{"meta":{"query_hash":"6783f1a3e53a","filters":{"topic":"Insurance, Mortality, Demography, Risk Management"},"cohort_total":1105,"direct_labels_cover":0,"predictions_cover":1105,"exported":1105,"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/6783f1a3e53a","api":"https://metacan.xera.ac/api/v1/cohort?topic=Insurance%2C+Mortality%2C+Demography%2C+Risk+Management"},"results":[{"id":"W1038830776","doi":"10.1017/cbo9780511807336.008","title":"Mortality Risk and Life Insurance","year":2012,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life insurance; Actuarial science; Business; Medicine","score_opus":0.03102816213873594,"score_gpt":0.2400045896089218,"score_spread":0.20897642747018585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1038830776","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":[0.0055728126,0.42164406,0.0060552717,0.025538377,0.0027855989,0.000023114466,0.0002090977,0.000071809365,0.5380999],"genre_scores_gemma":[0.11515624,0.39274856,0.007010661,0.013629687,0.007962446,0.00007651977,0.000492514,0.00011306074,0.4628103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9997851,0.00005113043,0.00000872287,0.000033488952,0.000098260214,0.000023366818],"domain_scores_gemma":[0.99975103,0.00017523063,0.000014138878,0.000011218712,0.000029180577,0.00001915029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033313455,0.0004768178,0.00032037066,0.0007046497,0.00055039383,0.0014905831,0.00032406952,0.0012402991,0.015547472],"category_scores_gemma":[0.0009227251,0.00012471816,0.00026154143,0.0008769355,0.001228367,0.0018094083,0.00072331977,0.0019256484,0.00352302],"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":[0.000012905916,0.000035165554,0.0005645287,0.000236534,0.00000857942,0.00010993969,0.0010016962,0.0008667961,0.0002993922,0.72519505,0.10600291,0.16566646],"study_design_scores_gemma":[0.000002944418,0.000021697148,0.001310318,0.00049824925,0.0000046092478,0.0003041048,0.00022425047,0.0002868669,0.000103352104,0.23658988,0.76064324,0.000010441447],"about_ca_topic_score_codex":0.0014283118,"about_ca_topic_score_gemma":0.0019323335,"teacher_disagreement_score":0.015547472,"about_ca_system_score_codex":0.0010380009,"about_ca_system_score_gemma":0.0007540693,"threshold_uncertainty_score":0.05201149},"labels":[],"label_agreement":null},{"id":"W109673681","doi":"","title":"Pricing of Equity-Linked Life Insurance Contracts with Flexible Guarantees","year":2004,"lang":"en","type":"article","venue":"Spectrum Research Repository (Concordia University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"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":"Life insurance; Equity (law); Endowment policy; Jump diffusion; Actuarial science; Business; Financial economics; Insurance policy; Economics; Jump; Microeconomics","score_opus":0.04450886057314695,"score_gpt":0.321874355036769,"score_spread":0.277365494463622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W109673681","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":[0.5039192,0.0011838757,0.48263717,0.0009793469,0.00012934045,0.00009482424,0.00011946599,0.00013313319,0.010803749],"genre_scores_gemma":[0.98905903,0.00021061562,0.0076185632,0.000032539858,0.00006354218,0.000027736869,0.00004227359,0.000015346235,0.0029303057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840254,0.0006845666,0.0000719055,0.00015810561,0.000482707,0.000200195],"domain_scores_gemma":[0.9959556,0.002031088,0.0006893398,0.00032597405,0.00026920432,0.000728873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033426513,0.0007608323,0.00088547956,0.00066863385,0.00051643717,0.002492384,0.001765367,0.0024415953,0.0025506916],"category_scores_gemma":[0.012280855,0.00052946736,0.0008080405,0.0006794164,0.002230095,0.003987427,0.0018254241,0.0017098254,0.00018706665],"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":[0.00043788794,0.00016549874,0.0021895058,0.00010378497,0.00008426899,0.0007170618,0.0002199478,0.28567284,0.004381023,0.68706113,0.0009221412,0.018044906],"study_design_scores_gemma":[0.00006159341,0.00011717784,0.0007128637,0.000017041124,0.000016876691,0.00012808762,0.000032773187,0.8577635,0.00049656676,0.13985601,0.0007745626,0.000022985287],"about_ca_topic_score_codex":0.00066566933,"about_ca_topic_score_gemma":0.00039492297,"teacher_disagreement_score":0.0033426513,"about_ca_system_score_codex":0.0012843937,"about_ca_system_score_gemma":0.00084949937,"threshold_uncertainty_score":0.017677903},"labels":[],"label_agreement":null},{"id":"W116368669","doi":"","title":"Опыт анализа динамики больших временных рядов демографических параметров стран мира и России","year":2013,"lang":"ru","type":"article","venue":"Пространство и Время","topic":"Insurance, Mortality, Demography, Risk Management","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":"Life expectancy; Population; Per capita; Demography; Fertility; Developed country; China; Developing country; Total fertility rate; Infant mortality; Geography; Economics; Research methodology; Family planning; Economic growth","score_opus":0.015007787919349473,"score_gpt":0.27493342018393235,"score_spread":0.2599256322645829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W116368669","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":[0.4625352,0.013688358,0.31966868,0.008706004,0.0019699826,0.0005997397,0.005818148,0.000773494,0.18624039],"genre_scores_gemma":[0.72260857,0.005838372,0.23479386,0.0003358197,0.00037105303,0.00073382235,0.001516958,0.00024711146,0.03355449],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981343,0.00043130128,0.00012241401,0.00035536458,0.00081972167,0.00013692667],"domain_scores_gemma":[0.99624383,0.0017648687,0.000530168,0.00050412235,0.00084306736,0.00011399746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027304695,0.0003512967,0.0003303174,0.0024894467,0.0007505578,0.0026406494,0.00041192214,0.0003607912,0.01333076],"category_scores_gemma":[0.007282987,0.00040684835,0.00078451674,0.0032568318,0.0010219729,0.0012654942,0.00084706635,0.0010332261,0.0036546043],"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":[0.00032208225,0.00022952058,0.06952848,0.0008505616,0.00024472323,0.00086270575,0.0054426156,0.006918642,0.011875182,0.173758,0.015420457,0.71454704],"study_design_scores_gemma":[0.00007946094,0.00037449968,0.15743594,0.0006736412,0.00035090285,0.0014701376,0.0067299334,0.015682664,0.023527917,0.16810744,0.6253203,0.00024718806],"about_ca_topic_score_codex":0.0059589217,"about_ca_topic_score_gemma":0.007948546,"teacher_disagreement_score":0.01333076,"about_ca_system_score_codex":0.00089191546,"about_ca_system_score_gemma":0.0022795745,"threshold_uncertainty_score":0.044595838},"labels":[],"label_agreement":null},{"id":"W129930478","doi":"10.2139/ssrn.224474","title":"An Econometric Model of Birth Inputs and Outputs: A Detailed Report","year":2000,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of British Columbia","funders":"","keywords":"Econometric model; Econometrics; Economics; Computer science","score_opus":0.012821772602163505,"score_gpt":0.2755061479775261,"score_spread":0.2626843753753626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W129930478","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":[0.10870901,0.0024683569,0.8321995,0.0043509277,0.00020121995,0.00037756405,0.014371727,0.0012786842,0.03604295],"genre_scores_gemma":[0.74724466,0.0055741407,0.08439617,0.0004238498,0.00042314144,0.0007761533,0.007451467,0.00034858153,0.15336192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993055,0.00029681603,0.000046458896,0.00013528868,0.00010684786,0.000109129534],"domain_scores_gemma":[0.997253,0.00213045,0.00023504683,0.00012942064,0.00020002935,0.000051976298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017726405,0.0007414992,0.0010011485,0.00097394275,0.00036774095,0.0018817612,0.0013281356,0.0019014603,0.022292765],"category_scores_gemma":[0.0065822904,0.00087371015,0.0015738545,0.002067364,0.0006334061,0.0018625063,0.0007927661,0.0014335274,0.0041512614],"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":[0.00009006582,0.00010849692,0.010035948,0.00016456566,0.0001485396,0.00044626248,0.00016022025,0.7973397,0.00081115804,0.15407863,0.0072462806,0.02937012],"study_design_scores_gemma":[0.000086204476,0.00008061575,0.008826316,0.00006121645,0.00017510453,0.00020800545,0.0001112751,0.88443905,0.0006983958,0.094702974,0.010538088,0.00007277229],"about_ca_topic_score_codex":0.030784616,"about_ca_topic_score_gemma":0.017763052,"teacher_disagreement_score":0.030784616,"about_ca_system_score_codex":0.0016572673,"about_ca_system_score_gemma":0.0022555937,"threshold_uncertainty_score":0.074576735},"labels":[],"label_agreement":null},{"id":"W131461519","doi":"","title":"Global