":[0.9995913,0.00016390003,0.00005124025,0.000035819867,0.00011478,0.00004306923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052647956,0.00052406685,0.0003945156,0.001244749,0.00031626137,0.0007204679,0.00048750988,0.00029772616,0.002543953],"category_scores_gemma":[0.0013545963,0.00012942246,0.00071131234,0.0010968659,0.00013088745,0.00028298594,0.00037757293,0.00049235317,0.00052813423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006368651,0.0003906765,0.5001051,0.00013853662,0.00039165613,0.0007604637,0.00008941936,0.41165382,0.0016378565,0.0006885838,0.010117008,0.07338991],"study_design_scores_gemma":[0.000022430928,0.000058940812,0.20984122,0.000030973843,0.000046516052,0.00006770163,0.00019556758,0.7868417,0.0008078362,0.00030902075,0.0017590587,0.000018981154],"about_ca_topic_score_codex":0.2474723,"about_ca_topic_score_gemma":0.1616071,"teacher_disagreement_score":0.2474723,"about_ca_system_score_codex":0.0011357069,"about_ca_system_score_gemma":0.0006173698,"threshold_uncertainty_score":0.4920637},"labels":[],"label_agreement":null},{"id":"W4200117174","doi":"10.32920/17315693","title":"Improving Fire Safety Systems Based On Internet Of Things And Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Firefighting; Smoke; Internet of Things; Fire safety; Computer science; Fire protection; The Internet; Architectural engineering; Snapshot (computer storage); Computer security; Simulation; Aeronautics; Real-time computing; Engineering; Civil engineering; World Wide Web; Geography; Operating system; Cartography","score_opus":0.005746415966130671,"score_gpt":0.1796836354198048,"score_spread":0.17393721945367413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200117174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16633958,0.0014960632,0.79811627,0.0016082063,0.0006702448,0.00026036784,0.0006145285,0.017696012,0.013198741],"genre_scores_gemma":[0.89713097,0.00053878076,0.09759854,0.00047069765,0.00005808137,0.00010091081,0.0008371083,0.00020482954,0.0030600969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.000034879322,0.00001684172,0.00007166313,0.00009660183,0.00005047813],"domain_scores_gemma":[0.9996983,0.0000700346,0.000039159568,0.000049356197,0.000114132854,0.000028964918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005664782,0.0008788008,0.00048826582,0.0005568205,0.00043349512,0.0007685382,0.00094767,0.0007055975,0.0018647291],"category_scores_gemma":[0.0013906381,0.00034995266,0.0007492776,0.00034959253,0.00030289136,0.0017307296,0.0010447069,0.0010517045,0.000575566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000510212,0.00077297504,0.009017117,0.00039692095,0.00035589671,0.00036590212,0.00016587917,0.45247102,0.040887143,0.005974293,0.0151709365,0.47391173],"study_design_scores_gemma":[0.000014848365,0.000116655305,0.0013552716,0.000017773216,0.000045586257,0.000035945985,0.000035036195,0.9824722,0.00932395,0.003927934,0.0026373568,0.0000173346],"about_ca_topic_score_codex":0.0035225197,"about_ca_topic_score_gemma":0.0055024205,"teacher_disagreement_score":0.0035225197,"about_ca_system_score_codex":0.0006408295,"about_ca_system_score_gemma":0.0005489523,"threshold_uncertainty_score":0.0070040226},"labels":[],"label_agreement":null},{"id":"W4200584442","doi":"10.32920/17315693.v1","title":"Improving Fire Safety Systems Based On Internet Of Things And Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Firefighting; Smoke; Internet of Things; Fire safety; Computer science; Fire protection; The Internet; Architectural engineering; Snapshot (computer storage); Computer security; Simulation; Real-time computing; Aeronautics; Engineering; Civil engineering; World Wide Web; Operating system; Geography; Cartography","score_opus":0.005746415966130671,"score_gpt":0.1796836354198048,"score_spread":0.17393721945367413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200584442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16633958,0.0014960632,0.79811627,0.0016082063,0.0006702448,0.00026036784,0.0006145285,0.017696012,0.013198741],"genre_scores_gemma":[0.89713097,0.00053878076,0.09759854,0.00047069765,0.00005808137,0.00010091081,0.0008371083,0.00020482954,0.0030600969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.000034879322,0.00001684172,0.00007166313,0.00009660183,0.00005047813],"domain_scores_gemma":[0.9996983,0.0000700346,0.000039159568,0.000049356197,0.000114132854,0.000028964918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005664782,0.0008788008,0.00048826582,0.0005568205,0.00043349512,0.0007685382,0.00094767,0.0007055975,0.0018647291],"category_scores_gemma":[0.0013906381,0.00034995266,0.0007492776,0.00034959253,0.00030289136,0.0017307296,0.0010447069,0.0010517045,0.000575566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000510212,0.00077297504,0.009017117,0.00039692095,0.00035589671,0.00036590212,0.00016587917,0.45247102,0.040887143,0.005974293,0.0151709365,0.47391173],"study_design_scores_gemma":[0.000014848365,0.000116655305,0.0013552716,0.000017773216,0.000045586257,0.000035945985,0.000035036195,0.9824722,0.00932395,0.003927934,0.0026373568,0.0000173346],"about_ca_topic_score_codex":0.0035225197,"about_ca_topic_score_gemma":0.0055024205,"teacher_disagreement_score":0.0035225197,"about_ca_system_score_codex":0.0006408295,"about_ca_system_score_gemma":0.0005489523,"threshold_uncertainty_score":0.0070040226},"labels":[],"label_agreement":null},{"id":"W4210265664","doi":"10.1109/safeprocess52771.2021.9693660","title":"Early Forest Fire Segmentation Based on Deep Learning","year":2021,"lang":"en","type":"article","venue":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Shaanxi Provincial Department of Education; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Segmentation; Upsampling; Computer science; Artificial intelligence; Feature (linguistics); Image segmentation; Fuse (electrical); Firefighting; Contraction (grammar); Path (computing); Computer vision; Pattern recognition (psychology); Engineering; Image (mathematics); Geography; Cartography","score_opus":0.00612974883506447,"score_gpt":0.2195253529233282,"score_spread":0.21339560408826372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210265664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08393259,0.0004747788,0.90955645,0.0001724159,0.00006728455,0.00008373425,0.00022191061,0.0029761675,0.0025146285],"genre_scores_gemma":[0.7211608,0.0004285697,0.27172473,0.00024730264,0.00005529215,0.00009811732,0.0012251275,0.00023691318,0.004823026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974114,0.000015326172,0.000013564543,0.000087735396,0.000067998095,0.00007420111],"domain_scores_gemma":[0.99980885,0.00004437157,0.000028599958,0.000031681855,0.0000624917,0.000024019904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083768,0.0009598699,0.0009034033,0.0011292999,0.0005025654,0.0008308478,0.0011983264,0.0008611197,0.0013698003],"category_scores_gemma":[0.00069234474,0.0004286591,0.0010168537,0.0008048862,0.00047045652,0.0014655608,0.00091461453,0.0010969009,0.00045268447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000399937,0.00022296389,0.00531201,0.000104660045,0.00011944382,0.00024398512,0.000117296004,0.2848082,0.040766425,0.002874827,0.0038935058,0.6611368],"study_design_scores_gemma":[0.000007114839,0.000031628937,0.0009423842,0.000008808456,0.000016600732,0.000060320555,0.0000143286425,0.9881374,0.008739991,0.0014296186,0.0006027357,0.00000913006],"about_ca_topic_score_codex":0.0098246215,"about_ca_topic_score_gemma":0.0149989445,"teacher_disagreement_score":0.0098246215,"about_ca_system_score_codex":0.0009587041,"about_ca_system_score_gemma":0.0010326107,"threshold_uncertainty_score":0.019534886},"labels":[],"label_agreement":null},{"id":"W4210672081","doi":"10.22214/ijraset.2021.37105","title":"Design and Analysis of Automatic Fire Extinguisher for Vehicles","year":2021,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Horizon College and Seminary","funders":"","keywords":"Automotive engineering; Smoke; Ignition system; Gasoline; Environmental science; Aeronautics; Engineering; Forensic engineering; Computer science; Waste management; Aerospace engineering","score_opus":0.04935428369209007,"score_gpt":0.3485538874593705,"score_spread":0.29919960376728044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210672081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08889257,0.0016294,0.8806531,0.00040498946,0.00021417966,0.00038110057,0.00021146364,0.0031275097,0.02448573],"genre_scores_gemma":[0.890594,0.00060607237,0.08950338,0.00010297173,0.000039713603,0.00026463708,0.00017328763,0.00011895638,0.018596984],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999616,0.000042476175,0.000014990613,0.0000793944,0.00020664438,0.00004043083],"domain_scores_gemma":[0.99982613,0.000034821933,0.000032267377,0.000014039178,0.00008477001,0.000008036965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002513811,0.0003968505,0.00043740487,0.00036434023,0.0004225087,0.0007451746,0.0010277809,0.0006730051,0.0047937175],"category_scores_gemma":[0.0003982736,0.00024088277,0.0005575545,0.00017480666,0.00018200336,0.00041375548,0.00023656465,0.00025500398,0.0009950066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054896704,0.00020718471,0.0036815803,0.0012616896,0.00017401371,0.00078842667,0.0003758442,0.3640021,0.3850583,0.015791811,0.0071771042,0.22093293],"study_design_scores_gemma":[0.00007124682,0.001320577,0.0028013117,0.00006094348,0.00012673448,0.00064186193,0.00009990208,0.87943786,0.079294965,0.001751528,0.034345966,0.00004712182],"about_ca_topic_score_codex":0.0019318067,"about_ca_topic_score_gemma":0.0011688247,"teacher_disagreement_score":0.0047937175,"about_ca_system_score_codex":0.00052096945,"about_ca_system_score_gemma":0.00048971263,"threshold_uncertainty_score":0.01603651},"labels":[],"label_agreement":null},{"id":"W4212958975","doi":"10.1109/access.2022.3151660","title":"Convolution Optimization in Fire Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Concordia University","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Convolution (computer science); Block (permutation group theory); Artificial intelligence; Deep learning; Computer engineering; FLOPS; Computation; Residual; Machine learning; Algorithm; Parallel computing; Artificial neural network","score_opus":0.02834709714939807,"score_gpt":0.24935254754816744,"score_spread":0.22100545039876937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212958975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095888235,0.002082681,0.8913389,0.0008652401,0.00018845308,0.00004041727,0.00031081715,0.0018073792,0.007477896],"genre_scores_gemma":[0.8639945,0.00097443466,0.123516455,0.0003815144,0.00014426868,0.00006377076,0.0007262037,0.00020560814,0.009993271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997948,0.000037543177,0.000012247338,0.000056125285,0.000043293312,0.000056077508],"domain_scores_gemma":[0.9997625,0.000105022984,0.000025218113,0.00003033695,0.000058652327,0.000018274519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061752927,0.0005789216,0.0007909833,0.00044810175,0.00027778375,0.0007874648,0.00074996945,0.000979914,0.002677795],"category_scores_gemma":[0.0014010533,0.00035817106,0.0007048714,0.00051854335,0.0005084913,0.0010170712,0.000615843,0.0009949058,0.0006460989],"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":[0.00022278032,0.00005559086,0.0018380288,0.00007838955,0.00007070972,0.000074805816,0.000028628141,0.8119663,0.0053839087,0.0091541065,0.003472917,0.16765389],"study_design_scores_gemma":[0.0000025123968,0.000006562338,0.00016756215,0.0000028630125,0.0000042159795,0.000011281279,0.0000022933561,0.99724245,0.0006987371,0.0015664088,0.00029332392,0.0000019049724],"about_ca_topic_score_codex":0.008127646,"about_ca_topic_score_gemma":0.0077074906,"teacher_disagreement_score":0.008127646,"about_ca_system_score_codex":0.0009316119,"about_ca_system_score_gemma":0.00089941814,"threshold_uncertainty_score":0.016160727},"labels":[],"label_agreement":null},{"id":"W4213187122","doi":"10.3390/rs14040992","title":"Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Remote sensing; Deep learning; Dilation (metric space); Artificial intelligence; Pixel; Satellite; Convolutional neural network; Satellite imagery; Pattern recognition (psychology); Geology; Mathematics","score_opus":0.010547777492436268,"score_gpt":0.19593840456683298,"score_spread":0.1853906270743967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213187122","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5327067,0.0009683394,0.4607792,0.0002856627,0.000080979386,0.00008276594,0.00037532076,0.0020104875,0.0027105638],"genre_scores_gemma":[0.94328296,0.00027372394,0.05415516,0.00005466601,0.000019841453,0.000031812015,0.00058634894,0.00003429257,0.0015612608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998466,0.000022164491,0.00000946673,0.000050259132,0.000036921185,0.00003457714],"domain_scores_gemma":[0.9998505,0.00004075065,0.000024439094,0.000019901387,0.000051583804,0.0000127612275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043405188,0.0007230352,0.0003805611,0.0005861541,0.00019018978,0.00035985032,0.0006558439,0.00038693665,0.000546478],"category_scores_gemma":[0.0007663468,0.00027513556,0.00074963534,0.00038201394,0.00020617836,0.0007480241,0.0004908918,0.00080718857,0.00020187309],"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":[0.00039320355,0.0003435119,0.009848288,0.00010158125,0.00020448984,0.00019625053,0.0000897989,0.56426775,0.023230769,0.00211085,0.0020758922,0.39713746],"study_design_scores_gemma":[0.000002544089,0.000012888987,0.0006179965,0.0000020382042,0.000007006418,0.000007851651,0.000004884919,0.9971078,0.0018339546,0.00029089846,0.0001090214,0.0000031172674],"about_ca_topic_score_codex":0.009237833,"about_ca_topic_score_gemma":0.010526766,"teacher_disagreement_score":0.009237833,"about_ca_system_score_codex":0.00066024327,"about_ca_system_score_gemma":0.0005098532,"threshold_uncertainty_score":0.018368125},"labels":[],"label_agreement":null},{"id":"W4214663719","doi":"10.3390/f13030383","title":"A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV","year":2022,"lang":"en","type":"article","venue":"Forests","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Global Positioning System; Computer vision; Firefighting; Geographic coordinate system; Artificial intelligence; Inertial measurement unit; Frame (networking); Remote sensing; Environmental science; Geography; Cartography","score_opus":0.005535185830692411,"score_gpt":0.19931333105789464,"score_spread":0.19377814522720224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214663719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14905573,0.0004741957,0.8463146,0.00009096789,0.000087575296,0.00008111032,0.00009486067,0.0019592284,0.001841674],"genre_scores_gemma":[0.768837,0.00027299047,0.22813703,0.00008198599,0.000024960951,0.000069621565,0.00025880217,0.000020388143,0.0022973102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986815,0.000012795856,0.000009221828,0.000041221276,0.000048204045,0.000020434132],"domain_scores_gemma":[0.9998777,0.000011752724,0.000022684018,0.000022302303,0.000050251685,0.000015297292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015598905,0.0003247546,0.0003313579,0.00052306947,0.0002471405,0.00022504997,0.00050365424,0.00028099696,0.00057273544],"category_scores_gemma":[0.00032081964,0.00017463624,0.00025023185,0.0002736875,0.00013942826,0.00047164978,0.00035973146,0.00023833619,0.00024255086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005362319,0.00016124739,0.0034489764,0.00012440453,0.0000377766,0.00027569674,0.00013576218,0.036409214,0.3270958,0.001816613,0.0026193613,0.6273388],"study_design_scores_gemma":[0.00004815901,0.00054071576,0.006209221,0.000018071414,0.00003879285,0.00066722825,0.00006232988,0.90613556,0.08196484,0.00057158753,0.0036990107,0.000044473672],"about_ca_topic_score_codex":0.0034363808,"about_ca_topic_score_gemma":0.003966987,"teacher_disagreement_score":0.0034363808,"about_ca_system_score_codex":0.0002853677,"about_ca_system_score_gemma":0.00042962926,"threshold_uncertainty_score":0.0068327785},"labels":[],"label_agreement":null},{"id":"W4214895504","doi":"10.3390/s22051977","title":"Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Deep learning; Computer science; Artificial intelligence; Segmentation; Machine learning; Transformer; Random forest; Architecture; Engineering; Geography","score_opus":0.012736050181385055,"score_gpt":0.19446363794009508,"score_spread":0.18172758775871004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214895504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08666422,0.0013433599,0.90501004,0.00022637,0.00009423771,0.000077048186,0.00029050492,0.0035894762,0.0027047156],"genre_scores_gemma":[0.8016551,0.0009020834,0.19130176,0.00025691398,0.00006079065,0.00006766306,0.0011606173,0.00016276726,0.004432309],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974424,0.000027250588,0.000015079288,0.00009309473,0.000058091748,0.000062206374],"domain_scores_gemma":[0.99982136,0.000047361988,0.000025649042,0.000026832122,0.000059364127,0.000019488698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044080516,0.0010229041,0.00078299624,0.0012142672,0.00029939532,0.0006956845,0.0011716154,0.0008266045,0.0014822545],"category_scores_gemma":[0.0006611985,0.00043525387,0.00097642804,0.000893971,0.00040010977,0.0010404077,0.0007075051,0.0009867112,0.00051996013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033281345,0.0002158444,0.003465004,0.00013574037,0.00013645148,0.00018910458,0.00010000985,0.36259538,0.026033321,0.0048799794,0.0036704554,0.5982459],"study_design_scores_gemma":[0.000003811832,0.000024578447,0.00033684904,0.000005384539,0.000011947159,0.00002922902,0.000011019966,0.99416745,0.0036280483,0.001326892,0.00045044976,0.0000043577766],"about_ca_topic_score_codex":0.011975346,"about_ca_topic_score_gemma":0.01393982,"teacher_disagreement_score":0.011975346,"about_ca_system_score_codex":0.0008337624,"about_ca_system_score_gemma":0.0009220286,"threshold_uncertainty_score":0.02381128},"labels":[],"label_agreement":null},{"id":"W4224113262","doi":"10.1049/ipr2.12491","title":"Multi‐step implicit Adams predictor‐corrector network for fire detection","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Predictor–corrector method; Computer science; Algorithm","score_opus":0.009394811562990166,"score_gpt":0.22268054826000316,"score_spread":0.213285736697013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224113262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11231379,0.0011390011,0.87212366,0.00045996462,0.00029186762,0.00012659744,0.0004226919,0.006717755,0.006404597],"genre_scores_gemma":[0.7862342,0.00042302167,0.20105664,0.0002820197,0.00007257454,0.00013234092,0.001000479,0.00012608612,0.010672616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999782,0.000021470914,0.000012831118,0.0000640301,0.00008125223,0.000038353723],"domain_scores_gemma":[0.9996705,0.00008175646,0.000041076888,0.000042173975,0.00014173533,0.000022778724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004034696,0.00096480123,0.00062449736,0.0005941485,0.00038825333,0.0005397466,0.0013919359,0.00070977723,0.0028987096],"category_scores_gemma":[0.0010771434,0.00044202188,0.0005466059,0.0003976985,0.00030150154,0.00091227423,0.00058076635,0.0009968699,0.0006055635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042050125,0.00020808463,0.004145932,0.00013997719,0.0001393936,0.00022914032,0.000065094006,0.46089536,0.022904154,0.004031053,0.007988861,0.49883246],"study_design_scores_gemma":[0.0000037606674,0.000017056585,0.00017567947,0.0000030019962,0.000005701185,0.00001046747,0.0000020174161,0.9967507,0.0024872608,0.0002684861,0.00027261119,0.000003295734],"about_ca_topic_score_codex":0.015484024,"about_ca_topic_score_gemma":0.018200975,"teacher_disagreement_score":0.015484024,"about_ca_system_score_codex":0.0007978954,"about_ca_system_score_gemma":0.0012015639,"threshold_uncertainty_score":0.030787826},"labels":[],"label_agreement":null},{"id":"W4225757380","doi":"10.4018/978-1-6684-5678-1.ch013","title":"Using Wireless Multimedia Sensor Networks to Enhance Early Forest Fire Detection","year":2022,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Fire detection; Computer science; Wireless sensor network; Wireless; Real-time computing; Modal; Field (mathematics); Multimedia; Telecommunications; Engineering; Computer network; Architectural engineering","score_opus":0.012810770650692655,"score_gpt":0.22642001806046477,"score_spread":0.2136092474097721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225757380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19500838,0.00874227,0.7580835,0.0003717667,0.00030052717,0.00020336913,0.00039137018,0.0020921146,0.034806583],"genre_scores_gemma":[0.69159776,0.0044901604,0.2852855,0.00021091239,0.0001374993,0.00010211426,0.00041183736,0.00008498817,0.017679168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999138,0.000016216298,0.0000031237273,0.000022774622,0.000035269146,0.000008790605],"domain_scores_gemma":[0.9999218,0.000035361678,0.000008507921,0.0000075670296,0.000022553306,0.000004314755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017353207,0.000511864,0.00017216116,0.0005026936,0.00010224241,0.0003768503,0.00046002868,0.00039279336,0.0016643759],"category_scores_gemma":[0.00021814655,0.00010431902,0.00016571581,0.0005115638,0.00008508655,0.0005759784,0.00025239962,0.00020530149,0.00048270385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020762943,0.00015349781,0.0022813347,0.00045841807,0.00004246381,0.0002800812,0.000110033434,0.020966588,0.20222135,0.0026944985,0.0023894026,0.76819474],"study_design_scores_gemma":[0.000038077636,0.00095398445,0.012489295,0.0001581025,0.00023245811,0.0014754935,0.00025068264,0.54347956,0.35834545,0.006258036,0.076248735,0.0000701767],"about_ca_topic_score_codex":0.00063631916,"about_ca_topic_score_gemma":0.0015247632,"teacher_disagreement_score":0.0016643759,"about_ca_system_score_codex":0.00016085457,"about_ca_system_score_gemma":0.00013181202,"threshold_uncertainty_score":0.0055678487},"labels":[],"label_agreement":null},{"id":"W4226185317","doi":"10.1109/cogmi52975.2021.00010","title":"FireWarn: Fire Hazards Detection Using Deep Learning Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada; Queen's University","funders":"Defence Research and Development Canada","keywords":"Smoke; Fire detection; Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Bounding overwatch; Contextual image classification; Pattern recognition (psychology); Test set; Image (mathematics); Computer vision; Remote sensing; Engineering; Geography","score_opus":0.019020538142838734,"score_gpt":0.205248325788981,"score_spread":0.18622778764614226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226185317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37310994,0.0037400466,0.5360866,0.0011214443,0.0007640715,0.00051842845,0.016863797,0.05426976,0.013525919],"genre_scores_gemma":[0.73586506,0.0008602201,0.21940766,0.0006798266,0.00012192211,0.00024566802,0.028425967,0.0005991429,0.013794469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997004,0.000033031818,0.000014395264,0.000111257556,0.00007298766,0.000067942376],"domain_scores_gemma":[0.99975973,0.0000633283,0.000029575898,0.0000537053,0.00006908404,0.000024557263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062630785,0.0014698873,0.0005989179,0.0010095461,0.00034490088,0.000746035,0.001802854,0.0011248656,0.0033799966],"category_scores_gemma":[0.0012056803,0.00060051633,0.0008817124,0.00054410973,0.0002758332,0.0013273206,0.0010050131,0.0015549258,0.0013610402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010303557,0.00090551184,0.017822418,0.0004529617,0.0005202933,0.0003379041,0.00009783568,0.33603913,0.024551664,0.0026168863,0.052532833,0.56309223],"study_design_scores_gemma":[0.000027584996,0.00008921717,0.0017325782,0.000028865412,0.00002819841,0.00005352736,0.000016561253,0.9844487,0.009712692,0.0011892425,0.002655618,0.000017202874],"about_ca_topic_score_codex":0.01934516,"about_ca_topic_score_gemma":0.033589244,"teacher_disagreement_score":0.01934516,"about_ca_system_score_codex":0.0011897116,"about_ca_system_score_gemma":0.0011030881,"threshold_uncertainty_score":0.038465142},"labels":[],"label_agreement":null},{"id":"W4231942784","doi":"10.1503/cmaj.131763","title":"Alcohol inhalation","year":2014,"lang":"en","type":"review","venue":"Canadian Medical Association Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Courtesy; Inhalation; Alcohol; Medicine; Computer science; Anesthesia; Chemistry; Law; Organic chemistry; Political science","score_opus":0.01369765899181757,"score_gpt":0.24922297476616515,"score_spread":0.23552531577434757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231942784","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014237447,0.8474198,0.0044315504,0.0034069084,0.004067721,0.00021326305,0.0010482109,0.0006752019,0.13731351],"genre_scores_gemma":[0.017970765,0.8546885,0.0034517914,0.005902408,0.0018757108,0.00021193175,0.0019495944,0.00016726933,0.11378198],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994293,0.00009001508,0.00005273758,0.00008826494,0.00027834746,0.00006129994],"domain_scores_gemma":[0.999658,0.00008640293,0.00005268148,0.00003116286,0.00013312604,0.000038645947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045975225,0.00088086893,0.0009581827,0.0012755248,0.00051337434,0.0011414477,0.0012336493,0.0014744911,0.06443334],"category_scores_gemma":[0.0013023436,0.00022411958,0.00079123856,0.00077833404,0.0004889668,0.001128158,0.001260948,0.0014249823,0.049574774],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000987603,0.00009151448,0.00029699426,0.0063555874,0.00005671667,0.00037037148,0.00007312261,0.00006920733,0.0032281124,0.0032994056,0.22249413,0.76356614],"study_design_scores_gemma":[0.000018705121,0.000074626514,0.000604815,0.0017351863,0.00002903487,0.0020532932,0.000053452113,0.000025401217,0.0014843717,0.0009407137,0.9929676,0.000012875854],"about_ca_topic_score_codex":0.0016579902,"about_ca_topic_score_gemma":0.0021713711,"teacher_disagreement_score":0.06443334,"about_ca_system_score_codex":0.00043322452,"about_ca_system_score_gemma":0.00090897584,"threshold_uncertainty_score":0.21555096},"labels":[],"label_agreement":null},{"id":"W4232615069","doi":"10.1117/3.853623.ch40","title":"Shot Noise","year":2010,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Noise (video); Computer science; Acoustics; Physics; Artificial intelligence","score_opus":0.015974874755665867,"score_gpt":0.19393990504546255,"score_spread":0.17796503028979668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232615069","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016233014,0.0069107097,0.07410308,0.00077933073,0.0017858563,0.00006232306,0.00022181077,0.0016653633,0.91284823],"genre_scores_gemma":[0.014244217,0.004365509,0.008061745,0.000615523,0.00032526202,0.00004205875,0.0003139239,0.00036292488,0.9716688],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995697,0.000025927737,0.000009944821,0.00007410453,0.00029032776,0.000030117919],"domain_scores_gemma":[0.99977666,0.00003472577,0.000010896001,0.000042191325,0.00011486097,0.000020744286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023240568,0.0012215995,0.0007185439,0.0012355656,0.001186663,0.0023249392,0.0010600202,0.001666269,0.087740734],"category_scores_gemma":[0.0007086074,0.00039753568,0.00043384114,0.00076756557,0.00089076,0.0015364344,0.0017021957,0.0014717116,0.05202322],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007082588,0.00007690747,0.00018100484,0.00035417403,0.000022698578,0.00024282854,0.00028324843,0.0019191877,0.021413038,0.11392803,0.22000454,0.6415035],"study_design_scores_gemma":[0.0000058037476,0.000049073773,0.00045264984,0.00017810476,0.000018088456,0.0009185381,0.00013224594,0.0025847007,0.010756628,0.03303359,0.9518401,0.00003059158],"about_ca_topic_score_codex":0.0013731205,"about_ca_topic_score_gemma":0.0022429246,"teacher_disagreement_score":0.087740734,"about_ca_system_score_codex":0.00080425444,"about_ca_system_score_gemma":0.0006691301,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4241236171","doi":"10.1142/9789812773081_0036","title":"ULTRA VIOLET DETECTION SENSORS","year":2006,"lang":"en","type":"article","venue":"Frontiers in Electronics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Computer science; Ultra violet; Remote sensing; Optoelectronics; Materials science; Geology","score_opus":0.0017409599764912477,"score_gpt":0.15391859158635876,"score_spread":0.15217763160986753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241236171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18678705,0.042397793,0.39773306,0.0047963317,0.005528755,0.00035872313,0.0016205714,0.006963676,0.35381398],"genre_scores_gemma":[0.5257581,0.011465535,0.13357715,0.003565707,0.00062421995,0.00020116745,0.001440686,0.00048927416,0.32287812],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985404,0.000113888076,0.00004083021,0.0003120136,0.00085569994,0.00013714658],"domain_scores_gemma":[0.99957603,0.00007616216,0.00007793358,0.0000651557,0.00015066295,0.00005408066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048385415,0.00089908356,0.00050549366,0.00090218225,0.00073983136,0.002350826,0.0015434427,0.001532941,0.0071469466],"category_scores_gemma":[0.0005784213,0.00074164616,0.00041690582,0.0007417426,0.0006124417,0.0019446411,0.00152882,0.0015797978,0.0050731874],"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":[0.00023321701,0.00007214529,0.0008100802,0.0003008397,0.000037567374,0.00015594353,0.00015708344,0.0005203197,0.85700244,0.021936893,0.011138407,0.10763507],"study_design_scores_gemma":[0.000007977436,0.00017767503,0.0011139767,0.000043160337,0.000024573426,0.0006968031,0.00006910294,0.002972258,0.8909236,0.0016794011,0.10224963,0.000041946285],"about_ca_topic_score_codex":0.00034808138,"about_ca_topic_score_gemma":0.0006968772,"teacher_disagreement_score":0.0071469466,"about_ca_system_score_codex":0.00096454937,"about_ca_system_score_gemma":0.0005311587,"threshold_uncertainty_score":0.023908973},"labels":[],"label_agreement":null},{"id":"W4248178777","doi":"10.2523/75946-ms","title":"Case Study: Including the Effects of Stagnant Water in Gas Gathering System Modeling","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPE Gas Technology Symposium","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Computer science; Environmental science; Petroleum engineering; Engineering","score_opus":0.01130659637303812,"score_gpt":0.19382981042958952,"score_spread":0.1825232140565514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248178777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.955597,0.000079635945,0.03387237,0.0003523066,0.00001794346,0.00019667878,0.00044401232,0.00041404518,0.009025958],"genre_scores_gemma":[0.9871947,0.000057993064,0.009945739,0.000019554926,0.0000029802218,0.000042419975,0.000100677746,0.000021275553,0.0026146853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948716,0.00019089018,0.000017597831,0.000057761743,0.00014660142,0.00009995517],"domain_scores_gemma":[0.9989766,0.0006362874,0.000074751835,0.000068814465,0.00015971759,0.00008387111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061205594,0.000765641,0.0003963167,0.0005773143,0.0013611673,0.0011268927,0.0012211488,0.0017910362,0.002424905],"category_scores_gemma":[0.001729537,0.00041998105,0.0005288515,0.00082326843,0.0009894232,0.00081938494,0.0006470682,0.0007920156,0.00016949816],"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":[0.00023990718,0.00017136468,0.011158227,0.00008350859,0.00003008552,0.0027494926,0.000670024,0.9695514,0.004054854,0.0032179377,0.00061292015,0.0074603655],"study_design_scores_gemma":[0.00007577991,0.0003842649,0.003831824,0.000022063161,0.000059809903,0.00037044688,0.0014807082,0.977209,0.010834463,0.0015513458,0.0041279504,0.00005232707],"about_ca_topic_score_codex":0.18959671,"about_ca_topic_score_gemma":0.22291209,"teacher_disagreement_score":0.18959671,"about_ca_system_score_codex":0.0032486273,"about_ca_system_score_gemma":0.002918961,"threshold_uncertainty_score":0.37698627},"labels":[],"label_agreement":null},{"id":"W4249109408","doi":"10.1115/icef2005-1302","title":"Further Development of a Smoke Sensor for Diesel Engines","year":2005,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Royal Military College of Canada","funders":"","keywords":"Smoke; Soot; Diesel fuel; Automotive engineering; Exhaust gas recirculation; Spark plug; SIGNAL (programming language); Diesel engine; Materials science; Insulator (electricity); Electrode; Diesel particulate filter; Environmental science; Engineering; Optoelectronics; Waste management; Combustion; Internal combustion engine; Mechanical engineering; Computer science; Chemistry","score_opus":0.017158920309274593,"score_gpt":0.2172859263329826,"score_spread":0.200127006023708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249109408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7919827,0.0017532461,0.19756858,0.0003847925,0.00023963586,0.00065123755,0.00022926829,0.00076281076,0.006427751],"genre_scores_gemma":[0.6967299,0.0015634266,0.29113108,0.00026492067,0.00006419548,0.00017637294,0.000559769,0.00011498234,0.0093953945],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966335,0.000040811934,0.0000163763,0.00005560441,0.00019292804,0.000031005235],"domain_scores_gemma":[0.999713,0.000060558206,0.000013639631,0.000024887238,0.00015224463,0.00003574056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000762615,0.00035821102,0.00041584842,0.00024243294,0.00018700032,0.0003340696,0.0006481629,0.00052162836,0.0016358435],"category_scores_gemma":[0.0006045097,0.00019271825,0.00036218172,0.00014477331,0.00018751096,0.0006496825,0.00029446906,0.00037238537,0.0004104179],"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":[0.000054604043,0.00009587367,0.00045634768,0.00010480932,0.00000806578,0.00006333007,0.00003564458,0.0004197169,0.9794586,0.0003041961,0.00007010027,0.018928695],"study_design_scores_gemma":[0.000031880645,0.0010734854,0.0014996937,0.00001051946,0.000019149697,0.00017958322,0.00002406569,0.007764787,0.98319083,0.00010860761,0.006085605,0.000011871803],"about_ca_topic_score_codex":0.0006515917,"about_ca_topic_score_gemma":0.001035431,"teacher_disagreement_score":0.0016358435,"about_ca_system_score_codex":0.00022942838,"about_ca_system_score_gemma":0.00043295618,"threshold_uncertainty_score":0.0054724813},"labels":[],"label_agreement":null},{"id":"W4251329488","doi":"10.2495/safe-v3-n4-278-289","title":"Abnormal pedestrians activities recognizer and tracker","year":2013,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Pedestrian; Human–computer interaction; Artificial intelligence; Computer vision; Transport engineering; Engineering","score_opus":0.0038749890257941456,"score_gpt":0.17960663229556137,"score_spread":0.17573164326976723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251329488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13249676,0.0011808599,0.8491828,0.00015235296,0.00040493195,0.00018263448,0.0010671115,0.009524835,0.0058077304],"genre_scores_gemma":[0.6458396,0.00078196934,0.33542082,0.00018378455,0.00019708085,0.00017346772,0.0019436645,0.00017667917,0.015282923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923563,0.000073060466,0.00004703195,0.00031989868,0.00023451206,0.00008988493],"domain_scores_gemma":[0.999582,0.000054571097,0.000059486018,0.00006373975,0.00018834311,0.00005190522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055606954,0.0006385515,0.0009938623,0.0019969558,0.00027356984,0.0006586557,0.0007039905,0.0008215021,0.0020392751],"category_scores_gemma":[0.0007785065,0.0003476899,0.00047741132,0.0007808398,0.00019459668,0.0006275184,0.0004652268,0.0005270296,0.0022653188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008279848,0.00033967485,0.024344955,0.00026246038,0.00012195778,0.00093118893,0.00015780056,0.009896902,0.19302674,0.0020821325,0.008311542,0.75969666],"study_design_scores_gemma":[0.00010805817,0.0010915247,0.08765411,0.00006992713,0.00027238874,0.004234538,0.000132033,0.67528355,0.20097934,0.0025437386,0.027508203,0.0001226562],"about_ca_topic_score_codex":0.0024990565,"about_ca_topic_score_gemma":0.0026187669,"teacher_disagreement_score":0.0024990565,"about_ca_system_score_codex":0.0003130847,"about_ca_system_score_gemma":0.00045930198,"threshold_uncertainty_score":0.006822109},"labels":[],"label_agreement":null},{"id":"W4283755424","doi":"10.1016/j.firesaf.2022.103629","title":"RGB image-based hybrid model for automatic prediction of flashover in compartment fires","year":2022,"lang":"en","type":"article","venue":"Fire Safety Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"National Research Council","keywords":"RGB color model; Artificial intelligence; Process (computing); Arc flash; Artificial neural network; Computer science; Computer vision; Test data; Simulation; Engineering","score_opus":0.013443776078712022,"score_gpt":0.21093443550858273,"score_spread":0.1974906594298707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283755424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65970194,0.0011276937,0.33150825,0.00016275804,0.00022323543,0.000059052894,0.00069393776,0.0022199159,0.0043031727],"genre_scores_gemma":[0.9911197,0.00015486094,0.007265833,0.000023888073,0.000014728486,0.000018637864,0.00019233867,0.000015173039,0.0011948317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999323,0.0000063401512,0.0000033382157,0.00002232853,0.000016055235,0.00001959114],"domain_scores_gemma":[0.9999269,0.000019522027,0.000009509839,0.000007034702,0.00002926132,0.000007786675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015406207,0.0005083326,0.00055369723,0.00046498986,0.00017742104,0.0005245096,0.00048654055,0.0004910759,0.0012609643],"category_scores_gemma":[0.00025675612,0.0002195398,0.00053763343,0.0003286234,0.00013434903,0.00037015346,0.00024994952,0.000390551,0.00032955586],"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":[0.0006076684,0.0002942257,0.011849348,0.00012375451,0.00015783223,0.00022792457,0.000064927524,0.79556423,0.031731505,0.00046109583,0.0018495198,0.15706801],"study_design_scores_gemma":[0.0000024698597,0.000020816196,0.0021267345,0.0000025359705,0.000010952837,0.000010013021,0.0000050651997,0.99648356,0.0012054426,0.00006374003,0.000064932756,0.0000037172902],"about_ca_topic_score_codex":0.015033074,"about_ca_topic_score_gemma":0.011653417,"teacher_disagreement_score":0.015033074,"about_ca_system_score_codex":0.00034334385,"about_ca_system_score_gemma":0.0003409063,"threshold_uncertainty_score":0.029891133},"labels":[],"label_agreement":null},{"id":"W4285099192","doi":"10.18280/jesa.550302","title":"A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Architectural engineering; Firefighting; Conceptual design; Computer science; Human–computer interaction; Artificial intelligence; Engineering; Geography; Cartography","score_opus":0.022641509822345918,"score_gpt":0.2425007720796135,"score_spread":0.2198592622572676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285099192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011320654,0.00045469188,0.97001463,0.0002532666,0.0001806762,0.0004450596,0.000085712774,0.0019645544,0.015280683],"genre_scores_gemma":[0.3902661,0.0006176261,0.5815659,0.00032501976,0.00005904707,0.0011116349,0.00023167,0.00014963024,0.025673289],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997009,0.000033680568,0.000018415905,0.00008738292,0.00012078581,0.000038723603],"domain_scores_gemma":[0.99984396,0.000020900361,0.000023337281,0.000016472559,0.000071598515,0.00002359576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030731413,0.0006229316,0.00046287375,0.00039259734,0.0004384901,0.00081764173,0.0017753502,0.00137538,0.0048203045],"category_scores_gemma":[0.00032009557,0.00039523427,0.00065515976,0.00013582986,0.0004684243,0.00062454614,0.0005666288,0.0005701684,0.0016678434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005722292,0.00036529222,0.0025582304,0.0027289067,0.0001441752,0.00231186,0.0008853086,0.12898538,0.4352953,0.07744186,0.009765841,0.33894563],"study_design_scores_gemma":[0.00021931704,0.0034063344,0.0043309946,0.0003047033,0.00024983753,0.0032704943,0.0002900429,0.6625977,0.11341795,0.010228251,0.20148696,0.00019745415],"about_ca_topic_score_codex":0.0014305898,"about_ca_topic_score_gemma":0.0011306179,"teacher_disagreement_score":0.0048203045,"about_ca_system_score_codex":0.0003606483,"about_ca_system_score_gemma":0.000740453,"threshold_uncertainty_score":0.01612556},"labels":[],"label_agreement":null},{"id":"W4285101200","doi":"10.1109/aiiot54504.2022.9817232","title":"Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"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":"NIST; Support vector machine; Logistic regression; Computer science; Artificial intelligence; Machine learning; Work (physics); Regression analysis; Fire safety; Training (meteorology); Aeronautics; Environmental science; Forensic engineering; Statistics; Engineering; Meteorology; Mathematics; Geography","score_opus":0.023280337687744502,"score_gpt":0.26984436472497875,"score_spread":0.24656402703723423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285101200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81510764,0.00035546115,0.18136007,0.00023677554,0.00006755635,0.00007701005,0.00028979083,0.0008410552,0.001664724],"genre_scores_gemma":[0.96364903,0.000116984564,0.03495471,0.00002532851,0.000016097722,0.000036630023,0.0003397538,0.000021359949,0.00084003585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916244,0.0003022462,0.00007334741,0.00016784137,0.00021157954,0.000082545936],"domain_scores_gemma":[0.996986,0.0019187152,0.0003769603,0.00019195369,0.00046133978,0.000065001565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018376722,0.00095877907,0.0005520212,0.0017548283,0.00027256255,0.0009947921,0.0005641833,0.00078298163,0.00073987216],"category_scores_gemma":[0.0058908844,0.00026179766,0.0007426805,0.0010132573,0.00025297108,0.0011997146,0.00039039575,0.00083730737,0.00052905193],"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":[0.00034731854,0.0006966229,0.17562333,0.00010434719,0.0002777185,0.00016769904,0.00022127885,0.5697105,0.007348499,0.0008550241,0.0010779407,0.24356972],"study_design_scores_gemma":[0.000004982996,0.00008513446,0.011147911,0.000010839664,0.000014791555,0.0000276369,0.00008651135,0.98605645,0.0020121667,0.00038070112,0.000157631,0.000015246718],"about_ca_topic_score_codex":0.009094297,"about_ca_topic_score_gemma":0.00768224,"teacher_disagreement_score":0.009094297,"about_ca_system_score_codex":0.00054457545,"about_ca_system_score_gemma":0.0005748763,"threshold_uncertainty_score":0.018082738},"labels":[],"label_agreement":null},{"id":"W4285105143","doi":"10.5383/juspn.16.01.005","title":"Fire Risk Prediction Using Cloud-based Weather Data Services","year":2022,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Norges Forskningsråd","keywords":"Environmental science; Meteorology; Risk assessment; Cloud computing; Forensic engineering; Engineering; Geography; Computer science; Computer security","score_opus":0.016713078529064958,"score_gpt":0.212630525431769,"score_spread":0.19591744690270405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285105143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8664759,0.0006404813,0.11336531,0.000634039,0.00029901424,0.00040930175,0.0043613845,0.0063379086,0.007476604],"genre_scores_gemma":[0.9853265,0.00015250208,0.012653488,0.000037171445,0.000014942888,0.00004226623,0.0012184823,0.00003730238,0.00051741116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965847,0.0000409255,0.000028020486,0.00010124765,0.000113843715,0.000057516674],"domain_scores_gemma":[0.9992317,0.00024968816,0.00009113668,0.00012570302,0.00019873815,0.00010314422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004829015,0.0009658861,0.0006217338,0.0005437611,0.00034415888,0.0010152197,0.0009269486,0.0005149483,0.0012860665],"category_scores_gemma":[0.0019306705,0.00021884676,0.0004910069,0.00044996224,0.00014836238,0.00095107115,0.00069606845,0.0006623451,0.0005421054],"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":[0.0006602689,0.0006620233,0.04062203,0.00013283316,0.0001070135,0.00029433472,0.00009151683,0.85854876,0.0092795435,0.0012195659,0.004036262,0.08434581],"study_design_scores_gemma":[0.000008743763,0.000024099929,0.0022870929,0.0000043401287,0.000006088125,0.000010276362,0.000012342435,0.9960294,0.001197614,0.00020024285,0.00021457227,0.000005321046],"about_ca_topic_score_codex":0.034308124,"about_ca_topic_score_gemma":0.017664203,"teacher_disagreement_score":0.034308124,"about_ca_system_score_codex":0.000821804,"about_ca_system_score_gemma":0.0007393034,"threshold_uncertainty_score":0.06821686},"labels":[],"label_agreement":null},{"id":"W4288047703","doi":"10.1109/icuas54217.2022.9836074","title":"Fire Monitoring with a Fixed-wing Unmanned Aerial Vehicle","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Unmanned Aircraft Systems (ICUAS)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Track (disk drive); Fixed wing; Computer science; Firefighting; SAFER; Real-time computing; Aerospace engineering; Simulation; Engineering; Wing; Computer security; Geography","score_opus":0.019990520406332275,"score_gpt":0.22764402425062955,"score_spread":0.20765350384429726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288047703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8557058,0.00018807624,0.13997136,0.00007010645,0.00003226313,0.000066478395,0.00013314465,0.000819352,0.0030134588],"genre_scores_gemma":[0.9602584,0.00006231012,0.039115276,0.000009705415,0.0000030733859,0.000014686892,0.00008657086,0.000012621242,0.0004372693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987006,0.000021346154,0.0000044581684,0.000029339573,0.000055768127,0.000019043362],"domain_scores_gemma":[0.9998666,0.000035706697,0.00002695059,0.000022484735,0.000036002708,0.000012215893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015858373,0.00031537705,0.00029640438,0.00038761707,0.00024892262,0.000359362,0.00047526884,0.0002804451,0.00043573522],"category_scores_gemma":[0.00036498654,0.00012994593,0.00024636858,0.00020106531,0.00017907713,0.00039286297,0.00027712536,0.00019453508,0.0001331253],"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":[0.00074661657,0.0002358445,0.02927583,0.00017256892,0.0001292017,0.00070043746,0.00024881584,0.5776864,0.12402715,0.00088453386,0.0011429548,0.26474965],"study_design_scores_gemma":[0.000019217654,0.00019871713,0.009628418,0.000008264395,0.000017717479,0.00009371294,0.000085855296,0.9647119,0.02430617,0.00025550995,0.0006592005,0.000015320831],"about_ca_topic_score_codex":0.0061318143,"about_ca_topic_score_gemma":0.0068406416,"teacher_disagreement_score":0.0061318143,"about_ca_system_score_codex":0.0002904102,"about_ca_system_score_gemma":0.00029334906,"threshold_uncertainty_score":0.012192249},"labels":[],"label_agreement":null},{"id":"W4288047839","doi":"10.1109/icuas54217.2022.9836119","title":"An Early Forest Fire