{"meta":{"query_hash":"0078a8797235","filters":{"venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board"},"cohort_total":25,"direct_labels_cover":0,"predictions_cover":25,"exported":25,"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/0078a8797235","api":"https://metacan.xera.ac/api/v1/cohort?venue=Transportation+Research+Board+87th+Annual+MeetingTransportation+Research+Board"},"results":[{"id":"W124411441","doi":"","title":"Microsimulating Residential Mobility and Spatial Search Behavior: Estimation of Continuous-Time Hazard and Discrete-Time Panel Logit Models for Residential Mobility","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":6,"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":"Random effects model; Econometrics; Logit; Mixed logit; Panel data; Hazard; Discrete choice; Duration (music); Statistics; Estimation; Parametric statistics; Logistic regression; Multilevel model; Computer science; Economics; Mathematics","score_opus":0.07875404281297609,"score_gpt":0.3900384895630609,"score_spread":0.3112844467500848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W124411441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852817,0.00028826483,0.005889727,0.0006177335,0.00009989132,0.0056733936,0.0016274865,0.00020705405,0.00031474596],"genre_scores_gemma":[0.9933263,0.00032308965,0.0036693132,0.000015860956,0.00022703788,0.00065191014,0.00079258875,0.00009019505,0.0009036716],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98716944,0.0021554683,0.0021434189,0.0018408957,0.004682016,0.0020087347],"domain_scores_gemma":[0.9901963,0.0027892247,0.00041436678,0.0007104894,0.0049510584,0.000938593],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.013368007,0.0005334583,0.0011177356,0.00093011255,0.0033211017,0.00029648462,0.0007796716,0.0006482598,0.0003305769],"category_scores_gemma":[0.0011113391,0.00057456555,0.00035356713,0.0013362089,0.005638,0.0021169418,0.00004406574,0.0013880071,0.000015086191],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007793194,0.0017946496,0.87297285,0.0022068126,0.00022638889,0.0001535135,0.077868395,0.00833554,0.017235784,0.0021104463,0.00082218397,0.008480242],"study_design_scores_gemma":[0.0030280119,0.00083776796,0.96763057,0.0002638546,0.000147862,5.983735e-7,0.0070367986,0.011172483,0.004438691,0.004584627,0.00018338484,0.00067534356],"about_ca_topic_score_codex":0.11066902,"about_ca_topic_score_gemma":0.04807562,"teacher_disagreement_score":0.09465773,"about_ca_system_score_codex":0.00025084446,"about_ca_system_score_gemma":0.0013664253,"threshold_uncertainty_score":0.99967057},"labels":[],"label_agreement":null},{"id":"W125736977","doi":"","title":"Porous Asphalt Pavement Designs: Canadian Climate Use","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Asphalt Pavement Performance Evaluation","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":"Asphalt; Environmental science; Stormwater; Pervious concrete; Durability; Stormwater management; Civil engineering; Low-impact development; Surface runoff; Geotechnical engineering; Engineering; Materials science; Cement","score_opus":0.12542508960403786,"score_gpt":0.36020829263034304,"score_spread":0.23478320302630518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125736977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990561,0.0004234581,0.0008795659,0.0005584336,0.0004468065,0.0030595104,0.0008771125,0.0007397754,0.002454334],"genre_scores_gemma":[0.9890373,0.0037507012,0.0030109917,0.0001495707,0.00028270547,0.0010994432,0.001638085,0.00023157723,0.0007996766],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.986923,0.0007632284,0.0017285822,0.0011397233,0.0057691494,0.0036763574],"domain_scores_gemma":[0.99267364,0.00078586844,0.000145408,0.00088094216,0.003825254,0.0016888964],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007070492,0.0006525249,0.0006499752,0.0026788895,0.0021800282,0.00024642897,0.00085319276,0.00047386167,0.0011222697],"category_scores_gemma":[0.0002681752,0.0007268907,0.00022388875,0.0031099555,0.0007697386,0.0024975229,0.000013818925,0.002205777,0.0008467699],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001274131,0.00086385274,0.67659277,0.0027826924,0.0007621225,0.002904172,0.04571269,0.14626302,0.024513839,0.0063782423,0.07915326,0.012799216],"study_design_scores_gemma":[0.0026456544,0.00083932694,0.92444766,0.00039178663,0.00006464603,0.000003846277,0.005122981,0.0063059265,0.0126664825,0.00029613977,0.045941614,0.0012739311],"about_ca_topic_score_codex":0.100166716,"about_ca_topic_score_gemma":0.33939427,"teacher_disagreement_score":0.2478549,"about_ca_system_score_codex":0.0011631205,"about_ca_system_score_gemma":0.0012885272,"threshold_uncertainty_score":0.99993116},"labels":[],"label_agreement":null},{"id":"W149133320","doi":"","title":"Defining the Range of Urban Congestion Impacts on Freight and Their Consequences for Business Activity","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":8,"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":"Business; Traffic congestion; Supply chain; Industrial organization; Transport engineering; Marketing; Engineering","score_opus":0.07738665282967314,"score_gpt":0.3188300408374085,"score_spread":0.24144338800773538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W149133320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886505,0.0017075224,0.0036132417,0.00085288886,0.00020833431,0.0020054989,0.0017405056,0.00024177851,0.0009797193],"genre_scores_gemma":[0.9957983,0.0023983726,0.0006853588,0.00003543587,0.00013553533,0.00044298373,0.00028289264,0.0000970253,0.00012409793],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9951203,0.0004330493,0.0008696944,0.00065474265,0.0017120356,0.0012101885],"domain_scores_gemma":[0.99332386,0.002959195,0.00014562276,0.00048210577,0.0026838097,0.0004054331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031688078,0.00044508287,0.0006302129,0.0007747148,0.0010200954,0.00006960345,0.00048879464,0.00031999103,0.000053393935],"category_scores_gemma":[0.00043250836,0.00034039756,0.0001793286,0.0016322674,0.0025437619,0.00047135228,0.0000055517276,0.0012472522,0.0000117998],"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":[0.01044823,0.0013914626,0.63389385,0.008318951,0.0012297643,0.00049188436,0.08243831,0.03326628,0.03920882,0.14743449,0.028642997,0.013234946],"study_design_scores_gemma":[0.002079244,0.00082330906,0.965948,0.0004171844,0.00005155879,0.0000026368828,0.0027805583,0.0014484143,0.019121146,0.0012875331,0.0055133835,0.00052703085],"about_ca_topic_score_codex":0.0022403987,"about_ca_topic_score_gemma":0.003998938,"teacher_disagreement_score":0.33205414,"about_ca_system_score_codex":0.00011136307,"about_ca_system_score_gemma":0.0003743529,"threshold_uncertainty_score":0.9999048},"labels":[],"label_agreement":null},{"id":"W1542555304","doi":"","title":"Probabilistic