Crisis in Fertility Theory: What Went Wrong? CRISE MONDIALE DANS LA THÉORIE DE FÉCONDITÉ: CE QUI NE VA PAS?","year":2008,"lang":"fr","type":"article","venue":"Canadian social science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fertility; Positive economics; Population; Politics; Sociology; Economics; Political science; Demography; Law","score_opus":0.014378133431029096,"score_gpt":0.27555892499504026,"score_spread":0.26118079156401114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W131461519","genre_codex":"commentary","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":[0.043081023,0.10276038,0.031235421,0.7533262,0.0023468889,0.000035475834,0.00022173497,0.000060315982,0.066932544],"genre_scores_gemma":[0.886184,0.060438085,0.0091213435,0.030536283,0.0041465457,0.00009907104,0.000112532965,0.00009569093,0.009266394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99810326,0.0011454958,0.000051222265,0.000251612,0.0002734251,0.00017484174],"domain_scores_gemma":[0.995188,0.0033102122,0.00029457995,0.00039211026,0.0005592998,0.00025585105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00921794,0.00037068702,0.0009029842,0.0015347134,0.0017459124,0.0026473224,0.0016256255,0.0029320577,0.0037202023],"category_scores_gemma":[0.013631834,0.00022240682,0.0007667701,0.0020374865,0.017321477,0.008328458,0.002690602,0.005559001,0.0003831694],"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":[0.000009201338,0.00000869268,0.0016113715,0.000073766474,0.000014847353,0.000043104646,0.0010799553,0.00064176053,0.00002422697,0.9781562,0.004580297,0.013756475],"study_design_scores_gemma":[0.000010868574,0.000015793697,0.0016655995,0.00017023455,0.000011006834,0.00007199913,0.0012051302,0.0009232193,0.000041739302,0.9643439,0.03152811,0.000012469023],"about_ca_topic_score_codex":0.014878269,"about_ca_topic_score_gemma":0.007381147,"teacher_disagreement_score":0.014878269,"about_ca_system_score_codex":0.005203271,"about_ca_system_score_gemma":0.00207855,"threshold_uncertainty_score":0.048749685},"labels":[],"label_agreement":null},{"id":"W132611911","doi":"10.1007/978-1-4020-4848-7_8","title":"Mortality at Extreme Ages and Data Quality: The Canadian Experience","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Exaggeration; Demography; Statistics; Psychology; Mathematics; Sociology","score_opus":0.36693967829719687,"score_gpt":0.42853905848759327,"score_spread":0.0615993801903964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W132611911","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":[0.47006828,0.12440459,0.0022841166,0.20542425,0.0011608028,0.00015156242,0.027832776,0.00030898626,0.16836473],"genre_scores_gemma":[0.8463817,0.113958485,0.0025052635,0.0052730395,0.00036647622,0.00006103611,0.0060346536,0.00018144294,0.02523785],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99310267,0.0006298121,0.0003191635,0.00036099507,0.004654738,0.000932627],"domain_scores_gemma":[0.9573326,0.0076948586,0.002620363,0.00083316746,0.023152528,0.008366446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010462962,0.0004261542,0.0007518561,0.004419467,0.0063368795,0.0059997984,0.002305465,0.0013082774,0.006366312],"category_scores_gemma":[0.031442907,0.00040988054,0.00057316764,0.0191878,0.0035972977,0.0021610588,0.0017815018,0.0020041228,0.00037978587],"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":[0.00031386028,0.00018528427,0.3180599,0.0006480797,0.00016335196,0.000649256,0.023246795,0.0019362438,0.00025326494,0.022626095,0.22774482,0.40417302],"study_design_scores_gemma":[0.000043844808,0.00006725971,0.7380459,0.0009867062,0.00011849743,0.00050289015,0.017865648,0.0011085324,0.00027278453,0.0023217108,0.23846658,0.00019966555],"about_ca_topic_score_codex":0.99745303,"about_ca_topic_score_gemma":0.9980982,"teacher_disagreement_score":0.10554447,"about_ca_system_score_codex":0.10554447,"about_ca_system_score_gemma":0.17196965,"threshold_uncertainty_score":0.7657823},"labels":[],"label_agreement":null},{"id":"W142522410","doi":"","title":"The Northern America Fertility Divide","year":2005,"lang":"en","type":"article","venue":"Policy review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Immigration; Population; Fertility; Geography; Government (linguistics); Total fertility rate; Development economics; Political science; Demographic economics; Economic growth; Demography; Economics; Sociology; Family planning; Law","score_opus":0.027610334068867773,"score_gpt":0.36668473714234223,"score_spread":0.3390744030734745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W142522410","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":[0.020880906,0.015873188,0.00031278253,0.03196125,0.0011866617,0.000064597494,0.009730779,0.00019920916,0.9197905],"genre_scores_gemma":[0.30030167,0.021452954,0.0009296854,0.021240141,0.0022717563,0.0003320309,0.013308375,0.00021308067,0.6399503],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99911755,0.00007453571,0.000016775772,0.0001230163,0.0002513319,0.0004168046],"domain_scores_gemma":[0.9993229,0.00005261412,0.000053719887,0.00003423516,0.000253042,0.00028353714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006896996,0.00030656063,0.00028849364,0.0017211288,0.004772728,0.0030997058,0.00086643914,0.00068272086,0.07335456],"category_scores_gemma":[0.0017837461,0.00013228547,0.00019920517,0.0020751306,0.0010147304,0.001276975,0.0021545098,0.0017515486,0.0107293],"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":[0.000050448325,0.000044024146,0.017222883,0.00011916711,0.000013235537,0.0003494282,0.004780982,0.000087747736,0.00020900402,0.13623694,0.6607649,0.18012121],"study_design_scores_gemma":[0.0000042576958,0.0000050015524,0.020523123,0.00008472621,0.000002568227,0.00007361464,0.001227058,0.000023443783,0.000038419927,0.0013710439,0.976642,0.0000047426556],"about_ca_topic_score_codex":0.3928179,"about_ca_topic_score_gemma":0.52857715,"teacher_disagreement_score":0.6071821,"about_ca_system_score_codex":0.009794886,"about_ca_system_score_gemma":0.010966614,"threshold_uncertainty_score":0.7810629},"labels":[],"label_agreement":null},{"id":"W1481977482","doi":"10.4054/mpidr-wp-2008-013","title":"Beyond the Kannisto-Thatcher Database on Old Age Mortality: an assessment of data quality at advanced ages","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":47,"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":"National Institute on Aging; Max-Planck-Institut für demografische Forschung","keywords":"Data quality; Database; Quality (philosophy); Quality assessment; Demography; Medicine; Computer science; Engineering; Operations management; External quality assessment; Sociology","score_opus":0.18166456564653247,"score_gpt":0.4818200956570313,"score_spread":0.3001555300104989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481977482","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":[0.8323036,0.019466903,0.043784242,0.011156155,0.00050114637,0.0012806022,0.06280087,0.00022597742,0.02848049],"genre_scores_gemma":[0.8993141,0.0059375614,0.044504188,0.0014224172,0.00027878225,0.0011400433,0.044267856,0.00014564948,0.0029894211],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.96501344,0.017958676,0.004730577,0.002403445,0.009124738,0.00076904334],"domain_scores_gemma":[0.87305117,0.056250945,0.022191068,0.021384858,0.024477722,0.002644324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035231777,0.00029128784,0.0012545974,0.008869326,0.0011872121,0.004513476,0.0019720842,0.0007322105,0.0016862763],"category_scores_gemma":[0.10516853,0.00027390206,0.001049806,0.01902667,0.0015053191,0.0035018893,0.0047310134,0.0010396815,0.00038013776],"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":[0.00040813326,0.00006441952,0.90180314,0.0010645019,0.0005266537,0.00024548217,0.0044987835,0.0023312054,0.00042268448,0.008968097,0.007892322,0.07177452],"study_design_scores_gemma":[0.000045151366,0.00014801769,0.94335735,0.0016099855,0.00041021634,0.00038982293,0.004466732,0.005277598,0.0013856194,0.006471032,0.03628113,0.0001573889],"about_ca_topic_score_codex":0.04284896,"about_ca_topic_score_gemma":0.062943086,"teacher_disagreement_score":0.04284896,"about_ca_system_score_codex":0.002572485,"about_ca_system_score_gemma":0.0067973416,"threshold_uncertainty_score":0.18632573},"labels":[],"label_agreement":null},{"id":"W1484074480","doi":"10.1080/10920277.2009.10597570","title":"Weighted