Detection System Based on DJI M300 Drone and H20T Camera","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Unmanned Aircraft Systems (ICUAS)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Computer science; Robustness (evolution); Artificial intelligence; Fire detection; Remote sensing; RGB color model; On board; Computer vision; Constant false alarm rate; False alarm; Engineering; Geography","score_opus":0.011847946850684287,"score_gpt":0.21343614996408072,"score_spread":0.20158820311339642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288047839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5663319,0.0014875199,0.39078122,0.0003685548,0.00044251047,0.0009554572,0.0014533816,0.014522186,0.023657223],"genre_scores_gemma":[0.8839815,0.00029500097,0.104479454,0.00034682784,0.000058192934,0.00022561052,0.0010648151,0.000077680634,0.009470906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998024,0.000013691498,0.000009665159,0.000066471235,0.00007681783,0.00003094621],"domain_scores_gemma":[0.99986684,0.000012918317,0.000012487878,0.000020334552,0.00005835681,0.000028951901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001633779,0.0005377277,0.00053086865,0.00060392,0.00030968647,0.00038342972,0.0007084575,0.00046270347,0.0032004546],"category_scores_gemma":[0.00024992094,0.00025190052,0.0002609424,0.00024634227,0.00012257753,0.00052737584,0.00045670278,0.00040931843,0.00095769664],"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":[0.0012475643,0.00057012023,0.013311332,0.0005153024,0.00014514536,0.0010001629,0.00035998414,0.00802339,0.4383775,0.0012601383,0.014159769,0.5210296],"study_design_scores_gemma":[0.000599086,0.0025116298,0.06674826,0.00012779886,0.00034301516,0.0037330743,0.00037963066,0.5736943,0.30821756,0.0010946343,0.04224373,0.00030731],"about_ca_topic_score_codex":0.0034813161,"about_ca_topic_score_gemma":0.006453191,"teacher_disagreement_score":0.0034813161,"about_ca_system_score_codex":0.00027997865,"about_ca_system_score_gemma":0.00034746868,"threshold_uncertainty_score":0.010706544},"labels":[],"label_agreement":null},{"id":"W4293053528","doi":"10.1109/civemsa53371.2022.9853707","title":"Warm Liquid Spill Detection and Tracking Using Thermal Imaging","year":2022,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Minimum bounding box; Context (archaeology); Unavailability; Pixel; Detector; Segmentation; Image (mathematics); Engineering","score_opus":0.010760178876051628,"score_gpt":0.1994002470377028,"score_spread":0.18864006816165116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293053528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2633126,0.0013929777,0.72401637,0.00018529406,0.0001170692,0.00016855069,0.00062736624,0.004162917,0.006016805],"genre_scores_gemma":[0.76444805,0.0010168056,0.22985974,0.00019837517,0.00006510923,0.00009819263,0.0010949103,0.00017298186,0.0030458746],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996816,0.000035462355,0.000014332692,0.00010177712,0.000114039554,0.000052781088],"domain_scores_gemma":[0.9997358,0.00005678639,0.00006829571,0.000036330814,0.000075607066,0.00002721644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036242485,0.00060940254,0.0005398425,0.0014300356,0.00027986147,0.00081457733,0.0005607781,0.000741555,0.0010147],"category_scores_gemma":[0.0008668297,0.00028622302,0.00054041296,0.0006757646,0.0003414322,0.00087587984,0.00070453994,0.00049755414,0.00064981834],"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":[0.0009140831,0.00030562488,0.0192288,0.00054908544,0.00018222547,0.00050548336,0.0001928832,0.042174134,0.5154599,0.0012700376,0.0038846915,0.41533297],"study_design_scores_gemma":[0.000029584984,0.0004409413,0.028182188,0.00011180706,0.00016919097,0.0011573918,0.00021895989,0.60089076,0.36119974,0.0017346885,0.0057701985,0.000094536794],"about_ca_topic_score_codex":0.0016942394,"about_ca_topic_score_gemma":0.0034466463,"teacher_disagreement_score":0.0016942394,"about_ca_system_score_codex":0.0002914445,"about_ca_system_score_gemma":0.0004428922,"threshold_uncertainty_score":0.0033945441},"labels":[],"label_agreement":null},{"id":"W4297833718","doi":"","title":"Experimental response of an optical sensor used to determine the moment of blast by sensing the flash of the explosion","year":2018,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Fire Detection and Safety Systems","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":"Impact","funders":"","keywords":"Flash (photography); Moment (physics); Materials science; Optics; Acoustics; Physics; Classical mechanics","score_opus":0.1676237257850141,"score_gpt":0.4715493559831496,"score_spread":0.3039256301981355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297833718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903439,0.00020231314,0.0075174617,0.000081682054,0.00007710039,0.000052767882,0.00017844097,0.00015048035,0.0013958669],"genre_scores_gemma":[0.99011177,0.00019908545,0.007945141,0.00011710999,0.000015089042,0.00006451479,0.00012324005,0.000024293133,0.0013997883],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941933,0.00007988737,0.000032412063,0.000135826,0.0002301363,0.000102415834],"domain_scores_gemma":[0.9988426,0.0005560029,0.00013004309,0.0000802744,0.00031068348,0.00008045405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042403408,0.00038431058,0.0002576819,0.00029564407,0.0003021389,0.00028742786,0.0006326311,0.0010047924,0.0022372238],"category_scores_gemma":[0.0014614494,0.00027349446,0.00020534103,0.00033176126,0.0003587941,0.0003836312,0.00029475446,0.0003504299,0.00031823033],"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":[0.00027569322,0.000069417525,0.0008203193,0.0000803867,0.0000133513295,0.00008800641,0.00013669675,0.00035261767,0.9950471,0.000072633855,0.00008791698,0.002955928],"study_design_scores_gemma":[0.00002087767,0.0013048633,0.005080394,0.0000131979805,0.000022175427,0.000116530275,0.00012002657,0.0044603446,0.9881031,0.000031622953,0.0007142087,0.000012647294],"about_ca_topic_score_codex":0.0008203451,"about_ca_topic_score_gemma":0.001010657,"teacher_disagreement_score":0.0022372238,"about_ca_system_score_codex":0.00042274964,"about_ca_system_score_gemma":0.0003314034,"threshold_uncertainty_score":0.007484257},"labels":[],"label_agreement":null},{"id":"W4300559870","doi":"","title":"Multimodal three-dimensional vision for wildland fires detection and analysis","year":2017,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Fire Detection and Safety Systems","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":"Université Laval; Université de Moncton","funders":"","keywords":"Computer science; Remote sensing; Computer vision; Artificial intelligence; Environmental science; Geology","score_opus":0.008652540659550819,"score_gpt":0.217643968543879,"score_spread":0.20899142788432817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300559870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10415164,0.002785126,0.8808706,0.00053722423,0.0002325162,0.00019301489,0.0014708277,0.002977347,0.0067817247],"genre_scores_gemma":[0.6367776,0.002684419,0.34838837,0.0005493049,0.00026136547,0.0002262633,0.0020246736,0.00028562237,0.008802366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997321,0.000046344303,0.000009888628,0.000064802145,0.000093651375,0.000053229967],"domain_scores_gemma":[0.99965787,0.000098609475,0.000027341215,0.000047197434,0.0001222765,0.000046788267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058965036,0.00063971494,0.00073804305,0.0015496742,0.00024362138,0.0012638614,0.00049100455,0.00090471783,0.004807806],"category_scores_gemma":[0.0010112099,0.0002486605,0.0007777209,0.0009928377,0.0003378459,0.00086779165,0.0009740888,0.0005855294,0.0015074852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045864214,0.00022388627,0.0018603532,0.00033506085,0.00013312192,0.00019498993,0.0001660383,0.029325213,0.2667883,0.0017716867,0.009646012,0.6890967],"study_design_scores_gemma":[0.000026931388,0.00020250308,0.014303957,0.00008451904,0.000116127594,0.00042424072,0.00015466906,0.89738184,0.071902975,0.0048522106,0.010478451,0.00007151399],"about_ca_topic_score_codex":0.0025656107,"about_ca_topic_score_gemma":0.003843984,"teacher_disagreement_score":0.004807806,"about_ca_system_score_codex":0.00032178304,"about_ca_system_score_gemma":0.0004906602,"threshold_uncertainty_score":0.016083717},"labels":[],"label_agreement":null},{"id":"W4312943501","doi":"10.1109/tetci.2022.3214826","title":"Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IntelliView Technologies (Canada); Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Robustness (evolution); Leak; Computer science; Leak detection; Gas leak; Artificial intelligence; Background subtraction; Pyramid (geometry); Convolutional neural network; Fusion mechanism; Liquefied natural gas; Computer vision; Natural gas; Real-time computing; Fusion; Engineering; Pixel","score_opus":0.013057235627575613,"score_gpt":0.24699071540988207,"score_spread":0.23393347978230647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312943501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026739309,0.0009936088,0.9682795,0.000183819,0.00005292174,0.000064909436,0.00016530264,0.0011597803,0.0023608678],"genre_scores_gemma":[0.70462024,0.0019917798,0.28668165,0.0002950766,0.00013383191,0.00013534287,0.000881126,0.00014745117,0.005113465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969673,0.000028359113,0.000012736701,0.00010528422,0.00010265218,0.000054263473],"domain_scores_gemma":[0.99986815,0.000019997777,0.000021718606,0.000013949347,0.000059886297,0.000016411583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044884926,0.0010533866,0.0008476797,0.0011433107,0.00030360694,0.0007280341,0.0011713108,0.0007373323,0.0010939956],"category_scores_gemma":[0.0006246488,0.0003355591,0.0010510985,0.00059031614,0.00040903778,0.0009357932,0.0009791763,0.0006545301,0.00035226461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036000644,0.0001487151,0.0046083024,0.00030603624,0.0001912702,0.0005906141,0.00022909758,0.34084865,0.07825321,0.011389353,0.004393228,0.5586815],"study_design_scores_gemma":[0.000006066065,0.000051352305,0.00084945315,0.000013148076,0.000047916077,0.00012802222,0.000021854095,0.9840514,0.010904481,0.0022268994,0.0016829025,0.000016522647],"about_ca_topic_score_codex":0.009670673,"about_ca_topic_score_gemma":0.0075306576,"teacher_disagreement_score":0.009670673,"about_ca_system_score_codex":0.00090134965,"about_ca_system_score_gemma":0.0009591514,"threshold_uncertainty_score":0.019228756},"labels":[],"label_agreement":null},{"id":"W4318814340","doi":"10.2139/ssrn.4293647","title":"Firefighting with a Distance-Based Restriction","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Memorial University of Newfoundland; Toronto Metropolitan University","funders":"","keywords":"Firefighting; Computer science; Geography; Cartography","score_opus":0.003500667869953082,"score_gpt":0.16454067450948828,"score_spread":0.1610400066395352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318814340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14140896,0.0001818531,0.7851038,0.0005091005,0.00029504966,0.00022748316,0.00037340162,0.001564375,0.070335984],"genre_scores_gemma":[0.88918614,0.00015273734,0.07605517,0.00022066619,0.00017911775,0.00012289,0.00040358747,0.00018478259,0.033494867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986767,0.0002223832,0.00007960457,0.00035044292,0.00035742327,0.0003134204],"domain_scores_gemma":[0.9971884,0.0007590568,0.0002449047,0.0012882103,0.00031959754,0.00019981965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009257857,0.0007684555,0.0008024696,0.00067447487,0.0009964718,0.0013882602,0.0019664632,0.0009960661,0.009380065],"category_scores_gemma":[0.0030752104,0.00028301345,0.0011064928,0.00046525788,0.0010876677,0.0020080102,0.002790977,0.0014509762,0.0025852616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003344442,0.0015037042,0.012300342,0.0005840406,0.0002308464,0.0014393413,0.00078859425,0.26988775,0.15681943,0.26065716,0.013881107,0.27856326],"study_design_scores_gemma":[0.00025934476,0.0021179125,0.008522768,0.00008177176,0.00020130792,0.0017026623,0.00037197012,0.79684734,0.046210367,0.108384304,0.035070762,0.00022956036],"about_ca_topic_score_codex":0.002597484,"about_ca_topic_score_gemma":0.0025378042,"teacher_disagreement_score":0.009380065,"about_ca_system_score_codex":0.00044169163,"about_ca_system_score_gemma":0.0010115914,"threshold_uncertainty_score":0.03137946},"labels":[],"label_agreement":null},{"id":"W4319316164","doi":"10.3390/f14020315","title":"Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Adaptability; Computer science; Fire detection; Environmental science; Scale (ratio); Remote sensing; Engineering; Ecology; Geography; Cartography; Architectural engineering","score_opus":0.023005523952650053,"score_gpt":0.22596853182747473,"score_spread":0.20296300787482469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319316164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18646786,0.0010378966,0.80263853,0.000310982,0.00017777803,0.0001161121,0.00044436604,0.0024573912,0.0063491226],"genre_scores_gemma":[0.9302177,0.00038737373,0.0639357,0.0001383371,0.000044838813,0.00009294691,0.00070669525,0.00006537331,0.0044110934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985075,0.000011145233,0.000007432607,0.00005707946,0.000041508767,0.00003213354],"domain_scores_gemma":[0.99987817,0.000020731757,0.000016903896,0.0000107038095,0.00005948929,0.000013947663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003497552,0.0006702068,0.00068961125,0.00061298796,0.0003212993,0.00058075227,0.0011510585,0.00047114675,0.0014239067],"category_scores_gemma":[0.0005129972,0.00027174858,0.00077088905,0.00037533106,0.0002659809,0.0006090222,0.0006286249,0.0006301807,0.0004466222],"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":[0.00033401948,0.00013896411,0.0063806335,0.00009414715,0.00011759217,0.00017137633,0.00007724433,0.698557,0.020560356,0.0028473353,0.0027561826,0.26796505],"study_design_scores_gemma":[0.0000028132013,0.000016098393,0.00048598705,0.000002053798,0.00000773898,0.0000133335925,0.00000294611,0.9983493,0.0007469574,0.00016012212,0.00020979697,0.0000028271304],"about_ca_topic_score_codex":0.028597763,"about_ca_topic_score_gemma":0.02113199,"teacher_disagreement_score":0.028597763,"about_ca_system_score_codex":0.0008325759,"about_ca_system_score_gemma":0.00074060686,"threshold_uncertainty_score":0.056862652},"labels":[],"label_agreement":null},{"id":"W4360616779","doi":"10.1016/j.jag.2023.103257","title":"A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning","year":2023,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":19,"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":"Smoke; Environmental science; Remote sensing; Pixel; Computer science; Artificial intelligence; Meteorology; Geography","score_opus":0.01234881451070842,"score_gpt":0.21401737021490924,"score_spread":0.20166855570420084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360616779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022788893,0.0001349106,0.97565275,0.00004139565,0.000021117721,0.00003224982,0.000058166497,0.000583698,0.0006867477],"genre_scores_gemma":[0.5393814,0.00036868211,0.4571989,0.0000772298,0.00005452524,0.000118761534,0.0005114394,0.00006113902,0.0022279639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972624,0.000030506731,0.000014190493,0.000075817465,0.00012504826,0.000028168275],"domain_scores_gemma":[0.99986494,0.000019592939,0.000020068957,0.000014082794,0.00007047471,0.000010850153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033863657,0.00069515157,0.00036773027,0.0012502,0.0003162988,0.00055486214,0.0008322032,0.0004089953,0.0005613862],"category_scores_gemma":[0.000543891,0.00024801126,0.000681613,0.0006992275,0.00026490758,0.0007249819,0.00062202476,0.00051729736,0.00031608442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016884848,0.0001799374,0.008285666,0.00018464682,0.00013168507,0.00021096643,0.00018694761,0.22418953,0.09130543,0.012229805,0.0017162635,0.66121024],"study_design_scores_gemma":[0.000009101914,0.00008572628,0.0035969391,0.000008416498,0.000025370407,0.0001133902,0.00003705205,0.9811122,0.011450431,0.0017388635,0.0017909745,0.000031572774],"about_ca_topic_score_codex":0.0117053,"about_ca_topic_score_gemma":0.010054319,"teacher_disagreement_score":0.0117053,"about_ca_system_score_codex":0.0004949039,"about_ca_system_score_gemma":0.00087863917,"threshold_uncertainty_score":0.023274362},"labels":[],"label_agreement":null},{"id":"W4361277442","doi":"10.3390/rs15071821","title":"Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Segmentation; Artificial intelligence; Market segmentation; Machine learning; Fire detection; Remote sensing; Geography; Engineering","score_opus":0.03648153055778519,"score_gpt":0.23401522949377343,"score_spread":0.19753369893598824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361277442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027936358,0.035401274,0.923838,0.0014643929,0.00030749783,0.00011409355,0.0012209864,0.0021012763,0.007616017],"genre_scores_gemma":[0.4345817,0.057792734,0.49040827,0.0011247662,0.00079273,0.0002697199,0.0058536483,0.00033421395,0.008842151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995683,0.00007091177,0.00003771336,0.00011685176,0.00015185778,0.000054539443],"domain_scores_gemma":[0.99959785,0.00018523465,0.000052815936,0.000034830857,0.000107872926,0.000021427952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008706055,0.0011357713,0.0007527676,0.0020521863,0.00026165516,0.0012542224,0.0010094787,0.001140633,0.0014028123],"category_scores_gemma":[0.0014530122,0.00035292798,0.0009420118,0.0015924823,0.00045012523,0.0012896169,0.00077998755,0.0013353945,0.0007680375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008576652,0.00014348594,0.0032609913,0.0009376808,0.0001786006,0.000120312034,0.00009871769,0.1132469,0.009186572,0.0082117105,0.0115209455,0.85300845],"study_design_scores_gemma":[0.000015149319,0.0000725161,0.004106024,0.0003734289,0.000112571965,0.00019454346,0.00009522976,0.93799704,0.012373132,0.022753993,0.02185885,0.00004746117],"about_ca_topic_score_codex":0.0067628836,"about_ca_topic_score_gemma":0.008104663,"teacher_disagreement_score":0.0067628836,"about_ca_system_score_codex":0.0007807003,"about_ca_system_score_gemma":0.000790758,"threshold_uncertainty_score":0.013447046},"labels":[],"label_agreement":null},{"id":"W4366420362","doi":"10.3390/f14040833","title":"An Improved Forest Fire and Smoke Detection Model Based on YOLOv5","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Smoke; Computer science; Feature (linguistics); Deep learning; Environmental science; Feature extraction; Fire prevention; Artificial intelligence; Remote sensing; Geography; Meteorology; Engineering","score_opus":0.011462857470473585,"score_gpt":0.22014604875625665,"score_spread":0.20868319128578305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366420362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14960036,0.0009033526,0.83523566,0.00057762425,0.00021458775,0.000060898623,0.00046507924,0.002274164,0.010668276],"genre_scores_gemma":[0.95326847,0.00026704668,0.038301535,0.00020916543,0.000036465204,0.000046928824,0.00046434178,0.000067761684,0.007338366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987876,0.000008618057,0.0000048829716,0.000045411005,0.00003073668,0.00003157054],"domain_scores_gemma":[0.999905,0.000019448958,0.000012603586,0.000006794769,0.00004480435,0.000011335675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023449975,0.00056646037,0.0006085473,0.0004112651,0.00030656325,0.00058748387,0.0014444952,0.00071908394,0.00206847],"category_scores_gemma":[0.00036746726,0.00030236857,0.0008539467,0.00023871646,0.00030554578,0.00070937193,0.0006134365,0.0007328996,0.00041850517],"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":[0.00020927837,0.00009075241,0.0044489154,0.00008721035,0.000079068894,0.00015755695,0.00006569942,0.8590981,0.014202525,0.0073255664,0.0029475952,0.11128774],"study_design_scores_gemma":[0.0000030537562,0.000012219791,0.00020476316,0.0000024763726,0.0000082199085,0.00001250088,0.0000017618369,0.9985044,0.00062871503,0.000379932,0.00023884962,0.0000030907695],"about_ca_topic_score_codex":0.025085315,"about_ca_topic_score_gemma":0.020637048,"teacher_disagreement_score":0.025085315,"about_ca_system_score_codex":0.0009340624,"about_ca_system_score_gemma":0.00094044907,"threshold_uncertainty_score":0.049878657},"labels":[],"label_agreement":null},{"id":"W4366547628","doi":"10.3390/f14040838","title":"Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Government of Jiangsu Province","keywords":"Computer science; Fire detection; Bottleneck; Smoke; Convolutional neural network; Environmental science; Remote sensing; Artificial intelligence; Architectural engineering; Engineering; Geography; Meteorology","score_opus":0.011669285678232355,"score_gpt":0.22149456004721185,"score_spread":0.2098252743689795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366547628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05470003,0.0005391935,0.9392155,0.0002473151,0.00007944879,0.000046170204,0.000093666415,0.0009409783,0.0041377037],"genre_scores_gemma":[0.92738664,0.00039255677,0.06617346,0.00013395578,0.000045838082,0.00007219794,0.00014573595,0.000060474897,0.0055891266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977726,0.00002246299,0.0000135744285,0.00006845087,0.00006672291,0.000051536113],"domain_scores_gemma":[0.9997695,0.00007206372,0.00003335691,0.000019912834,0.00008319358,0.000021911055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061332656,0.0006766716,0.0007400675,0.0005810767,0.00034307933,0.00081631134,0.0017455494,0.0007955502,0.0013522151],"category_scores_gemma":[0.000822959,0.0003693973,0.00081896776,0.00037123307,0.00045726492,0.0009785654,0.000874566,0.0009064608,0.00034808207],"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":[0.00028542322,0.00014264717,0.0042158486,0.000116747884,0.00009202996,0.000248529,0.00010375912,0.82568103,0.020923408,0.011736003,0.0015630367,0.13489154],"study_design_scores_gemma":[0.000002210527,0.000009582022,0.000078310266,0.0000012394045,0.00000510443,0.000012881545,0.0000010461217,0.99878603,0.00058195327,0.0003850358,0.00013445634,0.0000021082378],"about_ca_topic_score_codex":0.0113146985,"about_ca_topic_score_gemma":0.007238693,"teacher_disagreement_score":0.0113146985,"about_ca_system_score_codex":0.0009021204,"about_ca_system_score_gemma":0.0010135566,"threshold_uncertainty_score":0.022497654},"labels":[],"label_agreement":null},{"id":"W4367180406","doi":"10.1201/9781003420200-15","title":"Utilization and Application of Infrared Techniques in Forest Fire Detection and Suppression Operations","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Infrared; Remote sensing; Environmental science; Forestry; Computer science; Geography; Optics; Physics","score_opus":0.01489484887367488,"score_gpt":0.22347098888066022,"score_spread":0.20857614000698535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367180406","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011874948,0.024955122,0.03227095,0.00060814107,0.00021926386,0.00008076596,0.0000842629,0.00030828174,0.92959833],"genre_scores_gemma":[0.0908811,0.05714255,0.059800517,0.0005060915,0.00016650875,0.00006919334,0.00029469427,0.00021321021,0.79092616],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996742,0.000036131216,0.000006483706,0.000028371784,0.00022915065,0.000025709156],"domain_scores_gemma":[0.99987376,0.00005303906,0.000009567123,0.000012887938,0.000040505132,0.000010182993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029773242,0.0003894288,0.00013651895,0.0013301674,0.000513107,0.0015200893,0.0008215988,0.00043547506,0.008414569],"category_scores_gemma":[0.00022031419,0.00018924857,0.00017877787,0.0017391674,0.0005395465,0.0008846283,0.00043577616,0.00051917566,0.004276811],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000315262,0.00012358189,0.0011173653,0.0003718097,0.000005815156,0.00030031046,0.0017317698,0.0027428016,0.019589735,0.04678071,0.037191167,0.89001346],"study_design_scores_gemma":[0.0000034251877,0.00010966461,0.0047880355,0.0005131,0.000013685355,0.0011119315,0.00086707686,0.0033050962,0.012950722,0.009573222,0.9667354,0.000028604614],"about_ca_topic_score_codex":0.008692968,"about_ca_topic_score_gemma":0.026084697,"teacher_disagreement_score":0.008692968,"about_ca_system_score_codex":0.001242586,"about_ca_system_score_gemma":0.00093073724,"threshold_uncertainty_score":0.028149545},"labels":[],"label_agreement":null},{"id":"W4380538199","doi":"10.3390/rs15123083","title":"Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plume; Convolutional neural network; Point cloud; Environmental science; Cloud computing; Key (lock); Computer science; Remote sensing; Meteorology; Artificial intelligence; Geology; Geography","score_opus":0.05423479803878462,"score_gpt":0.22914241533072735,"score_spread":0.17490761729194274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380538199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42994237,0.0013725181,0.55667526,0.00040048172,0.0001422568,0.0000717272,0.001265382,0.0054108314,0.004719172],"genre_scores_gemma":[0.9484762,0.00024086102,0.04801761,0.00007432585,0.000021140564,0.000022916549,0.0010857125,0.000040685318,0.0020205125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998105,0.000020331585,0.000008468008,0.000059782018,0.00006103394,0.000039918887],"domain_scores_gemma":[0.99982965,0.000045879846,0.000027053627,0.000020135558,0.000063664585,0.00001361754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003449209,0.0007332863,0.00034204504,0.000674879,0.00018693811,0.00041771476,0.00065309094,0.00056513964,0.0009056506],"category_scores_gemma":[0.0007168186,0.0002387977,0.00034865594,0.00059195625,0.00015600392,0.0006406136,0.00050067337,0.00081610173,0.00035016256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005252034,0.0003421526,0.013048801,0.00014065235,0.000091628506,0.00022714454,0.00007554599,0.4344179,0.043422423,0.0015385603,0.0034796277,0.50269043],"study_design_scores_gemma":[0.000003017325,0.00001905272,0.0017143592,0.0000042041056,0.000007119244,0.000012636882,0.00000912488,0.992021,0.0055920295,0.0003486235,0.00026337962,0.0000054809543],"about_ca_topic_score_codex":0.01598659,"about_ca_topic_score_gemma":0.02164137,"teacher_disagreement_score":0.01598659,"about_ca_system_score_codex":0.0007743676,"about_ca_system_score_gemma":0.00067566737,"threshold_uncertainty_score":0.031787097},"labels":[],"label_agreement":null},{"id":"W4382052671","doi":"10.1109/ispce57441.2023.10158761","title":"Flame Detection Technology Overview &amp; Certification Requirements","year":2023,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Intertek (Canada)","funders":"","keywords":"Certification; Computer science; Systems engineering; Engineering","score_opus":0.060842760712990517,"score_gpt":0.2756664330763431,"score_spread":0.21482367236335256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382052671","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013305313,0.008882165,0.23361766,0.02123894,0.003940001,0.0041589313,0.009227811,0.016874485,0.6887546],"genre_scores_gemma":[0.17246135,0.0134733105,0.19129153,0.018033953,0.002508979,0.00525139,0.023236938,0.003983379,0.5697592],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99302024,0.0005049797,0.00023332088,0.00037715025,0.0053748726,0.0004894477],"domain_scores_gemma":[0.9871852,0.0019552107,0.0007320869,0.00081184926,0.008843004,0.0004727393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004712772,0.0009876456,0.0006559104,0.0033800562,0.0017111534,0.002648223,0.0020252918,0.0066815913,0.058102388],"category_scores_gemma":[0.009284763,0.0007961638,0.0007730552,0.00088132056,0.000833351,0.003747127,0.0016827784,0.0028408493,0.05781052],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036965456,0.0005722554,0.0015836307,0.0015066789,0.000019840703,0.00090320717,0.00021818379,0.00443095,0.073703885,0.03207712,0.64030594,0.2443087],"study_design_scores_gemma":[0.000080168575,0.00029601945,0.0014988238,0.0006024651,0.000022261629,0.0013911556,0.00009990871,0.0042318944,0.035107065,0.0058486755,0.95075,0.000071445946],"about_ca_topic_score_codex":0.0031461017,"about_ca_topic_score_gemma":0.0037788837,"teacher_disagreement_score":0.058102388,"about_ca_system_score_codex":0.0011805828,"about_ca_system_score_gemma":0.0041950108,"threshold_uncertainty_score":0.19437182},"labels":[],"label_agreement":null},{"id":"W4384920015","doi":"10.23977/jaip.2023.060503","title":"Design study of fire risk early warning robot","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; Manual fire alarm activation; ALARM; Fire detection; Firefighting; Risk analysis (engineering); False alarm; Flexibility (engineering); Fire protection; Computer science; Engineering; Computer security; Forensic engineering; Artificial intelligence; Architectural engineering; Business; Civil engineering; Telecommunications; Geography; Cartography","score_opus":0.07059250718815392,"score_gpt":0.31282653661124893,"score_spread":0.24223402942309502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384920015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03198974,0.00050613296,0.95255435,0.0003012793,0.00015879938,0.0005595056,0.000049820457,0.0010332726,0.012847053],"genre_scores_gemma":[0.75821316,0.00068764016,0.22073887,0.00017895432,0.0000660303,0.0014398725,0.00013585541,0.000076138385,0.018463517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992976,0.00014408723,0.00004067812,0.00017460814,0.0002637525,0.000079260564],"domain_scores_gemma":[0.999519,0.0000961966,0.00007127574,0.00004537073,0.0002127231,0.000055520297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008266388,0.00078203203,0.00065229164,0.0004898354,0.00083256286,0.000882709,0.0016219884,0.0011330858,0.0051342864],"category_scores_gemma":[0.0009308144,0.000393185,0.00054338365,0.0001627195,0.0005045944,0.0008259923,0.00075399276,0.00046475098,0.0009746031],"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":[0.0010141834,0.00052688125,0.0081883045,0.0028809214,0.00020765256,0.0024303591,0.001759061,0.3946987,0.23575984,0.04372212,0.0049175876,0.30389446],"study_design_scores_gemma":[0.00023955485,0.0032784871,0.00358111,0.000141241,0.0001994743,0.0011896597,0.00033118494,0.91842467,0.036528267,0.0054455893,0.030525947,0.00011487458],"about_ca_topic_score_codex":0.0022753077,"about_ca_topic_score_gemma":0.0010836249,"teacher_disagreement_score":0.0051342864,"about_ca_system_score_codex":0.00040083495,"about_ca_system_score_gemma":0.0011986244,"threshold_uncertainty_score":0.017175913},"labels":[],"label_agreement":null},{"id":"W4385380254","doi":"10.1007/s42979-023-02040-4","title":"A Feasibility Study on Translation of RGB Images to Thermal Images: Development of a Machine Learning Algorithm","year":2023,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Fire Detection and Safety Systems","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; National Research Council Canada","funders":"National Research Council Canada","keywords":"Computer science; Artificial intelligence; RGB color model; Artificial neural network; Pixel; Deep learning; Translation (biology); Image translation; Computer vision; Image (mathematics); Thermal","score_opus":0.02915621989495577,"score_gpt":0.2666425342806018,"score_spread":0.23748631438564605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385380254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12301057,0.0002280106,0.8724614,0.00047551453,0.00011342116,0.00020322003,0.000028115825,0.0009844343,0.0024954097],"genre_scores_gemma":[0.7925496,0.00013329406,0.20512627,0.00013780913,0.000032651504,0.00014292811,0.00007282094,0.000052538464,0.0017520741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996352,0.000111842295,0.00002587504,0.0000957767,0.00009638089,0.000034883884],"domain_scores_gemma":[0.9991078,0.0004083072,0.000058107053,0.00010102833,0.00028606804,0.000038655147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011955217,0.00058013404,0.0004036214,0.00033329305,0.00022489083,0.00047249917,0.0006779006,0.0008758486,0.0018755597],"category_scores_gemma":[0.0027057582,0.0002239521,0.0004254859,0.0002900162,0.0003765275,0.0008804128,0.00054923963,0.0009302992,0.00035224226],"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":[0.0004041265,0.00030117316,0.0029001932,0.00014671318,0.000057693484,0.00022672967,0.00008206876,0.6808037,0.026227714,0.0065464224,0.0016839047,0.28061956],"study_design_scores_gemma":[0.0000043634445,0.00002926553,0.00008006067,0.0000016927925,0.000002437849,0.000007951945,0.0000034530826,0.9978339,0.0017420222,0.00019915483,0.00009432328,0.000001444379],"about_ca_topic_score_codex":0.0031621694,"about_ca_topic_score_gemma":0.0012818042,"teacher_disagreement_score":0.0031621694,"about_ca_system_score_codex":0.00052825426,"about_ca_system_score_gemma":0.00067479187,"threshold_uncertainty_score":0.0063225627},"labels":[],"label_agreement":null},{"id":"W4385434591","doi":"10.1007/978-981-99-2680-0_4","title":"Design and Development of a ML-Based Safety-Critical Fire Detection System","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Random forest; Computer science; Lasso (programming language); Decision tree; Support vector machine; Artificial intelligence; Machine learning; Regression; Fire detection; Linear regression; Regression analysis; Data mining; Engineering; Statistics; Mathematics","score_opus":0.017502563101296352,"score_gpt":0.20334073560350666,"score_spread":0.1858381725022103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385434591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02329077,0.00019424084,0.95399547,0.00020255665,0.00011209232,0.00030655312,0.00014440344,0.0142285405,0.007525421],"genre_scores_gemma":[0.40494165,0.00022390844,0.57458353,0.0004734273,0.00007719884,0.00040777633,0.0005001541,0.0008152038,0.01797712],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957913,0.000047967453,0.000029648832,0.00009946379,0.00019587255,0.00004787103],"domain_scores_gemma":[0.9994795,0.00011418657,0.00004616231,0.00006158259,0.00024963223,0.000049053368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045755706,0.00047030812,0.00056874444,0.0004104782,0.0004457586,0.0011103817,0.0021511998,0.00084824,0.0078749405],"category_scores_gemma":[0.0007966427,0.00042915897,0.00034524052,0.00018912285,0.00030592937,0.0008166264,0.0006877079,0.0007141917,0.0034790488],"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":[0.0007365203,0.00042180062,0.0024310213,0.0006767555,0.00014188272,0.0006477854,0.00053460593,0.079051286,0.4631225,0.012315975,0.011102474,0.4288173],"study_design_scores_gemma":[0.00022667831,0.0010834275,0.0017669568,0.00006586569,0.00017651166,0.00056768785,0.00009494198,0.63799185,0.29230535,0.0020881402,0.06356786,0.00006476716],"about_ca_topic_score_codex":0.0020662379,"about_ca_topic_score_gemma":0.0013430952,"teacher_disagreement_score":0.0078749405,"about_ca_system_score_codex":0.0004808286,"about_ca_system_score_gemma":0.000964964,"threshold_uncertainty_score":0.0263443},"labels":[],"label_agreement":null},{"id":"W4385445599","doi":"10.1002/cjce.25053","title":"Design and simulation study on the heating performance of mesh heating elements","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Heating element; RADIUS; Power (physics); Resistor; Induction heating; Series (stratigraphy); Mechanics; Materials science; Electric heating; Line (geometry); Mechanical engineering; Nuclear engineering; Structural engineering; Electrical engineering; Composite material; Electromagnetic coil; Engineering; Computer science; Geometry; Thermodynamics; Mathematics; Voltage; Physics","score_opus":0.019708147894950113,"score_gpt":0.21175585356065868,"score_spread":0.19204770566570856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385445599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70733285,0.00036083654,0.27370334,0.0001363807,0.00010538121,0.00022264759,0.00023208298,0.0009736484,0.01693284],"genre_scores_gemma":[0.97426593,0.000065911685,0.02393541,0.000012721971,0.0000041705443,0.00008363894,0.000065675886,0.0000526372,0.001513902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966943,0.00010218475,0.000018468687,0.000044694843,0.00011356313,0.00005165223],"domain_scores_gemma":[0.9991371,0.00043102042,0.000083869636,0.00012877185,0.00018549319,0.000033798864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067106035,0.00040070064,0.0004935918,0.0004607247,0.00031732116,0.00065688504,0.000621758,0.00057568337,0.0023721647],"category_scores_gemma":[0.0016882858,0.00029960065,0.0005348452,0.00026743845,0.00039219036,0.00039978034,0.00031862105,0.00025934362,0.00030932148],"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":[0.00023300713,0.00006123224,0.0024872068,0.00013556468,0.000027064263,0.00010918117,0.00012305917,0.9504245,0.033095982,0.0015720733,0.00027786847,0.011453432],"study_design_scores_gemma":[0.000020090925,0.00018164312,0.00073359313,0.000012236618,0.0000149432,0.00001945297,0.00002638818,0.9857926,0.012196001,0.00019256458,0.0008019833,0.000008494832],"about_ca_topic_score_codex":0.0018207758,"about_ca_topic_score_gemma":0.00094954233,"teacher_disagreement_score":0.0023721647,"about_ca_system_score_codex":0.0004739268,"about_ca_system_score_gemma":0.0004262556,"threshold_uncertainty_score":0.007935703},"labels":[],"label_agreement":null},{"id":"W4385466031","doi":"10.1007/978-3-031-37742-6_19","title":"Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Convolutional neural network; Fire detection; Transformer; Deep learning; Aerial imagery; Computer vision; Image segmentation; Pixel; Deep neural networks; Firefighting; Pattern recognition (psychology); Cartography; Geography; Engineering","score_opus":0.01175150991333566,"score_gpt":0.23497606220175607,"score_spread":0.22322455228842042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385466031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04241428,0.0011761136,0.93752676,0.00025709782,0.00029397948,0.00011280605,0.0008899518,0.0059080417,0.01142096],"genre_scores_gemma":[0.5149167,0.0019075142,0.45109296,0.00036998553,0.00020640592,0.00012924094,0.0036511316,0.00067723484,0.027048746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997929,0.0000085740185,0.000008513009,0.000077457626,0.00005615993,0.00005653167],"domain_scores_gemma":[0.9998228,0.000033183704,0.000018967825,0.00003405386,0.000073997915,0.000017040667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029629507,0.001037907,0.00076015864,0.0011976435,0.00032464904,0.0013552185,0.0012659583,0.0007787565,0.0053094397],"category_scores_gemma":[0.00056499016,0.0006982886,0.001088763,0.0010662351,0.00033027423,0.0012052171,0.0008123074,0.0009447077,0.0026848582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026777285,0.00015011386,0.0016293066,0.00015908133,0.00011331934,0.00014155924,0.00004487857,0.06314127,0.067816734,0.006335007,0.010108364,0.85009265],"study_design_scores_gemma":[0.0000088489005,0.000063374304,0.0016919601,0.000031099928,0.00005287557,0.00013978528,0.000024449475,0.9489527,0.037481945,0.0060469997,0.005487048,0.000018869807],"about_ca_topic_score_codex":0.009657238,"about_ca_topic_score_gemma":0.016266676,"teacher_disagreement_score":0.009657238,"about_ca_system_score_codex":0.0008672791,"about_ca_system_score_gemma":0.00089060044,"threshold_uncertainty_score":0.019202054},"labels":[],"label_agreement":null},{"id":"W4385628550","doi":"10.23977/jeis.2023.080302","title":"Forest Fire Protection System Based on LoRa Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"ALARM; Base station; Fire prevention; Environmental science; Smoke; Computer science; Real-time computing; Manual fire alarm activation; Transmission (telecommunications); Cloud computing; Point cloud; Remote sensing; Telecommunications; Meteorology; Engineering; Architectural engineering; Geography; Electrical engineering","score_opus":0.005407625189596176,"score_gpt":0.19269898201859847,"score_spread":0.1872913568290023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385628550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2515593,0.0033329895,0.58686227,0.0017364305,0.00097628636,0.000749339,0.0010193628,0.057138328,0.09662569],"genre_scores_gemma":[0.9527753,0.000659482,0.028020008,0.00032563924,0.0001430027,0.00020592801,0.0005119927,0.00014691602,0.0172117],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996693,0.000060021543,0.000019176648,0.00005697149,0.00010864912,0.00008582337],"domain_scores_gemma":[0.9997105,0.00003013805,0.000035832785,0.000045167817,0.00013968226,0.000038625312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023222451,0.00043397016,0.0004172173,0.00075993995,0.0006476862,0.00062517653,0.00061760284,0.00034762523,0.004481259],"category_scores_gemma":[0.00040662175,0.00014733944,0.00032891397,0.00030023084,0.00017439877,0.00075106084,0.0006769958,0.00043058046,0.001946055],"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":[0.0016177127,0.0005743812,0.018291332,0.0011572727,0.00025040933,0.0019896165,0.0010427854,0.028958334,0.21109878,0.012058705,0.08724978,0.6357109],"study_design_scores_gemma":[0.00068547454,0.0022125966,0.01926519,0.00026146017,0.0006149945,0.00449479,0.0007095947,0.44265044,0.24584685,0.0047471295,0.27809852,0.00041313926],"about_ca_topic_score_codex":0.0021718163,"about_ca_topic_score_gemma":0.001398954,"teacher_disagreement_score":0.004481259,"about_ca_system_score_codex":0.0003636594,"about_ca_system_score_gemma":0.00043793352,"threshold_uncertainty_score":0.014991283},"labels":[],"label_agreement":null},{"id":"W4386070826","doi":"10.11159/icbes23.134","title":"A Robust Approach to Segment Human Skin and Burnt Region from Chaos Background Using Classification Trees","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"CHAOS (operating system); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer security","score_opus":0.03685666570100672,"score_gpt":0.2199094440638193,"score_spread":0.18305277836281258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386070826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08562919,0.0008435827,0.907798,0.00027183242,0.00010403348,0.00012845098,0.0006811286,0.0027685412,0.0017753278],"genre_scores_gemma":[0.4948149,0.00061585446,0.4979351,0.00025097953,0.00012349799,0.00013103196,0.0032035387,0.00027067002,0.0026545175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994286,0.00007433562,0.000033827946,0.00018727437,0.00016295991,0.0001129444],"domain_scores_gemma":[0.9995078,0.00011116135,0.000073655225,0.0000724956,0.00019738378,0.00003745877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006947115,0.0007604202,0.00083080074,0.0022082205,0.00054607686,0.00086264603,0.0009930335,0.00095572695,0.0007932879],"category_scores_gemma":[0.001064769,0.0003034614,0.0009714659,0.0013125982,0.00030996266,0.00074611785,0.0005773688,0.00092996244,0.00077348517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040173138,0.00031311973,0.014448331,0.0002322893,0.00017554394,0.0003546917,0.00023253087,0.14359532,0.09402317,0.0029716417,0.008796358,0.73445535],"study_design_scores_gemma":[0.000009916606,0.000084220526,0.0057689934,0.00002945822,0.000043624706,0.00025272265,0.00010047773,0.9679083,0.019489164,0.0027641116,0.0035254373,0.000023535493],"about_ca_topic_score_codex":0.0057147387,"about_ca_topic_score_gemma":0.010039797,"teacher_disagreement_score":0.0057147387,"about_ca_system_score_codex":0.00048261802,"about_ca_system_score_gemma":0.00071520585,"threshold_uncertainty_score":0.01136297},"labels":[],"label_agreement":null},{"id":"W4386127629","doi":"10.11159/icepr23.117","title":"Develop Smoke Detection Model Using GEMS to Respond Climate Change","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Smoke; Computer science; Climate change; Environmental science; Meteorology; Geology; Oceanography; Geography","score_opus":0.0525346058119176,"score_gpt":0.26381471460184347,"score_spread":0.21128010878992587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386127629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09458019,0.0007642694,0.8949163,0.0004616254,0.00017036898,0.00007354846,0.00037822858,0.0022484795,0.0064069894],"genre_scores_gemma":[0.9300477,0.00037981904,0.06273369,0.00016640782,0.000062598076,0.00012282412,0.00046259185,0.0000865799,0.0059378124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998995,0.0000121271005,0.0000061692517,0.000038886075,0.000023332615,0.000019906689],"domain_scores_gemma":[0.9998864,0.000044625485,0.000014763394,0.0000044232984,0.000040352694,0.000009330934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032105768,0.0006048212,0.00052652875,0.00041897,0.00035485186,0.0005963785,0.0010667146,0.0008963409,0.0022358212],"category_scores_gemma":[0.00057009887,0.00030994904,0.0007675509,0.00027606418,0.00022835856,0.0005853419,0.00052977505,0.0006041019,0.0004409063],"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":[0.000052645693,0.000040938972,0.005242735,0.00004556522,0.000034618635,0.00009494099,0.00004198978,0.9578909,0.0028641536,0.0022596046,0.0010567396,0.030375259],"study_design_scores_gemma":[0.0000016922162,0.0000035345652,0.00010138546,9.683278e-7,0.0000026866971,0.0000033632143,0.000001749066,0.99938715,0.00013424964,0.0002291069,0.00013291849,0.0000012190877],"about_ca_topic_score_codex":0.01575978,"about_ca_topic_score_gemma":0.00925191,"teacher_disagreement_score":0.01575978,"about_ca_system_score_codex":0.0005665303,"about_ca_system_score_gemma":0.0005291752,"threshold_uncertainty_score":0.03133607},"labels":[],"label_agreement":null},{"id":"W4386325323","doi":"10.18280/ts.400424","title":"Real-Time Detection of Safety Hazards in Coal Mines Utilizing an Enhanced YOLOv3 Algorithm","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coal mining; Computer science; Algorithm; Coal; Artificial intelligence; Environmental science; Mining engineering; Engineering; Waste management","score_opus":0.011290716756579026,"score_gpt":0.22727070198669388,"score_spread":0.21597998523011486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063614674,0.00015425144,0.9339368,0.00010031093,0.0000351089,0.000055060285,0.000057279525,0.0010155721,0.001030946],"genre_scores_gemma":[0.62102515,0.00011739039,0.3753127,0.00015396999,0.000031941785,0.000098716395,0.0002678307,0.0001043181,0.0028879906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982893,0.000023303082,0.000009148483,0.000049992366,0.00005120094,0.00003734826],"domain_scores_gemma":[0.999716,0.0001149273,0.000034766803,0.000018288978,0.00009435855,0.000021569296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064926804,0.00046119993,0.0005519881,0.0005781163,0.00025622034,0.0005576331,0.0008194119,0.0006104338,0.0008830955],"category_scores_gemma":[0.0010691509,0.00019220515,0.00042561023,0.00032038256,0.00025046268,0.0004502737,0.00057881896,0.000572801,0.0002735959],"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":[0.0005983419,0.00015532017,0.0054768543,0.000092549766,0.000068846115,0.00015223802,0.00012092933,0.4386867,0.06325424,0.0038176565,0.002037719,0.48553863],"study_design_scores_gemma":[0.0000072893413,0.000026777516,0.000510031,0.00000231904,0.000004793971,0.000017737046,0.000005198301,0.99642867,0.0024984656,0.00025684715,0.00023843472,0.0000035510102],"about_ca_topic_score_codex":0.0076967976,"about_ca_topic_score_gemma":0.008941381,"teacher_disagreement_score":0.0076967976,"about_ca_system_score_codex":0.00051794446,"about_ca_system_score_gemma":0.00091733615,"threshold_uncertainty_score":0.015304029},"labels":[],"label_agreement":null},{"id":"W4387665333","doi":"10.21203/rs.3.rs-3130718/v1","title":"Enhancing