Models for Discriminating Road Surface Conditions Based on Friction Measurements","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":6,"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":"Road surface; Snow; Probabilistic logic; Statistical model; Logit; Snow cover; Aggregate (composite); Environmental science; Cover (algebra); Logistic regression; Field (mathematics); Statistics; Meteorology; Econometrics; Mathematics; Engineering; Geography; Civil engineering","score_opus":0.12359395727321183,"score_gpt":0.3612138661551467,"score_spread":0.2376199088819349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542555304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98666346,0.000015062099,0.004612417,0.0009935896,0.00034720046,0.003988205,0.00079253054,0.00028509824,0.0023024376],"genre_scores_gemma":[0.99086183,0.000033019925,0.0059767156,0.00009153717,0.00013621328,0.001406037,0.0010415985,0.000115590665,0.00033747888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9893285,0.0011194582,0.0012122268,0.0014120144,0.005252234,0.0016755605],"domain_scores_gemma":[0.9958376,0.0010278684,0.00026794965,0.000664799,0.0016005025,0.0006012486],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0056897076,0.00046516495,0.0004987398,0.00064112275,0.0026986327,0.00012995115,0.0006119303,0.00030948952,0.00075362565],"category_scores_gemma":[0.0006946942,0.0004827951,0.0002675607,0.0018266821,0.001387515,0.0012663427,0.000016608332,0.0009365226,0.00024379857],"study_design_candidate":"observational","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":[0.002753767,0.001260762,0.280876,0.00058218534,0.00009060863,0.000073560615,0.008137491,0.6624459,0.029276242,0.0029552276,0.010289206,0.0012590319],"study_design_scores_gemma":[0.0035774396,0.0015776219,0.9230186,0.0003198637,0.000073711184,0.0000018601451,0.0028960172,0.050661243,0.008968063,0.0056529227,0.0023686583,0.00088401424],"about_ca_topic_score_codex":0.011693007,"about_ca_topic_score_gemma":0.0095325755,"teacher_disagreement_score":0.6421426,"about_ca_system_score_codex":0.00077477284,"about_ca_system_score_gemma":0.0003374087,"threshold_uncertainty_score":0.99976236},"labels":[],"label_agreement":null},{"id":"W1597932877","doi":"","title":"Modeling and Analysis of Truck Weight and Credential Screening System","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transport Systems and Technology","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":"Truck; Credential; Bridge (graph theory); Port (circuit theory); Engineering; Process (computing); Computer science; Transport engineering; Simulation; Automotive engineering; Operations research; Computer security; Electrical engineering","score_opus":0.042578619550724006,"score_gpt":0.3094898080093311,"score_spread":0.2669111884586071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597932877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97516817,0.0014148707,0.020798389,0.00010274675,0.00010365219,0.00088728033,0.00055176014,0.00049947837,0.0004736797],"genre_scores_gemma":[0.9943772,0.0016440891,0.0031914683,0.0000045417523,0.00007425955,0.00019069576,0.00033226333,0.00009324576,0.00009223444],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99371994,0.00034005757,0.0014612544,0.0008695946,0.002397489,0.0012116844],"domain_scores_gemma":[0.99689233,0.0003947471,0.000107873384,0.0004943035,0.0015893474,0.0005213745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027327072,0.0004021843,0.0010197139,0.003465764,0.0007447856,0.000058595822,0.00039269292,0.00044524437,0.000059855684],"category_scores_gemma":[0.000055628087,0.00042235813,0.00023973626,0.0036793863,0.00090563536,0.0005582872,0.000012139891,0.0012361094,0.000005480334],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097263296,0.00020214469,0.7807159,0.0043759067,0.0033573955,0.0007424009,0.02349173,0.15571006,0.007389243,0.017149908,0.00051744014,0.005375274],"study_design_scores_gemma":[0.0018896236,0.00033716386,0.80164534,0.00043545052,0.000512576,0.000005926839,0.01027167,0.18135256,0.0015643499,0.00011869449,0.0012154644,0.00065116555],"about_ca_topic_score_codex":0.007993132,"about_ca_topic_score_gemma":0.0054583894,"teacher_disagreement_score":0.025642488,"about_ca_system_score_codex":0.00008505903,"about_ca_system_score_gemma":0.0001151654,"threshold_uncertainty_score":0.9998228},"labels":[],"label_agreement":null},{"id":"W199108350","doi":"","title":"Drowsy Drivers: Effect of Light and Circadian Rhythm on Crash Occurrence","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Sleep and Work-Related Fatigue","field":"Psychology","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":"Circadian rhythm; Alertness; Morning; Crash; Rhythm; Logistic regression; Poison control; Medicine; Injury prevention; Psychology; Demography; Audiology; Environmental health; Internal medicine; Psychiatry; Computer science","score_opus":0.05174774857792221,"score_gpt":0.38106997550976773,"score_spread":0.32932222693184554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W199108350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869198,0.0005641859,0.00008384662,0.0011383052,0.00041070982,0.0021450703,0.0005410063,0.0002032917,0.00799379],"genre_scores_gemma":[0.99721265,0.00076996256,0.00021251536,0.00007101897,0.00017062556,0.0005307922,0.00037705607,0.000088548986,0.00056683854],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9889925,0.0023388616,0.0013457683,0.0015215806,0.0038282159,0.001973051],"domain_scores_gemma":[0.99275124,0.0029980058,0.00028786098,0.00088722096,0.0020816915,0.0009939555],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052910894,0.0005703227,0.00086783385,0.0020922287,0.0011457757,0.000066880006,0.0006946694,0.0006688365,0.0011566838],"category_scores_gemma":[0.00048572823,0.00054794445,0.00029837366,0.002903769,0.0015319817,0.0005273661,0.000015783375,0.0027924455,0.00045897407],"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":[0.0053201746,0.00095513184,0.8611899,0.00050413713,0.00061668357,0.0020198266,0.06086787,0.00023364148,0.0021294374,0.01219759,0.02864665,0.025318943],"study_design_scores_gemma":[0.0060200267,0.0055261035,0.9629043,0.0010693038,0.00010656282,0.0000057678976,0.0061520906,0.000018401302,0.00945945,0.00018370818,0.0077171745,0.0008371349],"about_ca_topic_score_codex":0.0036716254,"about_ca_topic_score_gemma":0.0016569959,"teacher_disagreement_score":0.10171436,"about_ca_system_score_codex":0.00013049378,"about_ca_system_score_gemma":0.00032325726,"threshold_uncertainty_score":0.9997564},"labels":[],"label_agreement":null},{"id":"W2151235206","doi":"","title":"Macrolevel Collision Prediction Models to Evaluate