Pricing Functionals With Applications to Insurance","year":2009,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":91,"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; York University","funders":"","keywords":"Actuarial science; Econometrics; Computer science; Economics; Mathematical economics","score_opus":0.012912493590566242,"score_gpt":0.2899613732737655,"score_spread":0.27704887968319925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484074480","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":[0.054875113,0.0011763622,0.9315199,0.0017123641,0.00020773512,0.000040107745,0.00005001067,0.000082020415,0.010336352],"genre_scores_gemma":[0.83367276,0.002442043,0.15167181,0.0004540415,0.0007251291,0.0001651237,0.00009672265,0.00015300645,0.010619402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987381,0.0006864156,0.00005456539,0.000106111096,0.00034209824,0.000072753406],"domain_scores_gemma":[0.9939851,0.0041844156,0.00034655584,0.00031648693,0.0008720181,0.00029540164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004739922,0.00070180383,0.0006459733,0.0020898737,0.00071116904,0.0016673417,0.0009439443,0.0010300492,0.002837547],"category_scores_gemma":[0.0140412375,0.00036311397,0.0008327259,0.0016918345,0.0021125216,0.003313383,0.0019491581,0.001959301,0.00022028752],"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":[0.000007762567,0.00002187555,0.0003699073,0.000025831412,0.000016829576,0.0000758767,0.000075227945,0.03146394,0.0004541828,0.9572599,0.00044978684,0.0097788945],"study_design_scores_gemma":[0.0000051538796,0.0000140822995,0.0002497562,0.000015881806,0.0000054499815,0.000055418885,0.00003017999,0.1829825,0.000109349145,0.81518483,0.00133817,0.000009280869],"about_ca_topic_score_codex":0.0012230916,"about_ca_topic_score_gemma":0.000820471,"teacher_disagreement_score":0.004739922,"about_ca_system_score_codex":0.0011103975,"about_ca_system_score_gemma":0.00077425246,"threshold_uncertainty_score":0.025067449},"labels":[],"label_agreement":null},{"id":"W1487883497","doi":"","title":"Multiperiod Statistical Risk Management Methods and Equity-Linked Life Insurance","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Equity (law); Actuarial science; Life insurance; Quantile; Investment management; Econometrics; Economics; Stock (firearms); Risk management; Business; Financial economics; Finance; Engineering; Political science","score_opus":0.05574827697127892,"score_gpt":0.4342932980715196,"score_spread":0.3785450211002407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1487883497","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":[0.0067499313,0.0016651506,0.9889262,0.00081477384,0.000142478,0.000028376417,0.000047019446,0.000083289575,0.0015427923],"genre_scores_gemma":[0.34089643,0.0043304577,0.6441709,0.0006670322,0.0015731807,0.00039730422,0.00028847242,0.00019780868,0.007478422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533564,0.0029751027,0.00016480601,0.00029711318,0.0011094374,0.0001179371],"domain_scores_gemma":[0.9792203,0.015170248,0.002272199,0.0019979947,0.0010726127,0.0002665914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011376528,0.00070734735,0.0005239405,0.002430841,0.00040457599,0.0013465079,0.0017004602,0.0011629205,0.0041061817],"category_scores_gemma":[0.029123677,0.00038646074,0.0010019834,0.0024161944,0.0015141482,0.0024546282,0.0016001839,0.0023287383,0.0005754135],"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":[0.000034664292,0.00011082115,0.0039498056,0.00014321628,0.00012428092,0.00014085574,0.0002454254,0.08517413,0.0013183949,0.7778246,0.0026643993,0.1282694],"study_design_scores_gemma":[0.000018053393,0.00007438093,0.0025114233,0.00006082188,0.000025928919,0.000120741955,0.00006914775,0.53075653,0.00082050514,0.45858738,0.0069096065,0.000045375797],"about_ca_topic_score_codex":0.0014984737,"about_ca_topic_score_gemma":0.0014820399,"teacher_disagreement_score":0.011376528,"about_ca_system_score_codex":0.0010665719,"about_ca_system_score_gemma":0.0010379436,"threshold_uncertainty_score":0.060165584},"labels":[],"label_agreement":null},{"id":"W1493457221","doi":"10.1002/9780470012505.tai012","title":"Inflation Impact on Aggregate Claims","year":2004,"lang":"en","type":"other","venue":"Encyclopedia of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université Laval; Concordia University","funders":"","keywords":"Inflation (cosmology); Aggregate (composite); Economics; Monetary economics; Offset (computer science); Interest rate; Computer science","score_opus":0.009666799246920176,"score_gpt":0.3133403636104768,"score_spread":0.30367356436355664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493457221","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":[0.9612059,0.0022558738,0.0015244161,0.0012657255,0.00007130516,0.000015709818,0.0021859203,0.00011305655,0.03136213],"genre_scores_gemma":[0.99810386,0.00020606394,0.00005947208,0.00003582921,0.00005197081,0.0000026189434,0.00049741636,0.000008846035,0.0010339707],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983564,0.00038780048,0.00011707127,0.00013809452,0.00079363014,0.00020699149],"domain_scores_gemma":[0.98602355,0.007303762,0.0031881568,0.00066516886,0.0021839396,0.0006354317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001185636,0.00013475587,0.00028043386,0.0013014512,0.00022627854,0.0016835051,0.00017620751,0.0003451803,0.0068301884],"category_scores_gemma":[0.010328328,0.000078834724,0.00028736432,0.0013041532,0.00026496517,0.0005425109,0.00082038384,0.000803585,0.0010420355],"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":[0.003241797,0.00048560128,0.76345855,0.00024194957,0.00046085374,0.001337335,0.0008920274,0.026818512,0.0071148444,0.013365293,0.009581149,0.17300206],"study_design_scores_gemma":[0.00002859786,0.00028877115,0.9627621,0.000048897957,0.00011541778,0.00072604953,0.00036355417,0.019106442,0.0021410561,0.009374549,0.005012033,0.000032474272],"about_ca_topic_score_codex":0.0023313076,"about_ca_topic_score_gemma":0.0009896627,"teacher_disagreement_score":0.0068301884,"about_ca_system_score_codex":0.00051715167,"about_ca_system_score_gemma":0.0002485427,"threshold_uncertainty_score":0.022849262},"labels":[],"label_agreement":null},{"id":"W1493538317","doi":"10.1111/j.1539-6975.2012.01469.x","title":"Mortality Portfolio Risk Management","year":2012,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":47,"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 New Brunswick; University of Manitoba","funders":"","keywords":"CVAR; Downside risk; Portfolio; Actuarial science; Diversification (marketing strategy); Risk management; Variance (accounting); Economics; Life insurance; Econometrics; Expected shortfall; Business; Financial economics; Finance","score_opus":0.018121243164823064,"score_gpt":0.30958329037993576,"score_spread":0.2914620472151127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493538317","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":[0.01330992,0.0007321755,0.979188,0.00047101596,0.00009361492,0.000039300845,0.0000733189,0.0001407996,0.0059517412],"genre_scores_gemma":[0.67638564,0.0014753998,0.307117,0.0002987746,0.000515235,0.00016819884,0.00030786166,0.00014046667,0.013591423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980751,0.00072322995,0.000086436834,0.00021679628,0.000796391,0.000102125065],"domain_scores_gemma":[0.9983917,0.00057406776,0.00028118296,0.0002625086,0.00040116746,0.00008931448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028965413,0.00057940924,0.00074441225,0.00092550926,0.00032915865,0.0015258946,0.0012151407,0.00086180225,0.0035802412],"category_scores_gemma":[0.00637737,0.0002898556,0.0005699285,0.00076704344,0.000394861,0.0014555369,0.0015904617,0.0011270923,0.00053989526],"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":[0.000059762333,0.000089696696,0.004021716,0.000088350884,0.00018514594,0.00013224328,0.00009363994,0.5298656,0.0041941623,0.15437728,0.006300944,0.30059135],"study_design_scores_gemma":[0.000012090452,0.000058708734,0.0010154679,0.000018196832,0.000026510776,0.00009965628,0.000017006261,0.93203485,0.0016823704,0.0594666,0.005547848,0.000020730386],"about_ca_topic_score_codex":0.0006562684,"about_ca_topic_score_gemma":0.00052680966,"teacher_disagreement_score":0.0035802412,"about_ca_system_score_codex":0.00070501276,"about_ca_system_score_gemma":0.0008837996,"threshold_uncertainty_score":0.0153185725},"labels":[],"label_agreement":null},{"id":"W1495493530","doi":"10.2470/rf.v2013.n1.1","title":"Life