University Security: A Machine Learning and IoT Driven Face Recognition System for Surveillance and Attendance","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Internet of Things; Facial recognition system; Computer science; Attendance; Face (sociological concept); Security system; Computer security; Machine learning; Artificial intelligence; Pattern recognition (psychology); Political science; Sociology","score_opus":0.03939696181343349,"score_gpt":0.293723180858352,"score_spread":0.2543262190449185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387665333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4897784,0.00060583773,0.47137195,0.0006431531,0.0005971304,0.00030192558,0.0014179013,0.023349289,0.011934501],"genre_scores_gemma":[0.8852336,0.00016109365,0.103454635,0.0003102984,0.00009469457,0.00010428281,0.00075303554,0.00014137493,0.009746985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997961,0.000023672841,0.000007907992,0.000056091914,0.00007269904,0.00004359948],"domain_scores_gemma":[0.999856,0.000024909703,0.000013951824,0.00002388692,0.00005596116,0.000025218236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022661536,0.00034592446,0.00039262878,0.00042669085,0.00020440298,0.00042324618,0.00065389345,0.00069891446,0.003966311],"category_scores_gemma":[0.0003910916,0.00016172427,0.00028879032,0.00028073776,0.00009598885,0.00043648935,0.0004848024,0.00043673682,0.002287244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010168565,0.0009634811,0.0066037057,0.000103815684,0.000086093896,0.00027562844,0.00009860139,0.0065351194,0.37902996,0.0006961396,0.013488383,0.5911023],"study_design_scores_gemma":[0.00011151052,0.00093566236,0.031546336,0.000024012066,0.00013444145,0.00078804244,0.00009395735,0.64369303,0.3112389,0.0008205652,0.010513117,0.00010048163],"about_ca_topic_score_codex":0.0016124839,"about_ca_topic_score_gemma":0.0025272449,"teacher_disagreement_score":0.003966311,"about_ca_system_score_codex":0.00026635063,"about_ca_system_score_gemma":0.00028234994,"threshold_uncertainty_score":0.01326859},"labels":[],"label_agreement":null},{"id":"W4387744936","doi":"10.3390/f14102089","title":"A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Pyramid (geometry); Feature (linguistics); Pooling; Fire detection; Feature extraction; Data mining; Artificial intelligence; Engineering","score_opus":0.014493658751937663,"score_gpt":0.21897853893233762,"score_spread":0.20448488018039995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387744936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07793376,0.00088116765,0.91730165,0.00030775004,0.00011422208,0.000044470933,0.00015409199,0.00102759,0.002235381],"genre_scores_gemma":[0.92621523,0.0005001721,0.067402124,0.00019709324,0.00009862639,0.00009550432,0.0004915471,0.000074577074,0.004925173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957424,0.00005316652,0.000029090485,0.00015748394,0.00010097743,0.00008512028],"domain_scores_gemma":[0.99944454,0.00017427493,0.0000603238,0.00005414073,0.0002304964,0.000036226276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014279513,0.0011201319,0.0014290151,0.00078272575,0.0007098165,0.0010283078,0.0016845273,0.0010189343,0.0012673459],"category_scores_gemma":[0.0019732774,0.00047776708,0.0012827604,0.00072927325,0.0003974512,0.0014747812,0.001027312,0.0018785067,0.00040107174],"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":[0.0001645666,0.00009246487,0.003443988,0.000037647565,0.00011860921,0.000071752795,0.000058284448,0.8254461,0.0029233764,0.0021878222,0.0015925089,0.16386282],"study_design_scores_gemma":[0.0000016813744,0.0000088018105,0.00014697565,0.0000015350594,0.000008131716,0.000005591795,0.0000018385506,0.9993093,0.00017771832,0.00026750192,0.000068489084,0.000002553831],"about_ca_topic_score_codex":0.023115238,"about_ca_topic_score_gemma":0.01634768,"teacher_disagreement_score":0.023115238,"about_ca_system_score_codex":0.00085545995,"about_ca_system_score_gemma":0.0011367205,"threshold_uncertainty_score":0.04596138},"labels":[],"label_agreement":null},{"id":"W4388023395","doi":"10.18280/ts.400544","title":"Enhanced Campus Security Target Detection Using a Refined YOLOv7 Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Anhui University; Hefei Normal University; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Remote sensing; Geology","score_opus":0.014031492774003443,"score_gpt":0.21111910258510347,"score_spread":0.19708760981110002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388023395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10832705,0.00090619497,0.87938356,0.00021380727,0.00014635501,0.00016106546,0.00041891643,0.004813376,0.0056296806],"genre_scores_gemma":[0.4911167,0.0007137337,0.4885578,0.0003854401,0.00009304312,0.00013021618,0.0035751548,0.00038044134,0.015047525],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996061,0.00003713715,0.000014427383,0.000116415475,0.00013829216,0.00008768866],"domain_scores_gemma":[0.9997639,0.00003372573,0.000023860654,0.00004157552,0.0001143865,0.000022571432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004483686,0.00083421107,0.0009506159,0.0014055235,0.00030728834,0.0008174423,0.0014020136,0.0008752808,0.0019047935],"category_scores_gemma":[0.0007149411,0.00032346023,0.0007720521,0.00066890696,0.00032165207,0.0007603818,0.0012262943,0.00076071155,0.0019748218],"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":[0.00040070064,0.0002063407,0.0032500376,0.0001602149,0.00012829875,0.00018656303,0.0000959052,0.06226106,0.10312603,0.0035851323,0.006227332,0.8203724],"study_design_scores_gemma":[0.000018474988,0.00017608014,0.0028603356,0.000024067105,0.000052663938,0.00027530003,0.000056773082,0.9532181,0.035843108,0.0013086494,0.0061428277,0.000023655983],"about_ca_topic_score_codex":0.007809317,"about_ca_topic_score_gemma":0.014680951,"teacher_disagreement_score":0.007809317,"about_ca_system_score_codex":0.0005600434,"about_ca_system_score_gemma":0.0011803843,"threshold_uncertainty_score":0.015527725},"labels":[],"label_agreement":null},{"id":"W4388514100","doi":"10.18280/ria.370525","title":"Utilising Deep Convolutional Neural Networks for Classifying Fire Disasters Through Surveillance: An Indoor and Outdoor Perspective to Predict Man-Made or Natural Disaster","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Convolutional neural network; Natural disaster; Computer science; Artificial intelligence; Geography; Meteorology","score_opus":0.0406861484312016,"score_gpt":0.28602253094251584,"score_spread":0.24533638251131423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388514100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59322107,0.0025325199,0.3908643,0.0012347607,0.0004717108,0.00012924382,0.001035606,0.0023197236,0.008191006],"genre_scores_gemma":[0.9529251,0.0007095858,0.04121377,0.00019082223,0.00006561667,0.00003385684,0.0013503458,0.00003860592,0.0034723184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981695,0.000025246007,0.000010701746,0.00005708694,0.000034312896,0.00005566395],"domain_scores_gemma":[0.9997725,0.0000791201,0.000032817385,0.000026806385,0.000066395434,0.000022398963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052248576,0.00097624294,0.00040293497,0.00075026037,0.00029570694,0.0007953675,0.000715424,0.00074412004,0.00062653597],"category_scores_gemma":[0.0008726419,0.00027965612,0.0005161493,0.0004839995,0.0003064931,0.00096765364,0.0006193276,0.0011750718,0.00029161738],"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":[0.00060724176,0.0006468221,0.022684311,0.00013345886,0.00021449979,0.0004521111,0.00016519717,0.559597,0.023895064,0.0023632718,0.0067383987,0.38250256],"study_design_scores_gemma":[0.0000034897298,0.000030427564,0.0011676704,0.000012474864,0.000017550954,0.000021441256,0.000027932365,0.9947449,0.0029552388,0.00065293367,0.00035970876,0.0000061941564],"about_ca_topic_score_codex":0.014698392,"about_ca_topic_score_gemma":0.019529855,"teacher_disagreement_score":0.014698392,"about_ca_system_score_codex":0.0007518231,"about_ca_system_score_gemma":0.00082824443,"threshold_uncertainty_score":0.029225647},"labels":[],"label_agreement":null},{"id":"W4389128676","doi":"10.3390/fire6120455","title":"BoucaNet: A CNN-Transformer for Smoke Recognition on Remote Sensing Satellite Images","year":2023,"lang":"en","type":"article","venue":"Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoke; Haze; Deep learning; Computer science; Fire detection; Artificial intelligence; Ignition system; Transformer; Environmental science; Satellite; Firefighting; Remote sensing; Engineering; Meteorology; Cartography; Geography; Waste management; Aerospace engineering; Architectural engineering","score_opus":0.031075164337789687,"score_gpt":0.2408779017424404,"score_spread":0.20980273740465072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389128676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10757014,0.0024277815,0.82305413,0.00043586743,0.0007605884,0.0004920662,0.0030593395,0.046722896,0.015477198],"genre_scores_gemma":[0.66450375,0.0010771265,0.29931188,0.000841552,0.00011562069,0.00029094092,0.008546028,0.0010054511,0.024307594],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998324,0.000013373996,0.0000070385327,0.000062879844,0.000042156673,0.000042142605],"domain_scores_gemma":[0.9998735,0.000023719174,0.000013906333,0.000024128749,0.000047835518,0.000016852375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003491184,0.0016204001,0.000500189,0.0009628095,0.00030514318,0.0005541848,0.001747839,0.00072099094,0.005434363],"category_scores_gemma":[0.00090192957,0.00046784282,0.00067314913,0.0005313187,0.0003035748,0.0010286723,0.00078782055,0.00088780833,0.001925259],"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":[0.00052813446,0.0003524982,0.005703831,0.0004027787,0.00027743547,0.00027025375,0.000071889706,0.08580799,0.06866843,0.004732364,0.032191053,0.8009934],"study_design_scores_gemma":[0.00002709763,0.00012406029,0.0016051945,0.00003378439,0.00005133099,0.00016302783,0.00002505856,0.9563736,0.031067714,0.0025080629,0.007997985,0.000023092864],"about_ca_topic_score_codex":0.024780989,"about_ca_topic_score_gemma":0.043052837,"teacher_disagreement_score":0.024780989,"about_ca_system_score_codex":0.0010200674,"about_ca_system_score_gemma":0.0011009098,"threshold_uncertainty_score":0.04927349},"labels":[],"label_agreement":null},{"id":"W4389224949","doi":"10.1080/01431161.2023.2283904","title":"CT-Fire: a CNN-Transformer for wildfire classification on ground and aerial images","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Firefighting; Computer science; Environmental science; Fire detection; Transformer; Remote sensing; Aerial imagery; Artificial intelligence; Aerial image; Benchmark (surveying); Ecosystem; Cartography; Geography; Image (mathematics); Ecology; Engineering","score_opus":0.021016009060219828,"score_gpt":0.26434953097108227,"score_spread":0.24333352191086244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389224949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20146266,0.001564997,0.7638897,0.00029584375,0.00040983566,0.0004119513,0.002279421,0.020408928,0.009276688],"genre_scores_gemma":[0.7421931,0.000767503,0.23900138,0.0003754492,0.00011349527,0.00017663169,0.0059296954,0.00039845658,0.011044193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980456,0.000012839473,0.0000089936575,0.00007182555,0.000055859855,0.000045986675],"domain_scores_gemma":[0.99985373,0.000023659986,0.000019136482,0.000035132183,0.000050784187,0.0000174763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005031569,0.001326249,0.0004922633,0.00095819647,0.00028539874,0.00045556616,0.0014736889,0.00061655126,0.002884557],"category_scores_gemma":[0.0008482368,0.0003446302,0.0007327896,0.0006267614,0.00022526273,0.0012330055,0.00067249767,0.00081054564,0.0010501827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048477948,0.00035483344,0.007436433,0.00016240464,0.0002885638,0.00022107408,0.000042516996,0.06975053,0.044466525,0.0018777617,0.015696792,0.8592177],"study_design_scores_gemma":[0.000035599474,0.00015865511,0.0027073422,0.000015231915,0.00007739588,0.00025781043,0.000023799208,0.9641339,0.027930927,0.0014347662,0.0032045397,0.000020026195],"about_ca_topic_score_codex":0.01084006,"about_ca_topic_score_gemma":0.022166647,"teacher_disagreement_score":0.01084006,"about_ca_system_score_codex":0.00067297945,"about_ca_system_score_gemma":0.00085549016,"threshold_uncertainty_score":0.021553934},"labels":[],"label_agreement":null},{"id":"W4389796970","doi":"10.21275/sr23907110409","title":"Optimized Design of Lightning Protection using Rolling Sphere Method","year":2023,"lang":"en","type":"article","venue":"International Journal of Science and Research (IJSR)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Lightning (connector); Meteorology; Aeronautics; Computer science; Environmental science; Engineering; Geography; Physics; Power (physics)","score_opus":0.16016955150060858,"score_gpt":0.41425048612990273,"score_spread":0.25408093462929415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389796970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0781459,0.0006698208,0.8893769,0.00009721296,0.000083440915,0.000105848216,0.00010805576,0.0011319488,0.03028092],"genre_scores_gemma":[0.8549807,0.0005393043,0.13582972,0.00003990761,0.00002988685,0.00010826226,0.0001439644,0.00014015203,0.008188147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997913,0.00004542202,0.000010591734,0.000036745972,0.00008402978,0.000031862128],"domain_scores_gemma":[0.999863,0.00003054704,0.000026551395,0.000015860629,0.000052046078,0.000011886813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019470356,0.00067310553,0.00064152153,0.0004537714,0.00023501815,0.0006639808,0.0006951947,0.0005367223,0.004863774],"category_scores_gemma":[0.00036639738,0.00029948752,0.0005760204,0.00039049366,0.00022079406,0.00044881375,0.00030193792,0.0002598857,0.0012245534],"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":[0.00054207444,0.00009032157,0.0012508663,0.00050994626,0.00008898616,0.00047864005,0.00022999712,0.66765517,0.14226659,0.012786258,0.0029672836,0.17113388],"study_design_scores_gemma":[0.00005136145,0.0005457203,0.0009411879,0.00002733171,0.00006525788,0.00028671112,0.00009535102,0.9573902,0.02682682,0.001956721,0.011781406,0.000032044914],"about_ca_topic_score_codex":0.0014549245,"about_ca_topic_score_gemma":0.0012098667,"teacher_disagreement_score":0.004863774,"about_ca_system_score_codex":0.00030068398,"about_ca_system_score_gemma":0.00044107673,"threshold_uncertainty_score":0.016270936},"labels":[],"label_agreement":null},{"id":"W4390493546","doi":"10.1109/induscon58041.2023.10374950","title":"Modelo de alerta de intensidade de risco de fogo utilizando algoritmo de regressão logística ordinal de classificação","year":2023,"lang":"pt","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Ontario Tech University","funders":"","keywords":"Logistic regression; Computer science; Geography; Mathematics; Statistics","score_opus":0.039239762004664974,"score_gpt":0.276949246358162,"score_spread":0.237709484353497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390493546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63739663,0.00170224,0.34897345,0.0016575899,0.00031916454,0.00021285874,0.0016905578,0.0014696545,0.0065778894],"genre_scores_gemma":[0.9778686,0.00037207117,0.018008774,0.00005354068,0.000062635525,0.00012994619,0.0006794896,0.000031003106,0.0027938772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995023,0.00017557458,0.00003478557,0.00013721625,0.000073679694,0.00007645513],"domain_scores_gemma":[0.99816835,0.0013200754,0.00012342846,0.000054387383,0.00028842434,0.000045345732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002319719,0.0009928118,0.0007678011,0.0009525433,0.000299215,0.001715267,0.0012871062,0.00075525086,0.0027357112],"category_scores_gemma":[0.0047108266,0.0002910036,0.0011653717,0.00073923543,0.0002579228,0.0009696725,0.00041857496,0.0011748406,0.0006081214],"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":[0.0007096546,0.00033488683,0.04764371,0.00024892436,0.0002559087,0.00018643605,0.00016749837,0.8657707,0.0016689277,0.0026714203,0.0021156168,0.078226335],"study_design_scores_gemma":[0.000005100449,0.00004178993,0.0021266895,0.0000119972465,0.00002446339,0.000016820339,0.000024359304,0.99694353,0.00017894719,0.00045768972,0.00016276026,0.000005850873],"about_ca_topic_score_codex":0.018800955,"about_ca_topic_score_gemma":0.010264455,"teacher_disagreement_score":0.018800955,"about_ca_system_score_codex":0.00096137956,"about_ca_system_score_gemma":0.0009878235,"threshold_uncertainty_score":0.03738302},"labels":[],"label_agreement":null},{"id":"W4391021147","doi":"10.1109/smarttechcon57526.2023.10391500","title":"Fire Monitoring Method of Ancient Building Repair Stage Based on Machine Learning Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Horizon College and Seminary","funders":"","keywords":"Vulnerability (computing); Control (management); Firefighting; Fire protection; Computer science; Artificial intelligence; Fire control; Engineering; Machine learning; Architectural engineering; Civil engineering; Computer security","score_opus":0.016886314864696966,"score_gpt":0.26700972632663106,"score_spread":0.25012341146193406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391021147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096825264,0.00042075245,0.89661163,0.000110345485,0.00010762744,0.00011779869,0.00007094133,0.0012543487,0.0044811494],"genre_scores_gemma":[0.81836486,0.00031036348,0.17503743,0.00006691552,0.000055183227,0.00016564546,0.0001908534,0.000048870796,0.0057598264],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99957305,0.000036285845,0.000037619026,0.00015139433,0.0001338135,0.0000677786],"domain_scores_gemma":[0.9995641,0.00008429881,0.00005014087,0.000026863261,0.00025024737,0.000024328117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004940783,0.00060452294,0.0007869622,0.0013100179,0.00050322694,0.00068447494,0.0008968056,0.0006380876,0.0020749024],"category_scores_gemma":[0.001122284,0.00024364807,0.00058458,0.0006950625,0.00028419343,0.0007580586,0.00033553067,0.00045975624,0.000578729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036808604,0.00016130961,0.011898967,0.00017606039,0.00006998271,0.00021497192,0.00024071762,0.14766392,0.021947747,0.0025051534,0.0025382221,0.812215],"study_design_scores_gemma":[0.000018086677,0.00008665648,0.0044259294,0.000015131164,0.000030066727,0.00013413314,0.0000494454,0.98418564,0.009254282,0.0007352558,0.0010446002,0.000020824647],"about_ca_topic_score_codex":0.004101602,"about_ca_topic_score_gemma":0.0029310375,"teacher_disagreement_score":0.004101602,"about_ca_system_score_codex":0.00055245735,"about_ca_system_score_gemma":0.00065732404,"threshold_uncertainty_score":0.008155465},"labels":[],"label_agreement":null},{"id":"W4391232616","doi":"10.1007/978-3-031-48161-1_13","title":"Fire and Smoke Image Recognition","year":2024,"lang":"en","type":"book-chapter","venue":"Digital innovations in architecture, engineering and construction","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Smoke; Computer science; Artificial intelligence; Engineering; Waste management","score_opus":0.008350443838424423,"score_gpt":0.17936823445554706,"score_spread":0.17101779061712263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391232616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03517906,0.0062080887,0.72124106,0.00071120344,0.0016173436,0.00028329054,0.0011662339,0.008461112,0.22513273],"genre_scores_gemma":[0.1787313,0.006065877,0.30850622,0.0007984011,0.0005904284,0.0001194838,0.0039183507,0.0008451475,0.50042474],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999833,0.000008621395,0.000005035841,0.000039582847,0.00009172794,0.000022032495],"domain_scores_gemma":[0.9998952,0.000017843586,0.000006838609,0.000026412537,0.00004375902,0.000009931498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018556304,0.0005924355,0.0004171465,0.0010401827,0.00027775014,0.00091232033,0.0007702527,0.00088254124,0.021719895],"category_scores_gemma":[0.00028046142,0.00027157788,0.00052222406,0.00068828097,0.00030877843,0.00067872583,0.00046148352,0.00058709824,0.01516753],"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":[0.000109868466,0.0000621684,0.00038151254,0.00013671305,0.000017621269,0.00013676952,0.000033139728,0.0018564599,0.10259853,0.0044982624,0.022283344,0.86788565],"study_design_scores_gemma":[0.000021729873,0.0002805777,0.012870046,0.00015148868,0.000102185455,0.0032856376,0.00015907855,0.15873615,0.43962714,0.010685994,0.37398615,0.000093862705],"about_ca_topic_score_codex":0.0015224693,"about_ca_topic_score_gemma":0.0037453864,"teacher_disagreement_score":0.021719895,"about_ca_system_score_codex":0.00022036838,"about_ca_system_score_gemma":0.00021811559,"threshold_uncertainty_score":0.07266033},"labels":[],"label_agreement":null},{"id":"W4391477578","doi":"10.54254/2755-2721/34/20230301","title":"Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"","keywords":"Firefighting; Convolutional neural network; Automotive industry; Robot; Computer science; Artificial intelligence; Computer vision; Patrolling; RGB color model; Factory (object-oriented programming); Fire detection; Engineering; Architectural engineering","score_opus":0.015205076487066222,"score_gpt":0.24608612909627223,"score_spread":0.230881052609206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391477578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2860166,0.0033006726,0.70184,0.000412552,0.00017337289,0.00007010705,0.00012093271,0.0017538603,0.0063119526],"genre_scores_gemma":[0.9214347,0.001714428,0.071155354,0.00009222292,0.00003610744,0.000035349003,0.00017311928,0.000034146833,0.0053245225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998747,0.000009470192,0.000007179864,0.000043226923,0.00003758623,0.000027921807],"domain_scores_gemma":[0.9998628,0.00003119247,0.000019682566,0.000013373637,0.000062247156,0.000010680752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002455024,0.0005301238,0.00029949838,0.00036952813,0.00021323188,0.00040243304,0.00069657515,0.00044299016,0.0007184781],"category_scores_gemma":[0.00037705607,0.00022694381,0.0004113202,0.0003630802,0.00023631351,0.00062486826,0.00018974167,0.0004365917,0.00014281482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003548942,0.00024047043,0.009372835,0.00028130348,0.00020902965,0.0002461932,0.00013696673,0.34961045,0.09801066,0.0041547483,0.0023676339,0.5350147],"study_design_scores_gemma":[0.0000055926894,0.00008337983,0.0022455028,0.000006752256,0.000036668258,0.000040177296,0.000015979535,0.98047423,0.015745042,0.00034414695,0.0009939928,0.0000084623725],"about_ca_topic_score_codex":0.019074826,"about_ca_topic_score_gemma":0.013742638,"teacher_disagreement_score":0.019074826,"about_ca_system_score_codex":0.00067313924,"about_ca_system_score_gemma":0.0007768373,"threshold_uncertainty_score":0.037927628},"labels":[],"label_agreement":null},{"id":"W4391604377","doi":"10.18260/1-2--42385","title":"Board 103: Solar-Powered Car Speed Radar Measurement, Display, and Logging System","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"National Science Foundation","keywords":"Radar; Wind speed; Computer science; Microprocessor; Real-time computing; Speed measurement; Data logger; Photovoltaic system; Automotive engineering; Simulation; Engineering; Computer hardware; Electrical engineering; Telecommunications; Meteorology; Operating system; Geography","score_opus":0.014184384360633481,"score_gpt":0.19765948723482757,"score_spread":0.18347510287419408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391604377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26125082,0.00061075663,0.57978797,0.00059283,0.00061428343,0.002298734,0.0035814077,0.093106896,0.0581563],"genre_scores_gemma":[0.8683916,0.00023785452,0.07590181,0.00045790328,0.00015180341,0.0006624218,0.0031925123,0.00067416433,0.05033004],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991824,0.00010721531,0.00006155524,0.00016377642,0.00040330726,0.000081650636],"domain_scores_gemma":[0.9989593,0.00012572558,0.00009164743,0.00013432109,0.0005598549,0.00012912546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056058384,0.0006049313,0.0007001975,0.00097562507,0.00032010747,0.00093688286,0.0010302622,0.0006030391,0.026161596],"category_scores_gemma":[0.0012357668,0.00036009177,0.0001979572,0.00047319013,0.00017451255,0.00068378315,0.00052826863,0.0004863685,0.012961342],"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":[0.0021343369,0.00073625776,0.028654749,0.00060292176,0.0001294482,0.0010215202,0.00051948737,0.005886179,0.3822608,0.0030995714,0.068365544,0.50658923],"study_design_scores_gemma":[0.00100503,0.005376745,0.06500083,0.00016065933,0.00024899188,0.0048981463,0.00029949605,0.2494553,0.43953416,0.0010126344,0.23276101,0.00024697764],"about_ca_topic_score_codex":0.001607276,"about_ca_topic_score_gemma":0.0010457982,"teacher_disagreement_score":0.026161596,"about_ca_system_score_codex":0.00029813094,"about_ca_system_score_gemma":0.00054809486,"threshold_uncertainty_score":0.08751923},"labels":[],"label_agreement":null},{"id":"W4391730305","doi":"10.21611/qirt.2023.03","title":"DC-Fire: a Deep Convolutional Neural Network for Wildland Fire Recognition on Aerial Infrared Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Remote sensing; Infrared; Computer vision; Pattern recognition (psychology); Environmental science; Geology; Astronomy","score_opus":0.01769248785108983,"score_gpt":0.21559504856080008,"score_spread":0.19790256070971024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391730305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17377867,0.0022738841,0.7963088,0.00033421745,0.00043745132,0.00024031835,0.0035798708,0.012742597,0.010304101],"genre_scores_gemma":[0.60522157,0.0012852465,0.36761442,0.00047301917,0.00010694368,0.00013780314,0.008157364,0.00031619865,0.01668743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998894,0.0000073678807,0.0000041881785,0.000038469505,0.000035239307,0.000025374344],"domain_scores_gemma":[0.999899,0.000020251737,0.000015801246,0.000018728364,0.0000331837,0.000012934488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029320002,0.0008052019,0.00037239195,0.0007568438,0.00026411997,0.0003786442,0.0009994493,0.0004754707,0.0017770081],"category_scores_gemma":[0.00045715662,0.00024300013,0.00047154573,0.0005056334,0.00020493848,0.0006510718,0.0005311686,0.0007836847,0.00075903774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038046262,0.00032790733,0.009730002,0.00021511025,0.00027652466,0.00025342946,0.000051476167,0.104159094,0.055879552,0.0029270162,0.018013312,0.8077861],"study_design_scores_gemma":[0.00002081414,0.000095938085,0.0034990734,0.000032027416,0.00004974393,0.0002231291,0.000020532252,0.94945806,0.03741242,0.0016122868,0.007546579,0.00002948785],"about_ca_topic_score_codex":0.012416252,"about_ca_topic_score_gemma":0.027230911,"teacher_disagreement_score":0.012416252,"about_ca_system_score_codex":0.0006313517,"about_ca_system_score_gemma":0.0006123722,"threshold_uncertainty_score":0.024687946},"labels":[],"label_agreement":null},{"id":"W4391877934","doi":"10.1109/csrswtc60855.2023.10427221","title":"Smoke Detection Model Based on Adaptive Feature Extraction Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Canadian Allergy, Asthma and Immunology Foundation","keywords":"Computer science; Feature extraction; Smoke; Extraction (chemistry); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Data mining; Engineering","score_opus":0.01888767798471798,"score_gpt":0.22328175766465963,"score_spread":0.20439407967994166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391877934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08159683,0.0010151594,0.9090477,0.0005139024,0.00014860519,0.00008679529,0.00035656337,0.0023694662,0.0048650224],"genre_scores_gemma":[0.9350721,0.0005274259,0.055231947,0.0002870309,0.000061178995,0.00016980848,0.0005869292,0.000059892453,0.008003631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981457,0.00001268929,0.000008459373,0.00008195796,0.000043519736,0.000038830633],"domain_scores_gemma":[0.99986887,0.000032850014,0.000019234658,0.000009301806,0.000058924612,0.000010854812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027451277,0.0008961227,0.0007439999,0.0004966233,0.0002954519,0.0005605534,0.0016072524,0.0008234598,0.0017277193],"category_scores_gemma":[0.000546614,0.00041272966,0.0007776157,0.00038797417,0.0003200556,0.00087495026,0.00068233575,0.0009414218,0.00040162553],"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":[0.00027193074,0.00016708777,0.0038719615,0.00010244482,0.00010527185,0.00020417194,0.000047294332,0.755649,0.018453805,0.0034934767,0.0032121474,0.2144214],"study_design_scores_gemma":[0.0000027406381,0.000013710865,0.0002062954,0.000001948509,0.000008208912,0.000011791874,0.0000014040727,0.99858475,0.00071894453,0.00032677816,0.000120843484,0.0000027062144],"about_ca_topic_score_codex":0.012317883,"about_ca_topic_score_gemma":0.010694026,"teacher_disagreement_score":0.012317883,"about_ca_system_score_codex":0.0008711851,"about_ca_system_score_gemma":0.0007269588,"threshold_uncertainty_score":0.024492383},"labels":[],"label_agreement":null},{"id":"W4392159417","doi":"10.18280/i2m.230101","title":"Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection","year":2024,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Leak; Leak detection; Pipeline (software); Computer science; In situ; Embedded system; Real-time computing; Environmental science; Engineering; Operating system; Chemistry; Environmental engineering","score_opus":0.042079452176350646,"score_gpt":0.3017368412906354,"score_spread":0.2596573891142847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392159417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.701789,0.00050878694,0.28940555,0.00036932493,0.00021122779,0.0011701514,0.00029678494,0.003668547,0.0025805088],"genre_scores_gemma":[0.8646435,0.00032690528,0.12942465,0.00019106377,0.0000356108,0.00036166087,0.00031582007,0.00008635605,0.0046144444],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894935,0.00016232986,0.000068794085,0.00020066179,0.0005396973,0.000079150625],"domain_scores_gemma":[0.9989741,0.00012180042,0.00010173973,0.000087117594,0.0006352381,0.00008007819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014359108,0.0004991319,0.0006295477,0.00046490895,0.00032923743,0.0005693628,0.0012455697,0.0008148635,0.0014387758],"category_scores_gemma":[0.0014704689,0.00025258932,0.00036716962,0.00026430032,0.0002588713,0.0008194584,0.00046056684,0.00036380056,0.00050540216],"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":[0.00053895405,0.0006698707,0.007358104,0.0005814398,0.00006399153,0.00035009536,0.0005217383,0.005216762,0.9078603,0.00051309186,0.0012252277,0.07510049],"study_design_scores_gemma":[0.00019814231,0.0092686815,0.024429372,0.000075723365,0.00027673875,0.0008579792,0.00035802854,0.123262875,0.82554555,0.0001796634,0.015431058,0.000116275645],"about_ca_topic_score_codex":0.0016816012,"about_ca_topic_score_gemma":0.0010763366,"teacher_disagreement_score":0.0016816012,"about_ca_system_score_codex":0.00045049528,"about_ca_system_score_gemma":0.000808807,"threshold_uncertainty_score":0.00759387},"labels":[],"label_agreement":null},{"id":"W4392420697","doi":"10.4995/wdsa-ccwi2022.2022.14830","title":"Water for firefighting: A comparative study across several cities","year":2022,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Firefighting; Computer science; Architectural engineering; Geography; Engineering; Cartography","score_opus":0.03413298258766688,"score_gpt":0.2721060065505798,"score_spread":0.23797302396291292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392420697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978078,0.0006829248,0.000072937415,0.00012731191,0.0000066025405,0.00005095768,0.00026794858,0.000002617867,0.0009808557],"genre_scores_gemma":[0.99665546,0.0019490286,0.00019509895,0.00013758171,0.000016327524,0.00009117641,0.00037105655,0.000008399808,0.0005758783],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99830604,0.00052903104,0.00022614641,0.00026955362,0.0003314621,0.0003377866],"domain_scores_gemma":[0.99569464,0.0014524738,0.0013071098,0.0001949752,0.0008305961,0.00052021904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029149363,0.0004858623,0.00095305627,0.0072369697,0.0025900442,0.0030987335,0.0008364625,0.0011060259,0.0034887532],"category_scores_gemma":[0.0048782523,0.00058234803,0.0015093172,0.009981355,0.0015650481,0.003006539,0.0020882376,0.0008683346,0.0006566516],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011530587,0.0012078289,0.8759884,0.00189496,0.00087447366,0.0026190127,0.08726808,0.00024715514,0.0014139285,0.00084092584,0.0013074967,0.02518464],"study_design_scores_gemma":[0.00003096429,0.0008613682,0.8237679,0.00033062202,0.00026373944,0.00067011523,0.17112939,0.00014183944,0.0002401833,0.00009632565,0.0024097818,0.000057824804],"about_ca_topic_score_codex":0.031150717,"about_ca_topic_score_gemma":0.08715435,"teacher_disagreement_score":0.031150717,"about_ca_system_score_codex":0.0040513277,"about_ca_system_score_gemma":0.0021080351,"threshold_uncertainty_score":0.061938822},"labels":[],"label_agreement":null},{"id":"W4392477065","doi":"10.1007/978-3-031-46238-2_29","title":"Generative AI for Fire Safety","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Generative grammar; Fire safety; Computer science; Artificial intelligence; Medicine; Risk analysis (engineering)","score_opus":0.012102422160582414,"score_gpt":0.21105598218546295,"score_spread":0.19895356002488054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392477065","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023209276,0.006128053,0.41630766,0.002244431,0.00066543464,0.00003316883,0.00021555631,0.0011901949,0.57089466],"genre_scores_gemma":[0.18378037,0.007393915,0.14367475,0.0016763055,0.00067924435,0.00013750143,0.0009280833,0.0018107666,0.65991914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998242,0.000056624795,0.0000043843443,0.000037365608,0.00006439055,0.000012981851],"domain_scores_gemma":[0.99972206,0.00018426284,0.0000053948,0.000054215296,0.000025356045,0.000008717845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023342276,0.0006915102,0.0003421716,0.0004906509,0.0006255992,0.0015451926,0.00088082196,0.0010020159,0.030790048],"category_scores_gemma":[0.0011054924,0.0005382041,0.0005971401,0.0005535413,0.0018848188,0.0019629176,0.0010308007,0.002308479,0.008432579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010962579,0.00002635023,0.00010060025,0.00013949604,0.000015569232,0.000052754385,0.000282288,0.017894667,0.0012030461,0.8173489,0.049374983,0.11355042],"study_design_scores_gemma":[0.000003809524,0.0000066550692,0.00009833177,0.00006621886,0.000007683452,0.00009088281,0.000047240705,0.029102372,0.00091063575,0.8162277,0.15342687,0.000011692158],"about_ca_topic_score_codex":0.0028707285,"about_ca_topic_score_gemma":0.004694534,"teacher_disagreement_score":0.030790048,"about_ca_system_score_codex":0.0011922915,"about_ca_system_score_gemma":0.00047144282,"threshold_uncertainty_score":0.103003025},"labels":[],"label_agreement":null},{"id":"W4393160790","doi":"10.1609/aaai.v38i20.30227","title":"AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Cycling; Computer science; Stress (linguistics); Natural language processing; Artificial intelligence; Geography; Linguistics; Archaeology","score_opus":0.02082240897970628,"score_gpt":0.2772960067925049,"score_spread":0.2564735978127986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42269665,0.0012417658,0.5305351,0.00045221616,0.00037097107,0.00044125342,0.01131496,0.024523523,0.008423477],"genre_scores_gemma":[0.75106275,0.000350038,0.22669116,0.00029866988,0.000103251026,0.00027459554,0.01412674,0.00038788674,0.006704889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976546,0.00002127854,0.000010187388,0.00011381551,0.000047547783,0.000041758452],"domain_scores_gemma":[0.99977356,0.00005291528,0.000025558304,0.00003377398,0.00008788915,0.000026436286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003461262,0.0011108177,0.0005739316,0.0012908119,0.00026452268,0.0005451623,0.0014879282,0.0006188113,0.002572753],"category_scores_gemma":[0.00089337927,0.00029256588,0.0005337068,0.00064705533,0.00028564726,0.0007452993,0.0011405444,0.0008369201,0.0014638243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006484194,0.0010068098,0.03858912,0.00029590726,0.00020591274,0.00025314174,0.00020954231,0.061691515,0.044581663,0.001009966,0.0259463,0.82556164],"study_design_scores_gemma":[0.00005574897,0.00024019912,0.024632236,0.00004252904,0.00005610664,0.00010006928,0.00013504876,0.9537603,0.01311214,0.0029090678,0.0049158875,0.000040657495],"about_ca_topic_score_codex":0.020424332,"about_ca_topic_score_gemma":0.06636254,"teacher_disagreement_score":0.020424332,"about_ca_system_score_codex":0.0005767761,"about_ca_system_score_gemma":0.0007140382,"threshold_uncertainty_score":0.04061091},"labels":[],"label_agreement":null},{"id":"W4393377631","doi":"10.36227/techrxiv.171197744.44687303/v1","title":"SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Convolutional neural network; Deep learning; Artificial intelligence; Computer science; Machine learning; Pattern recognition (psychology)","score_opus":0.010677461515874828,"score_gpt":0.2027728080533611,"score_spread":0.19209534653748628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393377631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1476548,0.001965471,0.8299404,0.0006039873,0.00034720657,0.00017823794,0.0017052726,0.008604361,0.009000281],"genre_scores_gemma":[0.6733699,0.0009928781,0.30074844,0.0006135561,0.00010512696,0.00016274746,0.0048704837,0.00033668618,0.018800125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988866,0.000010924931,0.0000047804156,0.00003758002,0.000033785538,0.000024329289],"domain_scores_gemma":[0.99989665,0.000021946384,0.000013294104,0.000014098305,0.00004188487,0.000012136022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023030989,0.00094783615,0.00042736126,0.0006615073,0.00024590275,0.00053893635,0.0010282588,0.00060359976,0.001998966],"category_scores_gemma":[0.00050963816,0.00029831062,0.0004634627,0.00042939425,0.00024005692,0.00083272316,0.0005272829,0.00076609716,0.00063715084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045817433,0.00026295485,0.0060270275,0.00020394556,0.00022502174,0.00028378807,0.00008838783,0.2900667,0.031868327,0.005452105,0.016344842,0.6487187],"study_design_scores_gemma":[0.000009161728,0.000082367755,0.0010479048,0.000017730517,0.00002242226,0.00007171552,0.000020326606,0.98338497,0.009520886,0.0022148283,0.0035969536,0.000010738862],"about_ca_topic_score_codex":0.010294217,"about_ca_topic_score_gemma":0.025999557,"teacher_disagreement_score":0.010294217,"about_ca_system_score_codex":0.00065561075,"about_ca_system_score_gemma":0.00081441936,"threshold_uncertainty_score":0.020468593},"labels":[],"label_agreement":null},{"id":"W4394063389","doi":"10.36713/epra16328","title":"AUTOMATED HELMET MONITORING SYSTEM USING DEEP LEARNING","year":2024,"lang":"en","type":"article","venue":"EPRA International Journal of Research & Development (IJRD)","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Environmental science","score_opus":0.04344640917913179,"score_gpt":0.35896374295020467,"score_spread":0.3155173337710729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394063389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2900421,0.003927857,0.5798004,0.0012356074,0.0013238117,0.00072122127,0.012774148,0.0790093,0.031165557],"genre_scores_gemma":[0.8080378,0.0010615358,0.14336081,0.0011059801,0.00015057898,0.0005101294,0.01275601,0.0005675972,0.032449614],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997696,0.000016096836,0.000013301267,0.00007282966,0.00007407158,0.00005391937],"domain_scores_gemma":[0.9998661,0.000015423931,0.000014782917,0.000013265254,0.0000731916,0.000017194057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017757354,0.0010582221,0.0007462128,0.0010739655,0.00027982038,0.00038086195,0.0011885042,0.0006704959,0.0060530882],"category_scores_gemma":[0.00048005144,0.0003617623,0.00056221645,0.00041692212,0.0001403821,0.0006013686,0.0008401229,0.00058506464,0.0026057009],"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":[0.00085800927,0.0006496166,0.0126676345,0.00052247517,0.00018831577,0.00090903597,0.00008243069,0.039570782,0.04403441,0.0008111036,0.051714342,0.8479919],"study_design_scores_gemma":[0.000106216496,0.00041065205,0.009027296,0.00011214013,0.000105908395,0.0004950993,0.00008869044,0.9311759,0.041032806,0.0019723605,0.015397508,0.00007534996],"about_ca_topic_score_codex":0.0064614965,"about_ca_topic_score_gemma":0.010663879,"teacher_disagreement_score":0.0064614965,"about_ca_system_score_codex":0.0005871159,"about_ca_system_score_gemma":0.0007884988,"threshold_uncertainty_score":0.020249605},"labels":[],"label_agreement":null},{"id":"W4394748062","doi":"10.3390/fire7040135","title":"Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models","year":2024,"lang":"en","type":"article","venue":"Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoke; Environmental science; Fire detection; Computer science; Environmental resource management; Engineering; Meteorology; Geography; Architectural engineering","score_opus":0.019563697777185904,"score_gpt":0.2134469938236127,"score_spread":0.1938832960464268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394748062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5227502,0.0023899009,0.45356822,0.0006335295,0.00058754836,0.00019784906,0.0016059012,0.009849159,0.00841774],"genre_scores_gemma":[0.86085445,0.00041467996,0.12651178,0.0003305053,0.00006442682,0.00008957437,0.0033278775,0.00025116195,0.008155591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977404,0.000026251142,0.000012480337,0.00008377053,0.000047320915,0.000056124656],"domain_scores_gemma":[0.9996877,0.000094807685,0.00003140294,0.000039847804,0.00011517672,0.00003108777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070545706,0.0011705,0.00070298323,0.0006925793,0.00027986523,0.00073470274,0.0015364679,0.0009865378,0.0019162766],"category_scores_gemma":[0.001360415,0.0004435944,0.00091898174,0.00034118883,0.00030619843,0.00086015824,0.0008335777,0.001315753,0.0007712814],"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":[0.0007918847,0.00049821043,0.010437792,0.00020728314,0.00026242557,0.00015817255,0.00006830305,0.6436784,0.02817231,0.0017705638,0.007619666,0.30633494],"study_design_scores_gemma":[0.000010026495,0.00004321525,0.000408311,0.0000080843765,0.000014853627,0.000012310402,0.000007757812,0.9958651,0.0030007572,0.00023790951,0.0003867627,0.000004896861],"about_ca_topic_score_codex":0.016761307,"about_ca_topic_score_gemma":0.026559345,"teacher_disagreement_score":0.016761307,"about_ca_system_score_codex":0.0010685236,"about_ca_system_score_gemma":0.0012012853,"threshold_uncertainty_score":0.03332746},"labels":[],"label_agreement":null},{"id":"W4394819366","doi":"10.3390/fire7040140","title":"YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images","year":2024,"lang":"en","type":"article","venue":"Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Smoke; Firefighting; Environmental science; Vegetation (pathology); Fire detection; Terrain; Remote sensing; Computer science; Meteorology; Cartography; Geography; Engineering","score_opus":0.016451476070884263,"score_gpt":0.2222521695086211,"score_spread":0.20580069343773683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394819366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29247823,0.0042948145,0.6805966,0.00091909274,0.0004471176,0.00026913415,0.0022228262,0.0076108202,0.011161351],"genre_scores_gemma":[0.82305366,0.0013906086,0.1517959,0.0009348815,0.00019586342,0.00021223743,0.0057824478,0.00031747657,0.016317058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999853,0.000014719361,0.0000071543227,0.00006139464,0.000025253537,0.000038473645],"domain_scores_gemma":[0.9998136,0.00005573579,0.000026692027,0.000017367127,0.00007019334,0.000016393105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038262625,0.0009226371,0.00062392384,0.0007105962,0.00025576173,0.00076212507,0.0013842217,0.00073803146,0.002210629],"category_scores_gemma":[0.00082154834,0.00031990977,0.0009478096,0.00038453506,0.00031202892,0.0006978365,0.00068362383,0.0009086542,0.001099915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086811214,0.0004448998,0.012394344,0.00032289734,0.0002758981,0.0002032937,0.0001465056,0.4542476,0.030216316,0.00471354,0.011969949,0.4841967],"study_design_scores_gemma":[0.000008738746,0.000032218904,0.00086547266,0.000014811575,0.000018707024,0.000019182267,0.000010664431,0.9962424,0.0015867014,0.00046189342,0.00073411094,0.0000051716383],"about_ca_topic_score_codex":0.020212743,"about_ca_topic_score_gemma":0.038210664,"teacher_disagreement_score":0.020212743,"about_ca_system_score_codex":0.0007022363,"about_ca_system_score_gemma":0.00080142467,"threshold_uncertainty_score":0.04019016},"labels":[],"label_agreement":null},{"id":"W4394892808","doi":"10.1093/jbcr/irae036.240","title":"606 Artificial Intelligence-powered Mobile Tool for Burn Injury Evaluation for First Responders","year":2024,"lang":"en","type":"article","venue":"Journal of Burn Care & Research","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SKiN Health; University of Alberta","funders":"","keywords":"Medicine; Burn injury; Medical emergency; Intensive care medicine; Emergency