Road Safety Effects of Mobility Management Strategies: New Empirical Tools to Promote Sustainable Development","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":12,"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 British Columbia","funders":"","keywords":"Transport engineering; Collision; Per capita; Public transport; Traffic congestion; Macro; Mobility management; Computer science; Business; Engineering; Computer security; Telecommunications","score_opus":0.1127282553080234,"score_gpt":0.42144462979676556,"score_spread":0.3087163744887422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151235206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96561265,0.00012500724,0.018368285,0.0015254337,0.00017079333,0.009781802,0.00025534452,0.00028018304,0.0038805183],"genre_scores_gemma":[0.98163396,0.00029224032,0.010400065,0.0000900856,0.00017527486,0.001215689,0.00035230606,0.00008029509,0.0057600643],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9828447,0.001682217,0.0022134634,0.0018343156,0.008672871,0.0027524098],"domain_scores_gemma":[0.98935,0.00084015494,0.0002507065,0.00086802826,0.006602731,0.0020883514],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.012991181,0.00054750626,0.0008809104,0.0016602987,0.0029590225,0.00027673692,0.0013412357,0.00044962188,0.00025233737],"category_scores_gemma":[0.0005406051,0.0005635998,0.0002847409,0.0057595107,0.0009297553,0.0024789874,0.000049720475,0.0012256878,0.00008117078],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01355387,0.0039787847,0.2063015,0.0060018967,0.00057254906,0.001244821,0.6545272,0.04970258,0.0022651693,0.01650973,0.011807159,0.033534754],"study_design_scores_gemma":[0.0021398247,0.0009275552,0.9230356,0.00056912255,0.000056534962,1.2028259e-7,0.04586138,0.00014766479,0.003669237,0.0030369554,0.019974219,0.00058175373],"about_ca_topic_score_codex":0.032224618,"about_ca_topic_score_gemma":0.032682776,"teacher_disagreement_score":0.7167341,"about_ca_system_score_codex":0.0011275745,"about_ca_system_score_gemma":0.005023272,"threshold_uncertainty_score":0.99968153},"labels":[],"label_agreement":null},{"id":"W218013463","doi":"","title":"Age and Its Relation with Home Location, Household Structure, and Travel Behavior: 15 Years of Observation","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","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":"Population; Workforce; Baby boom; Scale (ratio); Travel behavior; Descriptive statistics; Boom; Demographic economics; Age structure; Geography; Demography; Business; Economics; Economic growth; Transport engineering; Sociology; Engineering; Statistics; Mathematics","score_opus":0.0988123739211161,"score_gpt":0.36288941166984867,"score_spread":0.2640770377487326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W218013463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995828,0.0006376032,0.00019350402,0.0004402025,0.00007007573,0.0020118814,0.0004724206,0.000121185796,0.00022517733],"genre_scores_gemma":[0.9960592,0.001193851,0.001243668,0.000028166354,0.000115913805,0.00018755453,0.0005534567,0.00006540566,0.00055281963],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9922638,0.00084364216,0.0010420403,0.00096980535,0.0038330853,0.0010476371],"domain_scores_gemma":[0.99464446,0.00071431446,0.00028480813,0.00037322455,0.0033747805,0.00060842925],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0034940029,0.0003084774,0.00053573976,0.0009319914,0.0014716625,0.000111057125,0.00042391967,0.00039720387,0.00009871682],"category_scores_gemma":[0.00029237705,0.00032240438,0.00008190119,0.0027486705,0.0023723873,0.001620901,0.000008819756,0.0011310439,0.0000048390048],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006274482,0.00021954736,0.94911623,0.00037183386,0.000047048467,0.00019137649,0.0399334,0.00017456242,0.002029276,0.005917365,0.00015287662,0.001219029],"study_design_scores_gemma":[0.0012897713,0.0003595084,0.9880615,0.0001654009,0.000055237328,5.233551e-7,0.0072914716,0.000030822288,0.00079960574,0.00083859015,0.00077041396,0.00033717204],"about_ca_topic_score_codex":0.01758181,"about_ca_topic_score_gemma":0.044742573,"teacher_disagreement_score":0.038945246,"about_ca_system_score_codex":0.0001293361,"about_ca_system_score_gemma":0.0008422021,"threshold_uncertainty_score":0.9999228},"labels":[],"label_agreement":null},{"id":"W2216742479","doi":"","title":"Developing Regional 24-Hour Profiles for Link-Based, Speed-Dependent Vehicle Emissions and Zone-Based Soaks","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Vehicle emissions and performance","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":"Environmental science; Dispersion (optics); Representation (politics); Meteorology; Transport engineering; Spatial dispersion; Vehicle miles of travel; Computer science; Engineering; Geography","score_opus":0.09577547485477113,"score_gpt":0.3552840530336089,"score_spread":0.25950857817883777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216742479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752269,0.0006486722,0.015758084,0.004454142,0.00019658313,0.002239309,0.0006572505,0.0005365767,0.00028251935],"genre_scores_gemma":[0.9801847,0.00095500797,0.015806038,0.00015150747,0.00034105103,0.0007265215,0.0007749234,0.00017296814,0.00088733697],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9929308,0.00033584176,0.0011238665,0.0009575218,0.002887169,0.0017647548],"domain_scores_gemma":[0.99464554,0.0012933707,0.00011715923,0.00051013107,0.0025199894,0.0009138159],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003146551,0.00049437536,0.000551375,0.0011824167,0.0019232663,0.00013349729,0.00056070293,0.00044209242,0.00016774378],"category_scores_gemma":[0.00020550984,0.0004969687,0.00021393591,0.001510569,0.0007195417,0.0006030738,0.000011143403,0.0016899209,0.000042024974],"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":[0.0071491688,0.0011609101,0.4201636,0.011257606,0.0005623151,0.0009700145,0.022939546,0.34406775,0.07181725,0.008554277,0.066653445,0.044704124],"study_design_scores_gemma":[0.01061675,0.0016591514,0.54980916,0.002264763,0.000091148,0.000006812633,0.00652997,0.16781068,0.07768326,0.0010021896,0.18003869,0.0024874082],"about_ca_topic_score_codex":0.0010701999,"about_ca_topic_score_gemma":0.0015739363,"teacher_disagreement_score":0.17625706,"about_ca_system_score_codex":0.00026134963,"about_ca_system_score_gemma":0.0012362499,"threshold_uncertainty_score":0.9997482},"labels":[],"label_agreement":null},{"id":"W344991833","doi":"","title":"Rail Shuttles: Concepts and Case Studies","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Railway Systems and Energy Efficiency","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":"Control reconfiguration; Interim; Service (business); Urban transit; Transport engineering; Transit (satellite); Rail transit; Public transport; Line (geometry); Transit system; Business; Operations research; Computer science; Engineering; Geography; Marketing","score_opus":0.08467940461300916,"score_gpt":0.38387217560009096,"score_spread":0.2991927709870818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W344991833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903741,0.004379188,0.0003959333,0.00032218965,0.0003443846,0.0010543759,0.00024056787,0.00055928517,0.0023300103],"genre_scores_gemma":[0.99080676,0.0058744303,0.0009994546,0.00003236851,0.00026090522,0.0004889349,0.00010781775,0.0001285445,0.0013007638],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9925984,0.00065047515,0.0012294503,0.0009491351,0.0028104945,0.0017620251],"domain_scores_gemma":[0.99458385,0.0012308314,0.00008963592,0.0005416955,0.002772766,0.00078124617],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0039722123,0.0004888983,0.0007226781,0.0011747967,0.0016089458,0.000107784086,0.00040600332,0.00032447246,0.00014113884],"category_scores_gemma":[0.00033535223,0.00047749473,0.00017093489,0.0020328616,0.001889617,0.00080342084,0.000014198094,0.0014954662,0.00010043029],"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":[0.0016616244,0.001127121,0.18317774,0.010093534,0.0020926315,0.065333135,0.411627,0.12998955,0.017100075,0.027435992,0.12067724,0.02968438],"study_design_scores_gemma":[0.012184046,0.0036620325,0.43870848,0.0023083824,0.00023046522,0.00079613685,0.3094516,0.013796593,0.012768234,0.0024803022,0.19838615,0.0052275583],"about_ca_topic_score_codex":0.0041930163,"about_ca_topic_score_gemma":0.008211954,"teacher_disagreement_score":0.25553074,"about_ca_system_score_codex":0.00019454399,"about_ca_system_score_gemma":0.00024665045,"threshold_uncertainty_score":0.99976766},"labels":[],"label_agreement":null},{"id":"W560679385","doi":"","title":"Innovative Tools for Presenting Traffic Performance on Congested Highway Corridors","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Traffic control and management","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":"Transport engineering; Traffic congestion; Upstream (networking); Highway Capacity Manual; State highway; Mile; Downstream (manufacturing); Level of service; Computer science; Engineering; Geography; Operations management; Telecommunications","score_opus":0.07082875524371529,"score_gpt":0.325971866976588,"score_spread":0.2551431117328727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W560679385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891493,0.00014616743,0.0007528319,0.0007433554,0.0004610967,0.004017599,0.00057163957,0.0010197854,0.0031382572],"genre_scores_gemma":[0.99300504,0.0007143958,0.0011421597,0.00007986027,0.00030621607,0.0021364111,0.0009004598,0.0001714063,0.0015440386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.991726,0.000389562,0.0013414662,0.001054207,0.003317732,0.0021710736],"domain_scores_gemma":[0.9926801,0.0021675488,0.0001390734,0.00060688483,0.0038768197,0.00052961434],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0035426102,0.00055871403,0.0006878508,0.0016344639,0.001321894,0.00020095016,0.00075689715,0.00041087298,0.00014496665],"category_scores_gemma":[0.00046318513,0.000575256,0.00023494601,0.0031032912,0.0006945591,0.001200627,0.000011395084,0.0022146427,0.00013283468],"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":[0.0038225432,0.00090049545,0.017226039,0.0030241823,0.00070332194,0.00041340938,0.025788292,0.812716,0.0035280115,0.0060268254,0.05532095,0.070529945],"study_design_scores_gemma":[0.0071725487,0.0021266316,0.7917212,0.00056489225,0.00006500788,0.0000018076892,0.007122235,0.032871984,0.004739038,0.00011390442,0.15206988,0.0014308351],"about_ca_topic_score_codex":0.00030529013,"about_ca_topic_score_gemma":0.0015387791,"teacher_disagreement_score":0.779844,"about_ca_system_score_codex":0.00027665036,"about_ca_system_score_gemma":0.00033605652,"threshold_uncertainty_score":0.99997824},"labels":[],"label_agreement":null},{"id":"W585070670","doi":"","title":"Joint or Solo: Structural Equations Model of Household Activity Time Allocation Patterns","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Demographics; Interdependence; Joint (building); Time allocation; Wife; Discrete choice; Representation (politics); Work (physics); Structural equation modeling; Econometrics; Survey data collection; Demographic economics; Computer science; Economics; Statistics; Mathematics; Demography; Engineering; Sociology; Machine learning","score_opus":0.17982109382130493,"score_gpt":0.3980514297020188,"score_spread":0.21823033588071386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585070670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97112435,0.00005615165,0.021009918,0.001880644,0.00015974998,0.0020829586,0.001620032,0.00042244242,0.001643723],"genre_scores_gemma":[0.9884704,0.00074996357,0.0045083337,0.00005420779,0.00019347006,0.00034705384,0.0010873914,0.00010439839,0.0044847983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98779094,0.0018035726,0.0015481138,0.0011073716,0.00601821,0.0017318085],"domain_scores_gemma":[0.9910922,0.0016063047,0.0005070357,0.00064047513,0.005356496,0.0007974887],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.006278934,0.00044718088,0.00072922243,0.0016753908,0.0031482268,0.00012809047,0.0007996082,0.00054103223,0.0007167739],"category_scores_gemma":[0.0010978545,0.00044852257,0.00030519624,0.0030026915,0.0018631218,0.001747398,0.0000117647305,0.0015115796,0.00006658359],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047414345,0.001494354,0.16666837,0.001198323,0.0004110371,0.00032558196,0.26954687,0.49053675,0.019443257,0.033336863,0.007378307,0.0049188724],"study_design_scores_gemma":[0.0048108497,0.0013560472,0.88958895,0.0007869255,0.00017721708,0.0000016786081,0.029542636,0.057167307,0.009635771,0.0022868128,0.0029716084,0.0016742154],"about_ca_topic_score_codex":0.029214272,"about_ca_topic_score_gemma":0.0378223,"teacher_disagreement_score":0.72292054,"about_ca_system_score_codex":0.00034436944,"about_ca_system_score_gemma":0.0027503325,"threshold_uncertainty_score":0.9997966},"labels":[],"label_agreement":null},{"id":"W599160489","doi":"","title":"Louisiana Highway Construction Cost Trends After Hurricanes Katrina and Rita","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Construction Project Management and Performance","field":"Decision Sciences","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":"Hurricane katrina; Index (typography); Quarter (Canadian coin); Transport engineering; Agency (philosophy); Cost estimate; Natural disaster; Engineering; Geography; Civil engineering; Environmental