Annuities: An Optimal Product for Retirement Income","year":2013,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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":"York University; Research Foundation of CFA Institute","keywords":"Product (mathematics); Life annuity; Actuarial science; Economics; Business; Finance; Pension; Mathematics","score_opus":0.038585959578098385,"score_gpt":0.3236337980601003,"score_spread":0.2850478384820019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495493530","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":[0.010024583,0.032644622,0.3109041,0.007423748,0.0021563212,0.00007669801,0.00093239866,0.0007292354,0.63510835],"genre_scores_gemma":[0.32935208,0.040754315,0.18219492,0.001970965,0.0035702125,0.00034112102,0.0010608255,0.001245003,0.43951052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996681,0.00006416217,0.000013883203,0.0000664779,0.00016636324,0.000021074002],"domain_scores_gemma":[0.9997832,0.000100758014,0.000023188515,0.000027552172,0.00004651077,0.000018707096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062151277,0.0007802084,0.00045982984,0.0006505747,0.000437758,0.0028756876,0.000718423,0.0009770864,0.023685638],"category_scores_gemma":[0.002361035,0.000368732,0.00037785777,0.00078448164,0.0009210478,0.0034809723,0.0008786651,0.0015556368,0.0069799456],"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":[0.000015372138,0.000011423745,0.00016925833,0.00007323776,0.000006829768,0.000024821999,0.00007910579,0.003228224,0.0004946763,0.8972517,0.034114853,0.06453051],"study_design_scores_gemma":[0.0000056067065,0.000032767173,0.0006509615,0.00018524026,0.000014138824,0.00029097698,0.00009084397,0.012678834,0.00063570065,0.6698723,0.31551883,0.000023900064],"about_ca_topic_score_codex":0.0005020354,"about_ca_topic_score_gemma":0.0007916484,"teacher_disagreement_score":0.023685638,"about_ca_system_score_codex":0.0009608896,"about_ca_system_score_gemma":0.0008836691,"threshold_uncertainty_score":0.07923633},"labels":[],"label_agreement":null},{"id":"W1499059929","doi":"","title":"Helen Macbeth and Paul Collinson, Eds., Human Population Dynamics: Cross-Disciplinary Perspectives","year":2003,"lang":"en","type":"article","venue":"The Canadian Journal of Sociology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Discipline; Sociology; Realm; Population; Social science; Cross disciplinary; Subject (documents); Epistemology; Demography; Law; Political science; Library science","score_opus":0.02245200449252908,"score_gpt":0.3395825476341959,"score_spread":0.3171305431416668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499059929","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":[0.00018401888,0.9797528,0.00087991334,0.0054002483,0.0015946747,0.000009497604,0.0001560676,0.000040524523,0.011982243],"genre_scores_gemma":[0.002414006,0.97504914,0.0012889323,0.0011013173,0.0017830014,0.00004169606,0.00020766119,0.00004003639,0.018074268],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99905604,0.00021921442,0.00008910005,0.00015705546,0.0004041749,0.00007442465],"domain_scores_gemma":[0.9975005,0.0015504058,0.00021589859,0.00009674782,0.0003493527,0.00028700146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022571,0.0027408325,0.0028541132,0.0056018177,0.0011498503,0.005978162,0.0024944867,0.0027487224,0.027843276],"category_scores_gemma":[0.0034353193,0.0020339051,0.00074599695,0.009886851,0.0024788326,0.008378291,0.0029058754,0.004034911,0.013193556],"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":[0.00003483292,0.00006727611,0.0009052027,0.0025997292,0.000054743094,0.0002038839,0.0024888103,0.000886363,0.00015415068,0.024301749,0.6717617,0.2965415],"study_design_scores_gemma":[0.000008359592,0.000019853622,0.0027549926,0.0033665248,0.00002715087,0.00049535325,0.0011578699,0.00026332433,0.00005300761,0.015635926,0.97619027,0.000027288139],"about_ca_topic_score_codex":0.016493117,"about_ca_topic_score_gemma":0.02426753,"teacher_disagreement_score":0.027843276,"about_ca_system_score_codex":0.0026419442,"about_ca_system_score_gemma":0.003331966,"threshold_uncertainty_score":0.09314501},"labels":[],"label_agreement":null},{"id":"W1499840552","doi":"10.1002/9780470015902.a0005206","title":"Insurance and Human Genetics: Insurance Market Perspective","year":2008,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph","funders":"","keywords":"Perspective (graphical); Life insurance; Actuarial science; Disability insurance; Health insurance; Business; Economics; Health care; Computer science; Economic growth","score_opus":0.017836116297683596,"score_gpt":0.3019201777310351,"score_spread":0.28408406143335146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499840552","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":[0.24617383,0.03130717,0.019694885,0.07007303,0.00055059005,0.00016734067,0.0031709722,0.00008621322,0.628776],"genre_scores_gemma":[0.97674155,0.008240554,0.0023911346,0.002336418,0.00050790654,0.000045350404,0.00022422701,0.000010718278,0.009502152],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898154,0.00054813136,0.000013531687,0.0000628957,0.00029685625,0.000096972166],"domain_scores_gemma":[0.99686486,0.0023164605,0.00023693118,0.00008240656,0.00028924682,0.00021007241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022444,0.0004780097,0.00044337037,0.002015021,0.0004953749,0.0031031407,0.000568554,0.0019073643,0.020850282],"category_scores_gemma":[0.0032439122,0.00013703191,0.00061445584,0.0018263462,0.0016351466,0.0017935213,0.0010577282,0.0012938115,0.000428177],"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":[0.0003276463,0.00054380065,0.0269218,0.00032970437,0.0003030415,0.00080884236,0.00022398558,0.05658013,0.0013714639,0.8274541,0.022449259,0.062686235],"study_design_scores_gemma":[0.00015748382,0.0005672027,0.04564496,0.00049266167,0.0002228063,0.0004311788,0.0018203112,0.06013009,0.0008445293,0.83848685,0.051130354,0.0000714906],"about_ca_topic_score_codex":0.009287587,"about_ca_topic_score_gemma":0.010382899,"teacher_disagreement_score":0.020850282,"about_ca_system_score_codex":0.0019653179,"about_ca_system_score_gemma":0.0015356059,"threshold_uncertainty_score":0.069751084},"labels":[],"label_agreement":null},{"id":"W1502771836","doi":"10.3386/w15170","title":"Market Valuation of Accrued Social Security Benefits","year":2009,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"Australian Government; U.S. Social Security Administration","keywords":"Valuation (finance); Social security; Business; Actuarial science; Economics; Finance; Market economy","score_opus":0.40456935506663017,"score_gpt":0.5438499606667133,"score_spread":0.13928060560008315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502771836","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":[0.64768666,0.0053330646,0.15707563,0.006662171,0.00055403844,0.00047500344,0.0045925006,0.00029623022,0.17732468],"genre_scores_gemma":[0.97977465,0.00085813977,0.0068865404,0.000077497374,0.00014440081,0.000083066785,0.0008863676,0.000025436439,0.011263963],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964187,0.0012848845,0.00017402225,0.000254693,0.0016579552,0.00020970724],"domain_scores_gemma":[0.98835164,0.006292988,0.0024143383,0.0007970334,0.0018112244,0.0003326723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005390979,0.00047856887,0.0005636501,0.0018691862,0.00041977694,0.0040211733,0.0009798104,0.0014675544,0.0067986012],"category_scores_gemma":[0.02549259,0.0002729581,0.000581257,0.0014041909,0.0012055252,0.0049430933,0.0007027756,0.001509111,0.00058830983],"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":[0.00048178562,0.00032800183,0.028057624,0.0001776161,0.00017657522,0.0005099939,0.0008480089,0.0855025,0.0049343286,0.80548835,0.006380404,0.067114785],"study_design_scores_gemma":[0.0001289678,0.00078821735,0.06332971,0.00028622666,0.000118168864,0.0006483001,0.0014128598,0.3284871,0.006132491,0.55669487,0.04177601,0.00019716525],"about_ca_topic_score_codex":0.0023682623,"about_ca_topic_score_gemma":0.0015519623,"teacher_disagreement_score":0.0067986012,"about_ca_system_score_codex":0.0029975465,"about_ca_system_score_gemma":0.0010652072,"threshold_uncertainty_score":0.02851051},"labels":[],"label_agreement":null},{"id":"W1506773677","doi":"10.1002/9780470245842.ch19","title":"Method