medicine; Surgery","score_opus":0.0912392549438501,"score_gpt":0.40853653408329343,"score_spread":0.31729727913944333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394892808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42889687,0.0025532695,0.3949665,0.0034512654,0.0009910273,0.006060254,0.013716442,0.10486175,0.044502575],"genre_scores_gemma":[0.7357362,0.00086064474,0.23644732,0.0010358125,0.0002117327,0.0026145289,0.004245689,0.0006728498,0.018175274],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948394,0.00018578672,0.000053300304,0.000081834725,0.00014876026,0.000046299585],"domain_scores_gemma":[0.99836665,0.0008156309,0.00012926033,0.00009726738,0.0004235414,0.00016763926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076354365,0.00094312266,0.00046700053,0.0016148777,0.00022883415,0.0007321286,0.0006933427,0.00072430965,0.020918842],"category_scores_gemma":[0.0037684792,0.00019572825,0.0004434591,0.00041276644,0.00011611005,0.0005273919,0.0007192827,0.00031408953,0.005976445],"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":[0.0027573411,0.00083544676,0.033832982,0.0016178854,0.00011067865,0.00222194,0.0010444429,0.004520683,0.035497535,0.0014250976,0.08014998,0.8359861],"study_design_scores_gemma":[0.0012014807,0.005818296,0.18946643,0.0025325303,0.0007036366,0.010110577,0.0035451343,0.45005295,0.094907105,0.008365127,0.2326644,0.00063233246],"about_ca_topic_score_codex":0.0005990101,"about_ca_topic_score_gemma":0.0006441848,"teacher_disagreement_score":0.020918842,"about_ca_system_score_codex":0.00028992002,"about_ca_system_score_gemma":0.00033120348,"threshold_uncertainty_score":0.0699805},"labels":[],"label_agreement":null},{"id":"W4395668758","doi":"10.18280/ijsse.140202","title":"Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Computer science; Algorithm; Feature extraction; Extraction (chemistry); Artificial intelligence; Chemistry","score_opus":0.00791583777943347,"score_gpt":0.20721320796620832,"score_spread":0.19929737018677485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395668758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13515551,0.0011287568,0.85308707,0.0005217121,0.00016962035,0.00007820583,0.00037685037,0.0025613997,0.006920844],"genre_scores_gemma":[0.9167515,0.0002754468,0.07670219,0.00023857586,0.00003716068,0.00009249174,0.00045904866,0.00006853446,0.0053750956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990153,0.000012557986,0.000005942958,0.000036954567,0.000019815923,0.000023224016],"domain_scores_gemma":[0.9998853,0.000055248132,0.000013612819,0.000007884314,0.000029925892,0.000008008014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024856688,0.00069342233,0.00051280187,0.00032747877,0.00016854954,0.0004244891,0.00089076505,0.00069501647,0.0014423728],"category_scores_gemma":[0.0005575229,0.00028405123,0.00070550037,0.00028416037,0.00023723638,0.00049250544,0.00031178715,0.0010157736,0.0003042502],"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":[0.00013321363,0.000072477225,0.0016986034,0.00006550444,0.00006366074,0.00021105407,0.000047691326,0.84951,0.011224235,0.0022000207,0.0022240176,0.13254957],"study_design_scores_gemma":[0.0000013418144,0.000007010425,0.00010258911,0.0000018701448,0.000002650519,0.000011745964,0.0000014460553,0.99883956,0.0006730907,0.00027777176,0.000079264886,0.0000017101192],"about_ca_topic_score_codex":0.0070219426,"about_ca_topic_score_gemma":0.008891873,"teacher_disagreement_score":0.0070219426,"about_ca_system_score_codex":0.0004985468,"about_ca_system_score_gemma":0.00045653127,"threshold_uncertainty_score":0.01396215},"labels":[],"label_agreement":null},{"id":"W4396214135","doi":"10.1007/s10586-024-04504-5","title":"Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets","year":2024,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Computer science; Petri net; Firefighting; Drone; Fire detection; Key (lock); Enhanced Data Rates for GSM Evolution; Edge computing; Variety (cybernetics); Systems engineering; Real-time computing; Distributed computing; Computer security; Artificial intelligence; Architectural engineering","score_opus":0.02002648129740907,"score_gpt":0.2476591701216259,"score_spread":0.22763268882421683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396214135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9441174,0.00017439989,0.050191723,0.00006895317,0.000063551306,0.0001271582,0.00020782459,0.0019366974,0.0031122107],"genre_scores_gemma":[0.9928699,0.000043330903,0.0063280202,0.000011198113,0.0000037521752,0.00001699614,0.00009977752,0.000013225202,0.0006138278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996902,0.000062463136,0.000020245036,0.00006837921,0.000088269626,0.0000704931],"domain_scores_gemma":[0.99925333,0.00033220154,0.00006463943,0.00006123581,0.00019741598,0.000091120746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721661,0.0004996698,0.00034983526,0.0004926766,0.0004062827,0.0005245626,0.0006437926,0.00029892928,0.0013108411],"category_scores_gemma":[0.0009448059,0.00012052511,0.00022821515,0.00031408385,0.00020501687,0.00048358418,0.0002562323,0.00024895585,0.00015399467],"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":[0.0075192633,0.0012290163,0.028166668,0.00042972303,0.00026577595,0.00090339896,0.00018106033,0.6775572,0.09740335,0.002685714,0.0026220114,0.18103689],"study_design_scores_gemma":[0.000058840837,0.0005415022,0.00535375,0.0000061202963,0.000073762414,0.000067225075,0.000059077836,0.96936786,0.023733057,0.00027082046,0.00045415654,0.000013918025],"about_ca_topic_score_codex":0.012394975,"about_ca_topic_score_gemma":0.008114866,"teacher_disagreement_score":0.012394975,"about_ca_system_score_codex":0.0006843014,"about_ca_system_score_gemma":0.0007699768,"threshold_uncertainty_score":0.024645627},"labels":[],"label_agreement":null},{"id":"W4396585407","doi":"10.3390/rs16091627","title":"SWIFT: Simulated Wildfire Images for Fast Training Dataset","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Swift; Training (meteorology); Remote sensing; Environmental science; Computer science; Meteorology; Geology; Geography","score_opus":0.02115629115829634,"score_gpt":0.2519840643115534,"score_spread":0.23082777315325706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396585407","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21547115,0.0022006582,0.03520376,0.0017173929,0.0020288248,0.0013818796,0.6925013,0.03491582,0.014579245],"genre_scores_gemma":[0.12712781,0.0004896555,0.0516391,0.00034306536,0.00011078087,0.00057555793,0.81470925,0.0008190382,0.0041858577],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994974,0.00005108536,0.00003472706,0.0001446289,0.0001743844,0.00009780633],"domain_scores_gemma":[0.9995278,0.0000857497,0.000035974994,0.00013549309,0.00014708436,0.000067980865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093697355,0.0022610247,0.0008487368,0.0014681547,0.0006793487,0.00066373846,0.002386192,0.0016326187,0.0061855577],"category_scores_gemma":[0.0018042807,0.00045106286,0.0014356723,0.0012658867,0.0004768301,0.0009270731,0.00082811987,0.0020616679,0.0043086265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011131045,0.0021236083,0.01179275,0.0011732449,0.0003797339,0.0008889316,0.0001482931,0.08253071,0.015432905,0.0018698877,0.74749666,0.13505027],"study_design_scores_gemma":[0.0010131262,0.0006916948,0.042536486,0.0003360303,0.00017287991,0.001606412,0.00039426482,0.67196715,0.044281468,0.0048500514,0.23187402,0.00027647955],"about_ca_topic_score_codex":0.02721812,"about_ca_topic_score_gemma":0.067470096,"teacher_disagreement_score":0.02721812,"about_ca_system_score_codex":0.0010843606,"about_ca_system_score_gemma":0.0012889747,"threshold_uncertainty_score":0.054119408},"labels":[],"label_agreement":null},{"id":"W4396605531","doi":"10.1109/tie.2024.3387089","title":"Early Wildfire Detection and Distance Estimation Using Aerial Visible-Infrared Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Global Positioning System; Segmentation; Triangulation; False alarm; Remote sensing; Fire detection; Monocular; Feature (linguistics); Image segmentation; Satellite; Aerial image; Image (mathematics); Geography; Engineering","score_opus":0.014974593842789582,"score_gpt":0.22980196696646105,"score_spread":0.21482737312367145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396605531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05664058,0.0002448995,0.93970823,0.000051746356,0.000058426303,0.000038615224,0.0001363621,0.0017713092,0.0013498755],"genre_scores_gemma":[0.6289793,0.00024981226,0.36696735,0.00012215065,0.00006175917,0.00006115888,0.0006682248,0.00009442988,0.0027957165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969256,0.000020055453,0.000012099018,0.00009677632,0.00010639386,0.00007199779],"domain_scores_gemma":[0.9998679,0.000015411431,0.000029339062,0.00002554999,0.00004554735,0.000016244869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003008311,0.0008935879,0.00080463506,0.000955142,0.00030796614,0.00049787556,0.0011119128,0.0005701613,0.0009319324],"category_scores_gemma":[0.00045680138,0.00033176283,0.0007189425,0.00056053634,0.0002390321,0.0008259436,0.00086488633,0.0007287244,0.00054045336],"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":[0.00030019297,0.0002446319,0.007092697,0.00014835538,0.000101010795,0.0001925786,0.00010078476,0.10268049,0.06850339,0.0019025035,0.0024497358,0.81628364],"study_design_scores_gemma":[0.000017795808,0.00012106793,0.0056168763,0.000016539781,0.000032943193,0.00015102739,0.00004724704,0.96743125,0.022945715,0.0018997262,0.0017003608,0.000019437579],"about_ca_topic_score_codex":0.004142461,"about_ca_topic_score_gemma":0.008413232,"teacher_disagreement_score":0.004142461,"about_ca_system_score_codex":0.00030586813,"about_ca_system_score_gemma":0.00065962033,"threshold_uncertainty_score":0.008236706},"labels":[],"label_agreement":null},{"id":"W4398182925","doi":"10.1201/9781003488682-33","title":"Accident Evasion and Warning System","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Fire Detection and Safety Systems","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":"Horizon College and Seminary","funders":"","keywords":"Accident (philosophy); Evasion (ethics); Warning system; Computer security; Computer science; Business; Medicine; Telecommunications; Philosophy","score_opus":0.007117325536817088,"score_gpt":0.16818338476485367,"score_spread":0.16106605922803657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398182925","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012723774,0.014384561,0.37706852,0.0028567044,0.004432545,0.0008847447,0.0034679745,0.039996717,0.54418445],"genre_scores_gemma":[0.06531054,0.008541635,0.08990899,0.0014643127,0.00057873805,0.0004003737,0.0045183916,0.0010572023,0.82821983],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996736,0.00002441553,0.000015806485,0.00004891344,0.00020789608,0.000029325807],"domain_scores_gemma":[0.9997147,0.000043888565,0.000011739003,0.000029637586,0.00018488424,0.000015140718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028845103,0.0006145261,0.00045785136,0.0010632707,0.00048174753,0.0015400705,0.0010190236,0.0009495613,0.046623453],"category_scores_gemma":[0.00074409705,0.00025526452,0.00032820948,0.00060680544,0.00016527476,0.0010949867,0.00080712896,0.0007892084,0.036506277],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012447288,0.000111863264,0.00045738879,0.00056451326,0.000017839517,0.00026139934,0.00021086093,0.0022015905,0.020127716,0.0155142965,0.2606332,0.6997749],"study_design_scores_gemma":[0.000018155588,0.00014412821,0.001107301,0.00022064584,0.000034469915,0.00093078095,0.00008533428,0.012388654,0.014435677,0.004503562,0.9660877,0.000043564036],"about_ca_topic_score_codex":0.0011065919,"about_ca_topic_score_gemma":0.00081344997,"teacher_disagreement_score":0.046623453,"about_ca_system_score_codex":0.00039697404,"about_ca_system_score_gemma":0.00047960368,"threshold_uncertainty_score":0.15597093},"labels":[],"label_agreement":null},{"id":"W4398513078","doi":"10.7910/dvn/lozzcs/sp617d","title":"2019 Dusseault_Supplementary Mat. Hazards Database.pdf","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Fire Detection and Safety Systems","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":"University of Waterloo","funders":"","keywords":"Database; Computer science","score_opus":0.010287719637951775,"score_gpt":0.22395138764803854,"score_spread":0.21366366801008677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398513078","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006290516,0.000048012655,0.000034311295,0.000056935696,0.000021619071,0.0000038537787,0.9987459,0.00048406507,0.0005423309],"genre_scores_gemma":[0.0004170508,0.000058291123,0.00012666095,0.000056561144,0.0000104163855,0.000026395448,0.9984395,0.00010946548,0.00075572066],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992931,0.00010086443,0.000074334974,0.00024996372,0.00014027525,0.00014140969],"domain_scores_gemma":[0.9983388,0.00046148535,0.0001457061,0.00045539127,0.00038427234,0.00021426869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007547994,0.002270783,0.0012871841,0.0036395402,0.0007869757,0.0027506982,0.0027462284,0.0023283174,0.16298243],"category_scores_gemma":[0.004889912,0.0008193701,0.0012937345,0.00481302,0.00046334192,0.0017036069,0.0020024716,0.0014136584,0.1873362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043888922,0.000009716436,0.00043335964,0.000350856,0.000017188722,0.000011824828,0.000009807064,0.00016141083,0.000050505674,0.00028384003,0.99725646,0.00137127],"study_design_scores_gemma":[0.00022893466,0.000020189611,0.003156335,0.00023835016,0.000029273915,0.00005275384,0.00006319145,0.00053964776,0.0003510745,0.0014107445,0.9938777,0.00003186743],"about_ca_topic_score_codex":0.024402685,"about_ca_topic_score_gemma":0.03735723,"teacher_disagreement_score":0.16298243,"about_ca_system_score_codex":0.0015962367,"about_ca_system_score_gemma":0.0019539997,"threshold_uncertainty_score":0.54523057},"labels":[],"label_agreement":null},{"id":"W4399813544","doi":"10.23977/acss.2024.080402","title":"A Safety Helmet Detection Method Using Adjusted YOLOv8","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Aeronautics; Reliability engineering; Engineering","score_opus":0.018296548039023176,"score_gpt":0.27236484006716255,"score_spread":0.25406829202813935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399813544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2891819,0.0015078256,0.6819921,0.00038935427,0.00064546795,0.00034079817,0.0019947744,0.01650455,0.0074432585],"genre_scores_gemma":[0.5665917,0.0006820839,0.41223946,0.00029180798,0.00014034835,0.00024192446,0.0077025923,0.0006231878,0.011486879],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996979,0.000027719174,0.000018056968,0.00011350964,0.000080297636,0.0000624554],"domain_scores_gemma":[0.99970967,0.00003645592,0.00003661442,0.000036135254,0.00015553138,0.000025600359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033478884,0.0009264153,0.0006376064,0.0016634411,0.00042412928,0.0005028718,0.001274642,0.00062304793,0.0017035065],"category_scores_gemma":[0.0010449333,0.00033881466,0.0005613329,0.0007144834,0.0002790119,0.0007662647,0.0008407852,0.0005871488,0.0014516258],"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":[0.00058650255,0.00025797018,0.014314242,0.00021546161,0.00009669999,0.00020619259,0.00014674706,0.04638321,0.0604409,0.0014837473,0.014240842,0.8616275],"study_design_scores_gemma":[0.000101395846,0.0002879851,0.008486306,0.000061265535,0.00006499714,0.00031502932,0.00020368803,0.93531394,0.039736424,0.0014898974,0.013886643,0.00005241643],"about_ca_topic_score_codex":0.010476227,"about_ca_topic_score_gemma":0.017664997,"teacher_disagreement_score":0.010476227,"about_ca_system_score_codex":0.00044591137,"about_ca_system_score_gemma":0.0011076054,"threshold_uncertainty_score":0.020830512},"labels":[],"label_agreement":null},{"id":"W4399830825","doi":"10.1139/tcsme-2023-0175","title":"Autonomous firefighting using a quadruped robot","year":2024,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Firefighting; Payload (computing); Robot; Fire detection; Front (military); Computer science; Software; Simulation; Automotive engineering; Engineering; Artificial intelligence; Architectural engineering; Mechanical engineering; Computer security; Geography","score_opus":0.014541592549931747,"score_gpt":0.2069614370863527,"score_spread":0.19241984453642094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399830825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2990195,0.00035988298,0.68537396,0.00018570932,0.00024452596,0.00047921037,0.00024867154,0.0059384853,0.00814996],"genre_scores_gemma":[0.6978542,0.00023961767,0.29318365,0.00012765662,0.000040295363,0.0003061225,0.00025628743,0.00013185013,0.007860374],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998791,0.000014575711,0.000005277357,0.000028832646,0.00005015466,0.000022077535],"domain_scores_gemma":[0.99982774,0.000028156821,0.000019963927,0.000023631974,0.00005720959,0.00004328033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019201115,0.0003995034,0.00027534238,0.0002314526,0.00029041906,0.00023546495,0.00052382436,0.00042853082,0.0030938159],"category_scores_gemma":[0.00021863758,0.00020956674,0.00033498264,0.000054511038,0.0003151066,0.00032963787,0.00041333464,0.00043608097,0.0011157788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002715022,0.00016787878,0.002424968,0.00042672537,0.00005634008,0.00075781555,0.0003487574,0.021189416,0.83914393,0.002607402,0.0025171228,0.13008818],"study_design_scores_gemma":[0.00042084444,0.0072517856,0.013589799,0.00018299652,0.00013792556,0.0041351104,0.00033418267,0.47922745,0.4193988,0.0024969939,0.07258828,0.0002357913],"about_ca_topic_score_codex":0.0008322088,"about_ca_topic_score_gemma":0.0006789064,"teacher_disagreement_score":0.0030938159,"about_ca_system_score_codex":0.0001129346,"about_ca_system_score_gemma":0.00039614332,"threshold_uncertainty_score":0.01034981},"labels":[],"label_agreement":null},{"id":"W4399880721","doi":"10.1109/lgrs.2024.3417624","title":"Application of Explainable Artificial Intelligence in Predicting Wildfire Spread: An ASPP-Enabled CNN Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.012590446472268186,"score_gpt":0.2216278704247412,"score_spread":0.20903742395247302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399880721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2311825,0.0011935704,0.76014954,0.0009941622,0.0001260232,0.00006792907,0.0005259617,0.0014980034,0.004262423],"genre_scores_gemma":[0.9393133,0.00037393504,0.057818655,0.00015665949,0.00006399294,0.00004396497,0.00041273414,0.000029498988,0.0017871781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998653,0.000027200347,0.000008508641,0.00004901052,0.000023398574,0.000026636064],"domain_scores_gemma":[0.9996062,0.0001987298,0.000055688048,0.00003830067,0.00008331194,0.000017849079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065202685,0.0008754268,0.00047780332,0.00070428115,0.00021265709,0.0006224349,0.0008822915,0.00079576025,0.0010237171],"category_scores_gemma":[0.0013948506,0.0003247908,0.0007395867,0.00054011866,0.0003217318,0.0011860196,0.0004943014,0.0007487197,0.00016304875],"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":[0.00009286843,0.00007214882,0.0054030456,0.000051412233,0.00013281986,0.00016311716,0.000030457632,0.87355316,0.0035886667,0.0037085991,0.00099565,0.11220802],"study_design_scores_gemma":[0.0000012050482,0.0000065128015,0.0002742354,0.0000016296791,0.0000071289887,0.000006352935,0.0000017065851,0.9984212,0.000380393,0.0008191386,0.000078945,0.0000015677914],"about_ca_topic_score_codex":0.011291204,"about_ca_topic_score_gemma":0.011331252,"teacher_disagreement_score":0.011291204,"about_ca_system_score_codex":0.0008723987,"about_ca_system_score_gemma":0.0005865339,"threshold_uncertainty_score":0.022450984},"labels":[],"label_agreement":null},{"id":"W4400287115","doi":"10.1121/10.0027509","title":"Toward audio-based sensing for pedestrian detection","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Fire Detection and Safety Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Pedestrian detection; Computer science; Human–computer interaction; Engineering; Transport engineering","score_opus":0.015366869129684184,"score_gpt":0.23198533451087403,"score_spread":0.21661846538118984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400287115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042495463,0.0039661746,0.94113994,0.001192548,0.00047888505,0.00012040323,0.00022531771,0.0012324917,0.009148768],"genre_scores_gemma":[0.45447895,0.004118758,0.531244,0.0009195784,0.00066761655,0.00013195003,0.00035040735,0.00008892283,0.007999801],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935085,0.00015744154,0.000018682393,0.00013712338,0.00028953594,0.000046386667],"domain_scores_gemma":[0.99885523,0.0004949048,0.00008151787,0.000074243464,0.00044471337,0.000049369635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008393051,0.0008462298,0.00042357494,0.0010764673,0.00024761976,0.001192543,0.0008957445,0.0011835452,0.0025731067],"category_scores_gemma":[0.0020791069,0.00038379105,0.00039008903,0.00062853505,0.0005813678,0.0016254219,0.0008275629,0.00088438427,0.0017599058],"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":[0.00065642805,0.0004407211,0.005086725,0.00067280023,0.0000931648,0.00026196655,0.00018432071,0.030050881,0.37697116,0.010303821,0.006425926,0.5688521],"study_design_scores_gemma":[0.00009579637,0.0009595658,0.00666668,0.0002890558,0.000113814305,0.00078242033,0.00028595206,0.7763291,0.17463773,0.015882239,0.023808641,0.00014899098],"about_ca_topic_score_codex":0.0016361044,"about_ca_topic_score_gemma":0.002534782,"teacher_disagreement_score":0.0025731067,"about_ca_system_score_codex":0.00034869663,"about_ca_system_score_gemma":0.00038017944,"threshold_uncertainty_score":0.008607864},"labels":[],"label_agreement":null},{"id":"W4400423520","doi":"10.54963/ptnd.v3i2.271","title":"Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction","year":2024,"lang":"en","type":"article","venue":"Prevention and Treatment of Natural Disasters","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"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":"Support vector machine; Data mining; Computer science; Wireless sensor network; Algorithm; Artificial intelligence; Computer network","score_opus":0.024571125744813124,"score_gpt":0.2536984092588265,"score_spread":0.22912728351401335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400423520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07827038,0.003603149,0.91455925,0.0010134417,0.00009652762,0.00005166751,0.00006145279,0.00030682355,0.0020372486],"genre_scores_gemma":[0.7960795,0.0033221387,0.19894096,0.00012240619,0.00012636418,0.000054323013,0.00013177631,0.000023506647,0.0011989671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970585,0.00010952814,0.000022521614,0.000055071112,0.000083193205,0.000023854718],"domain_scores_gemma":[0.9992119,0.00048572806,0.000083081235,0.00003552651,0.00015805686,0.00002572473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011030071,0.0005863735,0.0005119735,0.00083522155,0.00022713518,0.00076139835,0.0005255568,0.00044798368,0.000436199],"category_scores_gemma":[0.0022650643,0.00018861069,0.00043726707,0.0008057603,0.0002452577,0.0012353879,0.00034217958,0.000697128,0.00012142744],"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":[0.000084731524,0.000214034,0.011826908,0.00013634314,0.00012257391,0.00009184249,0.00008432878,0.68331015,0.0027722856,0.006633551,0.001180324,0.29354298],"study_design_scores_gemma":[0.000001508283,0.000015254067,0.00037498705,0.0000075473076,0.000005214478,0.000008513534,0.000011443601,0.9974137,0.00035674186,0.001566164,0.00023689936,0.0000021122955],"about_ca_topic_score_codex":0.005187382,"about_ca_topic_score_gemma":0.0041418574,"teacher_disagreement_score":0.005187382,"about_ca_system_score_codex":0.0004774896,"about_ca_system_score_gemma":0.00055179745,"threshold_uncertainty_score":0.010314405},"labels":[],"label_agreement":null},{"id":"W4401244789","doi":"10.1016/j.aej.2024.07.066","title":"Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Garbage; Sustainable development; Computer science; Artificial intelligence; Residual neural network; Pattern recognition (psychology); Development (topology); Environmental science; Agricultural engineering; Machine learning; Mathematics; Engineering; Deep learning; Biology","score_opus":0.009260302313678038,"score_gpt":0.19434685646852018,"score_spread":0.18508655415484215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401244789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22422613,0.0015526961,0.7533281,0.00095609174,0.00025098457,0.0001413684,0.0005867436,0.0063827867,0.012575113],"genre_scores_gemma":[0.92442596,0.0003682328,0.06723604,0.0003004913,0.000038281047,0.00012471141,0.0009540618,0.000127278,0.0064250473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997899,0.000024766221,0.000012177643,0.00006993769,0.00004390246,0.000059414644],"domain_scores_gemma":[0.9998574,0.00003357769,0.000023188155,0.000015068817,0.000057534875,0.000013146805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044427533,0.0012720071,0.0008425279,0.00068647036,0.00029455722,0.0007797182,0.0012966297,0.00096537464,0.0018357534],"category_scores_gemma":[0.0007519306,0.0004259059,0.000984209,0.00047320974,0.00044392055,0.0008864593,0.0006365636,0.0007203354,0.0005028577],"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":[0.00009105197,0.00008147334,0.0018709102,0.0000744743,0.000054647204,0.00010554802,0.000020802456,0.91722894,0.004364703,0.0014366233,0.002207575,0.07246322],"study_design_scores_gemma":[0.000002370477,0.000009341593,0.00015549408,0.0000026399377,0.0000063160956,0.0000052803057,0.000003360226,0.9985953,0.0007171826,0.00035320528,0.00014684671,0.0000026058615],"about_ca_topic_score_codex":0.023084441,"about_ca_topic_score_gemma":0.02840482,"teacher_disagreement_score":0.023084441,"about_ca_system_score_codex":0.0011991091,"about_ca_system_score_gemma":0.0017787293,"threshold_uncertainty_score":0.045900106},"labels":[],"label_agreement":null},{"id":"W4401379778","doi":"10.1109/jiot.2024.3439228","title":"A Trustable Federated Learning Framework for Rapid Fire Smoke Detection at the Edge in Smart Home Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Smoke; Enhanced Data Rates for GSM Evolution; Edge computing; Fire detection; Human–computer interaction; Multimedia; Artificial intelligence; Architectural engineering; Engineering; Waste management","score_opus":0.011905559255668205,"score_gpt":0.2223884978677452,"score_spread":0.210482938612077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401379778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020821376,0.000105749634,0.97762907,0.00031496127,0.000019056812,0.000033146942,0.000038859587,0.0002410288,0.0007967693],"genre_scores_gemma":[0.8873673,0.000123103,0.11097665,0.0001413387,0.000041683372,0.00008064497,0.000094821575,0.000029399605,0.0011450889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875104,0.00050401397,0.000058469293,0.00029534608,0.00022596345,0.0001650942],"domain_scores_gemma":[0.996152,0.002324561,0.0004062677,0.00031149655,0.0005761495,0.00022955309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003148443,0.00067321624,0.0010067765,0.00064017205,0.00057625625,0.001241539,0.0017935017,0.0012643273,0.0016130382],"category_scores_gemma":[0.00854198,0.00035042857,0.00065533863,0.0005388059,0.0011781175,0.0018697592,0.002056471,0.0017123797,0.00018112962],"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":[0.00015334973,0.00009244085,0.001717918,0.000053849613,0.00005966324,0.00017341209,0.00017194825,0.90756965,0.0009541122,0.04412766,0.00078515813,0.04414088],"study_design_scores_gemma":[0.000005786828,0.000018508685,0.00006400504,0.0000033272088,0.0000053422114,0.000009714338,0.000008823573,0.9846954,0.00016844968,0.014900109,0.00011716427,0.0000035322626],"about_ca_topic_score_codex":0.0040305234,"about_ca_topic_score_gemma":0.004570277,"teacher_disagreement_score":0.0040305234,"about_ca_system_score_codex":0.001292658,"about_ca_system_score_gemma":0.0016461344,"threshold_uncertainty_score":0.016650736},"labels":[],"label_agreement":null},{"id":"W4401455951","doi":"10.1155/2024/7113084","title":"Method on Efficient Operation of Multiple Models for Vision‐Based In‐Flight Risky Behavior Recognition in UAM Safety and Security","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education, India; National Research Foundation of Korea; Ministry of Education; Soonchunhyang University; National Research Foundation","keywords":"Computer science; Computer security; Artificial intelligence; Computer vision; Engineering; Simulation","score_opus":0.01257343654217133,"score_gpt":0.27759350695818086,"score_spread":0.2650200704160095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401455951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020199021,0.00034259452,0.97540796,0.00019525771,0.00008255409,0.00007339819,0.000055460943,0.0018837238,0.001760014],"genre_scores_gemma":[0.6405385,0.00033364366,0.3510123,0.0003263347,0.00010052032,0.00022497957,0.000354325,0.00024618919,0.0068631913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996475,0.00004643102,0.000019506952,0.00012210928,0.00008871364,0.000075772536],"domain_scores_gemma":[0.99972373,0.000073547795,0.00003164392,0.000043713233,0.000089754394,0.00003757561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005708043,0.0011241533,0.00094915694,0.00069937384,0.0005507916,0.00080108485,0.0016236594,0.000941372,0.0025335697],"category_scores_gemma":[0.0010547382,0.0004964231,0.001113348,0.00044172173,0.00034264263,0.0009305917,0.000999595,0.0014839618,0.0008871699],"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":[0.00027787124,0.00023034062,0.0026329646,0.000094101335,0.00016558598,0.00019538503,0.00013379409,0.48819467,0.020303542,0.006634215,0.004258554,0.47687903],"study_design_scores_gemma":[0.00000698728,0.000021273461,0.00016192065,0.0000023109142,0.0000090996955,0.000020578007,0.0000062907143,0.9973398,0.0014187098,0.000629371,0.00037886252,0.0000048380552],"about_ca_topic_score_codex":0.018782796,"about_ca_topic_score_gemma":0.018276256,"teacher_disagreement_score":0.018782796,"about_ca_system_score_codex":0.00085127965,"about_ca_system_score_gemma":0.0018848556,"threshold_uncertainty_score":0.03734696},"labels":[],"label_agreement":null},{"id":"W4401645683","doi":"10.3390/en17164059","title":"A Review of Hydrogen Leak Detection Regulations and Technologies","year":2024,"lang":"en","type":"review","venue":"Energies","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Office of Energy Research and Development; Natural Resources Canada; National Research Council Canada","keywords":"Leak; Leak detection; Risk analysis (engineering); Business; Computer science; Environmental science; Engineering; Environmental engineering","score_opus":0.01983112006446955,"score_gpt":0.2627951068650693,"score_spread":0.24296398680059975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401645683","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005627126,0.9911288,0.0010219982,0.00063633325,0.00031564158,0.000029259898,0.00022456808,0.000036478625,0.0060442546],"genre_scores_gemma":[0.0016798398,0.9958254,0.00084435317,0.00026067306,0.00011271487,0.000023353025,0.0001678044,0.0000063183516,0.0010795883],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992711,0.000109496905,0.00012180398,0.000119823795,0.00032155224,0.000056183908],"domain_scores_gemma":[0.9990715,0.00046551984,0.00015074955,0.000035878555,0.00024532812,0.000031022744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011394747,0.0011391237,0.0011629963,0.0037801887,0.0005116901,0.0014733787,0.0011586407,0.0011747887,0.006013728],"category_scores_gemma":[0.0017404908,0.0005339638,0.0009941369,0.0036282819,0.00046675483,0.0019976357,0.0007568521,0.0012992376,0.0026942294],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072427734,0.00014257358,0.00039071738,0.10131194,0.00016559626,0.00035299856,0.0002382379,0.001273346,0.00785535,0.021042304,0.04417715,0.8229774],"study_design_scores_gemma":[0.0000036342062,0.000072897376,0.00042608485,0.0074065058,0.00012359151,0.00033194822,0.000073734445,0.00010194702,0.0015048004,0.0020188668,0.98791426,0.00002173873],"about_ca_topic_score_codex":0.0019273657,"about_ca_topic_score_gemma":0.0024833928,"teacher_disagreement_score":0.006013728,"about_ca_system_score_codex":0.0008373564,"about_ca_system_score_gemma":0.0028784478,"threshold_uncertainty_score":0.020117879},"labels":[],"label_agreement":null},{"id":"W4402314895","doi":"10.21203/rs.3.rs-4934969/v1","title":"Advancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"False positive paradox; Computer science; Smoke; Reliability (semiconductor); Inference; Object detection; Fire detection; Artificial intelligence; True positive rate; False positives and false negatives; Precision and recall; Data mining; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.01806385770021964,"score_gpt":0.3045765931961823,"score_spread":0.2865127354959627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402314895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18001255,0.0006458095,0.8115636,0.00031444544,0.0001255722,0.00007706691,0.00017932389,0.0030892761,0.0039922656],"genre_scores_gemma":[0.79401845,0.00019811263,0.20184049,0.00013052813,0.00007775334,0.00004897397,0.00029742892,0.0003768058,0.0030114795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931383,0.00018624822,0.000042203737,0.00014054343,0.000212439,0.000104786704],"domain_scores_gemma":[0.99714226,0.0015482197,0.00021749985,0.00031832347,0.0006275143,0.00014610315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020972276,0.0006414463,0.00076049886,0.0011409922,0.00039996902,0.0010822656,0.0014603254,0.0007720087,0.002335687],"category_scores_gemma":[0.0071587646,0.0003451808,0.0005652038,0.00036365684,0.00059508224,0.001252913,0.0014721103,0.0010388002,0.0006127426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016906078,0.0005005793,0.021668313,0.00036643955,0.00033487874,0.00027104493,0.0003379523,0.31517908,0.06516876,0.032160766,0.003396964,0.5589245],"study_design_scores_gemma":[0.00002238544,0.000099905345,0.0019172545,0.000015512833,0.00002901719,0.000050447667,0.000020528172,0.9749069,0.015627716,0.0063082445,0.0009716725,0.000030384648],"about_ca_topic_score_codex":0.0036624973,"about_ca_topic_score_gemma":0.0053714397,"teacher_disagreement_score":0.0036624973,"about_ca_system_score_codex":0.00070310285,"about_ca_system_score_gemma":0.0012767044,"threshold_uncertainty_score":0.0110913515},"labels":[],"label_agreement":null},{"id":"W4402474694","doi":"10.1109/ccece59415.2024.10667264","title":"Computer Vision Fire Hydrant Obstruction Detection System","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Carleton University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.0038283374388966324,"score_gpt":0.18023500843177018,"score_spread":0.17640667099287355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22405292,0.0019061744,0.6071489,0.0005467555,0.00069480087,0.0016616802,0.02549884,0.09472427,0.043765645],"genre_scores_gemma":[0.6131849,0.00074732373,0.30240187,0.0006930161,0.00014549999,0.0008408467,0.058221687,0.0007651942,0.022999575],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996898,0.000014608113,0.000014695711,0.00012986959,0.00008833199,0.00006280024],"domain_scores_gemma":[0.9998349,0.000010330924,0.000014289398,0.000025544226,0.00010155091,0.000013296402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022555051,0.0007119424,0.0007983377,0.0012206894,0.0003458099,0.00067963137,0.0008891925,0.00075324485,0.006978696],"category_scores_gemma":[0.00049221446,0.00026918922,0.0006414482,0.00049251603,0.00014231076,0.0005733441,0.0007930607,0.00067721587,0.0070278696],"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":[0.00077938184,0.000525817,0.011389074,0.0004027186,0.00016310657,0.00039158066,0.00009108496,0.028249905,0.09861822,0.0017104264,0.120634414,0.7370443],"study_design_scores_gemma":[0.00018905172,0.00042380684,0.030102504,0.00008051873,0.00012090186,0.0009259596,0.00016624355,0.8050523,0.09015871,0.0029647695,0.06970785,0.00010742421],"about_ca_topic_score_codex":0.010533061,"about_ca_topic_score_gemma":0.013522496,"teacher_disagreement_score":0.010533061,"about_ca_system_score_codex":0.0005668301,"about_ca_system_score_gemma":0.0006535355,"threshold_uncertainty_score":0.023346066},"labels":[],"label_agreement":null},{"id":"W4402927526","doi":"10.3390/fire7100343","title":"Deep Learning Approach for Wildland Fire Recognition Using RGB and Thermal Infrared Aerial Image","year":2024,"lang":"en","type":"article","venue":"Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Aerial image; RGB color model; Thermal infrared; Artificial intelligence; Remote sensing; Environmental science; Computer science; Computer vision; Infrared; Image (mathematics); Geology; Optics; Physics","score_opus":0.015881509606826015,"score_gpt":0.2135969920065807,"score_spread":0.19771548239975467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402927526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120553635,0.0027730162,0.86177415,0.0005492444,0.0002540486,0.0001618377,0.0010059399,0.006700266,0.006227843],"genre_scores_gemma":[0.7749083,0.0015258497,0.20644593,0.00053646456,0.00013597701,0.00021909556,0.0034151399,0.00014613073,0.01266711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997558,0.000017611714,0.000017118651,0.00008207735,0.00006455819,0.00006280378],"domain_scores_gemma":[0.99988806,0.000020944583,0.000016196776,0.000015272568,0.000046203288,0.000013280203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036704293,0.0011122173,0.00077290565,0.0009885776,0.00027113318,0.00063846755,0.0012141273,0.0009181083,0.0020618495],"category_scores_gemma":[0.00049765373,0.00046165314,0.0012451474,0.0008108844,0.0002561412,0.00084041397,0.0006750755,0.0016277357,0.00087942474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031726234,0.00039024602,0.004251286,0.00015941805,0.0001752373,0.00028949877,0.000090824906,0.26502222,0.022872292,0.0028342851,0.007207806,0.69638956],"study_design_scores_gemma":[0.000008133769,0.000033731227,0.0006441611,0.000010221159,0.000017346036,0.00003532279,0.000015571293,0.99418265,0.0031857935,0.0011110714,0.0007482647,0.0000076254414],"about_ca_topic_score_codex":0.014899076,"about_ca_topic_score_gemma":0.016428733,"teacher_disagreement_score":0.014899076,"about_ca_system_score_codex":0.00069771387,"about_ca_system_score_gemma":0.00093849865,"threshold_uncertainty_score":0.0296247},"labels":[],"label_agreement":null},{"id":"W4402992628","doi":"10.3390/math12193042","title":"An Explainable AI-Based Modified YOLOv8 Model for Efficient Fire Detection","year":2024,"lang":"en","type":"article","venue":"Mathematics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Fire detection; Artificial intelligence; Engineering; Architectural engineering","score_opus":0.019552704005812025,"score_gpt":0.24233017632409268,"score_spread":0.22277747231828066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402992628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08740215,0.00041148113,0.89764494,0.0007006112,0.00013066609,0.00013364859,0.00088397297,0.0050221737,0.007670377],"genre_scores_gemma":[0.791286,0.00035030697,0.19818483,0.00024539497,0.000046164096,0.00025958032,0.0014970333,0.00025162843,0.007879037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984086,0.000024878178,0.000012276378,0.00005747803,0.000040067247,0.00002442707],"domain_scores_gemma":[0.99967754,0.00013126219,0.000036627316,0.000040545063,0.000093161514,0.000020860722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003106727,0.00079255906,0.00041420577,0.00041619386,0.0002742449,0.00090404396,0.0014972999,0.0008046466,0.003377203],"category_scores_gemma":[0.0012539637,0.00035256834,0.001000951,0.00020280706,0.00036646865,0.000962971,0.00071540696,0.0010919846,0.00076807075],"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":[0.00019257219,0.000073176474,0.004517677,0.00014093061,0.00008365245,0.00020464648,0.00018471526,0.9025704,0.011519975,0.013953841,0.0023685994,0.06418977],"study_design_scores_gemma":[0.0000051389134,0.000012893845,0.00015701704,0.0000052749865,0.000010491604,0.000014627455,0.00000465702,0.9963542,0.0008698219,0.0017792168,0.0007822774,0.0000045138454],"about_ca_topic_score_codex":0.012979706,"about_ca_topic_score_gemma":0.0130348075,"teacher_disagreement_score":0.012979706,"about_ca_system_score_codex":0.0008181721,"about_ca_system_score_gemma":0.001007835,"threshold_uncertainty_score":0.025808334},"labels":[],"label_agreement":null},{"id":"W4403205954","doi":"10.2139/ssrn.4980597","title":"Exploring the Operational Logistics of Implementing Isolation Protocols at Equestrian Facilities","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Isolation (microbiology); Business; Operations management; Computer security; Computer science; Engineering","score_opus":0.07228780207709805,"score_gpt":0.2813809037577926,"score_spread":0.20909310168069456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403205954","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7979156,0.0004966823,0.13327692,0.0058686766,0.000076135155,0.00063142856,0.00028448788,0.00031039419,0.061139632],"genre_scores_gemma":[0.9767998,0.00022448302,0.01982257,0.00010590154,0.0000103848215,0.00007104166,0.000084464205,0.000037153106,0.0028442964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957753,0.0023792882,0.00012723422,0.00021039366,0.0006118573,0.00089598563],"domain_scores_gemma":[0.9873953,0.007952498,0.0012074742,0.00079302775,0.0021322316,0.00051948597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062930593,0.0006243147,0.00035340086,0.0008305407,0.0022866246,0.0053158673,0.0021706582,0.0015748393,0.011850368],"category_scores_gemma":[0.018827029,0.0004470311,0.0003717487,0.0009618683,0.001442813,0.007776654,0.0025849128,0.0017287781,0.0008491692],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044274665,0.0021363103,0.06511131,0.0011367017,0.00018366807,0.0024641799,0.008088743,0.4115289,0.017599292,0.22915626,0.0062394636,0.25192773],"study_design_scores_gemma":[0.00024979207,0.0037696566,0.02597357,0.00061267475,0.00020251631,0.000793883,0.0876559,0.7116848,0.02078853,0.12692632,0.021101354,0.00024098424],"about_ca_topic_score_codex":0.021025041,"about_ca_topic_score_gemma":0.017588852,"teacher_disagreement_score":0.021025041,"about_ca_system_score_codex":0.0036049369,"about_ca_system_score_gemma":0.0072935405,"threshold_uncertainty_score":0.041805327},"labels":[],"label_agreement":null},{"id":"W4403291004","doi":"10.1016/j.iot.2024.101402","title":"Automated image-based fire detection and alarm system using edge computing and cloud-based platform","year":2024,"lang":"en","type":"article","venue":"Internet of Things","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":10,"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 British Columbia, Okanagan Campus","funders":"University of British Columbia","keywords":"Cloud computing; Computer science; Enhanced Data Rates for GSM Evolution; ALARM; Edge computing; Computer vision; Engineering; Operating system; Aerospace engineering","score_opus":0.009076036553861407,"score_gpt":0.2222341212180368,"score_spread":0.21315808466417538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403291004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1801113,0.0006031053,0.7922157,0.00036820842,0.00030116996,0.0005262665,0.0005703995,0.01594014,0.009363772],"genre_scores_gemma":[0.8111763,0.00029711417,0.18283512,0.00036409072,0.00008633673,0.00016068779,0.000674565,0.00015393212,0.0042518037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996747,0.00003055075,0.000019155328,0.00007777695,0.00013722433,0.000060694532],"domain_scores_gemma":[0.9996656,0.0000469473,0.00004455827,0.000057788147,0.00013445105,0.000050650237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025690036,0.0004807541,0.00058295083,0.0008173438,0.0004463594,0.0007504455,0.0010644682,0.000498333,0.0016788769],"category_scores_gemma":[0.0005061306,0.00019081625,0.00029796356,0.00043488975,0.00016902787,0.0008730661,0.0008690556,0.00044519635,0.00071577897],"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":[0.0020494226,0.0011665949,0.015817497,0.00043556397,0.00019413937,0.0015642268,0.00032947084,0.034510702,0.28635314,0.0069040065,0.022999072,0.62767625],"study_design_scores_gemma":[0.00010152375,0.0005104616,0.0099862125,0.000053234253,0.00009071087,0.0007815166,0.00012861851,0.8099702,0.15948136,0.0027617381,0.016036995,0.00009738327],"about_ca_topic_score_codex":0.0015315958,"about_ca_topic_score_gemma":0.0019169598,"teacher_disagreement_score":0.0016788769,"about_ca_system_score_codex":0.0003196848,"about_ca_system_score_gemma":0.00047879491,"threshold_uncertainty_score":0.0056164265},"labels":[],"label_agreement":null},{"id":"W4403458627","doi":"10.1007/978-3-031-75540-8_18","title":"Smartphone-Based Fuel Identification Model for Wildfire Risk Assessment Using YOLOv8","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Identification (biology)","score_opus":0.021258766809355414,"score_gpt":0.2577889068896057,"score_spread":0.2365301400802503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403458627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15635478,0.00092637015,0.82625806,0.00019584522,0.00022435313,0.00009600494,0.0012759987,0.0036908058,0.010977742],"genre_scores_gemma":[0.94265395,0.0003440608,0.04736319,0.000061824925,0.00003280577,0.00014801596,0.0011168076,0.00007180153,0.008207547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999502,0.000005935351,0.000003056918,0.000019248704,0.000013508699,0.0000081020235],"domain_scores_gemma":[0.99994123,0.000017319226,0.000004846213,0.000004736222,0.000027597922,0.000004177621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011206086,0.00042802704,0.00058625365,0.00024818536,0.0001996265,0.00034756146,0.00058242795,0.0003884512,0.0042478936],"category_scores_gemma":[0.0002898661,0.00013977835,0.0004201713,0.0001603922,0.000076901146,0.00034357305,0.00028971827,0.00036608195,0.0011162264],"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":[0.00036546087,0.00012557412,0.005064106,0.00018864035,0.000081722304,0.00018904809,0.00007043069,0.761255,0.009892196,0.001922018,0.004559126,0.2162866],"study_design_scores_gemma":[0.000004091674,0.000023714276,0.0005279342,0.000004780154,0.000008852568,0.000020090798,0.0000066639473,0.99786216,0.0007335199,0.0002866497,0.0005167436,0.000004838651],"about_ca_topic_score_codex":0.012591385,"about_ca_topic_score_gemma":0.012489758,"teacher_disagreement_score":0.012591385,"about_ca_system_score_codex":0.00024534282,"about_ca_system_score_gemma":0.0003376506,"threshold_uncertainty_score":0.025036216},"labels":[],"label_agreement":null},{"id":"W4403675451","doi":"10.1109/case59546.2024.10711659","title":"An Analytical Threshold-based Classification Technique for Post-incident