science; Meteorology; Computer science; Archaeology","score_opus":0.13389305107511162,"score_gpt":0.4233531345661611,"score_spread":0.28946008349104946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W599160489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98401034,0.00048020185,0.0007716822,0.0038975861,0.0004781032,0.0014178157,0.00047215764,0.00025616647,0.008215953],"genre_scores_gemma":[0.98594755,0.001377994,0.0024976907,0.00010497555,0.000317267,0.00060944457,0.00019428643,0.00006710643,0.008883671],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9837558,0.0015843103,0.002065126,0.0019211702,0.008855438,0.0018181371],"domain_scores_gemma":[0.9900484,0.0020981673,0.00038690033,0.0010495232,0.0055041285,0.0009128625],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.011072001,0.00052818167,0.000788494,0.005064901,0.0022139847,0.0005266397,0.0010941275,0.00035756972,0.0029481596],"category_scores_gemma":[0.0009461657,0.00048098856,0.00029895877,0.007929738,0.0033407249,0.0022310694,0.0000515209,0.0018113495,0.00041851745],"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":[0.0022830723,0.00027181755,0.83004975,0.00017652583,0.000119661505,0.0005310978,0.01106631,0.00021249574,0.0002774696,0.009982347,0.024257753,0.120771706],"study_design_scores_gemma":[0.0015998706,0.00042495143,0.83825713,0.00007954394,0.000028923967,0.000011163687,0.008950572,0.0002914132,0.000313395,0.0021899617,0.14734633,0.0005067239],"about_ca_topic_score_codex":0.0049232487,"about_ca_topic_score_gemma":0.010400823,"teacher_disagreement_score":0.12308858,"about_ca_system_score_codex":0.0001719788,"about_ca_system_score_gemma":0.00050573365,"threshold_uncertainty_score":0.9997642},"labels":[],"label_agreement":null},{"id":"W600853639","doi":"","title":"Transfer Optimization Model for Intermodal Transit Services into the Suburbs","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Schedule; Transit (satellite); Transfer (computing); Computer science; Modal; Operations research; Transport engineering; Public transport; Genetic algorithm; Travel time; Service (business); Engineering; Business","score_opus":0.07590018077152863,"score_gpt":0.3873930371058333,"score_spread":0.3114928563343047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W600853639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60962135,0.0004399379,0.37027687,0.01074894,0.00037876083,0.0052873376,0.0007427257,0.00066066755,0.001843415],"genre_scores_gemma":[0.97587764,0.002069606,0.014399119,0.0003651367,0.00039804855,0.0015499621,0.0018117363,0.00015224665,0.0033764846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9881596,0.0016637952,0.0015582027,0.0013407957,0.0051469845,0.002130618],"domain_scores_gemma":[0.9898342,0.0018733145,0.00018438397,0.00064566004,0.006656964,0.0008054747],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009046519,0.00053047657,0.00062597846,0.0012393611,0.007297684,0.00031223713,0.0014092348,0.0006065332,0.00029209015],"category_scores_gemma":[0.0003006475,0.0004899747,0.00044854576,0.0030437927,0.0024585694,0.0021889894,0.000007118243,0.0016171491,0.00004289551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015395194,0.00027950772,0.010580027,0.0003725727,0.00010641661,0.00004015952,0.3153571,0.6488446,0.0002871211,0.019766552,0.0017264499,0.0011000092],"study_design_scores_gemma":[0.010539543,0.001699222,0.09806666,0.0010419379,0.000416015,0.0000023832445,0.22230238,0.5581819,0.0016845601,0.00735977,0.095641084,0.003064545],"about_ca_topic_score_codex":0.025006024,"about_ca_topic_score_gemma":0.08187749,"teacher_disagreement_score":0.3662563,"about_ca_system_score_codex":0.00030480573,"about_ca_system_score_gemma":0.0016233486,"threshold_uncertainty_score":0.9997552},"labels":[],"label_agreement":null},{"id":"W607179013","doi":"","title":"Using Macrolevel Collision Prediction Models to Conduct Road Safety Evaluation of Regional Transportation Plan","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","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":"Plan (archaeology); Macro; Transport engineering; Collision; Computer science; Transportation planning; Process (computing); Software; Engineering; Geography; Computer security","score_opus":0.2829392918846076,"score_gpt":0.3969075848026387,"score_spread":0.11396829291803112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607179013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9741558,0.00046165072,0.016474467,0.0002941185,0.00037193976,0.0034910669,0.0030184733,0.00046519926,0.0012673069],"genre_scores_gemma":[0.9903925,0.0009997579,0.0056390325,0.000031780022,0.00022600159,0.00044083284,0.0019205462,0.00017220955,0.0001773042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9852803,0.0011706026,0.002256058,0.0011806282,0.008467417,0.0016449963],"domain_scores_gemma":[0.990343,0.00052628346,0.0002276929,0.0007079313,0.0073141125,0.00088102836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008238192,0.0006063164,0.0008533342,0.0020470996,0.0011945936,0.000055602042,0.0006847414,0.00058100175,0.0003177816],"category_scores_gemma":[0.00014077449,0.0006502507,0.00033059198,0.0034939644,0.0007413183,0.0014433647,0.000008081417,0.001577893,0.000057996083],"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":[0.0016705841,0.0002845314,0.009464041,0.00044235776,0.00017667784,0.000044804536,0.023635779,0.9445268,0.009700876,0.0013896185,0.0042214845,0.0044424487],"study_design_scores_gemma":[0.004999412,0.00074945984,0.79409283,0.0007585392,0.00021147782,0.0000049473215,0.011533561,0.17261691,0.007878941,0.0008259071,0.005339625,0.000988359],"about_ca_topic_score_codex":0.0050285915,"about_ca_topic_score_gemma":0.006570891,"teacher_disagreement_score":0.7846288,"about_ca_system_score_codex":0.00070106937,"about_ca_system_score_gemma":0.0011950042,"threshold_uncertainty_score":0.99959487},"labels":[],"label_agreement":null},{"id":"W616410260","doi":"","title":"Exploring Route Choice Decision-Making Process: Comparison of Preplanned and Observed Routes Obtained Using Person-Based GPS","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","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":"Global Positioning System; Route planning; Computer science; Preference; Data collection; Process (computing); Operations research; Transport