and Theory in Paleodemography, with an Application to a Hunting, Fishing and Gathering Village from the Late Eastern Woodlands of North America","year":2007,"lang":"en","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Woodland; Osteology; Geography; Fishing; Context (archaeology); Fishing village; Archaeology; Wildlife; Fertility; Fishery; Forestry; Ecology; Population; Demography; Sociology; Biology","score_opus":0.0095148359742518,"score_gpt":0.28145377104865804,"score_spread":0.27193893507440625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506773677","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":[0.0023316212,0.0028508343,0.976715,0.0024860937,0.00052121666,0.00028878753,0.00030430712,0.00038078788,0.014121298],"genre_scores_gemma":[0.027878447,0.004853631,0.9525711,0.00086694397,0.00041226583,0.0028084407,0.00031480606,0.00038828823,0.009906172],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953244,0.0035939782,0.00023552998,0.00035677955,0.0004190567,0.00007024083],"domain_scores_gemma":[0.9871033,0.011179468,0.00026106072,0.00062732334,0.0006666108,0.00016224322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01079576,0.0012870898,0.0011591966,0.005507111,0.0018300845,0.00307112,0.0015307516,0.0019422809,0.013990084],"category_scores_gemma":[0.021629162,0.00081852736,0.0012476797,0.0074188435,0.00599801,0.0034874885,0.0023402593,0.0032880392,0.002795865],"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":[0.000023352728,0.00011071336,0.009090234,0.0005839597,0.00006955739,0.0004722731,0.0036065292,0.0072813053,0.0006030482,0.6530576,0.028748412,0.29635313],"study_design_scores_gemma":[0.00008249089,0.00009489845,0.012866717,0.0011088623,0.00007617209,0.0028961888,0.0035962553,0.050143484,0.00049063674,0.72558296,0.20291081,0.00015061793],"about_ca_topic_score_codex":0.007809649,"about_ca_topic_score_gemma":0.013290842,"teacher_disagreement_score":0.013990084,"about_ca_system_score_codex":0.0017981251,"about_ca_system_score_gemma":0.0033923343,"threshold_uncertainty_score":0.057094097},"labels":[],"label_agreement":null},{"id":"W1510165332","doi":"10.7202/010095ar","title":"Estimation de la mortalité selon l’âge et l’état de santé à partir d’une enquête longitudinale","year":2004,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humanities; Political science; Philosophy","score_opus":0.010384621555119187,"score_gpt":0.29488500986249666,"score_spread":0.28450038830737745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510165332","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":[0.9236315,0.0028251733,0.063840054,0.0013418094,0.00007824656,0.00013727906,0.005281623,0.00013771007,0.0027265777],"genre_scores_gemma":[0.96918416,0.0009081708,0.0219454,0.0001571926,0.000028442751,0.00024134711,0.0025859058,0.000019958132,0.0049293865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99825245,0.0010522531,0.00008643554,0.00026643503,0.0001964773,0.00014602735],"domain_scores_gemma":[0.9939977,0.0029658081,0.00085117656,0.00091732305,0.0010567666,0.00021128853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007237641,0.0004750979,0.00070441706,0.0009968161,0.0007852262,0.0009774202,0.0005674301,0.00058020477,0.0029887352],"category_scores_gemma":[0.013424376,0.00035219107,0.0012814605,0.0013332772,0.00033253166,0.00072284124,0.0009160793,0.0007426816,0.00045481746],"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":[0.00026432256,0.00010271584,0.9301508,0.00018749783,0.00087484333,0.000068695634,0.0010454549,0.015063433,0.0019250639,0.0015567283,0.0010990336,0.0476614],"study_design_scores_gemma":[0.000022823915,0.00028389072,0.9609623,0.0001861927,0.0004062911,0.000070427224,0.0013294707,0.0278563,0.0012936401,0.001641512,0.0058970046,0.000050136983],"about_ca_topic_score_codex":0.34212628,"about_ca_topic_score_gemma":0.40761796,"teacher_disagreement_score":0.34212628,"about_ca_system_score_codex":0.0018722126,"about_ca_system_score_gemma":0.003282869,"threshold_uncertainty_score":0.6802698},"labels":[],"label_agreement":null},{"id":"W1512796238","doi":"10.1111/rmir.12018","title":"Measuring Longevity Risk: An Application to the Royal Canadian Mounted Police Pension Plan","year":2014,"lang":"en","type":"article","venue":"Risk Management and Insurance Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Pension; Beneficiary; Longevity risk; Actuarial science; Liability; Longevity; Value (mathematics); Business; Economics; Finance; Medicine; Gerontology; Statistics","score_opus":0.021415304731115838,"score_gpt":0.27827356148914234,"score_spread":0.2568582567580265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1512796238","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":[0.66555244,0.24165608,0.0107130455,0.007701518,0.00035488693,0.0006979896,0.006230809,0.00011438286,0.066978894],"genre_scores_gemma":[0.9515622,0.038333938,0.00725838,0.00015629364,0.00006011909,0.000051809788,0.0007916185,0.0000046288274,0.0017809769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979418,0.00050916936,0.00009366393,0.00007880843,0.001286376,0.00009018919],"domain_scores_gemma":[0.99396175,0.0021980363,0.00076303346,0.00009829345,0.002818019,0.00016079075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005448303,0.0003281266,0.0003147667,0.0039867493,0.00069174357,0.0009817324,0.0005996087,0.00033445613,0.0011710062],"category_scores_gemma":[0.013503789,0.000081431375,0.00040974846,0.0046858927,0.00024872593,0.00030351983,0.00042441927,0.00041785475,0.000096068965],"study_design_candidate":"simulation_or_modeling","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":[0.00018207972,0.00017027983,0.29815805,0.0036161144,0.0006019921,0.00035907704,0.0032679623,0.023542896,0.00065229,0.014247782,0.019015787,0.63618565],"study_design_scores_gemma":[0.00004809659,0.00057509623,0.8400605,0.004895134,0.0011654105,0.00048855797,0.005554033,0.031445235,0.001879734,0.0048347553,0.108860485,0.00019299242],"about_ca_topic_score_codex":0.7831269,"about_ca_topic_score_gemma":0.819793,"teacher_disagreement_score":0.21687311,"about_ca_system_score_codex":0.00985274,"about_ca_system_score_gemma":0.011852472,"threshold_uncertainty_score":0.43630058},"labels":[],"label_agreement":null},{"id":"W1513303558","doi":"10.1080/10920277.2010.10597594","title":"Mortality Projections for Social Security Programs in Canada","year":2010,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Actua; Office of the Chief Medical Examiner","funders":"","keywords":"Projections of population growth; Baby boomers; Social security; Demography; Population projection; Mortality rate; Life expectancy; Population; Total fertility rate; Fertility; Population growth; Population ageing; Geography; Demographic economics; Economics; Research methodology; Sociology","score_opus":0.03631271882966235,"score_gpt":0.31689542335495924,"score_spread":0.2805827045252969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513303558","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":[0.6027109,0.005096258,0.016539415,0.014742981,0.00038681537,0.0004396044,0.25819978,0.001290272,0.1005939],"genre_scores_gemma":[0.90550274,0.0058666216,0.008814266,0.00050610513,0.000050826748,0.00019915558,0.052886575,0.00006936964,0.026104253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994228,0.00004943369,0.000015356254,0.000033188873,0.00031169518,0.0001675727],"domain_scores_gemma":[0.99877995,0.000031677693,0.000043385735,0.000011136537,0.0010056983,0.0001281703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006475717,0.0006044771,0.00023065884,0.0020274057,0.0014573102,0.0012248643,0.00090035575,0.00037088958,0.004470548],"category_scores_gemma":[0.0014147984,0.00017342603,0.00072714523,0.002411255,0.0001986248,0.00041941696,0.00067081675,0.00063102867,0.00067088864],"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":[0.00036364514,0.00013949831,0.18913987,0.00045418268,0.0003236786,0.0005732681,0.0009877088,0.44630972,0.0012258905,0.045226816,0.20079918,0.11445655],"study_design_scores_gemma":[0.00014677935,0.00014774868,0.31128284,0.000614205,0.0002493253,0.0002432716,0.003421446,0.47540987,0.0017968251,0.008231891,0.19823985,0.00021596966],"about_ca_topic_score_codex":0.99170214,"about_ca_topic_score_gemma":0.9917659,"teacher_disagreement_score":0.04025017,"about_ca_system_score_codex":0.04025017,"about_ca_system_score_gemma":0.045398146,"threshold_uncertainty_score":0.29203677},"labels":[],"label_agreement":null},{"id":"W1514003801","doi":"10.1080/10920277.2012.10590638","title":"The