Fall Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.021108685667242204,"score_gpt":0.271267261877636,"score_spread":0.2501585762103938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403675451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035527084,0.00024371302,0.9606126,0.00012569726,0.00010909025,0.00009843329,0.00010120596,0.001421901,0.00176028],"genre_scores_gemma":[0.43017155,0.00034493627,0.56564647,0.0001520238,0.00007732862,0.00014567073,0.00028132414,0.00012984933,0.0030508963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992895,0.00006708053,0.000055714594,0.0001571085,0.00034971084,0.00008079559],"domain_scores_gemma":[0.99907887,0.00025169732,0.00013035923,0.0001023294,0.00040121414,0.000035441954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082539674,0.0006292216,0.0005675666,0.0019446005,0.00051940116,0.00096401706,0.0010755709,0.00087499566,0.0017862796],"category_scores_gemma":[0.0028665042,0.00025343458,0.0005611457,0.0016336674,0.00045028146,0.0008467004,0.0006372714,0.00079630263,0.0012376274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033952293,0.00027095963,0.005803821,0.0001758258,0.00006130334,0.00023512976,0.00024650505,0.014848842,0.14251515,0.0051288414,0.0032447425,0.82712936],"study_design_scores_gemma":[0.000023626695,0.00039934248,0.01476361,0.000054644293,0.00008866278,0.0010818008,0.00021459803,0.8529075,0.11664262,0.0048617194,0.008888151,0.0000738103],"about_ca_topic_score_codex":0.001836636,"about_ca_topic_score_gemma":0.0019387567,"teacher_disagreement_score":0.0019446005,"about_ca_system_score_codex":0.0004749683,"about_ca_system_score_gemma":0.00081921404,"threshold_uncertainty_score":0.0059757233},"labels":[],"label_agreement":null},{"id":"W4403985214","doi":"10.23977/acss.2024.080614","title":"Evaluation on New Energy Vehicle Safety Early Warning System Based on Intelligent Optimization Algorithm","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; Computer science; Energy (signal processing); Algorithm; Optimization algorithm; Mathematical optimization; Mathematics; Telecommunications","score_opus":0.012679003656322357,"score_gpt":0.2350721009686588,"score_spread":0.22239309731233645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403985214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3872899,0.0016470437,0.5848267,0.00075443694,0.00033416023,0.0005519673,0.00020473066,0.0021244134,0.022266524],"genre_scores_gemma":[0.96536005,0.00038771404,0.030393321,0.00009158498,0.000027548409,0.00022713093,0.00026236297,0.0000423688,0.003207951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992749,0.00018574414,0.00006296261,0.0001094326,0.00027277946,0.000094083785],"domain_scores_gemma":[0.99897647,0.0002748971,0.00006418765,0.000034820925,0.00060044246,0.000049211747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343477,0.0008488108,0.0010067621,0.00088501605,0.00043997273,0.0009836189,0.000657721,0.00081193715,0.0029151968],"category_scores_gemma":[0.0025354875,0.00022828262,0.0007393728,0.0003434331,0.0002457183,0.0009565955,0.00045836403,0.00048476705,0.0003583753],"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":[0.00086044293,0.0003985793,0.006894127,0.00041568695,0.00016616833,0.00013143204,0.00008256061,0.86941993,0.00870193,0.0017810054,0.0021727206,0.10897536],"study_design_scores_gemma":[0.000036810874,0.00026035408,0.0015839704,0.000010212034,0.000047040896,0.000017935621,0.000025538717,0.99522746,0.0021857822,0.00017688794,0.00041633638,0.000011603864],"about_ca_topic_score_codex":0.009099579,"about_ca_topic_score_gemma":0.0032400494,"teacher_disagreement_score":0.009099579,"about_ca_system_score_codex":0.0007424053,"about_ca_system_score_gemma":0.00095415243,"threshold_uncertainty_score":0.018093228},"labels":[],"label_agreement":null},{"id":"W4404404921","doi":"10.2139/ssrn.5022694","title":"A Vision-Based Aerial-Ground Fusion Method for Fire Detection","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"","keywords":"Computer vision; Artificial intelligence; Fire detection; Fusion; Computer science; Sensor fusion; Remote sensing; Geography; Engineering; Architectural engineering","score_opus":0.007474401539856714,"score_gpt":0.26010254705449914,"score_spread":0.2526281455146424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404404921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03717209,0.00077860884,0.95751864,0.0001310896,0.00021319767,0.00006499911,0.00024867352,0.0013080164,0.0025647446],"genre_scores_gemma":[0.47594377,0.0009355387,0.5156861,0.00021874286,0.0002367213,0.00008866379,0.00086689735,0.00013068791,0.005892847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962425,0.000036413057,0.000014907833,0.00009632029,0.00017179335,0.000056224297],"domain_scores_gemma":[0.9997569,0.00003906,0.000024062516,0.00004563999,0.000116506126,0.00001783006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037227818,0.0006775157,0.0008310664,0.0014409971,0.00037110975,0.00074461516,0.0007079452,0.0009567582,0.0024439725],"category_scores_gemma":[0.0006518575,0.0003539042,0.0007220763,0.0012583098,0.00028783485,0.00081428984,0.0009864541,0.00075731426,0.0014285647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027431018,0.00014918951,0.0011272294,0.00013218996,0.00010182266,0.00008742265,0.00005181503,0.019806521,0.16323626,0.001630944,0.003234506,0.81016773],"study_design_scores_gemma":[0.000037815546,0.00025197375,0.0051277108,0.000029259374,0.00014409264,0.00040225487,0.000048300953,0.9066611,0.07914398,0.0029309618,0.005174879,0.000047646354],"about_ca_topic_score_codex":0.002230023,"about_ca_topic_score_gemma":0.0033236013,"teacher_disagreement_score":0.0024439725,"about_ca_system_score_codex":0.00026298745,"about_ca_system_score_gemma":0.0006143112,"threshold_uncertainty_score":0.0081759095},"labels":[],"label_agreement":null},{"id":"W4404407032","doi":"10.48550/arxiv.2411.08171","title":"Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Transfer of learning; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.09054915274491343,"score_gpt":0.20595721419488244,"score_spread":0.11540806144996901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404407032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8607585,0.005667031,0.11385039,0.0012101933,0.00044338012,0.0002727625,0.0013693728,0.005782067,0.010646233],"genre_scores_gemma":[0.96373516,0.0008717504,0.03000988,0.00016709242,0.00005622301,0.00006586156,0.0023236382,0.00018918782,0.0025812427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880075,0.00035476332,0.00008293925,0.00029089063,0.00032145824,0.00014916215],"domain_scores_gemma":[0.996786,0.0018333951,0.0001960869,0.00048147267,0.00058824755,0.0001148353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049969805,0.0017698118,0.0008844694,0.0018524077,0.0004991638,0.0010674782,0.0018083183,0.0015034333,0.0014127776],"category_scores_gemma":[0.008616183,0.00037022895,0.0008984738,0.0012210388,0.0007390495,0.003226972,0.0010258767,0.0016997398,0.00066749734],"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":[0.0008853011,0.00068222906,0.018052446,0.0003613782,0.00048027388,0.00016409176,0.0001364909,0.6407141,0.003732842,0.00311555,0.0072572366,0.32441807],"study_design_scores_gemma":[0.00001755565,0.0002798877,0.0028857116,0.0000299195,0.000040309224,0.000043632404,0.000049376027,0.990497,0.0039059322,0.001485729,0.0007465839,0.000018423309],"about_ca_topic_score_codex":0.014713398,"about_ca_topic_score_gemma":0.015851364,"teacher_disagreement_score":0.014713398,"about_ca_system_score_codex":0.002661586,"about_ca_system_score_gemma":0.0010395789,"threshold_uncertainty_score":0.02925551},"labels":[],"label_agreement":null},{"id":"W4404689294","doi":"10.1109/icds62089.2024.10756303","title":"Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Support vector machine; Transfer of learning; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.03859603357125322,"score_gpt":0.26433169126282646,"score_spread":0.22573565769157325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404689294","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85250884,0.0064180596,0.12083911,0.0010467856,0.00051258295,0.00032537102,0.0014149953,0.006209048,0.010725163],"genre_scores_gemma":[0.9608389,0.0008996694,0.03259035,0.00015959163,0.00005690675,0.00007273314,0.0024790487,0.00022114796,0.0026815247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987972,0.00034047753,0.00008395071,0.00029763393,0.00032605656,0.00015478938],"domain_scores_gemma":[0.9970421,0.0015898055,0.00018103233,0.0004841845,0.00059353356,0.00010924453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049616094,0.001855293,0.00097326643,0.0018680443,0.0005234473,0.0010906133,0.0017897997,0.0014991545,0.0014835105],"category_scores_gemma":[0.008451494,0.00035758203,0.0009387299,0.0012052555,0.00068308366,0.0031278117,0.000941699,0.0015802595,0.0007742042],"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":[0.0009737304,0.00066870556,0.017812142,0.00039657368,0.000508834,0.00020829629,0.00015754571,0.61726993,0.0046236534,0.0027022117,0.00765945,0.34701893],"study_design_scores_gemma":[0.000018022121,0.00032684254,0.0032367602,0.000032404412,0.00004709422,0.00006388653,0.00006550187,0.9894112,0.0045815306,0.0012844736,0.0009113182,0.000021062924],"about_ca_topic_score_codex":0.015156202,"about_ca_topic_score_gemma":0.01527105,"teacher_disagreement_score":0.015156202,"about_ca_system_score_codex":0.0023017325,"about_ca_system_score_gemma":0.0010494066,"threshold_uncertainty_score":0.03013599},"labels":[],"label_agreement":null},{"id":"W4404854193","doi":"10.1088/1742-6596/2885/1/012085","title":"A pilot study of vision-based real-time detection of invisible gas leak and fire","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"National Research Council Canada","funders":"","keywords":"Leak; Gas leak; Computer science; Environmental science; Artificial intelligence; Engineering; Chemistry; Environmental engineering","score_opus":0.01573643578671537,"score_gpt":0.23097495536741822,"score_spread":0.21523851958070284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404854193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97055054,0.00013140915,0.027455924,0.000045102635,0.00005074093,0.00028182592,0.000120485514,0.0002817851,0.0010820933],"genre_scores_gemma":[0.97615826,0.000089513334,0.022400143,0.000059416376,0.000021176877,0.00009187663,0.00012361383,0.000030753752,0.0010252185],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977654,0.0000537817,0.000009747959,0.0000655373,0.00004910076,0.00004528912],"domain_scores_gemma":[0.9994479,0.00017290495,0.000023584278,0.000051361072,0.0002225649,0.000081720165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072710635,0.00033895587,0.00043270882,0.00041310277,0.00021204846,0.00041157097,0.0005959078,0.0006764002,0.0017123846],"category_scores_gemma":[0.0010543957,0.00016934857,0.00029792407,0.00017044482,0.000314602,0.00041615934,0.00030705612,0.00036481398,0.00045297775],"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":[0.0031200277,0.003337084,0.0072417115,0.0005536912,0.000107314285,0.0009912146,0.0008038898,0.005910189,0.8669039,0.0003880233,0.00088171003,0.10976135],"study_design_scores_gemma":[0.00084183324,0.03747455,0.14381708,0.00010533785,0.00030597977,0.0036039026,0.0013276731,0.30156547,0.5041342,0.00058278005,0.006061199,0.00017996441],"about_ca_topic_score_codex":0.0017797855,"about_ca_topic_score_gemma":0.0014274591,"teacher_disagreement_score":0.0017797855,"about_ca_system_score_codex":0.00018070165,"about_ca_system_score_gemma":0.00030485503,"threshold_uncertainty_score":0.005728483},"labels":[],"label_agreement":null},{"id":"W4404854889","doi":"10.1088/1742-6596/2885/1/011001","title":"Proceedings of the 4<sup>th</sup> European Symposium on Fire Safety Science (ESFSS 2024)","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Engineering physics; Library science; Environmental science; Engineering; Computer science","score_opus":0.009928621633783033,"score_gpt":0.20092241566146163,"score_spread":0.1909937940276786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404854889","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012941078,0.028655961,0.003468828,0.018344266,0.8862751,0.00014024197,0.0009385426,0.00055731734,0.06032569],"genre_scores_gemma":[0.015212126,0.044256527,0.0031464486,0.0092241885,0.5211871,0.00033448188,0.0031995995,0.0012503886,0.40218908],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978988,0.00024120344,0.00016545717,0.00030572555,0.0011881724,0.00020064031],"domain_scores_gemma":[0.9939115,0.00095871865,0.0003112186,0.00023154398,0.0034141894,0.0011728165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027069899,0.0014163217,0.0011033225,0.001820145,0.0010996327,0.0061232513,0.0014366412,0.0026046862,0.0955744],"category_scores_gemma":[0.0048029213,0.00038239197,0.0012414693,0.0006700244,0.0007526568,0.0026134143,0.0018241283,0.0038697512,0.04913457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012932399,0.000030688327,0.00012344525,0.00043999287,0.000022621532,0.00010460579,0.00005272153,0.00011078242,0.0009767824,0.0012675687,0.9530337,0.043707825],"study_design_scores_gemma":[0.000014552911,0.000036865895,0.00035778503,0.00018762836,0.000016421774,0.00010829342,0.000058842525,0.000093251016,0.00048978673,0.0008487898,0.9977792,0.000008550476],"about_ca_topic_score_codex":0.00046141117,"about_ca_topic_score_gemma":0.00071349886,"teacher_disagreement_score":0.0955744,"about_ca_system_score_codex":0.001241312,"about_ca_system_score_gemma":0.0016134161,"threshold_uncertainty_score":0.31972826},"labels":[],"label_agreement":null},{"id":"W4404970970","doi":"10.3390/electronics13234768","title":"Gas Leakage Detection Using Tiny Machine Learning","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Leakage (economics); Computer science; Artificial intelligence; Materials science","score_opus":0.007215238789588896,"score_gpt":0.20573984822579255,"score_spread":0.19852460943620365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404970970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25204724,0.001741242,0.6899757,0.00086773105,0.00053530064,0.0002473236,0.0012370481,0.037063524,0.016284907],"genre_scores_gemma":[0.8925156,0.00040130096,0.101155214,0.0005264669,0.000038594964,0.00012775966,0.0007966788,0.00031139507,0.004126991],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997707,0.000022543427,0.000010580269,0.00007245805,0.000093868526,0.000029755192],"domain_scores_gemma":[0.99971575,0.00006948285,0.00006232876,0.000046906604,0.00008120386,0.000024391516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021680293,0.000622985,0.0004304395,0.0005881133,0.00018738746,0.00048697038,0.0010138458,0.0004176459,0.0020892385],"category_scores_gemma":[0.0010239391,0.00021499288,0.00024773594,0.00028128913,0.00029230298,0.0010533234,0.00056215585,0.00035231837,0.000571815],"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":[0.0010047806,0.000371336,0.018875893,0.0009310594,0.00024792543,0.0012009633,0.00024691023,0.14238954,0.18463582,0.0076471097,0.027968772,0.61447996],"study_design_scores_gemma":[0.000024064271,0.00016928885,0.0031992812,0.000040410643,0.00004677102,0.0002643701,0.00005028538,0.8988224,0.08454308,0.0028373403,0.009958397,0.000044207565],"about_ca_topic_score_codex":0.0018782216,"about_ca_topic_score_gemma":0.0033148571,"teacher_disagreement_score":0.0020892385,"about_ca_system_score_codex":0.0005272208,"about_ca_system_score_gemma":0.00041147118,"threshold_uncertainty_score":0.006989181},"labels":[],"label_agreement":null},{"id":"W4405490814","doi":"10.1109/iccspa61559.2024.10794365","title":"Image Enhancement for Better VRU Detection in Challenging Weather Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"General Motors (Canada); Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Remote sensing; Computer vision; Geology","score_opus":0.008353699645665314,"score_gpt":0.23114372690031973,"score_spread":0.2227900272546544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405490814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0980996,0.000783079,0.89304525,0.00023503575,0.00020646155,0.00013911405,0.00017952613,0.0034329612,0.0038790077],"genre_scores_gemma":[0.42235303,0.00054548844,0.57243305,0.00026046592,0.00012234038,0.00006721945,0.00041765516,0.00041698138,0.0033838463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960715,0.00004819514,0.000014858524,0.00011514856,0.00013395699,0.00008076033],"domain_scores_gemma":[0.99946505,0.00017188235,0.00007286498,0.00009069369,0.00017014673,0.000029369112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005884018,0.0008530238,0.00060421595,0.0009837075,0.00033953783,0.00078602275,0.0006568663,0.00059767027,0.0020208075],"category_scores_gemma":[0.0019719433,0.00028560642,0.00053563423,0.00028681033,0.00033392568,0.0009466341,0.00068005576,0.00095431297,0.0009664237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000441759,0.00017583063,0.0039417716,0.00026879238,0.000074699805,0.00023342099,0.00024292275,0.024402209,0.40173382,0.0022864344,0.0036166038,0.5625817],"study_design_scores_gemma":[0.0000293334,0.0005124833,0.015991518,0.000066708,0.000113093185,0.00089515495,0.0001403033,0.6349196,0.32803002,0.0019941549,0.017243462,0.000064270025],"about_ca_topic_score_codex":0.002049329,"about_ca_topic_score_gemma":0.0038055663,"teacher_disagreement_score":0.002049329,"about_ca_system_score_codex":0.00031333612,"about_ca_system_score_gemma":0.00036597517,"threshold_uncertainty_score":0.006760299},"labels":[],"label_agreement":null},{"id":"W4405778520","doi":"10.1109/access.2024.3522562","title":"SafeRespirator: Comprehensive Database for N95 Filtering Facepiece Respirator Leakage Detection Including Infrared, RGB Videos, and Quantitative Fit Testing","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Cégep de Rimouski; Université du Québec à Rimouski","funders":"Mitacs; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Respirator; Computer science; RGB color model; Database; Artificial intelligence; Computer vision; Materials science","score_opus":0.17382979672522983,"score_gpt":0.35587894818310606,"score_spread":0.18204915145787623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405778520","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008426507,0.003002175,0.010209463,0.00036728222,0.00024024004,0.00097017863,0.95295537,0.011722275,0.0121064335],"genre_scores_gemma":[0.013883557,0.0016878031,0.013251409,0.0003273588,0.00009594116,0.0013706009,0.9622984,0.0011122326,0.0059727808],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9978714,0.00028644822,0.0005992751,0.0003525718,0.0007712205,0.00011908959],"domain_scores_gemma":[0.98819155,0.0033942447,0.0013929413,0.0024557854,0.0038625202,0.0007030124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030077084,0.0015019637,0.0019303702,0.007695414,0.00053775025,0.0024515383,0.0031355615,0.0018300391,0.06431799],"category_scores_gemma":[0.015490064,0.0007102025,0.00083672476,0.0047370703,0.00033255573,0.0023309584,0.0030151731,0.00096816313,0.05480888],"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":[0.0017355516,0.0003804961,0.009718712,0.007980082,0.0003259752,0.0006234618,0.00026741513,0.0008523719,0.009157824,0.0016398326,0.75636095,0.21095729],"study_design_scores_gemma":[0.00050344976,0.000454966,0.043812312,0.0022175803,0.00035956604,0.0014846522,0.00034445766,0.0038807627,0.011909935,0.0026134239,0.9321096,0.00030928882],"about_ca_topic_score_codex":0.0042223227,"about_ca_topic_score_gemma":0.0057575735,"teacher_disagreement_score":0.06431799,"about_ca_system_score_codex":0.0007299237,"about_ca_system_score_gemma":0.0024958123,"threshold_uncertainty_score":0.21516508},"labels":[],"label_agreement":null},{"id":"W4405908300","doi":"10.1109/wf-iot62078.2024.10811141","title":"IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); Carleton University; University of Waterloo; Dalhousie University","funders":"","keywords":"Software deployment; Computer science; Interface (matter); Internet of Things; Wireless sensor network; Fire detection; Wildland–urban interface; Computer security; Environmental science; Computer network; Environmental resource management; Engineering; Architectural engineering; Software engineering","score_opus":0.00898354340357101,"score_gpt":0.2194260652966886,"score_spread":0.21044252189311757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61097866,0.00025796672,0.38468617,0.00033840808,0.0000542866,0.00012345426,0.00009328873,0.00056675414,0.0029010288],"genre_scores_gemma":[0.9453023,0.0000966925,0.05410962,0.000034215194,0.0000076284005,0.000024403644,0.000061469764,0.000031015054,0.0003326537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999539,0.00015026507,0.000024812245,0.000093753915,0.00012458398,0.000067527515],"domain_scores_gemma":[0.9976876,0.0014034517,0.00030382615,0.00021854723,0.00028126242,0.0001052647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015187807,0.00072627846,0.00045835986,0.000780014,0.00031413205,0.0007297432,0.00079995784,0.00046526716,0.00040819895],"category_scores_gemma":[0.0060527753,0.00023906014,0.0003160748,0.0004223458,0.00044076968,0.0012202407,0.00070017413,0.00044764197,0.00011122654],"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":[0.00012843423,0.00010358524,0.015462841,0.000043734024,0.000045461104,0.00009653816,0.00008749579,0.9208549,0.0035646523,0.001657914,0.00029159454,0.057662886],"study_design_scores_gemma":[0.000006193313,0.00006495029,0.002075436,0.000008211679,0.000010012334,0.000048790895,0.00006821197,0.99369836,0.002407989,0.0013409912,0.00026381767,0.0000068986515],"about_ca_topic_score_codex":0.004197089,"about_ca_topic_score_gemma":0.0069383164,"teacher_disagreement_score":0.004197089,"about_ca_system_score_codex":0.0007665228,"about_ca_system_score_gemma":0.0005684542,"threshold_uncertainty_score":0.008345306},"labels":[],"label_agreement":null},{"id":"W4406322613","doi":"10.1109/jstars.2025.3528652","title":"Edge Computing-Based Real-Time Forest Fire Detection Using UAV Thermal and Color Images","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Aeronautical Science Foundation of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Computer vision; Artificial intelligence; Remote sensing; Enhanced Data Rates for GSM Evolution; Environmental science; Geology","score_opus":0.01180441617033151,"score_gpt":0.21426519613081613,"score_spread":0.20246077996048462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406322613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23629247,0.0004393994,0.75619113,0.00010806574,0.00011905429,0.000110919274,0.0005031703,0.0032756906,0.002960023],"genre_scores_gemma":[0.7292642,0.00029445734,0.26702213,0.00009583381,0.00004666008,0.00007056254,0.0010036773,0.00009573807,0.00210675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998084,0.000013640282,0.000009185676,0.00007419521,0.000057034213,0.00003755654],"domain_scores_gemma":[0.9998567,0.000024026987,0.000021610136,0.000028134524,0.00005364877,0.000015848702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017171851,0.0007513453,0.0005325591,0.000849382,0.000268405,0.00041991664,0.0007231196,0.00033489487,0.0007647473],"category_scores_gemma":[0.00048064737,0.00022483552,0.0004763215,0.0005228427,0.00016281805,0.0006422187,0.00033415432,0.00044979993,0.00031501768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088411366,0.00033428878,0.0096988445,0.00013206124,0.00010279311,0.00033297343,0.00011418739,0.123912424,0.09270488,0.0013124106,0.0041351556,0.76633584],"study_design_scores_gemma":[0.000011668566,0.000061387575,0.004475807,0.0000064942437,0.000023211293,0.00012915784,0.000044161694,0.964573,0.029131593,0.0006440923,0.0008855095,0.000013904022],"about_ca_topic_score_codex":0.004719695,"about_ca_topic_score_gemma":0.0071552345,"teacher_disagreement_score":0.004719695,"about_ca_system_score_codex":0.00028142487,"about_ca_system_score_gemma":0.00032459613,"threshold_uncertainty_score":0.009384453},"labels":[],"label_agreement":null},{"id":"W4406407252","doi":"10.1007/s00170-024-14987-6","title":"Intelligent vacuum bagging leakage location prediction","year":2025,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leakage (economics); Computer science; Engineering; Artificial intelligence; Data mining","score_opus":0.004877325610982243,"score_gpt":0.22307406896491525,"score_spread":0.218196743353933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406407252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35455236,0.00155187,0.6332491,0.0002818591,0.0003185143,0.000063679174,0.00051140896,0.005630724,0.0038404062],"genre_scores_gemma":[0.9633658,0.00025004655,0.033263,0.00007474425,0.000047210655,0.000020670688,0.00036850313,0.000030475734,0.0025794883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.000023991817,0.000011117666,0.00006510461,0.00006814301,0.000043684824],"domain_scores_gemma":[0.9997185,0.000078225356,0.000048269878,0.00002940341,0.000104888444,0.000020747788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025299544,0.00071908644,0.00078689324,0.0009847371,0.00025854717,0.00058152905,0.00068913656,0.00060515624,0.0009786092],"category_scores_gemma":[0.0005826345,0.00027481627,0.00049391,0.0006192125,0.00014379731,0.00064706855,0.00047007776,0.000465141,0.0006807921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008612169,0.0005053547,0.035767287,0.00016840185,0.00010832145,0.00031538392,0.000050634826,0.16950154,0.05181428,0.0005671951,0.0059039854,0.73443645],"study_design_scores_gemma":[0.000008179004,0.00009902784,0.004881581,0.0000070464803,0.000028051949,0.00006116892,0.000013955865,0.9848914,0.00922686,0.00025377132,0.0005188914,0.000009990924],"about_ca_topic_score_codex":0.0029371195,"about_ca_topic_score_gemma":0.003275295,"teacher_disagreement_score":0.0029371195,"about_ca_system_score_codex":0.00029220685,"about_ca_system_score_gemma":0.0004097594,"threshold_uncertainty_score":0.0058400035},"labels":[],"label_agreement":null},{"id":"W4406508636","doi":"10.1016/s0197-2510(09)70029-4","title":"10.1016/s0197-2510(09)70029-4","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Transport engineering; Environmental science; Engineering","score_opus":0.003328319642718257,"score_gpt":0.13865915808372112,"score_spread":0.13533083844100285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406508636","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045087255,0.00038073823,0.00072579633,0.00038307943,0.00031242007,0.00011150373,0.00069773523,0.00084565504,0.99609226],"genre_scores_gemma":[0.0005379646,0.00015577672,0.0003051066,0.00018148341,0.00005847809,0.000052784773,0.00034602167,0.00013054008,0.9982318],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924314,0.000046111665,0.00006238283,0.0002778641,0.00019721,0.00017328284],"domain_scores_gemma":[0.9976902,0.0005431156,0.00016119592,0.00023070439,0.0005374145,0.00083737593],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011924919,0.0030077512,0.002024656,0.0029387441,0.0027434626,0.0042704316,0.0037937097,0.0071425214,0.9895229],"category_scores_gemma":[0.0019520276,0.0010432528,0.0015907525,0.0026557508,0.0022847317,0.005831006,0.0029594346,0.0029175347,0.9931324],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044688594,0.0002729676,0.0011095598,0.00074238144,0.00004891365,0.0003587923,0.00016201985,0.00053558085,0.002368889,0.0076594753,0.37009183,0.6162027],"study_design_scores_gemma":[0.000055209654,0.0001366512,0.00063814624,0.00035394027,0.000015304682,0.00038206173,0.0001408257,0.00024029783,0.00037889046,0.0006757726,0.9969578,0.000025216435],"about_ca_topic_score_codex":0.0050810953,"about_ca_topic_score_gemma":0.0037445985,"teacher_disagreement_score":0.010477126,"about_ca_system_score_codex":0.0013881505,"about_ca_system_score_gemma":0.0015902603,"threshold_uncertainty_score":0.014944375},"labels":[],"label_agreement":null},{"id":"W4406512897","doi":"10.1016/s0967-0653(98)80632-7","title":"10.1016/s0967-0653(98)80632-7","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; Emergency management; Business; Environmental science; Environmental planning; Environmental resource management; Computer science; Political science; Telecommunications","score_opus":0.00438298888951325,"score_gpt":0.14943545763355023,"score_spread":0.145052468744037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406512897","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047699767,0.00042397706,0.0007232891,0.00036764154,0.0003151505,0.000119390395,0.00058463257,0.0008857623,0.99610317],"genre_scores_gemma":[0.0005411042,0.00018806625,0.00032220507,0.00022064491,0.00006581341,0.000059222493,0.00030897604,0.00015360354,0.9981402],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923456,0.000048685328,0.00006760217,0.00029101668,0.0001907948,0.00016726847],"domain_scores_gemma":[0.9972922,0.00067296514,0.00018863885,0.00030056073,0.00066501513,0.00088056386],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012482896,0.0033895595,0.002079567,0.0030464677,0.0028549114,0.0044841827,0.0039159097,0.0069047376,0.9892954],"category_scores_gemma":[0.0019488911,0.0011008417,0.0016269208,0.0025259003,0.0024185174,0.0066347276,0.0035488533,0.0031398332,0.9934228],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043974048,0.00025879958,0.00095374254,0.0007279554,0.00004669089,0.0003271488,0.00015866841,0.00047054375,0.0023761662,0.0061085327,0.39673105,0.5914008],"study_design_scores_gemma":[0.00006289524,0.0001576523,0.0006629894,0.00041315894,0.000017227117,0.00042863452,0.00015006821,0.00022903824,0.00036104803,0.0006498725,0.99683964,0.000027745456],"about_ca_topic_score_codex":0.0041789864,"about_ca_topic_score_gemma":0.003274408,"teacher_disagreement_score":0.010704577,"about_ca_system_score_codex":0.0012166707,"about_ca_system_score_gemma":0.0011473435,"threshold_uncertainty_score":0.015268803},"labels":[],"label_agreement":null},{"id":"W4407129820","doi":"10.1109/cbmi62980.2024.10858873","title":"Fire Detection for Emergency Responders using X","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Computer science; Environmental science","score_opus":0.02297981979969318,"score_gpt":0.2562967221616824,"score_spread":0.23331690236198924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407129820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.869261,0.002005928,0.09297018,0.0014997174,0.0007231344,0.0004077582,0.0077396063,0.009710661,0.015682034],"genre_scores_gemma":[0.94494843,0.00049765577,0.039273217,0.00043557375,0.00020395141,0.00011783388,0.007309188,0.00008426189,0.007129702],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974555,0.00003938051,0.000011906847,0.000087949906,0.00005899274,0.00005624007],"domain_scores_gemma":[0.9997507,0.000077259516,0.00004411764,0.000036909056,0.00006265964,0.000028392738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041009838,0.00084355375,0.00050372217,0.0008972829,0.00036907522,0.000616844,0.00061247085,0.0009302673,0.0026697144],"category_scores_gemma":[0.0012160253,0.00015172905,0.0006642048,0.00031012524,0.00024844485,0.001014267,0.0008472832,0.00075825804,0.0018974455],"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":[0.0047267936,0.001046245,0.13374284,0.001215098,0.00035660996,0.0015486259,0.0008608085,0.0661222,0.07975875,0.0018130057,0.06483694,0.64397204],"study_design_scores_gemma":[0.00008975974,0.001129685,0.09581901,0.0002570195,0.00018429488,0.0012705114,0.0014708586,0.8372437,0.038916808,0.0033551168,0.020170499,0.0000928447],"about_ca_topic_score_codex":0.005771295,"about_ca_topic_score_gemma":0.011640902,"teacher_disagreement_score":0.005771295,"about_ca_system_score_codex":0.00035074525,"about_ca_system_score_gemma":0.000500672,"threshold_uncertainty_score":0.011475444},"labels":[],"label_agreement":null},{"id":"W4407414319","doi":"10.2514/6.2025-1730","title":"AI Assistance for Firefighting Enabled by Real-Time Satellite Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Lockheed Martin (Canada)","funders":"","keywords":"Firefighting; Computer science; Satellite; Real-time data; Real-time computing; Computer security; Engineering; World Wide Web; Geography; Aerospace engineering; Cartography","score_opus":0.010478028776090937,"score_gpt":0.24303210714190213,"score_spread":0.23255407836581118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407414319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58235747,0.0013426127,0.36078247,0.0046375943,0.00057339296,0.0003497156,0.0007315353,0.0090325875,0.040192597],"genre_scores_gemma":[0.87829584,0.00041056582,0.11450828,0.0002138999,0.00006731211,0.00005867781,0.00048296686,0.00010715194,0.0058552767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984443,0.00004797099,0.0000054585394,0.000020914347,0.00005399921,0.000027157039],"domain_scores_gemma":[0.99924195,0.00037500382,0.00003523211,0.000094307776,0.00017388005,0.000079578036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005302565,0.00034113973,0.00017604041,0.00043871568,0.00037761038,0.0006294001,0.0004523232,0.00036111227,0.003377166],"category_scores_gemma":[0.0019692923,0.00008995736,0.00013705072,0.0003615103,0.0004083361,0.0009872189,0.00066401716,0.0006111093,0.00054938777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019857932,0.00067082065,0.0118009625,0.00034821505,0.0001049351,0.0010549295,0.002552648,0.100252904,0.1435368,0.010947444,0.03746698,0.6892776],"study_design_scores_gemma":[0.00025836128,0.00061713357,0.012561976,0.00008487242,0.00006162469,0.00034250165,0.0021100019,0.82852226,0.071243174,0.014090929,0.07002086,0.0000863113],"about_ca_topic_score_codex":0.008570319,"about_ca_topic_score_gemma":0.01429016,"teacher_disagreement_score":0.008570319,"about_ca_system_score_codex":0.00045236567,"about_ca_system_score_gemma":0.0005746169,"threshold_uncertainty_score":0.017040849},"labels":[],"label_agreement":null},{"id":"W4407566436","doi":"10.1109/tim.2025.3541664","title":"Novel CNN-Based Approach for Burn Severity Assessment and Fine-Grained Boundary Segmentation in Burn Images","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Montreal Heart Institute; Université de Montréal; University of Alberta; SKiN Health","funders":"","keywords":"Burn-in; Segmentation; Computer science; Image segmentation; Boundary (topology); Pediatric burn; Artificial intelligence; Computer vision; Engineering; Reliability engineering; Medicine; Mathematics; Surgery","score_opus":0.0259327235583379,"score_gpt":0.2620148287273842,"score_spread":0.2360821051690463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407566436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15308149,0.002327398,0.82955205,0.00051071175,0.00022506846,0.00030251834,0.0010843768,0.006822016,0.006094498],"genre_scores_gemma":[0.695874,0.0011200212,0.29017684,0.0005385161,0.00016706252,0.00022644574,0.0029220632,0.0003630144,0.008611978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976856,0.000019337858,0.000011451905,0.00008981741,0.00005376076,0.00005713723],"domain_scores_gemma":[0.9998117,0.00003886768,0.000028958313,0.000027126513,0.000072782655,0.000020488375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042493726,0.0011271273,0.0006939859,0.0012561118,0.00029508115,0.00064934476,0.0009579371,0.0007512219,0.0019911067],"category_scores_gemma":[0.0008071235,0.00038533134,0.0008199715,0.0006147783,0.00026504727,0.0008403035,0.0006960091,0.00083822774,0.0006772255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000515605,0.00026392846,0.0067222477,0.00024026813,0.00018142635,0.00028840645,0.00014548031,0.12165988,0.08831215,0.002465908,0.0078351665,0.7713695],"study_design_scores_gemma":[0.000012288614,0.00008475902,0.002938938,0.00002214965,0.00005206174,0.00012435147,0.000027061562,0.9753464,0.017822111,0.0017370587,0.0018199766,0.000012846],"about_ca_topic_score_codex":0.0095535815,"about_ca_topic_score_gemma":0.013788646,"teacher_disagreement_score":0.0095535815,"about_ca_system_score_codex":0.0009116994,"about_ca_system_score_gemma":0.0007831633,"threshold_uncertainty_score":0.01899594},"labels":[],"label_agreement":null},{"id":"W4407847908","doi":"10.47392/irjash.2025.010","title":"Advanced Wildfire Detection Using Deep Learning Algorithms: A Comparative Study of CNN Variants","year":2025,"lang":"en","type":"article","venue":"International Research Journal on Advanced Science Hub","topic":"Fire Detection and Safety Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Algorithm; Machine learning; Pattern recognition (psychology)","score_opus":0.0650795787955893,"score_gpt":0.42045957736996753,"score_spread":0.35537999857437824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407847908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80404735,0.013368143,0.16260113,0.0008418788,0.00035247207,0.00019749053,0.0010419845,0.0020229674,0.015526672],"genre_scores_gemma":[0.91005206,0.0031026034,0.08266716,0.00016861172,0.00007928656,0.000053195967,0.0015997794,0.00012612366,0.0021512064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984908,0.00024475987,0.00015780941,0.00036702654,0.000572223,0.00016732466],"domain_scores_gemma":[0.9972899,0.0012461765,0.0002609436,0.00030821038,0.00075380126,0.00014092287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031944092,0.0015271673,0.0008231374,0.0027244512,0.0003433347,0.0013145724,0.0011531332,0.0010677258,0.000599795],"category_scores_gemma":[0.0056145974,0.00026319164,0.00059209025,0.0016310418,0.00047059872,0.0025704303,0.000841691,0.0009613906,0.0002721515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008904014,0.00044953983,0.046900634,0.0004544699,0.0005776567,0.00013778152,0.00010646404,0.35342595,0.004439415,0.00372679,0.0036671206,0.5852238],"study_design_scores_gemma":[0.000026783851,0.00046204193,0.009396114,0.0000866624,0.0001229169,0.00014368002,0.00008199947,0.9780604,0.007357483,0.0019692339,0.0022587318,0.000033913482],"about_ca_topic_score_codex":0.011864928,"about_ca_topic_score_gemma":0.013898876,"teacher_disagreement_score":0.011864928,"about_ca_system_score_codex":0.0014011286,"about_ca_system_score_gemma":0.00088918867,"threshold_uncertainty_score":0.023591757},"labels":[],"label_agreement":null},{"id":"W4407901639","doi":"10.1109/ictis62692.2024.10893909","title":"Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ministry of Higher Education and Scientific Research","keywords":"Smoke; Computer science; Fire detection; Building automation; Architectural engineering; Environmental science; Engineering; Waste management","score_opus":0.013321337410946592,"score_gpt":0.22077723652879613,"score_spread":0.20745589911784953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407901639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103458926,0.00026186364,0.8772054,0.0002897082,0.00010352774,0.00007149776,0.00035117767,0.01492715,0.0033307404],"genre_scores_gemma":[0.89171344,0.000077184086,0.10381407,0.00033500325,0.000030075538,0.000055979268,0.00063164823,0.00009370035,0.003248913],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976295,0.000022782786,0.00001103126,0.000092257884,0.00006035603,0.00005057586],"domain_scores_gemma":[0.9997985,0.000030508587,0.000021793858,0.0000652274,0.00006106232,0.00002293244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042424983,0.0005404153,0.0006017459,0.00037698756,0.0003653841,0.000587557,0.0014289797,0.00055842404,0.0016373035],"category_scores_gemma":[0.00074875157,0.00017675491,0.0004236363,0.00027011734,0.0002771922,0.0011714101,0.0010379362,0.000600401,0.00059565395],"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":[0.0008047619,0.00070409774,0.01106859,0.00012841803,0.00016265125,0.0004481315,0.00015129552,0.30419332,0.06171483,0.0040948577,0.010518887,0.60601014],"study_design_scores_gemma":[0.00001140166,0.000056072204,0.0010301028,0.00000518088,0.000019051027,0.00006970968,0.000019285615,0.9814559,0.014336206,0.0015525813,0.001432836,0.000011686522],"about_ca_topic_score_codex":0.0058520352,"about_ca_topic_score_gemma":0.0100985635,"teacher_disagreement_score":0.0058520352,"about_ca_system_score_codex":0.0008708755,"about_ca_system_score_gemma":0.00065672665,"threshold_uncertainty_score":0.011635959},"labels":[],"label_agreement":null},{"id":"W4407920598","doi":"10.18280/jesa.580113","title":"An Intelligent Surveillance Model for Wild Forest Fire Detection Using Deep Learning for Drone application","year":2025,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Deep learning; Artificial intelligence; Computer science; Remote sensing; Geography; Biology","score_opus":0.01842380443433099,"score_gpt":0.26678252443180034,"score_spread":0.24835871999746933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22219716,0.0012616366,0.76537395,0.00069520966,0.00018864166,0.00016458907,0.00041675498,0.0027121715,0.0069898036],"genre_scores_gemma":[0.93884015,0.0003815608,0.05446113,0.00015830033,0.000034076023,0.00014404918,0.00047720328,0.000027194475,0.00547637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989486,0.000009795244,0.000007164461,0.000039433897,0.000022308814,0.00002640203],"domain_scores_gemma":[0.99988365,0.00002771116,0.000014050513,0.000008704167,0.00005626517,0.000009559165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028300617,0.00055689324,0.0003874015,0.00034666318,0.0002818244,0.00051559805,0.0007598972,0.0005969338,0.0013997542],"category_scores_gemma":[0.00046412452,0.0002598535,0.0005431041,0.00022449534,0.00018496542,0.00056550524,0.0004258663,0.0008111317,0.00030051894],"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":[0.00019003145,0.00018801788,0.004353397,0.00006934869,0.00007387636,0.00013291457,0.000060279803,0.83932585,0.010753609,0.0021273938,0.0019589073,0.14076626],"study_design_scores_gemma":[0.0000019925,0.000013270362,0.00016851867,0.000002007233,0.0000056553663,0.0000060681214,0.0000019689223,0.99880505,0.0006936704,0.0001724727,0.00012762513,0.0000017065037],"about_ca_topic_score_codex":0.016278017,"about_ca_topic_score_gemma":0.015813397,"teacher_disagreement_score":0.016278017,"about_ca_system_score_codex":0.0009034588,"about_ca_system_score_gemma":0.0009447136,"threshold_uncertainty_score":0.032366574},"labels":[],"label_agreement":null},{"id":"W4408304410","doi":"10.1109/iecon55916.2024.10905606","title":"Autonomous Vision-Guided High-Precision Firefighting using Unmanned Aerial Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Computer science; Aeronautics; Artificial intelligence; Computer vision; Engineering; Geography; Cartography","score_opus":0.015781196341054685,"score_gpt":0.2537591020618384,"score_spread":0.2379779057207837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408304410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041488077,0.00028560273,0.9548121,0.00005136113,0.000072181465,0.000046223893,0.00003219893,0.000936062,0.002276216],"genre_scores_gemma":[0.77186626,0.0002521581,0.22452264,0.0000746786,0.000040824685,0.000087543245,0.00013936865,0.00005714787,0.002959245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998078,0.000017747245,0.0000055735386,0.000057202065,0.00007478107,0.00003691454],"domain_scores_gemma":[0.99992156,0.000012140004,0.000019522573,0.000013629667,0.00001945993,0.000013642802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015356489,0.00070859486,0.00046985375,0.00030293124,0.00029681952,0.00042021333,0.0006927087,0.00040243397,0.000615178],"category_scores_gemma":[0.0002217591,0.00025700717,0.0005291707,0.00014660452,0.0003212751,0.0003845906,0.0005636273,0.000686899,0.0002380959],"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":[0.00019158097,0.00011099855,0.0019960892,0.00015544277,0.00008333379,0.00039503034,0.00031133994,0.58242875,0.11400913,0.0060777776,0.002026257,0.29221433],"study_design_scores_gemma":[0.000016533164,0.00010489308,0.00079316203,0.000008509261,0.000010109122,0.00006713348,0.00003492442,0.98836386,0.0074029467,0.00089118944,0.0022943967,0.000012391438],"about_ca_topic_score_codex":0.006998468,"about_ca_topic_score_gemma":0.006327506,"teacher_disagreement_score":0.006998468,"about_ca_system_score_codex":0.00030748383,"about_ca_system_score_gemma":0.00057311903,"threshold_uncertainty_score":0.013915479},"labels":[],"label_agreement":null},{"id":"W4408438676","doi":"10.5194/egusphere-egu25-5629","title":"An enhanced NHI algorithm configuration for fire detection and mapping","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Computer science; Algorithm; Fire detection; Engineering","score_opus":0.010875909790027364,"score_gpt":0.23233147366382811,"score_spread":0.22145556387380075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408438676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06802663,0.00015091036,0.9205668,0.00008274822,0.00011757272,0.00023360061,0.000600818,0.0068661263,0.0033547855],"genre_scores_gemma":[0.21509407,0.00006516788,0.78013754,0.00007901852,0.000052502477,0.00027913638,0.0020119615,0.00033704363,0.0019435906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990421,0.00016985233,0.000093800474,0.00027987472,0.00032544025,0.000088865265],"domain_scores_gemma":[0.9989524,0.00020642686,0.000074732496,0.0001790231,0.00055038976,0.000036885067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015017206,0.0009034386,0.00062562746,0.0014651178,0.0004152352,0.0009102655,0.0013543236,0.00065012067,0.0026133945],"category_scores_gemma":[0.0035066798,0.0003580202,0.0005529812,0.0010048647,0.00022892146,0.0011204531,0.0008742631,0.00063680875,0.0012763774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072480045,0.00033415257,0.020827102,0.00015607859,0.00020700236,0.00026020975,0.00020869257,0.09904631,0.059242472,0.002315207,0.0072176787,0.8094604],"study_design_scores_gemma":[0.00007131357,0.00010716416,0.011030947,0.000014902048,0.000045381006,0.0002551831,0.00006727313,0.94078296,0.0398047,0.0013361971,0.006429651,0.000054353903],"about_ca_topic_score_codex":0.0054647527,"about_ca_topic_score_gemma":0.005304068,"teacher_disagreement_score":0.0054647527,"about_ca_system_score_codex":0.00047555982,"about_ca_system_score_gemma":0.00077570614,"threshold_uncertainty_score":0.010865927},"labels":[],"label_agreement":null},{"id":"W4408466818","doi":"10.5194/egusphere-egu25-17607","title":"Global Data-Driven Prediction of Fire Activity","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Environmental science; Computer science","score_opus":0.02902574271153894,"score_gpt":0.25523672064442215,"score_spread":0.22621097793288322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408466818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77951676,0.0012656237,0.1750148,0.0014645755,0.00033398415,0.000092134804,0.030866474,0.0024593016,0.008986487],"genre_scores_gemma":[0.95818204,0.0004107875,0.022232847,0.00007761962,0.000050074093,0.000027921189,0.01819973,0.00006933739,0.0007496505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986136,0.000026151009,0.0000072692933,0.000053745756,0.000036320653,0.00001508268],"domain_scores_gemma":[0.99954283,0.00017477962,0.000057068137,0.00007485365,0.00012204924,0.000028426064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006668456,0.0006834323,0.00025672797,0.000666858,0.00011657512,0.0006319288,0.00038845418,0.00036282054,0.00070222997],"category_scores_gemma":[0.0020087326,0.0001728245,0.00035631852,0.0008889731,0.0001695284,0.0008083131,0.00039080592,0.00059041096,0.00031163087],"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":[0.000053841224,0.000042393545,0.029105658,0.000048520433,0.000053086525,0.000045623565,0.000017987522,0.9243273,0.0013690385,0.0010864201,0.0030458788,0.040804263],"study_design_scores_gemma":[0.00000522179,0.000011478811,0.009901797,0.000008867261,0.000006981473,0.00001226092,0.000016287784,0.98580325,0.0009488172,0.0018200948,0.0014562856,0.000008628708],"about_ca_topic_score_codex":0.015905153,"about_ca_topic_score_gemma":0.017008772,"teacher_disagreement_score":0.015905153,"about_ca_system_score_codex":0.0004714349,"about_ca_system_score_gemma":0.0003790957,"threshold_uncertainty_score":0.03162515},"labels":[],"label_agreement":null},{"id":"W4408548687","doi":"10.32388/1heco0","title":"LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset","year":2025,"lang":"en","type":"preprint","venue":"Qeios","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Leak; Zero (linguistics); Subtraction; Shot (pellet); Computer science; Physics; Mathematics; Arithmetic; Materials science; Linguistics; Philosophy","score_opus":0.026320175560939376,"score_gpt":0.2720866948999259,"score_spread":0.2457665193389865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408548687","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060207367,0.004417259,0.08129725,0.001689334,0.0014563247,0.0011984193,0.7044769,0.1285336,0.01672351],"genre_scores_gemma":[0.031596687,0.00045038995,0.071112365,0.00050767564,0.000093803406,0.0007716651,0.8903824,0.0016721908,0.0034127315],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99756706,0.00041637933,0.00022116417,0.00081661576,0.0007384523,0.0002402464],"domain_scores_gemma":[0.99783355,0.0005593739,0.00014595855,0.0007491605,0.00056047225,0.00015146402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020586564,0.0038646923,0.0014458755,0.0037771086,0.0012970417,0.0027962283,0.005056243,0.0031183048,0.0064415406],"category_scores_gemma":[0.0056203348,0.0007060808,0.002129215,0.0023281812,0.0009074684,0.0022692312,0.003748254,0.0029781007,0.011601227],"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":[0.0015042631,0.0015436263,0.009662047,0.0025796362,0.0004274993,0.0006224841,0.00028319104,0.019679176,0.015064241,0.003391193,0.7559357,0.18930697],"study_design_scores_gemma":[0.0009014491,0.000620382,0.02625374,0.0006935685,0.00024995126,0.0017185134,0.00077494857,0.21373554,0.050449304,0.015198729,0.68898124,0.0004226841],"about_ca_topic_score_codex":0.01755566,"about_ca_topic_score_gemma":0.04658806,"teacher_disagreement_score":0.01755566,"about_ca_system_score_codex":0.0015403325,"about_ca_system_score_gemma":0.0017543781,"threshold_uncertainty_score":0.034906924},"labels":[],"label_agreement":null},{"id":"W4408633883","doi":"10.5267/j.ijdns.2024.10.004","title":"Employing CNN mobileNetV2 and ensemble models in classifying drones forest fire detection images","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deutscher Akademischer Austauschdienst","keywords":"Drone; Artificial intelligence; Remote sensing; Random forest; Computer science; Pattern recognition (psychology); Geography; Environmental science; Biology","score_opus":0.022651597877621783,"score_gpt":0.278784651806976,"score_spread":0.2561330539293542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408633883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85169965,0.004243281,0.119384155,0.0005340293,0.00054881064,0.00019881742,0.002545841,0.008827048,0.012018399],"genre_scores_gemma":[0.95506793,0.00051200006,0.034577116,0.00018627784,0.00006720548,0.00006701878,0.0045887497,0.000113315466,0.0048204567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.00003604682,0.00001832882,0.00013126548,0.00007043397,0.0000756803],"domain_scores_gemma":[0.9997489,0.000058078,0.00002609687,0.000043528085,0.00010141801,0.000021983775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084285316,0.001977374,0.00070696993,0.0016208977,0.0003058445,0.0008101599,0.0011549686,0.0007772816,0.0009870647],"category_scores_gemma":[0.0010223775,0.000366753,0.00061113376,0.00075683877,0.00021728406,0.0011018205,0.0007131409,0.0008184365,0.00055876025],"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":[0.0007485675,0.00035273065,0.026442908,0.00018188197,0.00042030177,0.00030872668,0.000108519605,0.48049784,0.012566203,0.0011967563,0.012412964,0.46476263],"study_design_scores_gemma":[0.000011818493,0.00008013286,0.0021642735,0.000016656972,0.000036880858,0.00004213007,0.000038746246,0.99275863,0.003455486,0.00039432113,0.0009885085,0.000012479566],"about_ca_topic_score_codex":0.02725671,"about_ca_topic_score_gemma":0.03989007,"teacher_disagreement_score":0.02725671,"about_ca_system_score_codex":0.0008608986,"about_ca_system_score_gemma":0.0008973757,"threshold_uncertainty_score":0.05419612},"labels":[],"label_agreement":null},{"id":"W4408843662","doi":"10.18280/ijsse.150213","title":"Authentication in Liveness Detection Utilizing CNN and MobileViT Algorithm","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Liveness; Computer science; Authentication (law); Computer security; Algorithm; Computer network; Distributed computing","score_opus":0.0031519122643232874,"score_gpt":0.20604997888825557,"score_spread":0.20289806662393228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408843662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31041005,0.0018158427,0.66329306,0.000536621,0.0004101791,0.0002204107,0.00056186394,0.009269743,0.013482235],"genre_scores_gemma":[0.91146183,0.00041199615,0.07836715,0.00020324031,0.000045323188,0.000062711806,0.0008369911,0.00007828569,0.00853245],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997327,0.000022953334,0.000014371723,0.00007593605,0.00008993359,0.0000640493],"domain_scores_gemma":[0.9998004,0.000027532053,0.00002750135,0.000042636824,0.000087334294,0.000014595803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004032697,0.00078804133,0.00047803792,0.00078948407,0.00029777954,0.0006477204,0.00094289304,0.00070411735,0.002664978],"category_scores_gemma":[0.0009830863,0.00022768916,0.000427335,0.0003920345,0.0002917681,0.0010805298,0.0007296525,0.0005588471,0.00091080036],"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":[0.0008598816,0.00023043794,0.0077235503,0.00013200885,0.00011304007,0.00033632733,0.00007832775,0.119739816,0.054137994,0.0039599445,0.0070833075,0.80560535],"study_design_scores_gemma":[0.000012698669,0.00011640663,0.0019327194,0.000012732514,0.000024227986,0.00016522991,0.000021733056,0.9739348,0.021340562,0.0009866136,0.0014389682,0.000013201353],"about_ca_topic_score_codex":0.005828795,"about_ca_topic_score_gemma":0.006025629,"teacher_disagreement_score":0.005828795,"about_ca_system_score_codex":0.00071617914,"about_ca_system_score_gemma":0.0005186635,"threshold_uncertainty_score":0.011589766},"labels":[],"label_agreement":null},{"id":"W4408873302","doi":"10.23977/jeis.2025.100109","title":"Design of Fire-extinguishing Car with Intelligent Tracking and Obstacle Avoidance","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstacle avoidance; Obstacle; Tracking (education); Collision avoidance; Computer science; Automotive engineering; Aeronautics; Environmental science; Artificial intelligence; Engineering; Computer security; Psychology; Geography; Mobile robot; Collision; Robot","score_opus":0.00732150204945532,"score_gpt":0.21556728154216712,"score_spread":0.2082457794927118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408873302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06415852,0.0013503903,0.8956714,0.0004655663,0.00038199217,0.0003798018,0.00011115513,0.0031263921,0.034354765],"genre_scores_gemma":[0.84788585,0.0006664464,0.13442151,0.00028231632,0.00010234898,0.00034131182,0.00017032407,0.0001041621,0.016025709],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996668,0.000026737556,0.000016505932,0.00009043897,0.00014410527,0.000055323468],"domain_scores_gemma":[0.999798,0.000008403671,0.00002118554,0.000012714785,0.00013419114,0.000025446809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022155873,0.0005841934,0.0006478207,0.0005090063,0.00066296716,0.00081947324,0.0021057643,0.00069344946,0.0024739148],"category_scores_gemma":[0.00020235061,0.00035816405,0.00048581552,0.00025837467,0.00024889532,0.00038907456,0.00046277844,0.00030985184,0.0010707965],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007565331,0.0003230943,0.0057622134,0.0010282292,0.00034432003,0.0014808024,0.00073358405,0.12679659,0.48645005,0.032836214,0.014108948,0.3293795],"study_design_scores_gemma":[0.00015673226,0.0014350472,0.0038510163,0.000073757976,0.00033688545,0.0020225313,0.0001735677,0.76074,0.14249095,0.002157013,0.0863662,0.00019633166],"about_ca_topic_score_codex":0.0037235762,"about_ca_topic_score_gemma":0.0025835999,"teacher_disagreement_score":0.0037235762,"about_ca_system_score_codex":0.00067854836,"about_ca_system_score_gemma":0.00069885614,"threshold_uncertainty_score":0.008276105},"labels":[],"label_agreement":null},{"id":"W4409054014","doi":"10.1007/s10694-025-01729-7","title":"Deep Residual Multi-resolution Features and Optimized Kernel ELM for Forest Fire Image Detection Using Imbalanced Database","year":2025,"lang":"en","type":"article","venue":"Fire Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Residual; Kernel (algebra); Artificial intelligence; Computer science; Database; Remote sensing; Pattern recognition (psychology); Data mining; Environmental science; Geography; Mathematics; Algorithm","score_opus":0.009353472908520998,"score_gpt":0.2441527616668966,"score_spread":0.23479928875837558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409054014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2867576,0.0021357762,0.69584674,0.00046740528,0.00029534093,0.00012412819,0.0020227206,0.009477843,0.002872424],"genre_scores_gemma":[0.7874863,0.00043077263,0.1976069,0.0002536254,0.00008787774,0.00009720985,0.0059995786,0.00030596738,0.00773176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995797,0.000042695483,0.000025287818,0.00013000882,0.000110312954,0.000111961795],"domain_scores_gemma":[0.999658,0.00007650635,0.000030261488,0.00008029221,0.00013197344,0.00002301031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092540955,0.00092176115,0.001084122,0.0011984748,0.00037820285,0.00080820563,0.0013565156,0.0007466296,0.002129437],"category_scores_gemma":[0.0011297421,0.00033498876,0.0008983985,0.00088882446,0.0002142431,0.0011816855,0.00092972507,0.0010505179,0.0013126151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013576369,0.00077627704,0.0047000702,0.00010386917,0.00021050093,0.00014451798,0.00007480167,0.074711405,0.03488582,0.001345311,0.011970899,0.86971885],"study_design_scores_gemma":[0.000021773958,0.000061665785,0.0020844098,0.000007009324,0.000045792003,0.000050786028,0.00003267888,0.98285633,0.012896602,0.00084265723,0.0010883703,0.0000118623775],"about_ca_topic_score_codex":0.008199512,"about_ca_topic_score_gemma":0.010829109,"teacher_disagreement_score":0.008199512,"about_ca_system_score_codex":0.0004907631,"about_ca_system_score_gemma":0.0010009148,"threshold_uncertainty_score":0.016303599},"labels":[],"label_agreement":null},{"id":"W4409187371","doi":"10.32628/cseit25112766","title":"Safety Detection System using Sound","year":2025,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","topic":"Fire Detection and Safety Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sound (geography); Computer science; Acoustics; Physics","score_opus":0.01637523631635023,"score_gpt":0.2855501689022718,"score_spread":0.26917493258592157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409187371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10792648,0.0014577357,0.81768376,0.0007063971,0.0011411272,0.0007015646,0.001224803,0.03593036,0.033227812],"genre_scores_gemma":[0.74240285,0.00083620177,0.21496518,0.0010036201,0.00043091268,0.00075544533,0.0016713494,0.0004547965,0.03747966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994374,0.00006172816,0.000028412713,0.0001467186,0.00027003622,0.000055642548],"domain_scores_gemma":[0.9995382,0.00010794593,0.000032454267,0.000043281172,0.00023259998,0.000045580553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000523744,0.0006959169,0.00078277686,0.0016704474,0.0004982411,0.0012460812,0.0008406923,0.00090936216,0.008762634],"category_scores_gemma":[0.000867475,0.00025413217,0.00043802554,0.00045449226,0.00024678,0.0010380079,0.0010801568,0.00042846834,0.0039266357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078681455,0.00030665047,0.0059450334,0.00057253195,0.00008154267,0.0005720424,0.00044775137,0.0035962276,0.2804984,0.0031087783,0.01536736,0.6887169],"study_design_scores_gemma":[0.00051903486,0.0038242997,0.019559313,0.00043062796,0.0006100324,0.0036807365,0.0008455886,0.32275224,0.469542,0.008935021,0.16892262,0.00037841918],"about_ca_topic_score_codex":0.00053556374,"about_ca_topic_score_gemma":0.0003830989,"teacher_disagreement_score":0.008762634,"about_ca_system_score_codex":0.00034578485,"about_ca_system_score_gemma":0.00042961558,"threshold_uncertainty_score":0.029313922},"labels":[],"label_agreement":null},{"id":"W4409733946","doi":"10.3390/rs17091503","title":"Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"Mitacs","keywords":"Computer science; Remote sensing; Artificial intelligence; Computer vision; Geography","score_opus":0.006363527452782268,"score_gpt":0.22702921092553716,"score_spread":0.2206656834727549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409733946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17748071,0.0018566443,0.81054115,0.00022136518,0.00009218998,0.00011363139,0.00033111902,0.005963551,0.0033996892],"genre_scores_gemma":[0.67569494,0.00081044296,0.31955242,0.00023482824,0.000047999372,0.000055052336,0.00071164523,0.00032954535,0.0025630859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999511,0.00010034448,0.00001770947,0.00015695416,0.00014047319,0.00007350838],"domain_scores_gemma":[0.999519,0.00012969579,0.000056949506,0.00011716658,0.00014340566,0.000033693326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010562761,0.0014017292,0.00085571187,0.0013849377,0.00029702307,0.0016939379,0.0007986093,0.0005729309,0.0014214437],"category_scores_gemma":[0.0019369132,0.00023907155,0.00070853,0.0009735535,0.00040761373,0.0021625832,0.0009914837,0.0007181653,0.00098335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004929951,0.00033567843,0.008561237,0.00024511974,0.00021591177,0.0000781508,0.0001283435,0.09035505,0.059459753,0.0028201286,0.0035076921,0.8337999],"study_design_scores_gemma":[0.000024896532,0.00017107146,0.0049482267,0.000032727028,0.00009415784,0.00019350802,0.00013355486,0.9555466,0.032316167,0.0038473117,0.0026579814,0.00003375585],"about_ca_topic_score_codex":0.0071233506,"about_ca_topic_score_gemma":0.009289421,"teacher_disagreement_score":0.0071233506,"about_ca_system_score_codex":0.00048517567,"about_ca_system_score_gemma":0.000715676,"threshold_uncertainty_score":0.014163792},"labels":[],"label_agreement":null},{"id":"W4409791017","doi":"10.61091/jcmcc127a-341","title":"Research on the construction of a working fluid system model for ultra-high temperature dense pressurized leakage prevention and plugging combined with multivariate nonlinear regression and machine learning optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Leakage (economics); Multivariate statistics; Nonlinear system; Nonlinear regression; Computer science; Materials science; Regression analysis; Machine learning; Physics","score_opus":0.02054433311103479,"score_gpt":0.2591609553704331,"score_spread":0.2386166222593983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02084794,0.00043500698,0.97587,0.00017423542,0.00005005357,0.00003909028,0.00005912653,0.00031534676,0.0022091696],"genre_scores_gemma":[0.89888406,0.001473693,0.09055466,0.000120057724,0.00009271045,0.00038158195,0.00041796223,0.0001148582,0.007960467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999503,0.00009939553,0.000032899366,0.00016214159,0.00014635676,0.000056289686],"domain_scores_gemma":[0.99964106,0.00013730244,0.00006015761,0.000018562348,0.00012428932,0.000018625793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008160562,0.0010857313,0.0012379681,0.00061089295,0.0005223709,0.0013039836,0.0011752823,0.0009537793,0.0016599986],"category_scores_gemma":[0.001308068,0.00059201155,0.0015322785,0.00059619045,0.00059936673,0.0016646477,0.00088140205,0.0013701525,0.00033404602],"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":[0.000016798898,0.00002317892,0.0009223099,0.00010008381,0.00003157071,0.000057273584,0.00004248875,0.98005116,0.0019822002,0.0031126863,0.0002894425,0.013370789],"study_design_scores_gemma":[0.0000016885908,0.0000071229956,0.00010244674,0.0000021047545,0.0000043716796,0.0000048404713,0.0000039840256,0.99919575,0.00020692543,0.000306983,0.00016049034,0.00000328766],"about_ca_topic_score_codex":0.017981688,"about_ca_topic_score_gemma":0.007211211,"teacher_disagreement_score":0.017981688,"about_ca_system_score_codex":0.0008542907,"about_ca_system_score_gemma":0.001990346,"threshold_uncertainty_score":0.035754025},"labels":[],"label_agreement":null},{"id":"W4410632549","doi":"10.22215/etd/2024-16436","title":"Automated Fine-tuning CNN Using Firefly Algorithm for Bearing Fault Diagnostics","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Fire Detection and Safety Systems","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":"Carleton University","funders":"","keywords":"Firefly algorithm; Bearing (navigation); Computer science; Fault (geology); Algorithm; Firefly protocol; Artificial intelligence; Real-time computing; Data mining; Seismology; Geology","score_opus":0.01568359292537812,"score_gpt":0.2694393076706225,"score_spread":0.2537557147452444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050219644,0.0003091966,0.94329697,0.00010805992,0.000071556395,0.00007425234,0.00005654372,0.0027744514,0.0030892838],"genre_scores_gemma":[0.6359827,0.00016407829,0.36046275,0.00015612207,0.000028283766,0.000116244686,0.00017476808,0.00025181248,0.0026632599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979955,0.00002780142,0.000010325792,0.000059518603,0.00006199352,0.0000407943],"domain_scores_gemma":[0.9996618,0.000118710144,0.000052058833,0.000051231702,0.000097463904,0.000018716744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006144974,0.0010922769,0.0005830536,0.0005902537,0.0002683216,0.00048026472,0.0007559061,0.0006123588,0.0013932945],"category_scores_gemma":[0.001539092,0.00032287423,0.0005863802,0.0002745934,0.0003591043,0.00060258154,0.0005138291,0.00080153043,0.00038484822],"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":[0.00009560255,0.00008349981,0.0020185106,0.00007040313,0.000058577254,0.00006963323,0.000065883294,0.72879314,0.04084398,0.0020522655,0.0019441432,0.22390434],"study_design_scores_gemma":[0.000005168217,0.000020503034,0.00020722642,0.000004162573,0.0000072894454,0.000011761387,0.0000039898323,0.9911724,0.007569964,0.00051130477,0.00048149406,0.000004771812],"about_ca_topic_score_codex":0.005558809,"about_ca_topic_score_gemma":0.00725393,"teacher_disagreement_score":0.005558809,"about_ca_system_score_codex":0.00084734935,"about_ca_system_score_gemma":0.00087826414,"threshold_uncertainty_score":0.0110529065},"labels":[],"label_agreement":null},{"id":"W4411172260","doi":"10.1109/jiot.2025.3578445","title":"Bayesian Optimization-Aided Hybrid Deep Learning Model for Lightweight UAV-Based Smoke Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; CARE Canada","funders":"University of Southern Mississippi","keywords":"Computer science; Bayesian optimization; Artificial intelligence; Smoke; Bayesian probability; Deep learning; Machine learning; Engineering","score_opus":0.008159244840038323,"score_gpt":0.21794129233590956,"score_spread":0.20978204749587123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411172260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017797854,0.00033124612,0.97917956,0.00018669608,0.000036406174,0.00002571277,0.00008046755,0.00069390563,0.0016682434],"genre_scores_gemma":[0.8196684,0.00046011878,0.17273621,0.00040629125,0.00006299544,0.00017567775,0.00046931938,0.00012823155,0.005892731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997929,0.000035140434,0.000010331514,0.00005184136,0.00006570446,0.000044128552],"domain_scores_gemma":[0.9997328,0.000111695335,0.0000312898,0.000016465687,0.00008792153,0.000019722733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051927735,0.0007875676,0.000735034,0.00036289578,0.00022465271,0.00060239114,0.0012688676,0.0007851278,0.0015666143],"category_scores_gemma":[0.001129868,0.00040959325,0.00056326715,0.00034750364,0.0003805312,0.000687116,0.00087677006,0.0011919815,0.00037507262],"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":[0.00009349892,0.00006820171,0.00093764934,0.00004813596,0.0000427775,0.0000498668,0.00002715188,0.9125825,0.0033473356,0.0033005981,0.0015069987,0.07799531],"study_design_scores_gemma":[0.0000016234942,0.000004587154,0.000033972923,0.0000013957433,0.0000021470382,0.0000026288055,9.344716e-7,0.9992993,0.0001874803,0.00039531037,0.000069260095,0.0000012857234],"about_ca_topic_score_codex":0.011106694,"about_ca_topic_score_gemma":0.0143376645,"teacher_disagreement_score":0.011106694,"about_ca_system_score_codex":0.00071883766,"about_ca_system_score_gemma":0.0011506742,"threshold_uncertainty_score":0.022084117},"labels":[],"label_agreement":null},{"id":"W4411570053","doi":"10.2316/j.2025.203-0588","title":"FOREIGN OBJECT INTRUSION DETECTION AND EARLY WARNING IN SUBSTATION VIDEO SURVEILLANCE BASED ON DEEP LEARNING, 46-57.","year":2025,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intrusion; Intrusion detection system; Warning system; Computer science; Computer security; Artificial intelligence; Object (grammar); Learning network; Telecommunications; Geology","score_opus":0.003041963615183587,"score_gpt":0.197626820981622,"score_spread":0.1945848573664384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411570053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35448122,0.008661716,0.6145458,0.0023313437,0.00093617616,0.00016045585,0.0010068946,0.002156635,0.015719742],"genre_scores_gemma":[0.9162295,0.0023190498,0.07045254,0.0001579492,0.00014037258,0.000031469262,0.0009148359,0.00006990569,0.0096844565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997874,0.000046335863,0.000016246846,0.000045383258,0.00005958973,0.000045063724],"domain_scores_gemma":[0.99959654,0.00012054076,0.000040540308,0.00003420765,0.00017101511,0.000037212914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312161,0.0005583136,0.0004811319,0.000770794,0.00029310276,0.00083967956,0.0003826746,0.00048525826,0.0011715669],"category_scores_gemma":[0.0017642345,0.00022541355,0.00048289303,0.00045536362,0.0003384242,0.0011913094,0.00040659987,0.00079405395,0.00042316047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006563829,0.0003741391,0.02210972,0.0002483559,0.00019270505,0.00033159298,0.00019561169,0.10059433,0.045036774,0.005740338,0.019854724,0.8046653],"study_design_scores_gemma":[0.000008895322,0.00009398412,0.009533505,0.000026635638,0.00006499171,0.000115820374,0.00008635743,0.96646047,0.018984096,0.0022441042,0.002363945,0.000017311657],"about_ca_topic_score_codex":0.010511494,"about_ca_topic_score_gemma":0.019193359,"teacher_disagreement_score":0.010511494,"about_ca_system_score_codex":0.0006594317,"about_ca_system_score_gemma":0.0007391238,"threshold_uncertainty_score":0.020900607},"labels":[],"label_agreement":null},{"id":"W4412219300","doi":"","title":"Fire Localisation And Mitigation Emergencies Satellites:A Constellation of CubeSats in LEO for Monitoring Wildfires in Near Real-Time","year":2023,"lang":"en","type":"article","venue":"VBN Forskningsportal (Aalborg Universitet)","topic":"Fire Detection and Safety Systems","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":"York University","funders":"","keywords":"Constellation; Environmental science; Remote sensing; Satellite constellation; Meteorology; Aeronautics; Astrobiology; Geography; Engineering; Astronomy; Physics","score_opus":0.008832765491010953,"score_gpt":0.2032314332238548,"score_spread":0.19439866773284387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412219300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7997673,0.0025347283,0.10738635,0.0015400684,0.0004924228,0.0011213439,0.012007565,0.005941801,0.06920844],"genre_scores_gemma":[0.81220204,0.0009610049,0.1633998,0.0003704677,0.00014090895,0.00034451703,0.009694182,0.00021132873,0.012675705],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997181,0.00005139275,0.000008899425,0.000054090826,0.00013021208,0.00003715354],"domain_scores_gemma":[0.99980766,0.000018344808,0.000035515262,0.0000285695,0.00006097838,0.0000488929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040819915,0.000573843,0.0002305542,0.0005221128,0.00038695565,0.0005254343,0.0003374733,0.0002845589,0.0019258143],"category_scores_gemma":[0.00034913476,0.0001475205,0.00024731326,0.0005337862,0.0002711802,0.0005710843,0.0008888321,0.0005212414,0.0006629188],"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":[0.0016778691,0.00049907045,0.10160521,0.00043755202,0.0002972326,0.0007502767,0.001519517,0.033171624,0.26921478,0.0069372356,0.05183702,0.53205264],"study_design_scores_gemma":[0.0005981107,0.0032405206,0.22734982,0.00026423688,0.0003692043,0.0017957623,0.0020036846,0.16130938,0.19278605,0.006467936,0.40360796,0.00020735346],"about_ca_topic_score_codex":0.0035805576,"about_ca_topic_score_gemma":0.008944389,"teacher_disagreement_score":0.0035805576,"about_ca_system_score_codex":0.00041296284,"about_ca_system_score_gemma":0.00066398585,"threshold_uncertainty_score":0.007119477},"labels":[],"label_agreement":null},{"id":"W4412391000","doi":"10.2305/eoha2567","title":"Detecting illegal campfires by drone-mounted thermal sensors in protected tropical rainforests","year":2025,"lang":"en","type":"article","venue":"PARKS","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"International Conservation Fund of Canada; Gordon and Betty Moore Foundation","keywords":"Drone; Rainforest; Tropical rain forest; Tropical rainforest; Geography; Environmental science; Ecology; Biology","score_opus":0.0036691588047948623,"score_gpt":0.21472204361878985,"score_spread":0.21105288481399498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412391000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99953616,0.000012998176,0.0001979285,0.000005177921,6.5492236e-7,0.0000059600575,0.000024713603,0.000003299949,0.00021318979],"genre_scores_gemma":[0.99932075,0.000019185782,0.00053857657,0.0000048508514,6.2419747e-7,0.000005080997,0.000034254026,7.5190263e-7,0.000075898395],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997392,0.0001277105,0.000011478511,0.000049923852,0.000039949897,0.000031711836],"domain_scores_gemma":[0.99935323,0.00029128173,0.00016940109,0.000049805825,0.00007938284,0.000056926325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054319,0.00021545212,0.00013653406,0.0002787537,0.00020113146,0.00023392138,0.00021243861,0.00015622737,0.00048202288],"category_scores_gemma":[0.0014648018,0.000096254196,0.00011349393,0.00015252308,0.00025226586,0.00029516697,0.00025709075,0.000119702396,0.000089073794],"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":[0.00033901233,0.00020198958,0.9583409,0.000052745672,0.000056850677,0.00008894643,0.0006315748,0.0072190184,0.0119263055,0.00004594592,0.00011702798,0.02097978],"study_design_scores_gemma":[0.000021213295,0.0005797695,0.95663947,0.000023671595,0.000038281138,0.00013268113,0.0016029573,0.036741532,0.0037789226,0.00007516593,0.00035173097,0.000014689341],"about_ca_topic_score_codex":0.011217957,"about_ca_topic_score_gemma":0.046266675,"teacher_disagreement_score":0.011217957,"about_ca_system_score_codex":0.0002451401,"about_ca_system_score_gemma":0.00018405587,"threshold_uncertainty_score":0.02230531},"labels":[],"label_agreement":null},{"id":"W4412952374","doi":"10.18280/i2m.240303","title":"Measurement and Performance Evaluation of an IoT-Integrated Dehumidification Control System for Airborne Infection Isolation Rooms: A Case Study at Betong Hospital","year":2025,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Isolation (microbiology); Internet of Things; Infection control; Medical emergency; Computer science; Medicine; Embedded system; Intensive care medicine","score_opus":0.02771915917158458,"score_gpt":0.27861542993953864,"score_spread":0.25089627076795407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412952374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909752,0.00010448002,0.0075901607,0.00006857229,0.000021373267,0.000106011146,0.000101639176,0.00014887593,0.00088366517],"genre_scores_gemma":[0.9926766,0.00008724695,0.0061956486,0.00003566706,0.000011345053,0.00004404118,0.00010089033,0.000019213121,0.0008293216],"study_design_codex":"bench_or_experimental","study_design_gemma":"case_report","domain_scores_codex":[0.9991062,0.00017304475,0.00006126172,0.00016777063,0.00037496912,0.00011676656],"domain_scores_gemma":[0.9993594,0.0001524457,0.00007589648,0.00009395554,0.00024045898,0.00007792137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057819096,0.00049279287,0.00049804046,0.00042580726,0.00039739016,0.00049729994,0.0006932654,0.0006606892,0.0009742597],"category_scores_gemma":[0.0009714748,0.00017745538,0.00028363906,0.0002637345,0.00032857584,0.0004915578,0.00054813636,0.00023593169,0.00035415802],"study_design_candidate":"case_report","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":[0.0015507258,0.002272191,0.080051295,0.0009824627,0.00015169306,0.0041581565,0.0024323396,0.015153894,0.75882035,0.00048938126,0.0016889026,0.13224861],"study_design_scores_gemma":[0.00027278185,0.014820939,0.24257725,0.000116974166,0.00032073748,0.0049630976,0.0044958107,0.13559757,0.5865263,0.0003281986,0.0097663365,0.00021413394],"about_ca_topic_score_codex":0.0015012324,"about_ca_topic_score_gemma":0.0018817711,"teacher_disagreement_score":0.0015012324,"about_ca_system_score_codex":0.00038077027,"about_ca_system_score_gemma":0.00034160638,"threshold_uncertainty_score":0.0032592416},"labels":[],"label_agreement":null},{"id":"W4413146664","doi":"10.1109/icoeca66273.2025.00187","title":"Object Detection and Hazard Alert System for Child Safety on Robot using YOLO","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Hazard; Robot; Object (grammar); Object detection; Computer vision; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.008874381527952628,"score_gpt":0.21287368749602606,"score_spread":0.20399930596807342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15805753,0.0021067294,0.6755891,0.0010166528,0.00054768677,0.0010929657,0.0026821636,0.11704883,0.04185835],"genre_scores_gemma":[0.58686376,0.001512853,0.3495409,0.0014135707,0.000114828166,0.0012673159,0.0051352885,0.0024769753,0.05167456],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968445,0.000024091676,0.00001639185,0.00008457293,0.00013073253,0.000059676284],"domain_scores_gemma":[0.99974245,0.00004506131,0.000041694377,0.000028028771,0.00010871283,0.00003400991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028099294,0.0007595023,0.0005589001,0.0005539012,0.00033054544,0.0005536872,0.001040478,0.00056702836,0.010429209],"category_scores_gemma":[0.00073912623,0.00035258697,0.00048527954,0.00019655318,0.00022732063,0.0007223202,0.0010908154,0.0005411663,0.0034635537],"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":[0.0016740776,0.00049284345,0.01582216,0.0019144439,0.00012409104,0.0023808377,0.0016172264,0.007972012,0.24376106,0.0032407658,0.08854816,0.6324523],"study_design_scores_gemma":[0.00038845418,0.0027243327,0.059082545,0.000725388,0.0004136531,0.0051480476,0.0008730669,0.28269315,0.35440308,0.003985394,0.28909326,0.00046961382],"about_ca_topic_score_codex":0.0030114185,"about_ca_topic_score_gemma":0.0037035295,"teacher_disagreement_score":0.010429209,"about_ca_system_score_codex":0.00036180316,"about_ca_system_score_gemma":0.00068235165,"threshold_uncertainty_score":0.03488916},"labels":[],"label_agreement":null},{"id":"W4413278230","doi":"10.1109/icip55913.2025.11084304","title":"Fine-Grained Spatial-Temporal Perception for Gas Leak Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"University of British Columbia","funders":"","keywords":"Computer science; Leak; Perception; Segmentation; Artificial intelligence; Computer vision; Environmental science; Psychology; Neuroscience","score_opus":0.007920565185328646,"score_gpt":0.23043179081316684,"score_spread":0.2225112256278382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413278230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027574629,0.0003587378,0.9682538,0.00013265264,0.000045391345,0.00005969426,0.00014041636,0.00229132,0.001143282],"genre_scores_gemma":[0.5650665,0.0006119026,0.42919898,0.0002980934,0.000093407194,0.00012950829,0.00090253534,0.000497364,0.0032017098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972934,0.000026221694,0.000012544932,0.00010116236,0.00007607671,0.000054738488],"domain_scores_gemma":[0.9996747,0.00010126339,0.00005068771,0.000052869185,0.00008423283,0.00003632028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004599859,0.0010083508,0.0008278283,0.0008809826,0.00033777618,0.0010250254,0.0013487926,0.0009771228,0.001362199],"category_scores_gemma":[0.0013059648,0.00049494236,0.00088665733,0.0006233711,0.0005946094,0.0014937844,0.0011862044,0.0011249953,0.0006413137],"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":[0.00053128164,0.00019102597,0.0032415485,0.00016716795,0.00007386428,0.00027007068,0.0002695923,0.4819955,0.088738576,0.0059791,0.0040620826,0.41448012],"study_design_scores_gemma":[0.0000069317293,0.000048987542,0.0005073282,0.000007921086,0.000012794419,0.00006124529,0.00002383656,0.98646355,0.009299927,0.0022573136,0.0013001754,0.0000099723175],"about_ca_topic_score_codex":0.0071694995,"about_ca_topic_score_gemma":0.010940882,"teacher_disagreement_score":0.0071694995,"about_ca_system_score_codex":0.00079605187,"about_ca_system_score_gemma":0.0014361694,"threshold_uncertainty_score":0.014255524},"labels":[],"label_agreement":null},{"id":"W4413327064","doi":"10.1016/j.dam.2025.08.026","title":"Firefighting with a distance-based restriction","year":2025,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Toronto Metropolitan University; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Mathematics; Firefighting; Combinatorics; Geography; Cartography","score_opus":0.004866721824933904,"score_gpt":0.19125592757207108,"score_spread":0.18638920574713716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413327064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24579518,0.00017741285,0.7131302,0.0010506958,0.00012929285,0.00028272544,0.00030661823,0.0002467723,0.03888111],"genre_scores_gemma":[0.94025636,0.00010452069,0.049349427,0.00019061237,0.000033395925,0.00017719866,0.00010652953,0.000035539135,0.009746475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857855,0.00058190286,0.00007879661,0.0003320348,0.00021664969,0.0002120488],"domain_scores_gemma":[0.99691606,0.0017041767,0.00033182488,0.00041572322,0.00015914923,0.0004731168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011948205,0.0007611237,0.0007002825,0.00024401493,0.00059155666,0.0012645045,0.001562885,0.0008056071,0.0060430607],"category_scores_gemma":[0.006659364,0.00030634878,0.00066178857,0.00023868073,0.002315969,0.0032636295,0.0022076734,0.0016963144,0.0006493608],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010870534,0.0002462725,0.00246642,0.00027214788,0.000100511425,0.0006991019,0.0009127587,0.23146914,0.014971914,0.7179339,0.0033482932,0.026492426],"study_design_scores_gemma":[0.0002200936,0.0004961016,0.0009186169,0.000032271313,0.00004166786,0.00043326704,0.00040998627,0.6046183,0.0025241885,0.3830463,0.007190637,0.000068565816],"about_ca_topic_score_codex":0.0022243953,"about_ca_topic_score_gemma":0.0016813547,"teacher_disagreement_score":0.0060430607,"about_ca_system_score_codex":0.00085226126,"about_ca_system_score_gemma":0.00076927536,"threshold_uncertainty_score":0.020215988},"labels":[],"label_agreement":null},{"id":"W4413565205","doi":"10.47611/jsrhs.v14i1.8612","title":"Enhanced Wildfire Detection Using a Mixture of Experts Approach","year":2025,"lang":"en","type":"article","venue":"Journal of Student Research","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"University of California, Santa Cruz","keywords":"Environmental science; Computer science; Remote sensing; Geography","score_opus":0.07436426699506894,"score_gpt":0.3877722693399863,"score_spread":0.3134080023449174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413565205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1233405,0.0010536403,0.8660958,0.0004263086,0.00012973518,0.00009065392,0.00039550097,0.0035301652,0.004937658],"genre_scores_gemma":[0.76280576,0.00038119062,0.2286295,0.00041806354,0.00015696224,0.000060340182,0.0010339374,0.00017489992,0.0063393232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999252,0.00016199717,0.000029321018,0.00022533073,0.00016973856,0.00016151942],"domain_scores_gemma":[0.99930775,0.0002684676,0.000059033187,0.000072956136,0.00022502869,0.00006673859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018684043,0.0013698923,0.0011410655,0.0021270136,0.00035992105,0.00093048194,0.0013323716,0.0014789018,0.0016440222],"category_scores_gemma":[0.0024162855,0.000565911,0.0017234482,0.00064697576,0.0002865187,0.001772335,0.00097447034,0.001452979,0.00091737375],"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":[0.0007895194,0.0004388137,0.010470251,0.000117802054,0.00048609552,0.00041391002,0.0002116229,0.49535054,0.018265972,0.0043740715,0.0073209032,0.46176052],"study_design_scores_gemma":[0.000005282736,0.000030187986,0.0006237125,0.0000072011544,0.00002704416,0.00006107855,0.000018711427,0.99466926,0.002596954,0.0014178258,0.00053105934,0.0000116506435],"about_ca_topic_score_codex":0.00722712,"about_ca_topic_score_gemma":0.012486689,"teacher_disagreement_score":0.00722712,"about_ca_system_score_codex":0.00054878125,"about_ca_system_score_gemma":0.0007873012,"threshold_uncertainty_score":0.014370084},"labels":[],"label_agreement":null},{"id":"W4413630458","doi":"10.1109/access.2025.3602259","title":"A Machine Learning Framework for Fire Risk Prediction With Response and Proximity Insights","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); Alberta Health Services; Ontario Tech University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence","score_opus":0.009608752485894536,"score_gpt":0.24420751881994848,"score_spread":0.23459876633405394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413630458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007286857,0.0001752558,0.99067056,0.000329378,0.000025784591,0.000043213608,0.00027524788,0.00059292454,0.00060066197],"genre_scores_gemma":[0.5199401,0.00043448142,0.47460005,0.0003263448,0.00018699632,0.00046221074,0.0017453702,0.00011580962,0.0021886316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901986,0.00036997607,0.00007414793,0.00026898365,0.00018663415,0.000080323414],"domain_scores_gemma":[0.99799156,0.0011860741,0.00022074612,0.00016200915,0.0003555746,0.00008405765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021563177,0.0011839174,0.0009816465,0.0014918625,0.0005921679,0.0014634187,0.0020176691,0.0012100091,0.0015763794],"category_scores_gemma":[0.00587258,0.00046006395,0.00105218,0.0014648492,0.0005454765,0.0015304353,0.0014707994,0.002298252,0.0006102505],"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":[0.000054960547,0.00015380191,0.0034862328,0.00004688783,0.00008480295,0.00006717557,0.000059054375,0.918367,0.0006157423,0.010178617,0.0019600403,0.06492564],"study_design_scores_gemma":[0.0000022571296,0.000009503143,0.00013297293,0.0000030855306,0.0000039268716,0.0000062726517,0.000003954335,0.9952512,0.00010689038,0.004245117,0.00023162048,0.0000032216406],"about_ca_topic_score_codex":0.013438443,"about_ca_topic_score_gemma":0.012902442,"teacher_disagreement_score":0.013438443,"about_ca_system_score_codex":0.0012276776,"about_ca_system_score_gemma":0.0013090955,"threshold_uncertainty_score":0.026720405},"labels":[],"label_agreement":null},{"id":"W4413652902","doi":"10.3390/fire8090341","title":"A Systematic Machine Learning Methodology for Enhancing Accuracy and Reducing Computational Complexity in Forest Fire Detection","year":2025,"lang":"en","type":"article","venue":"Fire","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University; Public Works and Government Services Canada; 3v Geomatics (Canada); Carleton University; University of Waterloo","funders":"University of Waterloo; Dalhousie University","keywords":"Computer science; Computational complexity theory; Machine learning; Artificial intelligence; Fire detection; Engineering; Algorithm; Architectural engineering","score_opus":0.04402203706261551,"score_gpt":0.29326486872803686,"score_spread":0.24924283166542135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413652902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020781713,0.00033753997,0.9766248,0.00023001306,0.00003069035,0.00013971448,0.00005675247,0.0009844529,0.0008144272],"genre_scores_gemma":[0.2573812,0.00027966566,0.74053735,0.00017771391,0.000051604973,0.0003149565,0.00022048116,0.00010219325,0.00093483756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984711,0.00052217615,0.00012206694,0.00029163918,0.00050388084,0.00008918046],"domain_scores_gemma":[0.9983286,0.0007208195,0.00018859048,0.00025475564,0.00047205458,0.00003503206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003063714,0.0011199105,0.0007341386,0.0011804192,0.0005843394,0.0010244518,0.0013630787,0.00066514045,0.00092656],"category_scores_gemma":[0.0072505553,0.00038152933,0.00076805544,0.0009816366,0.00062339066,0.0015155901,0.0008985817,0.0013407865,0.00049187726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018830002,0.00038033962,0.005955474,0.00027963394,0.0001607988,0.00011337142,0.00017203204,0.2358526,0.05241657,0.013636305,0.00301253,0.687832],"study_design_scores_gemma":[0.000020493804,0.0001824824,0.0012234241,0.000026740969,0.0000379224,0.000093391514,0.000032407468,0.96865326,0.02155611,0.0059279674,0.0022264207,0.000019306515],"about_ca_topic_score_codex":0.0034154544,"about_ca_topic_score_gemma":0.0062920153,"teacher_disagreement_score":0.0034154544,"about_ca_system_score_codex":0.0008221909,"about_ca_system_score_gemma":0.0024618662,"threshold_uncertainty_score":0.016202629},"labels":[],"label_agreement":null},{"id":"W4414013236","doi":"10.1016/j.jnlssr.2025.100254","title":"A lightweight four-channel multi-modal model to improve computational performance of automated fire detection","year":2025,"lang":"en","type":"article","venue":"Journal of Safety Science and Resilience","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia","keywords":"Modal; Computer science; Channel (broadcasting); Telecommunications; Materials science","score_opus":0.00774160419627315,"score_gpt":0.23267300860619555,"score_spread":0.2249314044099224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414013236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02780541,0.00028910278,0.9684388,0.0001326456,0.000069702546,0.000036115332,0.00007650087,0.0010699814,0.0020817274],"genre_scores_gemma":[0.77447784,0.000359494,0.218582,0.00021131235,0.00005562544,0.00011868457,0.00038121856,0.00015228418,0.005661517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998292,0.000024050536,0.000008439835,0.000049089536,0.000056486297,0.000032738684],"domain_scores_gemma":[0.999814,0.00005968851,0.000015480686,0.000025260886,0.00007062889,0.00001488993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003821119,0.0005766219,0.00051837735,0.000425121,0.00030223024,0.0006268334,0.0012198689,0.00060773233,0.0024850434],"category_scores_gemma":[0.0008315908,0.00028572598,0.0008431543,0.000327673,0.00027566258,0.0009496608,0.00075779285,0.0010569214,0.0006472428],"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":[0.00022848597,0.00011561097,0.0016812582,0.000084811465,0.00007907152,0.00010246189,0.00007288239,0.7604555,0.020678956,0.0070673027,0.0023777685,0.20705597],"study_design_scores_gemma":[0.0000012500459,0.0000059270365,0.000055660956,0.0000010750715,0.0000028512013,0.000006216897,0.000001685258,0.99861693,0.0007905681,0.00032328226,0.00019242491,0.000002048026],"about_ca_topic_score_codex":0.0113071585,"about_ca_topic_score_gemma":0.010588527,"teacher_disagreement_score":0.0113071585,"about_ca_system_score_codex":0.0006403875,"about_ca_system_score_gemma":0.0009608425,"threshold_uncertainty_score":0.022482693},"labels":[],"label_agreement":null},{"id":"W4414015726","doi":"10.11159/mvml25.108","title":"Multiple Image-Based Fire Head Detection and Contour-Based Spread Rate of Fire Head Area Estimation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Fire Agency; Ministry of Science and ICT, South Korea; Ministry of the Interior and Safety","keywords":"Head (geology); Computer science; Computer vision; Artificial intelligence; Fire detection; Image (mathematics); Geology; Engineering; Architectural engineering","score_opus":0.006632180850660629,"score_gpt":0.20373422223945745,"score_spread":0.1971020413887968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38000578,0.0007621928,0.61396295,0.000113103735,0.000086569664,0.00012152292,0.00051568466,0.0019327602,0.0024995012],"genre_scores_gemma":[0.836302,0.00033889382,0.16100697,0.000038841084,0.000040980412,0.000051704887,0.0004586358,0.00008358489,0.0016784633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958104,0.000032945263,0.000023905188,0.00013973824,0.00016052542,0.00006178628],"domain_scores_gemma":[0.9991986,0.00017850152,0.00015912451,0.00010596278,0.0003139708,0.00004385855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063661975,0.000610247,0.0007269616,0.0025083309,0.00017074232,0.00072192185,0.00077396695,0.0006272675,0.0010956826],"category_scores_gemma":[0.0019608196,0.0003231594,0.0005715213,0.0012220454,0.00025704663,0.0011536238,0.00045024906,0.00054937263,0.0004985038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006881142,0.00032509238,0.043373045,0.00027398427,0.00018995049,0.0003820646,0.0001787499,0.12658422,0.090582035,0.0010521568,0.0016024116,0.7347681],"study_design_scores_gemma":[0.000009452328,0.00007228,0.024440756,0.000017949003,0.00004872688,0.00035436547,0.000051512052,0.9421915,0.031727765,0.00043158777,0.0006290357,0.000025124704],"about_ca_topic_score_codex":0.003479745,"about_ca_topic_score_gemma":0.0042751506,"teacher_disagreement_score":0.003479745,"about_ca_system_score_codex":0.000449442,"about_ca_system_score_gemma":0.0003367277,"threshold_uncertainty_score":0.006918967},"labels":[],"label_agreement":null},{"id":"W4414015763","doi":"10.11159/mvml25.111","title":"A Comprehensive Analysis of Transfer Learning Algorithms for Image Segmentation of Irregular-Shaped Fire Object","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Fire Agency; Ministry of Science and ICT, South Korea; Ministry of the Interior and Safety","keywords":"Computer science; Image segmentation; Segmentation; Artificial intelligence; Image (mathematics); Object (grammar); Transfer of learning; Segmentation-based object categorization; Computer vision; Algorithm; Scale-space segmentation; Pattern recognition (psychology)","score_opus":0.006714744996796112,"score_gpt":0.21783054076741876,"score_spread":0.21111579577062264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044498213,0.002577638,0.9468739,0.00032966523,0.0000873848,0.000106337095,0.00011443762,0.0020969391,0.0033154532],"genre_scores_gemma":[0.64379334,0.0023466086,0.34526542,0.00034604504,0.00015079108,0.00017261562,0.00081528816,0.00045813428,0.0066517144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993505,0.00014382576,0.000038238693,0.00018710319,0.00020678778,0.00007350008],"domain_scores_gemma":[0.9981937,0.0009058317,0.00015160152,0.00020357224,0.00048427613,0.000060994138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027907754,0.0011914768,0.0009750619,0.0019591015,0.00048728034,0.0010736964,0.0017304949,0.0014651847,0.0018277777],"category_scores_gemma":[0.006989275,0.0004561926,0.0010710882,0.0014220042,0.0006558188,0.0019922869,0.0009538438,0.0014816194,0.00080737047],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018235507,0.00013370244,0.0022599823,0.000188344,0.00016969585,0.00008811675,0.000084879364,0.4117552,0.009831156,0.006368846,0.0021878614,0.5667499],"study_design_scores_gemma":[0.0000030203369,0.00005046782,0.0005346759,0.000012647663,0.000015161673,0.00004160299,0.000011318117,0.9918263,0.0039271014,0.0028900614,0.0006809428,0.000006724802],"about_ca_topic_score_codex":0.007981944,"about_ca_topic_score_gemma":0.005251102,"teacher_disagreement_score":0.007981944,"about_ca_system_score_codex":0.0017064234,"about_ca_system_score_gemma":0.0013694357,"threshold_uncertainty_score":0.015870988},"labels":[],"label_agreement":null},{"id":"W4414272352","doi":"10.1109/icct-europe63283.2025.11157690","title":"IoT-Integrated Multimodal Large Language Model for Real-Time Fire Detection and Risk Mitigation","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"","keywords":"Fire detection; Adaptability; Safeguarding; Resilience (materials science); Transformer; Risk management; Fire protection; Data modeling","score_opus":0.0032184992369537883,"score_gpt":0.20894972377113225,"score_spread":0.20573122453417847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414272352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014115236,0.00019788793,0.9738098,0.00035697414,0.000075855845,0.00012195173,0.00064125535,0.0071987943,0.0034822698],"genre_scores_gemma":[0.58605504,0.00038718965,0.40267602,0.00047914335,0.00007172227,0.00055194524,0.0023478612,0.0007977293,0.006633375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994438,0.00016254008,0.000044884637,0.00011661903,0.0001737862,0.00005824579],"domain_scores_gemma":[0.99926835,0.00028658728,0.000057060464,0.00018395855,0.00015767557,0.000046351663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081267825,0.00063899835,0.00048524205,0.0005475834,0.0004583494,0.0014047388,0.0012818939,0.0006887518,0.0032872658],"category_scores_gemma":[0.002736354,0.00026935842,0.0011848436,0.00039164282,0.0005151893,0.0026979463,0.0016233495,0.0012663902,0.0012319161],"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":[0.0009074977,0.0005749239,0.00823533,0.0005067341,0.00021493407,0.0012120121,0.0010673715,0.45954677,0.033076406,0.22114243,0.02746944,0.24604623],"study_design_scores_gemma":[0.000011172628,0.000025466386,0.00012908487,0.000014062667,0.000023010914,0.00006489564,0.00003904751,0.9701975,0.002769393,0.021424271,0.0052855797,0.00001644238],"about_ca_topic_score_codex":0.005984345,"about_ca_topic_score_gemma":0.012072851,"teacher_disagreement_score":0.005984345,"about_ca_system_score_codex":0.001013404,"about_ca_system_score_gemma":0.0017072471,"threshold_uncertainty_score":0.011898994},"labels":[],"label_agreement":null},{"id":"W4414406115","doi":"10.1109/eeite65381.2025.11166526","title":"Real-Time Leak Localization in N95 Respirators Using Infrared Imaging and Deep Learning with Optimal ROI Signal Correlation","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Université Laval; École de Technologie Supérieure; Institut de recherche Robert-Sauvé en santé et en sécurité du travail; Université du Québec à Rimouski","funders":"","keywords":"Leak; SIGNAL (programming language); Respirator; Deep learning; Tracking (education); Breathing; Leak detection; Pattern recognition (psychology)","score_opus":0.0036475776941003457,"score_gpt":0.193746413313866,"score_spread":0.19009883561976565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414406115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22421104,0.0006375459,0.77059877,0.000216196,0.000053367523,0.000049545717,0.00013207253,0.0025956426,0.0015057329],"genre_scores_gemma":[0.8949157,0.00023746495,0.10248635,0.0001280502,0.00002064512,0.000043977234,0.0002055278,0.00011050794,0.0018517341],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998547,0.000022026228,0.000006364168,0.000050916526,0.00004278162,0.00002327037],"domain_scores_gemma":[0.99984026,0.000053961143,0.00003817259,0.00001688811,0.000034487177,0.000016316557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033500214,0.000799368,0.00039256443,0.00042260255,0.00013486629,0.00044835318,0.00062044803,0.00051692844,0.0006698495],"category_scores_gemma":[0.0010016913,0.00022977384,0.0003413606,0.0002385374,0.00023740859,0.0005434932,0.0005988123,0.00045730613,0.00025471798],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007571807,0.0002765024,0.0121843945,0.00021917952,0.00010251412,0.00044726525,0.00018373961,0.29010227,0.15317714,0.0012792611,0.003023978,0.53824663],"study_design_scores_gemma":[0.000007339122,0.000101523714,0.0017492987,0.000009588007,0.0000145444255,0.00008464071,0.000018682465,0.9765771,0.02054463,0.0005063084,0.00037699923,0.000009426135],"about_ca_topic_score_codex":0.0018485623,"about_ca_topic_score_gemma":0.00350146,"teacher_disagreement_score":0.0018485623,"about_ca_system_score_codex":0.0003358057,"about_ca_system_score_gemma":0.00047530507,"threshold_uncertainty_score":0.0036756396},"labels":[],"label_agreement":null},{"id":"W4414538694","doi":"10.1109/icc52391.2025.11160857","title":"A Heterogeneous Data-Driven Multi-Sensor Collaborative Small Target Detection Method for Road Safety in Bad Weather","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"University of Ottawa","funders":"","keywords":"Fusion; Point cloud; Sensor fusion; Lidar; Image fusion; Channel (broadcasting); Image (mathematics)","score_opus":0.022499991078221107,"score_gpt":0.28228192295995297,"score_spread":0.2597819318817319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414538694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026982475,0.00018525745,0.9712785,0.000071316594,0.00005743755,0.000056929297,0.000059929866,0.0006459064,0.0006622267],"genre_scores_gemma":[0.6075952,0.00018400088,0.38855866,0.00012924577,0.000084796375,0.00014350435,0.00049457914,0.0000966461,0.002713347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901485,0.00011432043,0.000043471824,0.00040790203,0.00028524737,0.00013418772],"domain_scores_gemma":[0.9994399,0.00011925472,0.00005638703,0.00010016993,0.00023232771,0.000052026782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010710148,0.0010680219,0.0013052436,0.001120989,0.00087594334,0.00079507165,0.0021497817,0.00095746043,0.0010169618],"category_scores_gemma":[0.001516537,0.00045532457,0.0012380364,0.0010921364,0.00042268878,0.0017813279,0.0014294588,0.0011586744,0.00047532978],"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":[0.0004726221,0.00034299018,0.0035139616,0.000111991656,0.0002091669,0.00017399187,0.00024012524,0.23702797,0.04611978,0.0031897477,0.0030928347,0.7055047],"study_design_scores_gemma":[0.000015359901,0.000054136555,0.0008258659,0.0000027434824,0.00002650753,0.000031383453,0.000034328263,0.9892526,0.007821579,0.00118541,0.00073576963,0.00001440273],"about_ca_topic_score_codex":0.0063204225,"about_ca_topic_score_gemma":0.005029638,"teacher_disagreement_score":0.0063204225,"about_ca_system_score_codex":0.0007360767,"about_ca_system_score_gemma":0.0010737487,"threshold_uncertainty_score":0.012567282},"labels":[],"label_agreement":null},{"id":"W4414548368","doi":"10.1007/s10694-025-01810-1","title":"Automatic Flame Detection: Evaluation of Deep Learning Algorithms Using a Custom Thermal Image Dataset","year":2025,"lang":"en","type":"article","venue":"Fire Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Benchmark (surveying); Deep learning; Generalization; Novelty; Key (lock); Novelty detection; Resource (disambiguation)","score_opus":0.01183688334443765,"score_gpt":0.2632867660254859,"score_spread":0.25144988268104823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414548368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87235695,0.0021614328,0.08022109,0.0009138311,0.0008581703,0.00080355426,0.01891451,0.013864438,0.009905997],"genre_scores_gemma":[0.8465219,0.00056889583,0.096137285,0.0003555518,0.00008822082,0.00024198418,0.051052697,0.000376808,0.004656605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987684,0.00018241703,0.00009630063,0.00047273928,0.00033629246,0.00014372678],"domain_scores_gemma":[0.99828607,0.00054178457,0.00015687039,0.0003333763,0.0005541063,0.00012782331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002741505,0.0021015191,0.0007906333,0.0018366378,0.000642714,0.0010436419,0.002359813,0.0015150756,0.0017907849],"category_scores_gemma":[0.0040777507,0.00035861574,0.0011381215,0.0009938452,0.00073777226,0.0011556321,0.0012122788,0.0016811986,0.0011400627],"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":[0.0025743004,0.002711716,0.023863863,0.001083142,0.00096855656,0.00038497042,0.00012888065,0.59011614,0.02337641,0.0018132919,0.042503968,0.31047475],"study_design_scores_gemma":[0.00011412961,0.00036410886,0.007647794,0.00005502661,0.00006539241,0.0001282044,0.00007245898,0.9685336,0.019329222,0.00084924285,0.0028070046,0.000033924807],"about_ca_topic_score_codex":0.025505029,"about_ca_topic_score_gemma":0.027420333,"teacher_disagreement_score":0.025505029,"about_ca_system_score_codex":0.0015325871,"about_ca_system_score_gemma":0.001115335,"threshold_uncertainty_score":0.05071318},"labels":[],"label_agreement":null},{"id":"W4414787285","doi":"10.1109/iccv51701.2025.00591","title":"HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Haze; Ode; Visibility; Generalization; Adaptability; Benchmark (surveying); Inference; Trajectory","score_opus":0.012058679603003584,"score_gpt":0.25924332542359635,"score_spread":0.24718464582059277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414787285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02143538,0.00046838887,0.97392446,0.0002908813,0.00009084649,0.000049159626,0.0002593488,0.0019915346,0.0014899442],"genre_scores_gemma":[0.5274927,0.0011311271,0.45937756,0.00046586478,0.00013816175,0.00010964823,0.0019040564,0.0006921223,0.008688722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998123,0.000022926592,0.000009598621,0.00006752987,0.00006275308,0.000024856385],"domain_scores_gemma":[0.9995921,0.00013740183,0.000050818806,0.00008757462,0.00009171808,0.0000404119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062392984,0.0009808934,0.00068317907,0.00071373105,0.00030104053,0.00094927364,0.0018521035,0.0011445966,0.0011403832],"category_scores_gemma":[0.0021000677,0.00045363716,0.00095146307,0.0003617816,0.0006853235,0.0018497348,0.0015173221,0.0019847872,0.00043492767],"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":[0.000055696422,0.0001052051,0.0017726463,0.00015374756,0.000080453894,0.00009782086,0.000103256876,0.8687742,0.016189495,0.009980945,0.0036285047,0.09905809],"study_design_scores_gemma":[0.0000036309357,0.000012720811,0.00011536069,0.0000041303983,0.0000042510305,0.00002268569,0.00000413385,0.9957594,0.0014625342,0.0018875829,0.0007168195,0.000006622059],"about_ca_topic_score_codex":0.01176881,"about_ca_topic_score_gemma":0.0134278955,"teacher_disagreement_score":0.01176881,"about_ca_system_score_codex":0.0007047081,"about_ca_system_score_gemma":0.0012374353,"threshold_uncertainty_score":0.023400605},"labels":[],"label_agreement":null},{"id":"W4414805416","doi":"10.1016/j.rsase.2025.101739","title":"Comparative analysis of CNN architectures for satellite-based forest fire detection: A mobile-friendly approach using Sentinel-2 imagery","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"International Development Research Centre; Regional Universities Forum for Capacity Building in Agriculture; West African Science Service Centre on Climate Change and Adapted Land Use; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Convolutional neural network; Software deployment; Inference; Set (abstract data type); Reliability (semiconductor); Computational model; Deep learning; Key (lock); Artificial neural network","score_opus":0.011814348810212582,"score_gpt":0.23277816825305217,"score_spread":0.22096381944283958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414805416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9603357,0.0037237548,0.024956955,0.0002651,0.00021433631,0.000054957472,0.0012387132,0.00071664766,0.008493962],"genre_scores_gemma":[0.98026836,0.0012778653,0.013015004,0.000062299005,0.000040126055,0.00003797465,0.002049589,0.0000651596,0.0031836578],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998049,0.000027699836,0.0000137213865,0.000044237793,0.00005361888,0.000055772685],"domain_scores_gemma":[0.99950945,0.00018176145,0.00003364632,0.00003324906,0.00021571721,0.000026292268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062439026,0.00084044796,0.0003346507,0.0008520339,0.00019879361,0.00048069662,0.00050764746,0.000489845,0.002136023],"category_scores_gemma":[0.001470242,0.00018732887,0.00043096195,0.0004802393,0.00012802564,0.00064718665,0.00022429702,0.0002989421,0.00045747182],"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":[0.002168272,0.0004356731,0.037038233,0.00065771374,0.0007311775,0.00038371532,0.000119671124,0.43697655,0.02700063,0.0021177249,0.009232442,0.48313814],"study_design_scores_gemma":[0.00002993827,0.0005426301,0.025662735,0.00005531043,0.0002479986,0.000108057655,0.0001314695,0.9604509,0.010386826,0.0006025194,0.0017585172,0.000022978911],"about_ca_topic_score_codex":0.018674416,"about_ca_topic_score_gemma":0.025110312,"teacher_disagreement_score":0.018674416,"about_ca_system_score_codex":0.0006440464,"about_ca_system_score_gemma":0.0005249798,"threshold_uncertainty_score":0.03713143},"labels":[],"label_agreement":null},{"id":"W4415183442","doi":"10.1007/978-3-031-96767-2_22","title":"An Autonomous Structural Health Monitoring Strategy Utilizing Building Information Modeling Integrated with Internet-of-Things Data","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Structural health monitoring; Workflow; Cloud computing; Scripting language; Big data; Automation; Interface (matter); Leverage (statistics); Scalability; Python (programming language)","score_opus":0.018551476665322874,"score_gpt":0.24235772953980164,"score_spread":0.22380625287447878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415183442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052219093,0.00015056663,0.9397892,0.0001496226,0.00006536578,0.00005125987,0.00013732369,0.0017821863,0.005655329],"genre_scores_gemma":[0.81837374,0.00014531572,0.1779654,0.00007972537,0.000028948041,0.00005475636,0.00036672835,0.00006613526,0.0029192646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989176,0.000013374331,0.000004993887,0.00003334703,0.000042068612,0.000014424709],"domain_scores_gemma":[0.999908,0.00002427881,0.000011100099,0.000020130528,0.000026696558,0.000009861641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017895049,0.0005279198,0.00048730196,0.00044966527,0.0002892116,0.0006316223,0.0007061003,0.00043002918,0.0007517196],"category_scores_gemma":[0.0002694229,0.00024523103,0.00045422732,0.0004247121,0.00023071143,0.0009118506,0.0006453493,0.00029867288,0.00030130442],"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":[0.00021573072,0.00037423993,0.005300744,0.00013193619,0.00016265987,0.00039884273,0.00018931564,0.43946058,0.11986287,0.01086982,0.005723447,0.41730985],"study_design_scores_gemma":[0.0000039532506,0.000032311324,0.00096539315,0.0000036881463,0.000022670507,0.000043781933,0.000030245554,0.990026,0.005374014,0.0023803688,0.0011088932,0.000008646493],"about_ca_topic_score_codex":0.0027565814,"about_ca_topic_score_gemma":0.005840033,"teacher_disagreement_score":0.0027565814,"about_ca_system_score_codex":0.00020645378,"about_ca_system_score_gemma":0.0004032439,"threshold_uncertainty_score":0.005481124},"labels":[],"label_agreement":null},{"id":"W4415571745","doi":"10.5539/mas.v19n2p97","title":"An IoT-Based Framework for Wildfire Detection Using Multi-Sensors Integration and CNN Image Classification","year":2025,"lang":"","type":"article","venue":"Modern Applied Science","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Teknikal Malaysia Melaka","keywords":"Convolutional neural network; Scalability; Software deployment; Deep learning; Fire detection; Raspberry pi; Object detection; Contextual image classification; Big data","score_opus":0.031993476098896745,"score_gpt":0.3018683547011761,"score_spread":0.2698748786022794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415571745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028140044,0.00035402118,0.9501828,0.00015197086,0.00012020533,0.000139355,0.000669859,0.016015964,0.00422582],"genre_scores_gemma":[0.55366087,0.00051715743,0.43334907,0.00039602874,0.00008234477,0.00034528112,0.0030653714,0.0005970863,0.007986706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998661,0.0000072612147,0.000007078507,0.00004883527,0.00004691075,0.000023763623],"domain_scores_gemma":[0.99992454,0.000011000925,0.000011873483,0.000014219347,0.00002730056,0.000011073533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021713224,0.00084836205,0.00036462815,0.00062859827,0.00026432856,0.0005315694,0.0011092118,0.0005056445,0.0026228703],"category_scores_gemma":[0.00038351072,0.00038068992,0.000611287,0.00034978573,0.00022615238,0.0008475229,0.0008435393,0.00056774763,0.000853588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005829211,0.0006155109,0.00931552,0.00036485438,0.0003313309,0.00092706125,0.00020799544,0.23856436,0.14025734,0.0118681975,0.020768158,0.57619673],"study_design_scores_gemma":[0.000011509679,0.00004062027,0.0015850378,0.000014306921,0.000026257125,0.00009430302,0.000015063553,0.9756656,0.015001665,0.003529524,0.0039969673,0.000019144869],"about_ca_topic_score_codex":0.007792148,"about_ca_topic_score_gemma":0.014229555,"teacher_disagreement_score":0.007792148,"about_ca_system_score_codex":0.0005091784,"about_ca_system_score_gemma":0.000570945,"threshold_uncertainty_score":0.015493572},"labels":[],"label_agreement":null},{"id":"W4415968609","doi":"10.1109/iecon58223.2025.11221682","title":"Autonomous Leader-Follower UAV System for Real-Time Wildfire Detection and Suppression","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Payload (computing); Fire detection; Scalability; Object detection; Firefighting; Ground station","score_opus":0.006409095468392704,"score_gpt":0.21213425825443552,"score_spread":0.2057251627860428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415968609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41506043,0.0006807442,0.5600927,0.00022827521,0.00027371873,0.00027743218,0.0003419416,0.010992856,0.012051846],"genre_scores_gemma":[0.9353596,0.00008450839,0.05936841,0.00010374944,0.00002204405,0.00009873411,0.00023970446,0.000044303415,0.004678945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999001,0.0000086977225,0.0000038987937,0.00003225247,0.00003305367,0.00002196231],"domain_scores_gemma":[0.9999114,0.000012078584,0.000012483052,0.000014681751,0.00002872903,0.00002056373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014714878,0.00041691875,0.00036241984,0.00019491737,0.00034060935,0.00024366738,0.00054045673,0.00030906443,0.0016682779],"category_scores_gemma":[0.0002130492,0.00013477758,0.00013297674,0.00006726842,0.00012595045,0.00034025463,0.00046196155,0.0003304336,0.0005641359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010026854,0.00060578296,0.012381788,0.0002658224,0.00011892815,0.0011965203,0.00059229595,0.06057352,0.399968,0.003146575,0.018248305,0.50189984],"study_design_scores_gemma":[0.00019475448,0.001052386,0.0062496625,0.00001998623,0.00005984087,0.00040899313,0.0001269618,0.9063917,0.07018847,0.0014830326,0.013776258,0.000048025562],"about_ca_topic_score_codex":0.0015206598,"about_ca_topic_score_gemma":0.0030811704,"teacher_disagreement_score":0.0016682779,"about_ca_system_score_codex":0.00015520434,"about_ca_system_score_gemma":0.00042206526,"threshold_uncertainty_score":0.0055809617},"labels":[],"label_agreement":null},{"id":"W4415969525","doi":"10.1109/iecon58223.2025.11221667","title":"Dynamic Programming-Based Multi-Spot Path Planning and LQR Control for Autonomous UAV Firefighting","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"","keywords":"Trajectory; Firefighting; Controller (irrigation); Linear-quadratic regulator; Motion planning; Control theory (sociology); MATLAB; Dynamic programming; Quadratic programming","score_opus":0.01004790361130747,"score_gpt":0.2493258807010678,"score_spread":0.2392779770897603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415969525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00927849,0.00014320493,0.9883748,0.00005364534,0.00001976078,0.000028678485,0.000010447129,0.0002533931,0.0018374608],"genre_scores_gemma":[0.90504485,0.00022471276,0.09092143,0.000082944236,0.000033637025,0.00017450018,0.00005910788,0.000050090155,0.0034088215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996705,0.000082080755,0.00001306601,0.00008741592,0.000110008215,0.000036856043],"domain_scores_gemma":[0.99975175,0.0000984881,0.000058569418,0.000013470015,0.000064389635,0.0000134724305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005697987,0.00054563355,0.0005315963,0.00019154596,0.00023066832,0.0005284892,0.0005151272,0.00039085714,0.0010417184],"category_scores_gemma":[0.0006583281,0.00028482734,0.00037812893,0.00021165115,0.00041477848,0.00032486452,0.00051368296,0.0007328588,0.00022399695],"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":[0.00007999567,0.00006111521,0.00030080843,0.00011587414,0.000036226975,0.00007493893,0.00010733953,0.9275159,0.012016856,0.0045584664,0.00058604026,0.054546475],"study_design_scores_gemma":[0.000008074555,0.00006513472,0.00008251171,0.0000033022409,0.000004077817,0.00000855692,0.0000058577093,0.99827397,0.0007586894,0.00044758772,0.00033856297,0.0000037451139],"about_ca_topic_score_codex":0.004797404,"about_ca_topic_score_gemma":0.0029462664,"teacher_disagreement_score":0.004797404,"about_ca_system_score_codex":0.00038255952,"about_ca_system_score_gemma":0.0007964631,"threshold_uncertainty_score":0.009538949},"labels":[],"label_agreement":null},{"id":"W4415969745","doi":"10.1109/iecon58223.2025.11221647","title":"Depth-Homography Registration Framework and YOLOv8n-Coordinate Attention Forest Fire Detection for Visible-Infrared UAV Imagery","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fire detection; Multispectral image; RGB color model; Object detection; Calibration; Homography; Bounding overwatch; Image registration; Sensor fusion","score_opus":0.007545626718371791,"score_gpt":0.2357896961755845,"score_spread":0.22824406945721273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415969745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104908245,0.00024400062,0.9863051,0.00006390573,0.00003548602,0.00005143699,0.00014133366,0.0016005768,0.0010673681],"genre_scores_gemma":[0.40722585,0.0006696724,0.58224034,0.00023795465,0.00013303407,0.00024086854,0.0017871679,0.00047376443,0.006991309],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995685,0.00005511188,0.000016996899,0.00016640585,0.00012701888,0.00006594116],"domain_scores_gemma":[0.9997919,0.00003456756,0.000035303106,0.000051464034,0.00006533717,0.00002144491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046757693,0.0010509223,0.0010495851,0.0008717327,0.0003008331,0.000862651,0.0019301744,0.0007960363,0.0025346146],"category_scores_gemma":[0.0010386949,0.00057803857,0.001371317,0.00073308265,0.0004503386,0.0007554952,0.0014738734,0.0012305066,0.0011548146],"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":[0.0002807608,0.00012870666,0.0025355255,0.00016332071,0.0001326162,0.000146931,0.00017297483,0.34187073,0.024449686,0.008188956,0.0041880542,0.61774164],"study_design_scores_gemma":[0.000006386374,0.00004197145,0.0006980498,0.000009049951,0.000011378332,0.000059936243,0.000015339792,0.9928502,0.0035812377,0.0010289844,0.0016874804,0.000010005782],"about_ca_topic_score_codex":0.01628956,"about_ca_topic_score_gemma":0.017471552,"teacher_disagreement_score":0.01628956,"about_ca_system_score_codex":0.0007115768,"about_ca_system_score_gemma":0.001164545,"threshold_uncertainty_score":0.03238952},"labels":[],"label_agreement":null},{"id":"W4416074288","doi":"10.32628/ijsrset2513860","title":"Fire Detection Using Deep Learning","year":2025,"lang":"","type":"article","venue":"International Journal of Scientific Research in Science Engineering and Technology","topic":"Fire Detection and Safety Systems","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Fire detection; Convolutional neural network; Fire alarm system; Object detection; Artificial neural network; ALARM","score_opus":0.026664208028516625,"score_gpt":0.3369565944370934,"score_spread":0.3102923864085768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416074288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077468425,0.0013799318,0.9067348,0.00077279593,0.00027381247,0.00008478684,0.00073502056,0.0046708095,0.007879602],"genre_scores_gemma":[0.7971627,0.001008413,0.19000843,0.0006153099,0.00011161897,0.00007944099,0.0017359602,0.00016317047,0.009114833],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997254,0.000037763413,0.000013034243,0.0000708372,0.000094903175,0.000058141253],"domain_scores_gemma":[0.99968946,0.00007841945,0.000044926765,0.000041242452,0.00012138652,0.00002449059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000481078,0.0007107462,0.0005702483,0.0010163941,0.00031265366,0.00086730305,0.00088128494,0.00079184875,0.0019433515],"category_scores_gemma":[0.0010882335,0.00038874758,0.0007438953,0.00057705154,0.00029907437,0.0010553463,0.00087851693,0.0010973628,0.0007197476],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004007219,0.00026068807,0.008317702,0.00017478903,0.00022206624,0.0001667917,0.000054378896,0.30292183,0.028322292,0.006860976,0.012975549,0.6393222],"study_design_scores_gemma":[0.000005782218,0.00002525162,0.0006201649,0.000013456772,0.000011608929,0.000034766505,0.0000067007195,0.9883206,0.0066807577,0.0028734005,0.0013983317,0.000009323793],"about_ca_topic_score_codex":0.0066614333,"about_ca_topic_score_gemma":0.009089108,"teacher_disagreement_score":0.0066614333,"about_ca_system_score_codex":0.0011182149,"about_ca_system_score_gemma":0.00073375675,"threshold_uncertainty_score":0.013245344},"labels":[],"label_agreement":null},{"id":"W4416371779","doi":"10.2316/j.2026.206-1294","title":"BASKETBALL ROBOT TARGET DETECTION COMBINING ROS AND IMPROVED YOLOv5 ALGORITHM. 265-279","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Basketball; Noise (video); Feature (linguistics); Mobile robot","score_opus":0.004164945910901284,"score_gpt":0.21435564920605843,"score_spread":0.21019070329515716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08348639,0.0013629342,0.888842,0.00026305893,0.0003642785,0.00020404474,0.000830206,0.010002342,0.014644779],"genre_scores_gemma":[0.48883677,0.00076791097,0.47911167,0.00021891348,0.0001361274,0.00017011756,0.0047040856,0.0004441896,0.02561019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969494,0.000023798333,0.000011114023,0.000101121645,0.00011775093,0.000051291918],"domain_scores_gemma":[0.9998914,0.000011096548,0.000011062483,0.000013824637,0.000059714428,0.000012914964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003016939,0.00076988555,0.0006735528,0.0009913018,0.0004185989,0.0007220023,0.0009103935,0.00052706304,0.007100105],"category_scores_gemma":[0.00041211752,0.0003417157,0.0006268603,0.00045352688,0.00016202078,0.00052734016,0.0006701468,0.0005059735,0.0031442672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007050454,0.00023402544,0.006159288,0.00025024652,0.00019807724,0.00015927477,0.000048254104,0.04309437,0.100229524,0.0020290806,0.014263188,0.8326296],"study_design_scores_gemma":[0.00008843153,0.0003742133,0.0100714555,0.000052423286,0.000104669496,0.00021771787,0.0000782746,0.9141216,0.055494856,0.0011588449,0.018183177,0.000054329015],"about_ca_topic_score_codex":0.0076768957,"about_ca_topic_score_gemma":0.012024945,"teacher_disagreement_score":0.0076768957,"about_ca_system_score_codex":0.00038692853,"about_ca_system_score_gemma":0.00094622053,"threshold_uncertainty_score":0.023752213},"labels":[],"label_agreement":null},{"id":"W4416371835","doi":"10.2316/j.2026.206-1283","title":"A NEW MULTIMODAL PERCEPTION-BASED SENSOR FUSION COST MAP. 321-334","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensor fusion; Fusion; Field (mathematics); Key (lock); Image fusion; Feature (linguistics)","score_opus":0.006205274678054952,"score_gpt":0.23862828359326077,"score_spread":0.23242300891520581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009848665,0.0006530101,0.98254895,0.00018424213,0.00016380224,0.00007614055,0.00046601903,0.00087960233,0.0051795277],"genre_scores_gemma":[0.3749579,0.0012281357,0.602816,0.0002887025,0.00018793056,0.00037960534,0.0021444596,0.00038499467,0.01761226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956566,0.000054415028,0.000017794513,0.00008943741,0.0002263702,0.000046366516],"domain_scores_gemma":[0.9997805,0.000031137657,0.000013019133,0.000028868786,0.00013259635,0.000013882976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034246544,0.0009956212,0.0006525627,0.0009499418,0.000396014,0.0011247837,0.0011515756,0.00075169554,0.0071758935],"category_scores_gemma":[0.0012930336,0.00036393185,0.00069470424,0.0010599913,0.0002698769,0.0017700123,0.0014198357,0.0006700567,0.0025700624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040979765,0.0001845204,0.00066185783,0.00023199175,0.00012765675,0.00014657821,0.00007225998,0.084889665,0.052585352,0.014520714,0.010226874,0.8359427],"study_design_scores_gemma":[0.000023544748,0.00017911156,0.0023969184,0.000045851924,0.00008749687,0.00030559133,0.000060981965,0.93304425,0.0356429,0.011922315,0.01622936,0.00006166256],"about_ca_topic_score_codex":0.0037577336,"about_ca_topic_score_gemma":0.0043001515,"teacher_disagreement_score":0.0071758935,"about_ca_system_score_codex":0.00049946073,"about_ca_system_score_gemma":0.0008461483,"threshold_uncertainty_score":0.02400577},"labels":[],"label_agreement":null},{"id":"W4416371865","doi":"10.2316/j.2026.206-1237","title":"HUMAN FOLLOWING TASK BASED ON MULTI-SENSOR FUSION PLANNING ALGORITHM. 203-217","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Task (project management); Sensor fusion; Fusion; Key (lock); Automation","score_opus":0.011821549426975818,"score_gpt":0.27384215266997614,"score_spread":0.2620206032430003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06402681,0.00041326377,0.9274255,0.00017970364,0.00012053989,0.0001231803,0.00021145648,0.0016940194,0.005805545],"genre_scores_gemma":[0.77038115,0.00027968473,0.2212643,0.000072532894,0.000026140722,0.00011780295,0.00038148864,0.00008447427,0.007392417],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998783,0.000016439633,0.000005819222,0.00004446166,0.000031433286,0.000023478498],"domain_scores_gemma":[0.99986565,0.00003873161,0.000011856174,0.000017588622,0.000049167462,0.000016978034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002638391,0.00063241227,0.00059743586,0.00036106165,0.00049673574,0.00041341622,0.0006208047,0.0006095922,0.0035461346],"category_scores_gemma":[0.0004575708,0.00028644223,0.00043427333,0.00030137968,0.00017629137,0.00047420498,0.00044696423,0.0004253444,0.0007591039],"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":[0.0008531744,0.00025563335,0.0031444805,0.00018783312,0.00013011441,0.00032564497,0.00015845035,0.43551162,0.038083386,0.0026144683,0.0058171563,0.512918],"study_design_scores_gemma":[0.000016667997,0.00009557081,0.001138763,0.000007100083,0.00001839101,0.00006895666,0.000021764163,0.99021286,0.006717616,0.0009781752,0.00071515783,0.000009065422],"about_ca_topic_score_codex":0.009158936,"about_ca_topic_score_gemma":0.0076641263,"teacher_disagreement_score":0.009158936,"about_ca_system_score_codex":0.0002487321,"about_ca_system_score_gemma":0.0011116391,"threshold_uncertainty_score":0.018211246},"labels":[],"label_agreement":null},{"id":"W4416790707","doi":"10.1016/j.ifacol.2025.11.159","title":"A forest fire detection method by a modified YOLOv8 algorithm with feature alignment and fusion strategy","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"Aeronautical Science Foundation of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Feature (linguistics); Fire detection; Convolution (computer science); Feature vector; RGB color model; Feature extraction; Pattern recognition (psychology)","score_opus":0.004697220479562985,"score_gpt":0.2179939381937989,"score_spread":0.21329671771423592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416790707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04164226,0.00034621058,0.95475984,0.00006949072,0.00007028994,0.00007845247,0.00010167144,0.0014099497,0.0015217789],"genre_scores_gemma":[0.27864805,0.00030734992,0.7153325,0.00009551396,0.00005589544,0.00015005059,0.0007754787,0.00013203533,0.0045031304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951947,0.00003736534,0.00003142111,0.00014584031,0.00019571616,0.000070094226],"domain_scores_gemma":[0.9998254,0.000023477794,0.000020506934,0.000027806116,0.00008953439,0.000013299041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041845173,0.0007154386,0.0008693645,0.001250715,0.00039203765,0.0005623089,0.0010393764,0.0006034373,0.0015689185],"category_scores_gemma":[0.0006570864,0.0003281144,0.00083961035,0.0008016339,0.00023639125,0.00084710395,0.0007196111,0.0006808599,0.0008239214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003044761,0.00012298944,0.0020447855,0.00007404198,0.000081638565,0.00007320149,0.00006577916,0.027613346,0.060659204,0.0020050919,0.0023701782,0.90458536],"study_design_scores_gemma":[0.000041697505,0.00015809565,0.0034522507,0.000012330391,0.00004629088,0.00034069514,0.00004220053,0.9524833,0.03743866,0.0011687705,0.0047835475,0.000032157604],"about_ca_topic_score_codex":0.004466179,"about_ca_topic_score_gemma":0.005013052,"teacher_disagreement_score":0.004466179,"about_ca_system_score_codex":0.0003796988,"about_ca_system_score_gemma":0.0008611267,"threshold_uncertainty_score":0.008880377},"labels":[],"label_agreement":null},{"id":"W4417004085","doi":"10.1109/uemcon67449.2025.11267734","title":"Integration of AI and Sustainable Computing in Agricultural Electronics for Early Wildfire Smoke Detection and Mitigation","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Royal Military College of Canada","funders":"","keywords":"Leverage (statistics); Agriculture; Big data; Sustainable development; Greenhouse gas; Smoke; Resilience (materials science)","score_opus":0.003688401649522007,"score_gpt":0.211532546352046,"score_spread":0.207844144702524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417004085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06974616,0.0011760185,0.90445876,0.0008394663,0.00027623007,0.00012248824,0.00030439225,0.004616096,0.018460428],"genre_scores_gemma":[0.697814,0.00088537467,0.29217398,0.00036297852,0.00009018832,0.000113142,0.0005863869,0.00025043628,0.0077235396],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968207,0.000049258815,0.000018386567,0.00007823257,0.00013819378,0.000033890876],"domain_scores_gemma":[0.9994968,0.00019337036,0.000032185242,0.00012191448,0.00013593126,0.000019770367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034089864,0.00042851182,0.00029452887,0.00035936746,0.00027927136,0.0010849155,0.0008139787,0.00048188417,0.0030582484],"category_scores_gemma":[0.0013707412,0.00016197583,0.00042564046,0.0005928069,0.00033006113,0.0013364549,0.0005885852,0.0006717994,0.0009795504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003226533,0.0003346847,0.0045030653,0.0003972376,0.000085096275,0.00029169652,0.00015704455,0.36019862,0.07594298,0.03705822,0.008858758,0.51184994],"study_design_scores_gemma":[0.000009126246,0.00009282929,0.0010367194,0.00002052724,0.000014959558,0.00008339698,0.00004646954,0.9544422,0.020299831,0.012303901,0.011633679,0.000016341382],"about_ca_topic_score_codex":0.0028062246,"about_ca_topic_score_gemma":0.004693884,"teacher_disagreement_score":0.0030582484,"about_ca_system_score_codex":0.0006357469,"about_ca_system_score_gemma":0.0006890784,"threshold_uncertainty_score":0.010230899},"labels":[],"label_agreement":null},{"id":"W4417052945","doi":"10.1109/safeprocess67117.2025.11268117","title":"GhostGD-YOLOv8: An efficient algorithm for forest fire detection by UAV images","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Concordia University","funders":"Aeronautical Science Foundation of China","keywords":"Fire detection; Feature (linguistics); Object detection; Feature extraction; Task (project management); Warning system; Artificial neural network","score_opus":0.004540253059907825,"score_gpt":0.2212864199530473,"score_spread":0.21674616689313947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417052945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026497405,0.0005674625,0.9674259,0.000090843445,0.00009915223,0.0000921894,0.00010541462,0.0035793714,0.00154225],"genre_scores_gemma":[0.18812566,0.0003828692,0.80473495,0.00015580044,0.00004685872,0.00014754153,0.00082760537,0.00043415185,0.005144488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998386,0.00002077206,0.000009506425,0.0000489434,0.00005163935,0.00003048445],"domain_scores_gemma":[0.9998834,0.000025093526,0.000012861149,0.000023092673,0.000043593107,0.000011893893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003362364,0.0007347532,0.0007021634,0.00067450636,0.00028367314,0.00052209315,0.0013712393,0.0006130888,0.0021790846],"category_scores_gemma":[0.0006372945,0.000386734,0.00052806473,0.00044751033,0.00028154778,0.00074915047,0.00065620145,0.00064082403,0.00086652004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045739565,0.000081509854,0.0020631554,0.00014347944,0.00009666269,0.000106068466,0.00006517983,0.18403594,0.031839103,0.0039170817,0.0083730975,0.76882124],"study_design_scores_gemma":[0.000033488424,0.00006210793,0.00043306506,0.0000091566235,0.000013256889,0.00006748597,0.00001441079,0.98822635,0.0071082055,0.00077753287,0.0032436235,0.00001131623],"about_ca_topic_score_codex":0.0083028395,"about_ca_topic_score_gemma":0.015158571,"teacher_disagreement_score":0.0083028395,"about_ca_system_score_codex":0.0006055967,"about_ca_system_score_gemma":0.0012789598,"threshold_uncertainty_score":0.016508996},"labels":[],"label_agreement":null},{"id":"W4417509482","doi":"10.1109/csitss67709.2025.11295807","title":"A Literature Survey on Smart Emergency Management Systems for Stray Animals Using Community Reporting and Rescue Prioritization","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Horizon College and Seminary","funders":"","keywords":"Prioritization; Emergency management; Data collection; Scalability; Focus (optics); Internet of Things; Decision support system; Crisis management","score_opus":0.05320668963444254,"score_gpt":0.3043349212095434,"score_spread":0.2511282315751009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417509482","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006381155,0.95411354,0.014917054,0.0020420125,0.0011021012,0.00013023634,0.00089006836,0.00037060812,0.02005326],"genre_scores_gemma":[0.029185232,0.95548654,0.0082938,0.0011512947,0.0006332172,0.00009135052,0.0014154726,0.00005033715,0.0036927345],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994404,0.00013102009,0.00010706753,0.00010616718,0.00017521661,0.000040061743],"domain_scores_gemma":[0.9962908,0.0024608613,0.00027178728,0.00011214765,0.00078796805,0.000076422795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088838505,0.0007683233,0.00073934323,0.0043831086,0.000442964,0.0013815751,0.0009231727,0.0008889075,0.006361903],"category_scores_gemma":[0.0036951238,0.00032916997,0.0007651876,0.006260844,0.00029772762,0.0018812517,0.0005974299,0.00046804492,0.0022766201],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091152644,0.00007121454,0.0019023897,0.039911773,0.00011725301,0.00024900708,0.0004457146,0.001689986,0.0015361443,0.0048744944,0.038297735,0.9108132],"study_design_scores_gemma":[0.000013252161,0.00020097404,0.007476234,0.030298892,0.00063898606,0.0012507184,0.0013916917,0.0030686602,0.0020919403,0.0040498357,0.949434,0.000084925894],"about_ca_topic_score_codex":0.002558677,"about_ca_topic_score_gemma":0.0030046015,"teacher_disagreement_score":0.006361903,"about_ca_system_score_codex":0.00047643957,"about_ca_system_score_gemma":0.0016505183,"threshold_uncertainty_score":0.021282673},"labels":[],"label_agreement":null},{"id":"W597942715","doi":"","title":"Security for the Vancouver Olympics 2010","year":2010,"lang":"en","type":"article","venue":"Public transport international","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Unit (ring theory); Work (physics); Business; Service (business); Security service; Crowds; Law enforcement; Computer security; Public transport; Security forces; National security; State police; Software deployment; Public administration; Transport engineering; Engineering; Political science; Marketing; Law; Computer science; Information security; Politics","score_opus":0.009160343623217366,"score_gpt":0.20084398737426837,"score_spread":0.191683643751051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W597942715","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034209184,0.0055713993,0.001980617,0.014139471,0.007831774,0.0006173049,0.005997838,0.0009608825,0.92869157],"genre_scores_gemma":[0.0627128,0.0037943246,0.0028544776,0.0016525714,0.00058080704,0.00023333734,0.008979996,0.00024175542,0.91894984],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991898,0.000036181766,0.000022773329,0.00006019219,0.00043605987,0.00025495206],"domain_scores_gemma":[0.9991394,0.000012940041,0.000023826518,0.00002611551,0.00039529614,0.0004023117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059198786,0.00085339847,0.000142064,0.0012236579,0.0057205125,0.0039651673,0.0008075782,0.0009070366,0.0528522],"category_scores_gemma":[0.0012314211,0.00034807294,0.00025914342,0.000658102,0.00046722582,0.00064572465,0.0020845286,0.0016369855,0.01340931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070365786,0.00007436631,0.006071411,0.00011778972,0.00000998816,0.0002456129,0.00059952744,0.00027581232,0.0008543538,0.006770843,0.83228374,0.15262617],"study_design_scores_gemma":[0.00000911082,0.000027014765,0.01448183,0.00008190899,0.000002401411,0.00005717221,0.00058742147,0.00008782744,0.00016165589,0.0002145231,0.9842758,0.000013329846],"about_ca_topic_score_codex":0.72456837,"about_ca_topic_score_gemma":0.9136502,"teacher_disagreement_score":0.27543163,"about_ca_system_score_codex":0.008310786,"about_ca_system_score_gemma":0.01857718,"threshold_uncertainty_score":0.5541074},"labels":[],"label_agreement":null},{"id":"W6890028226","doi":"10.3389/fcvm.2023.955060.s003","title":"Table2_Perceived self-efficacy and empowerment in patients at increased risk of sudden cardiac arrest.docx","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Empowerment; Psychosocial; Psychological intervention; Multidisciplinary approach; Health care; Mental health; Genetic testing; Specialty","score_opus":0.009364282545640486,"score_gpt":0.2107555162316078,"score_spread":0.2013912336859673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890028226","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01303251,0.00040268817,0.00017851788,0.0016163377,0.00021383548,0.0010266544,0.96832824,0.00023390303,0.0149673605],"genre_scores_gemma":[0.16756603,0.0027455965,0.005350262,0.005013862,0.0007111087,0.016718484,0.736683,0.0005441086,0.06466757],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9996958,0.00004295858,0.00005144951,0.000032712003,0.00008127986,0.00009579019],"domain_scores_gemma":[0.9954971,0.0022321646,0.00046526146,0.0000889972,0.0013811507,0.00033536175],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006652964,0.0005510944,0.0009838023,0.001440556,0.0010777998,0.0009960192,0.0011171098,0.0006116897,0.46649975],"category_scores_gemma":[0.0077760825,0.00035058195,0.0013227888,0.0019270583,0.00016650141,0.0014281552,0.0007389005,0.0011893173,0.028962063],"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":[0.00074778084,0.00026091878,0.043324314,0.003839362,0.00009628095,0.000121598685,0.00039158016,0.0002897891,0.00006026027,0.0005854392,0.9174359,0.032846823],"study_design_scores_gemma":[0.003485848,0.00046546772,0.79860306,0.0074187913,0.00021394457,0.0006781615,0.0043715574,0.0016715068,0.00034284583,0.0026921458,0.1798666,0.00019013116],"about_ca_topic_score_codex":0.07831108,"about_ca_topic_score_gemma":0.14010218,"teacher_disagreement_score":0.46649975,"about_ca_system_score_codex":0.0015563716,"about_ca_system_score_gemma":0.0024763567,"threshold_uncertainty_score":0.76097333},"labels":[],"label_agreement":null},{"id":"W6891853645","doi":"10.48550/arxiv.1809.00675","title":"The 1989 and 2015 outbursts of V404 Cygni: a global study of wind-related optical features","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Fire Detection and Safety Systems","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":"University of Alberta","funders":"","keywords":"Spectral line; Emission spectrum; Line (geometry); Outflow; Spectroscopy; Optical spectra","score_opus":0.024892123907353514,"score_gpt":0.18019584727805904,"score_spread":0.15530372337070553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891853645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992052,0.00007628581,0.00007165015,0.000009429562,0.0000019807478,0.0000056001345,0.0003476505,0.0000062525874,0.00027604276],"genre_scores_gemma":[0.9972875,0.00013968258,0.00022754406,0.000015771824,0.000016020913,0.000009658248,0.0021429777,0.000006397108,0.00015439572],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987197,0.000009807564,0.000008630583,0.000044499604,0.000025000392,0.000040084622],"domain_scores_gemma":[0.9995634,0.000035140074,0.00021070115,0.000025522415,0.000041384912,0.00012396018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025065162,0.00025062796,0.0002708074,0.001717073,0.00029105716,0.0004132757,0.00015036267,0.00024301033,0.00025737623],"category_scores_gemma":[0.00040877683,0.0001188895,0.0002158218,0.0008233226,0.0002848328,0.00032733454,0.0007117555,0.00017862779,0.00008381713],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015083954,0.000062783765,0.9759064,0.000040756884,0.00007858066,0.00068943657,0.0011037703,0.00023551872,0.013199013,0.00008878306,0.00038491623,0.008059162],"study_design_scores_gemma":[5.218465e-7,0.000014157022,0.9994324,0.000002449124,0.000005014734,0.00009978579,0.000113319,0.00006211915,0.0001192578,0.000006611342,0.00014324344,0.0000012519534],"about_ca_topic_score_codex":0.0055102045,"about_ca_topic_score_gemma":0.007916525,"teacher_disagreement_score":0.0055102045,"about_ca_system_score_codex":0.0002406181,"about_ca_system_score_gemma":0.000119126336,"threshold_uncertainty_score":0.010956287},"labels":[],"label_agreement":null},{"id":"W6907849668","doi":"10.25446/oxford.25006382.v1","title":"2786: Philip Fallon (St. John's Ambulance)","year":2024,"lang":"en","type":"other","venue":"Figshare","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Biography; George (robot); Period (music); Theme (computing)","score_opus":0.014602254729134796,"score_gpt":0.20972978678244658,"score_spread":0.1951275320533118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907849668","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078583753,0.0029227047,0.0010957331,0.015521015,0.00857732,0.00008661769,0.0009362525,0.001281719,0.9687928],"genre_scores_gemma":[0.0017909104,0.00050255546,0.00013610478,0.001340181,0.00031226882,0.000010040603,0.000118485106,0.00010914725,0.99568033],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970204,0.00002750562,0.000011355606,0.00006841081,0.000119915036,0.00007082528],"domain_scores_gemma":[0.9990553,0.00006440157,0.0000347605,0.000024613555,0.00037124852,0.00044960334],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036670212,0.0008235061,0.00032596372,0.0007997272,0.0013366676,0.0021640686,0.0004687785,0.0015630212,0.7153863],"category_scores_gemma":[0.0012138764,0.00020484217,0.00015906198,0.0004764334,0.00035362734,0.0012661936,0.0013224751,0.0014566906,0.51095647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015299112,0.000011078639,0.0001089517,0.000022656122,5.857756e-7,0.00007168591,0.00003148462,0.00003685797,0.00011402512,0.0007392748,0.9593423,0.039505746],"study_design_scores_gemma":[0.000003557847,0.000013858223,0.00026666367,0.000043852408,7.086311e-7,0.00014556079,0.00006233284,0.00006291154,0.00006853586,0.00017340326,0.9991559,0.000002830826],"about_ca_topic_score_codex":0.008639464,"about_ca_topic_score_gemma":0.021731481,"teacher_disagreement_score":0.7153863,"about_ca_system_score_codex":0.0006171483,"about_ca_system_score_gemma":0.00093872,"threshold_uncertainty_score":0.40596694},"labels":[],"label_agreement":null},{"id":"W6964254452","doi":"10.25549/webster-c100-14002","title":"The Widening Divide, 1989-06","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Commission; Newspaper; Poverty; Inequality; Economic Justice; Variety (cybernetics)","score_opus":0.004337573956232618,"score_gpt":0.13691755715314227,"score_spread":0.13257998319690967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964254452","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014175217,0.00012350443,0.000024445124,0.00025799178,0.00004926282,0.000010573212,0.9971059,0.000073105184,0.0009377251],"genre_scores_gemma":[0.0018521922,0.00013043257,0.00008129975,0.000052828545,0.000023914554,0.00006041673,0.99656737,0.000020033007,0.0012114581],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991598,0.000090632624,0.00010520711,0.00018242988,0.00030283973,0.00015908889],"domain_scores_gemma":[0.99750096,0.0002032136,0.00039140947,0.00032437657,0.0012735048,0.0003064176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010016151,0.00089179183,0.0007619852,0.0031069296,0.001096484,0.0031039696,0.0017787582,0.0009132696,0.012807756],"category_scores_gemma":[0.0040964745,0.0006236314,0.0005228357,0.008909244,0.00027217952,0.0012625903,0.0020168552,0.0015306125,0.019656345],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003155541,0.00002588681,0.008744035,0.00010684417,0.0000107215965,0.000019702851,0.000055486562,0.00010268423,0.00001699704,0.00023561995,0.9879786,0.0026719503],"study_design_scores_gemma":[0.0001941867,0.000028129018,0.19910026,0.00030175742,0.000032266384,0.00006543393,0.00085110666,0.0007280341,0.0003034752,0.0004626517,0.79790044,0.00003235445],"about_ca_topic_score_codex":0.20184132,"about_ca_topic_score_gemma":0.30032614,"teacher_disagreement_score":0.20184132,"about_ca_system_score_codex":0.002518732,"about_ca_system_score_gemma":0.0030670697,"threshold_uncertainty_score":0.40133297},"labels":[],"label_agreement":null},{"id":"W6977353119","doi":"10.6084/m9.figshare.24979582","title":"Additional file 1 of Episodic disability questionnaire (EDQ) measurement properties among adults living with HIV in Canada, Ireland, United Kingdom, and United States","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Fire Detection and Safety Systems","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":true,"ca_institutions":"University Health Network; Casey House; McMaster University; University of Toronto","funders":"","keywords":"Human immunodeficiency virus (HIV); Sample (material); MEDLINE; Population; Public health","score_opus":0.021698996631009732,"score_gpt":0.17224994611973543,"score_spread":0.1505509494887257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977353119","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010025359,0.000018743078,0.0001292488,0.000090734495,0.000011236911,0.0001761494,0.9969267,0.00003148814,0.0016131691],"genre_scores_gemma":[0.018823193,0.0001836904,0.0016079209,0.00048659678,0.000073551986,0.004213313,0.96340805,0.000121707206,0.011081916],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9995573,0.0000833321,0.00009795567,0.000095053685,0.000097130454,0.00006925843],"domain_scores_gemma":[0.99126035,0.004869952,0.0010750176,0.00039558718,0.002158658,0.00024045495],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009988577,0.0006646807,0.0007805865,0.0015275432,0.001145318,0.00076924014,0.0009942709,0.0006949509,0.70783484],"category_scores_gemma":[0.016174324,0.00042231663,0.00061855954,0.0034671482,0.00017543901,0.0013053643,0.00063792133,0.0008666975,0.06489462],"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":[0.0001882919,0.00016430728,0.008007415,0.0008229514,0.000029445831,0.000042481537,0.00010772687,0.00022510481,0.00003734377,0.0005164379,0.9837255,0.0061330544],"study_design_scores_gemma":[0.007315992,0.0006778115,0.2895607,0.006654645,0.00031066686,0.0010481923,0.0024684444,0.0024316923,0.0007601434,0.008135875,0.68038046,0.000255421],"about_ca_topic_score_codex":0.033059146,"about_ca_topic_score_gemma":0.050973278,"teacher_disagreement_score":0.70783484,"about_ca_system_score_codex":0.0012285829,"about_ca_system_score_gemma":0.0020340802,"threshold_uncertainty_score":0.4167381},"labels":[],"label_agreement":null},{"id":"W7019808493","doi":"","title":"IRC and University of Ottawa investigate new fibre-optic fire-detection system","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Data collection","score_opus":0.0066915181994988224,"score_gpt":0.15495790856048447,"score_spread":0.14826639036098566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019808493","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26330552,0.0059576384,0.04550828,0.03869039,0.0051176124,0.0026439459,0.009683307,0.010413385,0.61867994],"genre_scores_gemma":[0.26703298,0.0014928565,0.04861163,0.002545416,0.00036043674,0.00030277693,0.0044205477,0.00031443502,0.67491895],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99492586,0.00020896873,0.00009422291,0.00065418635,0.0032493793,0.0008674255],"domain_scores_gemma":[0.99555737,0.0002476901,0.00012745774,0.00026787943,0.0030778192,0.00072174345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017939542,0.0008285658,0.0005812381,0.0016937549,0.0046549486,0.0033804162,0.0015680732,0.0024725408,0.026121905],"category_scores_gemma":[0.0018572782,0.0007050709,0.0006510762,0.00095068564,0.0015657307,0.0017491174,0.0018570547,0.0020289673,0.0058434443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004584073,0.0011206956,0.033138774,0.00073335937,0.00022459368,0.0022826253,0.0033721281,0.007145654,0.22344325,0.049282234,0.40218735,0.27248523],"study_design_scores_gemma":[0.0004199683,0.00077919423,0.029719526,0.00010252747,0.00018461088,0.00065603905,0.0011995956,0.013294627,0.078236595,0.0013530607,0.87380344,0.00025083026],"about_ca_topic_score_codex":0.5607517,"about_ca_topic_score_gemma":0.77291465,"teacher_disagreement_score":0.5607517,"about_ca_system_score_codex":0.019721795,"about_ca_system_score_gemma":0.030464737,"threshold_uncertainty_score":0.8836703},"labels":[],"label_agreement":null},{"id":"W7033299891","doi":"","title":"Performing Postracialism: Reflections on Antiblackness, Nation, and Education through Contemporary Blackface in Canada","year":2023,"lang":"en","type":"article","venue":"TSpace","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto; Social Sciences and Humanities Research Council of Canada; Canada Council for the Arts; Government of Ontario; Ontario Arts Council; Government of Canada","keywords":"Blackface; State (computer science); Black female; Race (biology); Focus (optics)","score_opus":0.045042050090586996,"score_gpt":0.31344900049221436,"score_spread":0.2684069504016274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033299891","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7311753,0.005381439,0.0012665004,0.08123183,0.0010464371,0.00009209941,0.00013957551,0.00008124722,0.17958556],"genre_scores_gemma":[0.95538753,0.0023278256,0.00034879244,0.0050928122,0.000049437807,0.000023402907,0.000029156514,0.000058294594,0.036682762],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9940806,0.0012721117,0.000068687026,0.000274537,0.0013208803,0.0029831857],"domain_scores_gemma":[0.9947929,0.0013769127,0.00028591033,0.00009730262,0.0014233673,0.0020235088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003111714,0.00054947287,0.0006067265,0.0018102351,0.08884293,0.011030178,0.0027986998,0.0035097585,0.0051017688],"category_scores_gemma":[0.0053170854,0.00047077797,0.0004133581,0.003599014,0.032332238,0.0027929551,0.006719204,0.007576503,0.000279253],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019748764,0.00003058918,0.00270466,0.000049476108,0.0000043142404,0.0006655062,0.9433911,0.00010100395,0.0004189527,0.026624128,0.016476173,0.0095144585],"study_design_scores_gemma":[0.0000018853358,0.000011011323,0.0038105708,0.0000796386,0.0000044746607,0.00008910751,0.90321934,0.00007796572,0.0001569755,0.00084784755,0.09167526,0.000025946038],"about_ca_topic_score_codex":0.9905814,"about_ca_topic_score_gemma":0.996792,"teacher_disagreement_score":0.16885659,"about_ca_system_score_codex":0.16885659,"about_ca_system_score_gemma":0.17369406,"threshold_uncertainty_score":0.96400857},"labels":[],"label_agreement":null},{"id":"W7097334482","doi":"","title":"Reward for Information Leading to False Fire Alarm Conviction","year":2015,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Conviction; ALARM; Key (lock); Harm","score_opus":0.021083198082166476,"score_gpt":0.22743604771555548,"score_spread":0.206352849633389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097334482","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030004436,0.00060267985,0.008874663,0.1020706,0.0029910041,0.0004984218,0.0027930452,0.0017218583,0.85044324],"genre_scores_gemma":[0.49636945,0.0005018764,0.0032426454,0.041629028,0.0015249243,0.00037098906,0.0010889987,0.00022879419,0.45504338],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99260944,0.0011975649,0.00028600494,0.00043594307,0.0036159118,0.0018551095],"domain_scores_gemma":[0.977986,0.009042152,0.0020166063,0.0012302065,0.007992425,0.0017327081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004073086,0.00040979768,0.00030069432,0.0008416104,0.003515081,0.0023981142,0.0013721603,0.007862816,0.07607282],"category_scores_gemma":[0.048921514,0.00052091933,0.0003559459,0.00033420912,0.00081123144,0.00093660667,0.0021743008,0.0041682743,0.028376503],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018235094,0.00008023384,0.012218407,0.0000464894,0.000017484675,0.0012308366,0.0004702994,0.00052286015,0.00069746305,0.02975285,0.9190755,0.03570517],"study_design_scores_gemma":[0.0002815916,0.00062387134,0.08262746,0.0006972294,0.00018032658,0.0036401532,0.0019143681,0.016516902,0.0065722805,0.036516584,0.85022974,0.00019954736],"about_ca_topic_score_codex":0.03053445,"about_ca_topic_score_gemma":0.051984183,"teacher_disagreement_score":0.07607282,"about_ca_system_score_codex":0.0037571304,"about_ca_system_score_gemma":0.0062833927,"threshold_uncertainty_score":0.2544889},"labels":[],"label_agreement":null},{"id":"W7100527443","doi":"","title":"By","year":2007,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Safeguard; Fire detection; Fire safety; Table (database); Fire protection","score_opus":0.0021995368647232585,"score_gpt":0.16391898455118598,"score_spread":0.1617194476864627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100527443","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013009238,0.0006367695,0.0006335609,0.0025021986,0.0011221041,0.000103502636,0.002173744,0.0007791334,0.99074805],"genre_scores_gemma":[0.0027833483,0.00047472262,0.0005264158,0.00054376817,0.000058226637,0.000038214377,0.000972198,0.00017439287,0.99442875],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990753,0.000050292543,0.00003883461,0.00024210902,0.00044916914,0.00014438237],"domain_scores_gemma":[0.9985569,0.00011348057,0.00004942554,0.00020634572,0.00088832155,0.00018545709],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007102526,0.000680761,0.0003916669,0.0012432815,0.002216975,0.0052954084,0.00095919013,0.0018538242,0.84363407],"category_scores_gemma":[0.0020692921,0.00033216222,0.00045113685,0.0010503037,0.00079325127,0.001656518,0.00289665,0.0014973874,0.70218766],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009235043,0.000116205825,0.0014607188,0.00015703526,0.000006753908,0.00016082072,0.00022314933,0.00007903762,0.002642248,0.012498231,0.7978116,0.1847518],"study_design_scores_gemma":[0.0000042662655,0.00001292258,0.00086093124,0.000055688008,0.0000013225655,0.000058755075,0.00009139904,0.00004043044,0.00024147498,0.00024740444,0.99838126,0.0000040450436],"about_ca_topic_score_codex":0.028080832,"about_ca_topic_score_gemma":0.043418795,"teacher_disagreement_score":0.15636593,"about_ca_system_score_codex":0.0029173559,"about_ca_system_score_gemma":0.0041914107,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7103203872","doi":"","title":"Advancing Forest Fires Classification using Neurochaos Learning","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"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":"Amrita Vishwa Vidyapeetham University","keywords":"Randomness; Random forest; Training (meteorology); Artificial neural network; Chaotic; Deep learning; Training set","score_opus":0.02743085268969208,"score_gpt":0.2571548356942548,"score_spread":0.2297239830045627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7103203872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5915734,0.005249438,0.38321152,0.0017608536,0.0004440819,0.00023953167,0.0009257598,0.004577576,0.012017837],"genre_scores_gemma":[0.94342077,0.0005334046,0.05156131,0.00030927936,0.00012385298,0.0000713733,0.0011459112,0.000050202023,0.0027839777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995757,0.00008343438,0.000036520698,0.000114008435,0.00011766953,0.00007274168],"domain_scores_gemma":[0.9992593,0.00031171163,0.00008973117,0.00005769617,0.00022393797,0.00005758502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010591904,0.00081130565,0.0007038185,0.0022516053,0.00047615854,0.0010944663,0.0010064748,0.0010756499,0.0009765597],"category_scores_gemma":[0.0022757764,0.00022934505,0.00080669334,0.00083053514,0.0004981034,0.0011615455,0.0008563571,0.0010351232,0.0004964231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046373834,0.0004165551,0.03236781,0.00015014072,0.0002068057,0.00031636815,0.00013131148,0.40833935,0.005923893,0.004044497,0.0077814506,0.5398581],"study_design_scores_gemma":[0.000007788918,0.000028680282,0.00091235974,0.000011234166,0.000008384708,0.00002614776,0.000026294096,0.9956791,0.0013937973,0.0013980573,0.00050255965,0.0000056381728],"about_ca_topic_score_codex":0.0071440167,"about_ca_topic_score_gemma":0.0068856883,"teacher_disagreement_score":0.0071440167,"about_ca_system_score_codex":0.0009045623,"about_ca_system_score_gemma":0.0011731324,"threshold_uncertainty_score":0.01420486},"labels":[],"label_agreement":null},{"id":"W7115887896","doi":"10.3390/ecsa-12-26597","title":"Forest Fire Monitoring from Unmanned Aerial Vehicles Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"University of Ottawa","funders":"","keywords":"Deep learning; Convolutional neural network; Overhead (engineering); Drone; Bottleneck; Benchmark (surveying); Segmentation; Artificial neural network; Pooling","score_opus":0.010295588846806408,"score_gpt":0.21968090868643772,"score_spread":0.20938531983963132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115887896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60766405,0.0026975186,0.36029848,0.0005189854,0.00027582524,0.00020461,0.004315793,0.013980626,0.010044199],"genre_scores_gemma":[0.91211754,0.0005249436,0.07836412,0.00015804435,0.000040319548,0.00006623876,0.006015857,0.000098736426,0.0026141899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981827,0.000017805258,0.000008222283,0.00006110529,0.000057143556,0.000037589092],"domain_scores_gemma":[0.99987996,0.00002562326,0.00002397449,0.000020925703,0.000037263635,0.000012240383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022008113,0.0010647208,0.00044659196,0.0009614194,0.00026182138,0.00052054384,0.0007708468,0.00046362905,0.00086060254],"category_scores_gemma":[0.0005618653,0.00025945788,0.0005263549,0.0005064136,0.0001567656,0.0008091122,0.0005276811,0.0006810747,0.00039461773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000566,0.0005337563,0.0143198995,0.00021409083,0.00022037467,0.00031520647,0.00008302759,0.2991296,0.029530214,0.0009048977,0.011184139,0.6429989],"study_design_scores_gemma":[0.000010882937,0.00004854516,0.003949051,0.000020003217,0.000018794455,0.000045586392,0.00003802984,0.98364985,0.010061891,0.0010028869,0.0011451541,0.000009444752],"about_ca_topic_score_codex":0.015242659,"about_ca_topic_score_gemma":0.029279582,"teacher_disagreement_score":0.015242659,"about_ca_system_score_codex":0.00072437414,"about_ca_system_score_gemma":0.0005983012,"threshold_uncertainty_score":0.030307889},"labels":[],"label_agreement":null},{"id":"W7118659488","doi":"10.18280/ijsse.151011","title":"Design and Implementation of a Smart Dual-Stage Fire Crisis Management System Using Raspberry Pi for Safety and Security Applications","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Raspberry pi; Management system; Crisis management; Fire safety; Fire protection; Emergency management","score_opus":0.009019551225401918,"score_gpt":0.2586185096664176,"score_spread":0.24959895844101568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118659488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2131158,0.00024376047,0.76274973,0.00030497415,0.00026308448,0.0009356216,0.00019145956,0.007990346,0.014205276],"genre_scores_gemma":[0.86163974,0.00009564113,0.126264,0.00019910203,0.000030570754,0.00039083796,0.0001270924,0.000091758506,0.011161233],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996939,0.00004200785,0.00002022207,0.000073840696,0.00012039073,0.00004967449],"domain_scores_gemma":[0.9998029,0.000024732033,0.000023457884,0.000026216248,0.00008988134,0.000032830496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024624483,0.0004259664,0.0005305507,0.0002983331,0.0004212736,0.00046815554,0.0012790541,0.00062367984,0.0047914432],"category_scores_gemma":[0.00025356922,0.000289907,0.00026834285,0.00013922312,0.00020449833,0.00037891738,0.00035705182,0.00031927117,0.001298499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013064023,0.0008414252,0.009032526,0.00079490925,0.00015348684,0.0013434618,0.0008326186,0.036047667,0.64176935,0.004708777,0.0075384304,0.29563096],"study_design_scores_gemma":[0.0006108395,0.004686825,0.019694965,0.000091161186,0.00038102543,0.0018775469,0.00036038793,0.5606272,0.34656075,0.0015128439,0.06341903,0.00017729824],"about_ca_topic_score_codex":0.0010541892,"about_ca_topic_score_gemma":0.0010066968,"teacher_disagreement_score":0.0047914432,"about_ca_system_score_codex":0.00020262496,"about_ca_system_score_gemma":0.0006553924,"threshold_uncertainty_score":0.01602894},"labels":[],"label_agreement":null},{"id":"W7123881907","doi":"10.1109/bdai66031.2025.11325611","title":"Long-Term Feature Point Tracking for Camera Pose Estimation in Forest Fire Scenes","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Ministry of Education and Child Care","funders":"Southeast University; National Natural Science Foundation of China","keywords":"Trajectory; Feature (linguistics); Pose; Benchmark (surveying); Focus (optics); Point cloud; Point (geometry); Visual odometry","score_opus":0.009996637704841927,"score_gpt":0.25284764202155724,"score_spread":0.2428510043167153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123881907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17867234,0.0021714584,0.8069687,0.00018569703,0.00019858438,0.00015203415,0.0020205039,0.007281911,0.002348737],"genre_scores_gemma":[0.74515414,0.0010276925,0.24398553,0.00010251214,0.00011300709,0.00010198616,0.006409799,0.0002453198,0.0028600614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950147,0.000048139977,0.000019281651,0.00023213893,0.00012602255,0.00007293591],"domain_scores_gemma":[0.9995431,0.00008374734,0.0000696895,0.00013748487,0.00012633693,0.000039724957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048297376,0.0010604816,0.000762405,0.0011632573,0.00045200228,0.0005611243,0.0011203821,0.00058037846,0.0013651795],"category_scores_gemma":[0.0016127517,0.00038285137,0.00054995366,0.0015264715,0.0002960782,0.0008024987,0.00062558515,0.0011252423,0.0014722112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004421733,0.0003522774,0.011046019,0.00017410489,0.00014946674,0.00024371204,0.00014426757,0.14002042,0.035155896,0.0011346928,0.0090561975,0.80208075],"study_design_scores_gemma":[0.000018888883,0.000107245745,0.008324768,0.00003365057,0.000025831463,0.0002680782,0.000094003204,0.97243667,0.013151288,0.0020480375,0.0034718043,0.000019837085],"about_ca_topic_score_codex":0.011629239,"about_ca_topic_score_gemma":0.02559595,"teacher_disagreement_score":0.011629239,"about_ca_system_score_codex":0.00043829452,"about_ca_system_score_gemma":0.00075948815,"threshold_uncertainty_score":0.023123085},"labels":[],"label_agreement":null},{"id":"W7125496013","doi":"10.1109/icrcv67407.2025.11349293","title":"A Label-Free Lightweight Prompt-Driven Cross-Modal Fire Detection on Robotic Edge Platforms","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Fire detection; Edge detection; Robot; Noise (video); Object detection","score_opus":0.011702540893216073,"score_gpt":0.24225274881142164,"score_spread":0.23055020791820557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125496013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05025807,0.0001663054,0.93832284,0.00014944245,0.00023207326,0.00012360913,0.00012290105,0.0046212804,0.006003438],"genre_scores_gemma":[0.6722698,0.00012000813,0.3143887,0.00035990163,0.00006867972,0.00016455622,0.00031721432,0.0003064845,0.012004631],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999432,0.000056454435,0.000012328883,0.00014291386,0.00025465927,0.00010165307],"domain_scores_gemma":[0.99962234,0.000090345035,0.00004218602,0.000087602406,0.00011501302,0.000042486085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039870478,0.00073813734,0.0008131066,0.00036789436,0.0004650483,0.00060367346,0.0017039882,0.0010196359,0.004708616],"category_scores_gemma":[0.00085985626,0.00039875723,0.00048569674,0.00016651554,0.000378339,0.0010814392,0.0023577553,0.0011078807,0.0022156981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014799834,0.0003266923,0.0018731148,0.00035452662,0.00006365812,0.0006963465,0.00017657499,0.039952103,0.52993023,0.0061686854,0.008777556,0.4102005],"study_design_scores_gemma":[0.00005840136,0.0006692222,0.0017843295,0.000038645176,0.000042892163,0.00063110807,0.0000773471,0.83796954,0.1457927,0.0044917013,0.008375875,0.00006822812],"about_ca_topic_score_codex":0.0006367262,"about_ca_topic_score_gemma":0.0012556136,"teacher_disagreement_score":0.004708616,"about_ca_system_score_codex":0.00028979813,"about_ca_system_score_gemma":0.00060200575,"threshold_uncertainty_score":0.015751898},"labels":[],"label_agreement":null},{"id":"W7125599476","doi":"10.1109/iciteics64870.2025.11341439","title":"Intelligent Surveillance Across Multi-Domain Environments Using Deep Learning Architectures","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Impact","funders":"","keywords":"Flagging; Deep learning; Spotting; Drone; Scalability; Object detection; Key (lock); Camouflage","score_opus":0.013597254296810416,"score_gpt":0.26078686658992506,"score_spread":0.24718961229311465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125599476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10204673,0.0009234306,0.8886125,0.0006212016,0.00009491181,0.000041793406,0.00018538225,0.00392909,0.0035449925],"genre_scores_gemma":[0.843711,0.00035375342,0.15144889,0.00027537157,0.00005539965,0.000036954414,0.0004760747,0.00008853709,0.0035539598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997522,0.000039212096,0.00001034916,0.00009644606,0.000042692223,0.00005902639],"domain_scores_gemma":[0.9997353,0.0000697664,0.000039055318,0.000058777783,0.00006493338,0.000032077594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052409683,0.00093581947,0.00053333666,0.0005590389,0.0004270442,0.0010222198,0.001047191,0.0008888948,0.0010130362],"category_scores_gemma":[0.0009126502,0.0005000586,0.0005596835,0.00059212954,0.00044288207,0.0019961474,0.0014619317,0.0015815,0.00032379283],"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":[0.0001785756,0.00024850454,0.003237911,0.00006452725,0.0001962446,0.00017177576,0.0001268955,0.66218644,0.018389817,0.0052722497,0.0041932184,0.30573383],"study_design_scores_gemma":[0.0000023420562,0.000014754135,0.00019521026,0.0000032469995,0.0000056835624,0.000008870892,0.000011413916,0.996111,0.0012427648,0.0021305352,0.00027105698,0.0000031208347],"about_ca_topic_score_codex":0.012201078,"about_ca_topic_score_gemma":0.017032053,"teacher_disagreement_score":0.012201078,"about_ca_system_score_codex":0.0011237218,"about_ca_system_score_gemma":0.00066439155,"threshold_uncertainty_score":0.024260104},"labels":[],"label_agreement":null},{"id":"W7127363025","doi":"10.1109/ccece64018.2025.11364433","title":"A Robust Two-Tier Computer Vision System for Energy-Efficient Fire Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"British Columbia Institute of Technology","funders":"","keywords":"Fire detection; Object detection; Complement (music); Detector; Simple (philosophy); Robustness (evolution); Key (lock)","score_opus":0.008114907318456854,"score_gpt":0.20608109749187584,"score_spread":0.197966190173419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127363025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009593761,0.00016149848,0.979183,0.0001032924,0.00010479762,0.000094865296,0.00014755863,0.009289758,0.0013215017],"genre_scores_gemma":[0.40179425,0.00018650224,0.5913595,0.00045330485,0.000078899255,0.00023582004,0.00078425254,0.0004262903,0.004681242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950755,0.000039472496,0.000020759877,0.00016519734,0.0001860042,0.00008105118],"domain_scores_gemma":[0.9995844,0.000057387824,0.000037269037,0.000114804825,0.00015554142,0.000050612998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069587707,0.0010567376,0.001159661,0.0006537623,0.00045912797,0.0011528675,0.0031169385,0.0011575398,0.004692135],"category_scores_gemma":[0.0012313712,0.0005934516,0.0006482626,0.00054560363,0.00035030433,0.0016130013,0.001719969,0.0015311248,0.0023303428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010311917,0.00091936154,0.0031313624,0.00027884042,0.00031839154,0.00035397933,0.00008166564,0.19067664,0.179438,0.01319165,0.021581363,0.58899754],"study_design_scores_gemma":[0.000020334937,0.000066376044,0.00045189288,0.0000062542863,0.00001750542,0.00007510209,0.00000522626,0.9772554,0.01810169,0.0018139367,0.0021589373,0.00002728862],"about_ca_topic_score_codex":0.005181544,"about_ca_topic_score_gemma":0.009002408,"teacher_disagreement_score":0.005181544,"about_ca_system_score_codex":0.00096378056,"about_ca_system_score_gemma":0.0012898743,"threshold_uncertainty_score":0.015696764},"labels":[],"label_agreement":null},{"id":"W7127367876","doi":"10.1109/icarce67182.2025.11362232","title":"EdgeFlame: A Physics-Inspired Framework for Zero-Shot Fire and Smoke Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Drone; Scalability; Smoke; Key (lock); Deep learning; Fire detection; Latency (audio)","score_opus":0.03438619916795335,"score_gpt":0.268009501300649,"score_spread":0.23362330213269564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127367876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02447997,0.0005699621,0.9618451,0.00020664559,0.00012123902,0.00011292348,0.00087853783,0.008860225,0.0029253834],"genre_scores_gemma":[0.5300404,0.000543617,0.4532974,0.0008499453,0.000109499866,0.00029088007,0.0053940997,0.0007834567,0.008690662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985576,0.00001722452,0.000004279793,0.00006340117,0.000038143557,0.000021173677],"domain_scores_gemma":[0.9999068,0.00002935137,0.000009760318,0.00001941086,0.000022959652,0.00001172727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003654804,0.0010169956,0.0006404873,0.0007326517,0.00034268937,0.0006312967,0.0022540113,0.0009967521,0.0032768284],"category_scores_gemma":[0.00095433876,0.00038709323,0.0009881078,0.00033452886,0.0004005555,0.0012916519,0.0013016614,0.0013280445,0.00089570903],"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":[0.00031260913,0.00044728065,0.0041547515,0.0002847949,0.00028522025,0.00023785995,0.0001443675,0.35996908,0.033933293,0.010765384,0.023358699,0.5661067],"study_design_scores_gemma":[0.000008002262,0.000032364744,0.0004370016,0.000009525922,0.000007908248,0.000048494738,0.00001077128,0.9918888,0.0022748152,0.0034807362,0.0017918807,0.000009755757],"about_ca_topic_score_codex":0.009059578,"about_ca_topic_score_gemma":0.021374373,"teacher_disagreement_score":0.009059578,"about_ca_system_score_codex":0.00056829146,"about_ca_system_score_gemma":0.0006515594,"threshold_uncertainty_score":0.018013716},"labels":[],"label_agreement":null},{"id":"W7127416744","doi":"10.56220/uwjst.v5i0.81","title":"Steering Mechanism for an IC engine powered tracked firefighting UGV","year":2021,"lang":"","type":"article","venue":"University of Wah Journal of Science and Technology (UWJST)","topic":"Fire Detection and Safety Systems","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":"The Journal of Student Science and Technology","funders":"","keywords":"Firefighting; Teleoperation; Robot; Rescue robot; Mechanism (biology); Telerobotics; Search and rescue; Focus (optics); Transmission (telecommunications)","score_opus":0.0097018516350573,"score_gpt":0.19211426112097874,"score_spread":0.18241240948592144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127416744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48677912,0.00085929636,0.48515633,0.0004477833,0.000414184,0.00059094885,0.00021394738,0.003386328,0.022152048],"genre_scores_gemma":[0.9544635,0.00018583251,0.032589417,0.00004600616,0.0000144470205,0.000088678345,0.000062812775,0.000021494387,0.012527765],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999835,0.000017639752,0.000010097386,0.000036284415,0.00007390982,0.00002700611],"domain_scores_gemma":[0.9997956,0.00002423232,0.000043857726,0.00003494796,0.00006979637,0.00003147484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021218396,0.00038543917,0.00021061601,0.00041151882,0.0003042125,0.00032864252,0.00094506063,0.0006784297,0.003559883],"category_scores_gemma":[0.0003492729,0.0001802728,0.00034006784,0.000109827124,0.000313658,0.0003319055,0.0003312363,0.00021164327,0.0006200469],"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":[0.000469229,0.00015327554,0.002856312,0.0004854392,0.000055969624,0.0016540393,0.0005757306,0.020134766,0.83355474,0.009408089,0.0028077725,0.12784468],"study_design_scores_gemma":[0.00024448388,0.0067318077,0.015684286,0.00022451709,0.00024796568,0.0052235927,0.0006422344,0.35457605,0.5239684,0.0023104935,0.08991636,0.00022987643],"about_ca_topic_score_codex":0.0012636611,"about_ca_topic_score_gemma":0.00074782805,"teacher_disagreement_score":0.003559883,"about_ca_system_score_codex":0.00020915832,"about_ca_system_score_gemma":0.00038592468,"threshold_uncertainty_score":0.011909008},"labels":[],"label_agreement":null},{"id":"W7129677005","doi":"10.1109/iceconf65644.2025.11379688","title":"Development of Smart Fire Detection System with Security Information and Loss Analysis through Deep Learning Methods","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Horizon College and Seminary","funders":"","keywords":"Deep learning; Fire detection; Authentication (law); Data loss; Big data; Security analysis; Information security; Fuzzy logic","score_opus":0.006042468957605877,"score_gpt":0.2386310002048971,"score_spread":0.23258853124729123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129677005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034937304,0.00012490961,0.9568818,0.00018192916,0.00005936695,0.00013672309,0.00013670145,0.005031546,0.0025097907],"genre_scores_gemma":[0.57731545,0.00021508266,0.41231722,0.00029730672,0.000036604717,0.000326312,0.0007253929,0.00014355303,0.008622968],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996697,0.000028868728,0.000024156707,0.00008620065,0.00014533153,0.000045743724],"domain_scores_gemma":[0.9997385,0.000041966443,0.00002475517,0.00003545612,0.00013545822,0.000023726805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006212788,0.00048854516,0.00049953075,0.00047626704,0.0003207383,0.00056613033,0.0009753755,0.0005622684,0.0019662795],"category_scores_gemma":[0.0007483367,0.00030327684,0.00050559995,0.00028859434,0.00021011455,0.0012228644,0.0006584881,0.0007316376,0.00058587623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003614873,0.00039390512,0.008783777,0.00018268707,0.00016178373,0.00023595581,0.00018559245,0.22989003,0.06740629,0.0063336687,0.0077700154,0.67829484],"study_design_scores_gemma":[0.000015948815,0.00006779285,0.0007778217,0.0000065244785,0.000016943977,0.000044639986,0.0000076420865,0.9824233,0.014042452,0.0007589703,0.0018249644,0.000012921783],"about_ca_topic_score_codex":0.004469608,"about_ca_topic_score_gemma":0.0045223483,"teacher_disagreement_score":0.004469608,"about_ca_system_score_codex":0.00069406594,"about_ca_system_score_gemma":0.00096721906,"threshold_uncertainty_score":0.008887172},"labels":[],"label_agreement":null},{"id":"W7132712517","doi":"","title":"Detecting flashover in a room fire based on the sequence of thermal infrared images using convolutional neural networks","year":2022,"lang":"en","type":"article","venue":"NPARC","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arc flash; Convolutional neural network; Deep learning; RGB color model; Fire detection; Artificial neural network; Extinguishment","score_opus":0.018923865658866168,"score_gpt":0.21170045638438817,"score_spread":0.192776590725522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132712517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7771312,0.0014595296,0.2136624,0.00027418297,0.00023192883,0.00009649459,0.00089712755,0.0024394954,0.0038075575],"genre_scores_gemma":[0.970123,0.0005110299,0.026523568,0.000104214654,0.000044051914,0.000029013896,0.0009830478,0.000032639997,0.0016494415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998147,0.000009158618,0.00001180453,0.00005895239,0.000052788622,0.0000526239],"domain_scores_gemma":[0.9998324,0.00002805571,0.00004059735,0.000014916157,0.000056059835,0.000027902845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019800117,0.00090984453,0.0005653352,0.0013562797,0.00022232815,0.00043595518,0.0005862055,0.00055852305,0.0007097056],"category_scores_gemma":[0.00063948845,0.00028113794,0.0005567839,0.00053293264,0.00018678256,0.000497565,0.00038440272,0.0007309515,0.0002589246],"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":[0.001667488,0.0009407382,0.051442325,0.00035051405,0.0003416015,0.0022030047,0.00024642746,0.20051754,0.20673524,0.0010405264,0.005823847,0.5286907],"study_design_scores_gemma":[0.000009470324,0.00012836885,0.018322285,0.000027189464,0.00006939783,0.00024377138,0.000055328874,0.9506199,0.029429898,0.0004182828,0.00065485376,0.000021262107],"about_ca_topic_score_codex":0.008881104,"about_ca_topic_score_gemma":0.013337712,"teacher_disagreement_score":0.008881104,"about_ca_system_score_codex":0.0004885319,"about_ca_system_score_gemma":0.00045619934,"threshold_uncertainty_score":0.01765877},"labels":[],"label_agreement":null},{"id":"W7133526476","doi":"10.1109/wf-pst65083.2025.00035","title":"AI-Powered Early Fire Detection: Refining Datasets and Deploying YOLOv11 for Improved Accuracy","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Royal Military College of Canada","funders":"","keywords":"Refining (metallurgy); Matching (statistics); Process (computing); Key (lock); Feature (linguistics)","score_opus":0.011201760652993617,"score_gpt":0.2557875951261121,"score_spread":0.2445858344731185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133526476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4799775,0.0020184913,0.41073963,0.0017708302,0.0014509007,0.0011552672,0.016553935,0.06356938,0.022764038],"genre_scores_gemma":[0.5812798,0.00048942055,0.3596603,0.00087063457,0.00013349538,0.0006534161,0.04545879,0.0013050603,0.010149068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991855,0.0001167452,0.000053419324,0.00033618868,0.00016275444,0.00014551447],"domain_scores_gemma":[0.9989812,0.0002285005,0.00007046279,0.0003049384,0.00034660782,0.00006819115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016493173,0.0015637232,0.00078143383,0.0016815563,0.0009159593,0.001528558,0.0024924953,0.0010883792,0.0027519425],"category_scores_gemma":[0.004418699,0.00042568284,0.00087140716,0.00092652696,0.0005781435,0.0021498236,0.0017942803,0.0020887076,0.0024019068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014674276,0.0016723033,0.048315544,0.0006343716,0.00033246382,0.00032072506,0.0003873441,0.12337573,0.047749434,0.004879267,0.08799753,0.68286777],"study_design_scores_gemma":[0.00009119939,0.00017196176,0.0059914235,0.00008645923,0.000059887014,0.00008857448,0.00012525407,0.94788706,0.028970119,0.002212051,0.014262985,0.00005299354],"about_ca_topic_score_codex":0.029302185,"about_ca_topic_score_gemma":0.06056271,"teacher_disagreement_score":0.029302185,"about_ca_system_score_codex":0.0013818627,"about_ca_system_score_gemma":0.0015793232,"threshold_uncertainty_score":0.058263242},"labels":[],"label_agreement":null},{"id":"W7134944141","doi":"10.1109/icdmw69685.2025.00434","title":"Early Wildfire Detection with UAVs using a Frame Difference Method","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Quest University Canada","funders":"","keywords":"Frame (networking); Noise (video); Field (mathematics); Drone","score_opus":0.010794254907399988,"score_gpt":0.24452274849409752,"score_spread":0.23372849358669753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134944141","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099119104,0.00022322874,0.8988406,0.00009551277,0.00008370247,0.000033202654,0.000070354195,0.0005406246,0.0009936546],"genre_scores_gemma":[0.75336254,0.00013539293,0.24539392,0.000059124643,0.000040441813,0.000029824843,0.0001376056,0.00003068613,0.0008105258],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99985826,0.00001595452,0.000007145067,0.000040349554,0.000047276364,0.000030942832],"domain_scores_gemma":[0.999835,0.00005478047,0.000027616448,0.000022321334,0.00003828979,0.00002189822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026875842,0.0003909505,0.00050667796,0.0005420209,0.00020614476,0.00038264977,0.0007117925,0.00044711784,0.0006036944],"category_scores_gemma":[0.0007424796,0.00022789832,0.00034439503,0.00035810447,0.00023775018,0.0005667658,0.00050412567,0.00055818405,0.0001482839],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004455566,0.00021684058,0.00664227,0.000094715,0.000086434215,0.00035109167,0.00015069968,0.42388648,0.07238572,0.0061519537,0.0025087644,0.48707944],"study_design_scores_gemma":[0.0000036257716,0.000017424545,0.0003787201,0.000002255034,0.000002888598,0.000016534685,0.0000054340276,0.9960259,0.0027959298,0.00055056124,0.00019749955,0.0000031863856],"about_ca_topic_score_codex":0.0046570017,"about_ca_topic_score_gemma":0.004937896,"teacher_disagreement_score":0.0046570017,"about_ca_system_score_codex":0.00029999536,"about_ca_system_score_gemma":0.00039886523,"threshold_uncertainty_score":0.00925976},"labels":[],"label_agreement":null},{"id":"W7163162160","doi":"10.1109/icsm64417.2025.11541547","title":"Segmentation of cars covered with smoke","year":2025,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Ross Video (Canada); University of Ottawa","funders":"","keywords":"Segmentation; Training (meteorology); Training set; Image segmentation; Pattern recognition (psychology)","score_opus":0.0064018328166486485,"score_gpt":0.2136664147525034,"score_spread":0.20726458193585473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163162160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89951104,0.0011022063,0.08420935,0.00049255247,0.00034470693,0.00021602151,0.004732668,0.004083916,0.0053076055],"genre_scores_gemma":[0.9396386,0.00053500826,0.047494475,0.00021683268,0.00004444582,0.00004658153,0.0093756365,0.00045908423,0.002189256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959916,0.000032947526,0.00001851283,0.00015106858,0.00006652683,0.00013192843],"domain_scores_gemma":[0.9996536,0.000091804155,0.000029254577,0.00006239881,0.00012319797,0.00003976847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063081493,0.0011760909,0.0007521614,0.0017056786,0.00041983614,0.0012629637,0.00088191614,0.0010858678,0.0015181596],"category_scores_gemma":[0.0011248495,0.00043260882,0.001068109,0.0007724893,0.00057936984,0.000766495,0.0006618741,0.0010865383,0.0007952501],"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":[0.002234625,0.00048448812,0.024899025,0.0010876425,0.0005813073,0.0020735294,0.0008509383,0.5019261,0.22224253,0.002695473,0.013626665,0.22729772],"study_design_scores_gemma":[0.000043568118,0.0002311694,0.021304267,0.00015858808,0.00014765213,0.00055373745,0.0005613908,0.84853727,0.11993922,0.0017027457,0.0067305444,0.00008985685],"about_ca_topic_score_codex":0.017685562,"about_ca_topic_score_gemma":0.025219284,"teacher_disagreement_score":0.017685562,"about_ca_system_score_codex":0.00087858725,"about_ca_system_score_gemma":0.0010136246,"threshold_uncertainty_score":0.03516525},"labels":[],"label_agreement":null},{"id":"W7164157907","doi":"10.4050/f-0079-2023-1298","title":"Analysis of Aerial Firefighting with Rotorcraft Platforms","year":2023,"lang":"","type":"article","venue":"","topic":"Fire Detection and Safety Systems","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":"Lockheed Martin (Canada)","funders":"","keywords":"Firefighting; Human life; Wildfire suppression; Fire protection; Water supply; Aerial survey","score_opus":0.011961453699849505,"score_gpt":0.21370593980477584,"score_spread":0.20174448610492635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164157907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9642117,0.00023276723,0.017192855,0.00023495556,0.00005012421,0.00009444985,0.00031318847,0.00012020652,0.017549679],"genre_scores_gemma":[0.9945615,0.00008002804,0.0011089252,0.000018583713,0.000007118483,0.000018832436,0.0001187248,0.000023427736,0.0040628826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99986684,0.000018978542,0.0000036529643,0.000020285723,0.00003410041,0.00005612521],"domain_scores_gemma":[0.9996674,0.00016409956,0.00004918214,0.000013664345,0.00006365577,0.00004200101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029412733,0.00074393145,0.00048644058,0.0006208112,0.00053479837,0.00090038276,0.0005381723,0.0010464082,0.0043349667],"category_scores_gemma":[0.00087717234,0.00035551997,0.00087894354,0.0002893652,0.0005010883,0.00057251763,0.0005105486,0.0006318249,0.0004985128],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078755606,0.00006727335,0.004870917,0.000047449957,0.000026310754,0.00047845082,0.000051279025,0.9858292,0.003614798,0.0011747371,0.00061015715,0.0031505907],"study_design_scores_gemma":[0.0000071745226,0.00005828884,0.0024845344,0.0000052450796,0.0000065295994,0.000028207429,0.00007028851,0.9964456,0.0004643255,0.0002133716,0.00020918275,0.000007395126],"about_ca_topic_score_codex":0.036410913,"about_ca_topic_score_gemma":0.015084816,"teacher_disagreement_score":0.036410913,"about_ca_system_score_codex":0.0009246441,"about_ca_system_score_gemma":0.00056279334,"threshold_uncertainty_score":0.07239801},"labels":[],"label_agreement":null},{"id":"W754511852","doi":"10.1007/s11760-015-0789-x","title":"Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods","year":2015,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pixel; Fire detection; Computer science; Benchmark (surveying); Artificial intelligence; Context (archaeology); Image processing; Image (mathematics); Computer vision; Pattern recognition (psychology); Engineering; Geography; Cartography","score_opus":0.043414980956500655,"score_gpt":0.32028904228795296,"score_spread":0.2768740613314523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W754511852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09412407,0.008341282,0.8898052,0.00028232808,0.00016202676,0.00018801744,0.00017475734,0.0012816944,0.0056406474],"genre_scores_gemma":[0.5752493,0.005771736,0.41624302,0.00018651124,0.00023856311,0.000114866474,0.00038511667,0.0001995234,0.0016113797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741685,0.000677728,0.00021572717,0.0003617903,0.0012309307,0.00009695879],"domain_scores_gemma":[0.9874872,0.008671405,0.00059694756,0.0006160615,0.0025182082,0.00011015772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043504275,0.000745825,0.0015553471,0.00460373,0.0003410607,0.0019881858,0.0014015656,0.0011495348,0.0010773686],"category_scores_gemma":[0.010743982,0.00030526976,0.0010198321,0.0022263771,0.00057585683,0.00181306,0.000366349,0.00062855094,0.000544172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060768623,0.00032629754,0.01236157,0.00053907995,0.000412334,0.00009818686,0.00010640269,0.029553514,0.01051172,0.0020534345,0.00083128776,0.94259846],"study_design_scores_gemma":[0.00006700231,0.0006295933,0.028250387,0.00015058939,0.00060970674,0.00066762074,0.0001814841,0.93888754,0.023384813,0.0038197127,0.0032495775,0.00010199188],"about_ca_topic_score_codex":0.0030185406,"about_ca_topic_score_gemma":0.0021037764,"teacher_disagreement_score":0.00460373,"about_ca_system_score_codex":0.00045528443,"about_ca_system_score_gemma":0.00069544284,"threshold_uncertainty_score":0.023007512},"labels":[],"label_agreement":null}]}