engineering; Engineering; Telecommunications","score_opus":0.34711760445906215,"score_gpt":0.4585523472302456,"score_spread":0.11143474277118348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616410260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938165,0.0005091057,0.0014228295,0.000642521,0.00017526935,0.0021870448,0.00032666366,0.00026238803,0.0006577006],"genre_scores_gemma":[0.9932524,0.00016879865,0.005619147,0.00003349778,0.00020240768,0.0003494561,0.00018415014,0.000091667825,0.000098473436],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.987158,0.0014847312,0.0017606786,0.001439195,0.006160277,0.0019970892],"domain_scores_gemma":[0.9881135,0.004525817,0.00050700275,0.00061362685,0.005413213,0.0008267997],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007227532,0.00049708807,0.0010023537,0.0014234555,0.0031917973,0.00019355862,0.0010303694,0.00038237227,0.00023288856],"category_scores_gemma":[0.0022283325,0.0005233651,0.00029941276,0.003813445,0.0026780737,0.0024648563,0.000012517899,0.0016189229,0.000008587507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013815535,0.00043240035,0.91170925,0.0006494606,0.0000688049,0.00008525965,0.07763674,0.0043123728,0.0009562441,0.00043681948,0.00008336614,0.0022477373],"study_design_scores_gemma":[0.0016428747,0.00038751285,0.9244818,0.0010008353,0.0000634514,1.3927594e-7,0.066033736,0.0022212756,0.0024143448,0.0005460347,0.00065341796,0.00055456464],"about_ca_topic_score_codex":0.037303265,"about_ca_topic_score_gemma":0.05781245,"teacher_disagreement_score":0.020509183,"about_ca_system_score_codex":0.00032241282,"about_ca_system_score_gemma":0.0021147996,"threshold_uncertainty_score":0.99972177},"labels":[],"label_agreement":null},{"id":"W618236167","doi":"","title":"Association of Highway Traffic Volumes with Cold and Snow and Their Interactions","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Vehicle emissions and performance","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":"Snow; Environmental science; Meteorology; Precipitation; Traffic flow (computer networking); Traffic volume; Volume (thermodynamics); Cold front; Geography; Transport engineering; Engineering; Computer science","score_opus":0.028274889107436814,"score_gpt":0.2982643170228421,"score_spread":0.2699894279154053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W618236167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99666494,0.00045147966,0.00026537807,0.00073761557,0.000073609655,0.0007011549,0.00035361547,0.0002000226,0.0005522047],"genre_scores_gemma":[0.99419314,0.0029280472,0.00086507556,0.000015170512,0.00007779028,0.00017404312,0.0001097364,0.000065628104,0.0015713832],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99626887,0.0002785038,0.0006733452,0.00048033704,0.0014578901,0.0008410651],"domain_scores_gemma":[0.9964679,0.0010695573,0.00011123614,0.00026754182,0.0016637658,0.00042002863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019244725,0.00028398016,0.0004323613,0.0007755757,0.00069924566,0.000060530987,0.00020821352,0.00019710195,0.000119732824],"category_scores_gemma":[0.00011249154,0.0002475716,0.00007185894,0.0012704557,0.00054455874,0.0008949553,0.000006775427,0.0011869904,0.000009820739],"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":[0.0020729604,0.00072193326,0.7308579,0.0036460054,0.0009830563,0.00020021717,0.079453014,0.05163309,0.06660284,0.0022658505,0.028348835,0.033214264],"study_design_scores_gemma":[0.0030059053,0.0011269638,0.86020935,0.00081633945,0.000051605766,0.0000061541073,0.013121031,0.017915769,0.03137235,0.00009901638,0.07149497,0.00078056654],"about_ca_topic_score_codex":0.00091239566,"about_ca_topic_score_gemma":0.0053437813,"teacher_disagreement_score":0.12935139,"about_ca_system_score_codex":0.00012578182,"about_ca_system_score_gemma":0.00018035958,"threshold_uncertainty_score":0.9999977},"labels":[],"label_agreement":null},{"id":"W623357792","doi":"","title":"Assessing Impact of Automated Commercial Environment Truck e-Manifest","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","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":"Truck; Motor carrier; Business; Government (linguistics); Productivity; Workload; Transport engineering; Engineering; Economics; Economic growth","score_opus":0.08947271147459201,"score_gpt":0.3721812952857497,"score_spread":0.2827085838111577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W623357792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99007773,0.000117193595,0.0001498664,0.0006675018,0.00018865401,0.0013032298,0.000264287,0.00054537813,0.006686178],"genre_scores_gemma":[0.9967869,0.0002672544,0.0005910054,0.00010420478,0.00067352445,0.00020622546,0.0009514811,0.00011992067,0.00029948886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99176985,0.00025277,0.0018104843,0.0010295731,0.0031240815,0.0020132607],"domain_scores_gemma":[0.9960294,0.000560704,0.00052697567,0.00070101017,0.0019331601,0.00024879593],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0041827504,0.0005745999,0.0009281124,0.0023038231,0.0015189141,0.00030778185,0.0009529736,0.00036605087,0.001286457],"category_scores_gemma":[0.00018388972,0.0005666646,0.0005880492,0.0021170035,0.0016467684,0.0038979256,0.000029368544,0.0014126536,0.0005059526],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010055763,0.0007712554,0.96336055,0.00070213736,0.00024540798,0.00028593894,0.0045301113,0.0071584005,0.0035169073,0.005035494,0.01187091,0.0015172948],"study_design_scores_gemma":[0.0017659048,0.000203491,0.98244214,0.0001646887,0.000054694898,0.0000013062626,0.00276213,0.0012022121,0.00042783984,0.0005111126,0.009929735,0.00053475145],"about_ca_topic_score_codex":0.061345927,"about_ca_topic_score_gemma":0.007464677,"teacher_disagreement_score":0.05388125,"about_ca_system_score_codex":0.00031617662,"about_ca_system_score_gemma":0.00058804697,"threshold_uncertainty_score":0.99978095},"labels":[],"label_agreement":null},{"id":"W631880921","doi":"","title":"Active transit signal priority for streetcars: experience in Melbourne and Toronto","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Traffic congestion; Bus priority; Operations research; Transit (satellite); Computer science; Traffic signal; Futures contract; Engineering; Business; Real-time computing; Finance","score_opus":0.09827760439205936,"score_gpt":0.4243807061306364,"score_spread":0.32610310173857704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W631880921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881784,0.0005210416,0.0037558107,0.0015589299,0.00015110456,0.0032030323,0.00059744134,0.00022455341,0.0018097241],"genre_scores_gemma":[0.99008673,0.0026718092,0.0043990356,0.000065363936,0.00021960911,0.0010219153,0.0005029266,0.00007004925,0.0009625496],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99067134,0.001343799,0.0011846218,0.001228695,0.003679307,0.0018922612],"domain_scores_gemma":[0.993296,0.0018746788,0.00019638901,0.00032922524,0.0034737461,0.00082990975],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005787619,0.00038627695,0.00061287096,0.00082578085,0.0026569576,0.000150302,0.0005810076,0.00047660392,0.00038067077],"category_scores_gemma":[0.00047132338,0.0004368768,0.00018473122,0.0018097576,0.0019433673,0.0021444294,0.000005883423,0.0011247152,0.000010561484],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036817156,0.0006914582,0.29435524,0.00039251804,0.00009067802,0.00021553946,0.666253,0.0034204032,0.0010263101,0.01697031,0.0015247638,0.011378085],"study_design_scores_gemma":[0.0032640216,0.00059989543,0.8049741,0.00025577983,0.000028709579,6.030472e-7,0.16469978,0.0004575558,0.00097318867,0.0010488594,0.02305638,0.00064117677],"about_ca_topic_score_codex":0.109428115,"about_ca_topic_score_gemma":0.42243513,"teacher_disagreement_score":0.5106188,"about_ca_system_score_codex":0.00047780454,"about_ca_system_score_gemma":0.0013152192,"threshold_uncertainty_score":0.9998083},"labels":[],"label_agreement":null},{"id":"W641488488","doi":"","title":"Effect