Impact of the Automatic Balancing Mechanism for the Public Pension in Japan on the Extreme Elderly","year":2012,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Social security; Pension; Dependency ratio; Government (linguistics); Mechanism (biology); Affect (linguistics); Demographics; Old Age Security; Economics; Financial security; Fertility; Business; Demographic economics; Finance; Demography; Birth rate; Psychology; Sociology; Population","score_opus":0.03776710667564167,"score_gpt":0.308536286899815,"score_spread":0.27076918022417334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514003801","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":[0.97498024,0.0011685303,0.0065144324,0.0043741562,0.00008414011,0.000016497157,0.00013756506,0.000023072758,0.012701394],"genre_scores_gemma":[0.9982685,0.00042851557,0.00057854125,0.00010936075,0.000032974818,0.0000069113835,0.000029361154,0.0000033309357,0.0005425832],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994659,0.00025103093,0.00002646303,0.000057782112,0.00008879425,0.000110161316],"domain_scores_gemma":[0.99890697,0.0003880234,0.0002822598,0.00007502615,0.0002104317,0.0001373836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017240684,0.00021466351,0.00018966912,0.00037577897,0.0006223681,0.0011320367,0.00039665741,0.0006498407,0.0014509023],"category_scores_gemma":[0.0042668423,0.00013448349,0.00043735647,0.00030039175,0.00070344965,0.0011762207,0.0010041915,0.00046651997,0.00010334751],"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":[0.0010800553,0.00043074306,0.37209463,0.00035783628,0.00023230043,0.003993728,0.0035850387,0.21475556,0.010165137,0.28498834,0.011298284,0.09701836],"study_design_scores_gemma":[0.00017208599,0.0007430425,0.5207489,0.00021473649,0.00040029737,0.0008405935,0.00644087,0.33899415,0.0032702584,0.1113404,0.016626494,0.0002081889],"about_ca_topic_score_codex":0.015679419,"about_ca_topic_score_gemma":0.014946888,"teacher_disagreement_score":0.015679419,"about_ca_system_score_codex":0.0014138651,"about_ca_system_score_gemma":0.0012814868,"threshold_uncertainty_score":0.031176329},"labels":[],"label_agreement":null},{"id":"W1514322394","doi":"10.1080/10920277.2011.10597614","title":"Longevity Risk and Capital Markets","year":2011,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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 Manitoba","funders":"","keywords":"Longevity; Longevity risk; Business; Economics; Risk analysis (engineering); Medicine; Gerontology","score_opus":0.01699035277314767,"score_gpt":0.2563190564023504,"score_spread":0.23932870362920275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514322394","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":[0.023126733,0.4524966,0.00728867,0.24687096,0.042758957,0.000029535715,0.00092068955,0.0001626478,0.22634517],"genre_scores_gemma":[0.40971467,0.25268394,0.0023433792,0.018028203,0.14715499,0.00006085257,0.00082136557,0.000116531635,0.16907616],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999524,0.00013307406,0.000028151326,0.000053155436,0.00020235813,0.000059373866],"domain_scores_gemma":[0.9979589,0.00096867356,0.00027253767,0.00010434488,0.0004579427,0.00023768756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013459855,0.0002916335,0.0003366094,0.0013533102,0.00066890183,0.0027339503,0.00030985303,0.0013376481,0.014895094],"category_scores_gemma":[0.005232432,0.00009291138,0.00024943365,0.001246454,0.0009067924,0.002391879,0.0006610268,0.0015673976,0.0015161198],"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":[0.00004017435,0.000045757497,0.0026431973,0.00015666841,0.00003209418,0.00010590952,0.00034555662,0.0007810295,0.0002329122,0.32943422,0.5364308,0.12975176],"study_design_scores_gemma":[0.000012934505,0.00004703905,0.008988513,0.00027491892,0.000014245607,0.00022060893,0.0003021639,0.0010999321,0.00011422365,0.33567145,0.65323025,0.000023731594],"about_ca_topic_score_codex":0.0010555706,"about_ca_topic_score_gemma":0.0014168202,"teacher_disagreement_score":0.014895094,"about_ca_system_score_codex":0.0009822213,"about_ca_system_score_gemma":0.0013335389,"threshold_uncertainty_score":0.049829006},"labels":[],"label_agreement":null},{"id":"W1515975278","doi":"10.7202/010853ar","title":"Intérêt de l’analyse des causes multiples dans l’étude de la mortalité aux grands âges : l’exemple français","year":2005,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Humanities; Philosophy; Medicine","score_opus":0.014781796534741336,"score_gpt":0.28912595003420327,"score_spread":0.27434415349946195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515975278","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":[0.85729855,0.08019925,0.015816484,0.017183226,0.0011370542,0.00023797067,0.0062173693,0.0000919035,0.021818131],"genre_scores_gemma":[0.9763762,0.012765526,0.0035723154,0.0014449435,0.0006295375,0.00014640906,0.0010969428,0.000043796983,0.0039244164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928277,0.003390613,0.00070498924,0.0011088606,0.0013901783,0.00057752745],"domain_scores_gemma":[0.9691844,0.0151551645,0.007641811,0.0014387117,0.0054432712,0.0011366806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012187374,0.0007993516,0.001062109,0.003057305,0.0014086834,0.0026510376,0.0006945821,0.0009488297,0.005451107],"category_scores_gemma":[0.020441372,0.0002983664,0.0024713743,0.0034215448,0.0009536785,0.0011925662,0.0013508507,0.0014299061,0.0005235825],"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":[0.00022934448,0.000058202277,0.92183286,0.0013371136,0.002081896,0.00056342094,0.005964644,0.0005544759,0.00048242713,0.0030665195,0.0027703182,0.06105877],"study_design_scores_gemma":[0.000016562459,0.00024481473,0.9666563,0.0012445601,0.0011155682,0.0008598118,0.0030627523,0.0006864032,0.00037630624,0.0015306739,0.024160223,0.000046036414],"about_ca_topic_score_codex":0.13172144,"about_ca_topic_score_gemma":0.17119095,"teacher_disagreement_score":0.13172144,"about_ca_system_score_codex":0.0028312642,"about_ca_system_score_gemma":0.0055615744,"threshold_uncertainty_score":0.26190948},"labels":[],"label_agreement":null},{"id":"W1518331148","doi":"10.2139/ssrn.1628279","title":"Estimating Dynamic Models with Aggregate Shocks and an Application to Mortgage Default in Colombia","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Economics; Aggregate (composite); Econometrics; Default; Monetary economics; Finance","score_opus":0.005915401131700121,"score_gpt":0.28957143886742,"score_spread":0.28365603773571985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518331148","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":[0.9666121,0.00085442147,0.027647085,0.0010860757,0.00004567297,0.00008819704,0.001483087,0.00020442656,0.0019789916],"genre_scores_gemma":[0.99171466,0.00033229095,0.0054053115,0.000027147462,0.000025085423,0.000049597522,0.0008393386,0.00002681604,0.0015796724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.00032050756,0.00004761013,0.00015163807,0.000035046098,0.0000952263],"domain_scores_gemma":[0.9929321,0.0058952267,0.0004525938,0.00020194068,0.00025612678,0.00026197068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001902838,0.00090747396,0.0015873014,0.0009517885,0.0007965622,0.0025359397,0.001490336,0.0017428203,0.0030854032],"category_scores_gemma":[0.0133144,0.00079381577,0.00079252856,0.0012553589,0.0006790759,0.0012885217,0.0013700769,0.0014109141,0.00020448974],"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":[0.0002845337,0.00013980824,0.039709866,0.000089927074,0.00015475627,0.0002764805,0.00022792781,0.9396861,0.00024266863,0.006522906,0.0014741094,0.011191019],"study_design_scores_gemma":[0.000045571425,0.000031918767,0.008084213,0.0000108583645,0.000032825432,0.000020206526,0.0002313571,0.9885076,0.000075147334,0.0025305753,0.0004020048,0.000027634433],"about_ca_topic_score_codex":0.31755793,"about_ca_topic_score_gemma":0.22674477,"teacher_disagreement_score":0.31755793,"about_ca_system_score_codex":0.003747512,"about_ca_system_score_gemma":0.0014607954,"threshold_uncertainty_score":0.63141906},"labels":[],"label_agreement":null},{"id":"W1528472061","doi":"10.58079/ou99","title":"Dynamic dependence ordering for Archimedean copulas and distorted copulas","year":2008,"lang":"en","type":"preprint","venue":"OpenEdition (OpenEdition)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"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; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Copula (linguistics); Statistical physics; Tail dependence; Econometrics; Mathematics; Economics; Applied mathematics; Mathematical economics; Statistics; Physics; Multivariate statistics","score_opus":0.026092234761983638,"score_gpt":0.3020796037441982,"score_spread":0.27598736898221454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528472061","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":[0.07625755,0.0013755555,0.90040106,0.0011863306,0.0001824445,0.000053328826,0.00055095565,0.000113674454,0.019879106],"genre_scores_gemma":[0.90264523,0.0026525243,0.07805566,0.00044910406,0.00048919144,0.00014246177,0.0013499622,0.00020505652,0.014010839],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971328,0.0011293471,0.0001522506,0.00036602418,0.00087767886,0.0003418568],"domain_scores_gemma":[0.98915267,0.0052122036,0.001600486,0.0012278737,0.002085129,0.00072162156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038506472,0.0009575799,0.0007667292,0.0019030727,0.0007043973,0.0022724818,0.0011370683,0.00074327,0.0061939936],"category_scores_gemma":[0.018118238,0.00053406326,0.0012364502,0.002196674,0.0021888013,0.0039277305,0.001582825,0.0029491591,0.00061656005],"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":[0.000033512308,0.000016713406,0.001360105,0.00004051402,0.000027772507,0.00015927713,0.00014800503,0.022759538,0.0009564232,0.9655694,0.0013120668,0.0076166806],"study_design_scores_gemma":[0.000010535634,0.000033621753,0.001537395,0.000019738649,0.000014135166,0.00017165476,0.00007654059,0.15093325,0.0005518049,0.84256446,0.00405157,0.000035317647],"about_ca_topic_score_codex":0.0028042211,"about_ca_topic_score_gemma":0.001512594,"teacher_disagreement_score":0.0061939936,"about_ca_system_score_codex":0.0020561158,"about_ca_system_score_gemma":0.0011511232,"threshold_uncertainty_score":0.020720959},"labels":[],"label_agreement":null},{"id":"W1531173525","doi":"","title":"Over the moon about even numbers","year":2000,"lang":"en","type":"article","venue":"Europe PMC (PubMed Central)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Rounding; Preference; Computer science; Gestational age; Statistics; Pregnancy; Mathematics; Biology","score_opus":0.013787927728250833,"score_gpt":0.2501377011442916,"score_spread":0.2363497734160408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531173525","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":[0.022445276,0.093920715,0.008575094,0.75953674,0.028209042,0.000018852814,0.00059830927,0.00014640449,0.08654953],"genre_scores_gemma":[0.48118147,0.11469843,0.0064835898,0.2833472,0.08892018,0.00010060369,0.00043819688,0.0006170027,0.024213273],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919666,0.0031471082,0.00044806596,0.0012858447,0.002787923,0.0003645018],"domain_scores_gemma":[0.9474818,0.0363857,0.0036637452,0.004517801,0.006179756,0.00177118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01515288,0.00038023724,0.00066848187,0.002227565,0.0028082244,0.0055226586,0.00095972954,0.0025424177,0.011940023],"category_scores_gemma":[0.08593346,0.00032915175,0.00028167802,0.0022203245,0.017252922,0.012364175,0.003657182,0.008373189,0.0026208935],"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":[0.00047876823,0.00002842499,0.0098349545,0.00072099816,0.00007476699,0.00055131904,0.005948306,0.00025756695,0.0006377415,0.30219227,0.31762394,0.361651],"study_design_scores_gemma":[0.000035099732,0.0000663321,0.0065470957,0.0015944878,0.00004109966,0.0012759127,0.0054298267,0.0002130136,0.0006999093,0.30955932,0.67446244,0.00007542275],"about_ca_topic_score_codex":0.002592397,"about_ca_topic_score_gemma":0.0031044746,"teacher_disagreement_score":0.01515288,"about_ca_system_score_codex":0.0018806959,"about_ca_system_score_gemma":0.0011374442,"threshold_uncertainty_score":0.080137074},"labels":[],"label_agreement":null},{"id":"W1531186856","doi":"","title":"\"Multiperiod Statistical Risk Management Methods and Equity-Linked Life Insurance\"(in Japanese)","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Equity (law); Life insurance; Actuarial science; Quantile; Economics; Investment management; Econometrics; Stock (firearms); Business; Financial economics; Finance; Political science; Geography","score_opus":0.05287010066607218,"score_gpt":0.42761341329268593,"score_spread":0.37474331262661376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531186856","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":[0.021957045,0.008051857,0.9594685,0.003881093,0.0012479206,0.000056370674,0.00016819387,0.00019733289,0.004971567],"genre_scores_gemma":[0.39790416,0.009068274,0.5676671,0.0016987367,0.0030545287,0.000303781,0.00034333722,0.00024869072,0.01971139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998589,0.0007188735,0.00007326484,0.00014984018,0.00040631698,0.00006283251],"domain_scores_gemma":[0.9970427,0.0017282179,0.000421595,0.00033564388,0.0003984395,0.000073400304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047477246,0.00047391688,0.00041624502,0.0014940589,0.00034872658,0.0010150205,0.0009063161,0.0006572715,0.0041571204],"category_scores_gemma":[0.010849897,0.00029116348,0.00079626695,0.0017473641,0.0015810777,0.0017182251,0.0010527596,0.0012837725,0.0005571104],"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":[0.000050833914,0.00008741447,0.006654688,0.0002957264,0.00014891372,0.00027527797,0.0005220347,0.034355972,0.0017770908,0.7159161,0.016595146,0.22332083],"study_design_scores_gemma":[0.00002983818,0.00013718077,0.015844261,0.00013228162,0.00009957611,0.0003450514,0.00023017604,0.42673928,0.0020695927,0.5066951,0.0475782,0.000099468],"about_ca_topic_score_codex":0.0039056614,"about_ca_topic_score_gemma":0.0047576446,"teacher_disagreement_score":0.0047477246,"about_ca_system_score_codex":0.0010538754,"about_ca_system_score_gemma":0.00092525,"threshold_uncertainty_score":0.025108635},"labels":[],"label_agreement":null},{"id":"W1531267011","doi":"10.2139/ssrn.1335454","title":"Complete Market Valuation of the Ruin-Contingent Life Annuity (RCLA)","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Annuity; Actuarial science; Life annuity; Valuation (finance); Economics; Financial economics; Business; Finance; Pension","score_opus":0.02270781748268383,"score_gpt":0.2853589306841683,"score_spread":0.2626511132014845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531267011","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":[0.43739742,0.003789087,0.46241257,0.0038807134,0.00034064354,0.0001799944,0.0016138512,0.0002436395,0.090142086],"genre_scores_gemma":[0.97540057,0.0007561999,0.010604034,0.00009516819,0.00022795396,0.000056135148,0.00025170317,0.000036269892,0.012571951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991875,0.00035992998,0.00003388248,0.00014475556,0.00017337633,0.000100477926],"domain_scores_gemma":[0.9969177,0.0015575478,0.00042151922,0.0003531364,0.00039355597,0.0003564025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003279337,0.00066594087,0.00129823,0.00072793354,0.00038589726,0.003278614,0.0013603333,0.0024214159,0.0063119684],"category_scores_gemma":[0.011420562,0.0005127056,0.00052654307,0.0005591148,0.001374366,0.0055218814,0.0009767853,0.0015802837,0.0004934768],"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":[0.00015287624,0.00008299254,0.0008303691,0.000101616446,0.000042690717,0.00025588868,0.00018161413,0.05160295,0.0017770245,0.92583036,0.003080574,0.016060976],"study_design_scores_gemma":[0.000036769932,0.00013125266,0.0011933113,0.000045294393,0.000023753226,0.00026017116,0.00008028948,0.3538485,0.0005367561,0.6394586,0.004351956,0.000033303048],"about_ca_topic_score_codex":0.00063859625,"about_ca_topic_score_gemma":0.00042594122,"teacher_disagreement_score":0.0063119684,"about_ca_system_score_codex":0.0009966865,"about_ca_system_score_gemma":0.0009445498,"threshold_uncertainty_score":0.02111566},"labels":[],"label_agreement":null},{"id":"W1539798016","doi":"10.1080/10920277.2011.10597616","title":"Measuring