of Winter Events on Highway Performance in the Province of Alberta","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Smart Materials for Construction","field":"Environmental Science","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":"Performance measurement; Snow; Asset (computer security); Duration (music); Environmental science; Transport engineering; Suite; Control (management); Environmental resource management; Asset management; Computer science; Meteorology; Geography; Engineering; Business","score_opus":0.02245535849226479,"score_gpt":0.31415533123467326,"score_spread":0.2916999727424085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W641488488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950907,0.000014694644,0.000020086763,0.0003382448,0.0001260578,0.0019865602,0.00007708808,0.000027271935,0.002319302],"genre_scores_gemma":[0.9989226,0.000118916694,0.00016100089,0.000022964516,0.000046149147,0.00030261764,0.00009361068,0.00003979308,0.00029233398],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99106675,0.002060595,0.0011353823,0.0006933024,0.0041056927,0.0009382497],"domain_scores_gemma":[0.99669003,0.0019053434,0.00026090242,0.0006128871,0.00035462354,0.00017620955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074876584,0.00029253788,0.00049075007,0.00060651364,0.0003917217,0.000016909928,0.00087815244,0.00018932686,0.000492516],"category_scores_gemma":[0.00038573903,0.00022259312,0.00016541778,0.0017551137,0.0014823597,0.00059725274,0.000019000896,0.00091235817,0.00016829149],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028507032,0.0002881607,0.98087585,0.00043987684,0.000022245862,0.000054697546,0.0061839186,0.0020070802,0.0049508875,0.00026697252,0.00089748774,0.0011621134],"study_design_scores_gemma":[0.0013738956,0.002565003,0.9488323,0.0002435707,0.000013271106,0.0000015353565,0.000546764,0.00006988842,0.044596035,0.000084994,0.0014865095,0.00018623669],"about_ca_topic_score_codex":0.014741312,"about_ca_topic_score_gemma":0.017898811,"teacher_disagreement_score":0.039645147,"about_ca_system_score_codex":0.00017124122,"about_ca_system_score_gemma":0.00015176913,"threshold_uncertainty_score":0.9987957},"labels":[],"label_agreement":null},{"id":"W649290231","doi":"","title":"Sustainable Transportation: Theory or Practice? Perspective of Planners and Policy Makers","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","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":"Sustainability; Government (linguistics); Terminology; Politics; Sustainable development; Urban planning; Public administration; Environmental planning; Public policy; Political science; Business; Economic growth; Public relations; Economics; Engineering; Geography","score_opus":0.04182864691639104,"score_gpt":0.3986749669323411,"score_spread":0.35684632001595007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649290231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803232,0.0002607272,0.00027886676,0.0033767943,0.000056411936,0.0019598715,0.00041126707,0.00010320527,0.0132296765],"genre_scores_gemma":[0.98624134,0.002075396,0.0022007166,0.00020450211,0.000112478134,0.00026599472,0.00019857885,0.00008376315,0.008617231],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99062234,0.001394531,0.0010350117,0.0010419595,0.004201423,0.0017047407],"domain_scores_gemma":[0.99568665,0.001760763,0.0002998478,0.00044681397,0.0009985404,0.00080738065],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.0048884396,0.00042615677,0.00057602837,0.0007577068,0.0014883637,0.00007072406,0.000546931,0.00031482542,0.0015147234],"category_scores_gemma":[0.0009000527,0.0003913763,0.00018358315,0.0027709773,0.0036448322,0.0019307391,0.000025652607,0.0012780658,0.00008814857],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015046393,0.0025251117,0.54792535,0.0008364491,0.00057570136,0.0030824926,0.29046372,0.0031849833,0.005433358,0.114149876,0.011978737,0.0047978405],"study_design_scores_gemma":[0.002080298,0.0012913882,0.75559884,0.000084814594,0.00005847887,0.000004310428,0.21822567,0.00001972142,0.0012997513,0.0061811195,0.014686608,0.00046901332],"about_ca_topic_score_codex":0.06096632,"about_ca_topic_score_gemma":0.009445075,"teacher_disagreement_score":0.20767349,"about_ca_system_score_codex":0.0008021613,"about_ca_system_score_gemma":0.0006363821,"threshold_uncertainty_score":0.9998538},"labels":[],"label_agreement":null},{"id":"W75323213","doi":"","title":"Review of Current Damage-Level Criteria for Longitudinal Barrier Repair","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Safety and Impact Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Loss and damage; Liability; Business; Risk analysis (engineering); Forensic engineering; Engineering; Accounting","score_opus":0.17226362562546912,"score_gpt":0.42701295697777525,"score_spread":0.25474933135230615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75323213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9079837,0.038509928,0.030913515,0.0011762381,0.0008887288,0.006724012,0.010708278,0.0014143037,0.0016812556],"genre_scores_gemma":[0.92920715,0.0603213,0.0056451387,0.00010065545,0.0003004555,0.001126795,0.002616509,0.00019515103,0.00048682018],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9905109,0.0006448175,0.0024065163,0.0010192555,0.0036428373,0.0017757019],"domain_scores_gemma":[0.99000967,0.0012104986,0.00023997758,0.000904213,0.006769229,0.0008664066],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0062596495,0.00058393046,0.0011590164,0.0016429572,0.00088122534,0.000045917397,0.00078202144,0.0003061406,0.00102239],"category_scores_gemma":[0.0006518606,0.0006102961,0.0009265676,0.0034469964,0.0009989735,0.0011205202,0.000008261453,0.0015342901,0.00006590517],"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":[0.0048180916,0.0021807402,0.32279655,0.15806274,0.0026323174,0.00047571474,0.03665075,0.014602958,0.017256036,0.010868313,0.41361335,0.01604243],"study_design_scores_gemma":[0.002864856,0.0008157221,0.8349349,0.007748807,0.00031814995,0.0000021179214,0.0022918268,0.0018236019,0.006198302,0.0005930685,0.14122185,0.0011867959],"about_ca_topic_score_codex":0.00097522826,"about_ca_topic_score_gemma":0.0024489826,"teacher_disagreement_score":0.51213837,"about_ca_system_score_codex":0.00019416506,"about_ca_system_score_gemma":0.000674091,"threshold_uncertainty_score":0.9998908},"labels":[],"label_agreement":null},{"id":"W756876472","doi":"","title":"Comparative Long-Term Performance of Canada’s First Stone-Mix Asphalt Freeway Project","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Asphalt Pavement Performance Evaluation","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":"Asphalt; Rut; SMA*; Durability; Aggregate (composite); Christian ministry; Cement; Cracking; Environmental science; Geotechnical engineering; Engineering; Materials science; Forensic engineering; Composite material; Computer science","score_opus":0.0880624471624863,"score_gpt":0.3643017895857081,"score_spread":0.2762393424232218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W756876472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99229234,0.00045048934,0.00045659425,0.00022936912,0.0003160266,0.0028019962,0.0005193426,0.0002835828,0.0026502556],"genre_scores_gemma":[0.9945053,0.0016552111,0.0008921506,0.000021759362,0.00018220776,0.0007331484,0.00084378355,0.00011885551,0.0010475998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.988246,0.0005467165,0.0017758433,0.00087886973,0.0064974884,0.0020550408],"domain_scores_gemma":[0.99358904,0.00067793427,0.00025294945,0.000724057,0.0041939816,0.0005620175],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003985714,0.00057210244,0.00087749027,0.0013707606,0.0013539394,0.00005491818,0.0009011113,0.0002971613,0.00046536693],"category_scores_gemma":[0.0001310134,0.00060730486,0.0001781073,0.0027930252,0.0009616916,0.0013747098,0.00001662358,0.0018268845,0.000059594448],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017391357,0.00061552576,0.7828031,0.0047007534,0.00049372227,0.0002993574,0.04107702,0.12387049,0.005292036,0.00043245286,0.037146486,0.0015299041],"study_design_scores_gemma":[0.0017954817,0.0008640923,0.94327325,0.00047146936,0.00003701344,0.0000017373334,0.0029511424,0.008752502,0.038018167,0.000013259408,0.0031987946,0.00062311505],"about_ca_topic_score_codex":0.09109542,"about_ca_topic_score_gemma":0.64546466,"teacher_disagreement_score":0.5543693,"about_ca_system_score_codex":0.0008003246,"about_ca_system_score_gemma":0.0031052867,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W789223033","doi":"","title":"Comparison of GPS and Driver-Reported Urban Commercial Vehicle Tours and Stops","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","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":"Global Positioning System; Dwell time; Data collection; Computer science; Identification (biology); Transport engineering; Pencil (optics); Geography; Engineering; Statistics; Telecommunications; Psychology; Mathematics","score_opus":0.07124735779824873,"score_gpt":0.369409815186017,"score_spread":0.29816245738776825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W789223033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99267703,0.00057780504,0.0021624211,0.00048211127,0.00011371626,0.0011585339,0.00018979024,0.0011370541,0.0015015301],"genre_scores_gemma":[0.99614793,0.0017456035,0.0013732428,0.000027154325,0.00007732188,0.00017460033,0.00019841132,0.00006944964,0.00018629491],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99496835,0.00034913942,0.0010672369,0.00059653673,0.0021255976,0.0008931287],"domain_scores_gemma":[0.99738455,0.00043958033,0.000121919715,0.00036893357,0.0011894554,0.00049556803],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020391515,0.00030903926,0.0005718923,0.0010781812,0.000622983,0.00005649029,0.0003129406,0.00025759666,0.000054917757],"category_scores_gemma":[0.00011544821,0.000339307,0.000098149234,0.0011654369,0.0011852704,0.000560096,0.00001704764,0.0011863086,0.000008207421],"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":[0.00062546163,0.0004732232,0.8477471,0.0011766831,0.00027880896,0.00020328058,0.029472465,0.0017528718,0.009230743,0.0044467812,0.08754763,0.017044967],"study_design_scores_gemma":[0.0012833871,0.00052937213,0.9674363,0.0001519026,0.000042384232,0.0000012847241,0.0051639997,0.00396135,0.0051972475,0.000115021074,0.015772352,0.00034537562],"about_ca_topic_score_codex":0.0016089126,"about_ca_topic_score_gemma":0.002915843,"teacher_disagreement_score":0.11968924,"about_ca_system_score_codex":0.00007885891,"about_ca_system_score_gemma":0.00011376858,"threshold_uncertainty_score":0.9999059},"labels":[],"label_agreement":null},{"id":"W805067053","doi":"","title":"Growth and Change of Retail Opportunity in Greater Toronto Area","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","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":"Metropolitan area; Pace; Economic geography; Geography; Regional science; Business; Marketing","score_opus":0.20452241129078924,"score_gpt":0.3578139035987746,"score_spread":0.15329149230798533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W805067053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916619,0.00068495877,0.000015051601,0.0015751777,0.000105071704,0.0017790801,0.00012979747,0.00013251495,0.0039164363],"genre_scores_gemma":[0.9967151,0.0015268101,0.00021821157,0.00009603109,0.00013872377,0.00066023687,0.00022150148,0.000079418896,0.00034396545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9929553,0.00029466386,0.0012557259,0.0009919539,0.0031028658,0.001399461],"domain_scores_gemma":[0.9952583,0.00042864395,0.0002806764,0.0004904873,0.0033350945,0.00020683518],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00399622,0.0004280385,0.00075416913,0.0015579304,0.0007425352,0.000106236555,0.0005590023,0.0002580776,0.00074175774],"category_scores_gemma":[0.0005865927,0.00042938875,0.00016941078,0.0020848752,0.001361509,0.002787486,0.00005175615,0.0010548525,0.000036882357],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061351404,0.00024444022,0.9835513,0.0008184667,0.000044598877,0.00034306973,0.007523648,0.0000023261532,0.00065769494,0.002233826,0.0004170034,0.0035500801],"study_design_scores_gemma":[0.001473923,0.00013427869,0.98611873,0.00026328515,0.000041886935,6.559414e-7,0.007924277,0.000048425558,0.00022661731,0.0002402616,0.0031360353,0.00039161186],"about_ca_topic_score_codex":0.16618182,"about_ca_topic_score_gemma":0.18602853,"teacher_disagreement_score":0.019846713,"about_ca_system_score_codex":0.00013502198,"about_ca_system_score_gemma":0.00022872981,"threshold_uncertainty_score":0.99981576},"labels":[],"label_agreement":null}]}