Basis Risk in Longevity Hedges","year":2011,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":236,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hedge; Basis risk; Longevity; Longevity risk; Population; Index (typography); Econometrics; Factor analysis; Statistics; Portfolio; Actuarial science; Mathematics; Demography; Economics; Biology; Computer science; Financial economics; Ecology; Capital asset pricing model","score_opus":0.05608101450985869,"score_gpt":0.2750022189912478,"score_spread":0.21892120448138913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539798016","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":[0.96432465,0.00027800273,0.03233334,0.00012968337,0.00001246629,0.00002567448,0.00019387962,0.00004811404,0.0026543934],"genre_scores_gemma":[0.99750465,0.000029515153,0.0021958477,0.00000582484,0.0000037022605,0.0000028085383,0.00005153507,0.0000018661925,0.00020419247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991303,0.00026236355,0.00005181264,0.000109617395,0.00035530332,0.00009056749],"domain_scores_gemma":[0.9946346,0.0028457579,0.0012725518,0.00047524268,0.000579687,0.0001920936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039764754,0.00034795995,0.00048786943,0.0011300865,0.00035980952,0.0012057045,0.0005054691,0.0007022989,0.00087113533],"category_scores_gemma":[0.012699909,0.00015241303,0.00035080712,0.0010156082,0.0005971185,0.0010931643,0.0007750342,0.00069954176,0.00008055523],"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":[0.00066963315,0.00021695938,0.40099972,0.00009051331,0.00028016837,0.00031046165,0.00073966885,0.46097788,0.0043426696,0.042103544,0.0013642692,0.08790447],"study_design_scores_gemma":[0.000017768823,0.00047408373,0.14768386,0.000046630772,0.000067541754,0.00018965003,0.0005439867,0.8157479,0.0028159998,0.031048993,0.0012867854,0.00007674929],"about_ca_topic_score_codex":0.01141015,"about_ca_topic_score_gemma":0.009743761,"teacher_disagreement_score":0.01141015,"about_ca_system_score_codex":0.0014116794,"about_ca_system_score_gemma":0.0006597724,"threshold_uncertainty_score":0.022687495},"labels":[],"label_agreement":null},{"id":"W1540301262","doi":"","title":"Stochastic Conditional Duration Models with Mixture Processes","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Weibull distribution; Gamma distribution; Econometrics; Duration (music); Generalized gamma distribution; Mathematics; Conditional probability distribution; Statistics; Bayesian inference; Bayesian probability; Computer science","score_opus":0.00862254094133715,"score_gpt":0.2442067302055928,"score_spread":0.23558418926425564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540301262","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":[0.059366845,0.00079848693,0.9352725,0.00070903986,0.00010298372,0.00009573239,0.00072347303,0.00036375664,0.0025671471],"genre_scores_gemma":[0.85717064,0.001387241,0.12558448,0.0002928015,0.00037028154,0.0005194321,0.0020330374,0.00018121707,0.012460861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740857,0.0010903344,0.00012436906,0.0006516877,0.00041027137,0.0003147202],"domain_scores_gemma":[0.9865725,0.009600574,0.0015072604,0.0008962012,0.0009402244,0.00048321052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007072865,0.0012449364,0.0017194157,0.0021654582,0.0007089221,0.0028579498,0.0040431553,0.0028576222,0.005641058],"category_scores_gemma":[0.020552889,0.0011782482,0.0021187651,0.0022149074,0.0022122208,0.004915243,0.0023015058,0.0037033593,0.0011199355],"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":[0.00018544887,0.00008168139,0.005839294,0.00009345546,0.0001212302,0.00023385613,0.00046477118,0.49480614,0.0007369,0.47837698,0.0015878916,0.017472224],"study_design_scores_gemma":[0.000022464115,0.000026285965,0.0008695265,0.00002025303,0.000026205867,0.00007424391,0.000030128987,0.92225677,0.00010735259,0.07527347,0.0012618136,0.00003149089],"about_ca_topic_score_codex":0.010541329,"about_ca_topic_score_gemma":0.00578137,"teacher_disagreement_score":0.010541329,"about_ca_system_score_codex":0.0018035569,"about_ca_system_score_gemma":0.0012436315,"threshold_uncertainty_score":0.037405312},"labels":[],"label_agreement":null},{"id":"W1549339412","doi":"10.4054/demres.2000.2.2","title":"Mortality statistics for the oldest-old","year":2000,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Census; Demography; Mortality rate; Data quality; Geography; Statistics; Population; Economics; Economy; Mathematics; Sociology","score_opus":0.14133479297770105,"score_gpt":0.4569539540480254,"score_spread":0.3156191610703244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549339412","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":[0.32252124,0.01091342,0.005020617,0.0010245766,0.00028848872,0.00019076616,0.62210524,0.00071024516,0.037225384],"genre_scores_gemma":[0.59777313,0.008030916,0.0052963244,0.00028674203,0.000113137685,0.00014392495,0.37471464,0.00007409045,0.013567081],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989858,0.000049599035,0.00009491892,0.00006779668,0.00061886286,0.00018292089],"domain_scores_gemma":[0.99404514,0.00029712776,0.00054264185,0.00018261571,0.0045323237,0.00040014554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013762928,0.00036131387,0.0003388783,0.01063303,0.0009851585,0.00063688465,0.00078338356,0.00016468609,0.0023069507],"category_scores_gemma":[0.005209269,0.00011271448,0.0005753419,0.0075307363,0.00017856101,0.000293221,0.00041130272,0.0004770701,0.00044293422],"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":[0.00027963275,0.0000510806,0.78084385,0.0007641975,0.00029559253,0.00013410798,0.0012642095,0.004454779,0.0010090664,0.009912197,0.0871347,0.11385665],"study_design_scores_gemma":[0.000011649072,0.000032252858,0.9292179,0.000099689554,0.00005791496,0.00014849649,0.00028502886,0.00091450015,0.0003434794,0.0002901011,0.06856809,0.00003102834],"about_ca_topic_score_codex":0.9711893,"about_ca_topic_score_gemma":0.9718518,"teacher_disagreement_score":0.9711893,"about_ca_system_score_codex":0.009584923,"about_ca_system_score_gemma":0.013285915,"threshold_uncertainty_score":0.06954378},"labels":[],"label_agreement":null},{"id":"W1550752614","doi":"10.2139/ssrn.2739726","title":"Purchasing Term Life Insurance to Reach a Bequest Goal While Consuming","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Purchasing; Term (time); Bequest; Business; Beneficiary; Actuarial science; Marketing; Economics; Finance","score_opus":0.016365219562626307,"score_gpt":0.28749574543814227,"score_spread":0.27113052587551595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550752614","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":[0.97571427,0.00010790452,0.0009778407,0.0020841302,0.000050659175,0.00005513873,0.000244618,0.00003755311,0.020727776],"genre_scores_gemma":[0.986932,0.00009127714,0.001920698,0.00047636667,0.00004484135,0.00002872475,0.00027967186,0.000008593158,0.010217862],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975675,0.00006302794,0.000015013413,0.000026920558,0.000058933852,0.00007939951],"domain_scores_gemma":[0.9980623,0.00034957894,0.0004509271,0.00012254874,0.00018505471,0.00082964567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009982854,0.00014746537,0.00018547529,0.0003224396,0.00084373786,0.0009251627,0.00036363513,0.0008428057,0.012424885],"category_scores_gemma":[0.0041694664,0.00011562183,0.00022875083,0.0002182304,0.00015399717,0.0006600532,0.00047890106,0.0014552778,0.0024072232],"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":[0.00049152214,0.006934759,0.8376669,0.000057095152,0.000107923915,0.00064560794,0.0026822444,0.0008218393,0.003485683,0.0065076244,0.010568393,0.13003024],"study_design_scores_gemma":[0.00007944202,0.0021753344,0.9469833,0.000088420355,0.00010592334,0.0012656859,0.005419493,0.009633951,0.0018543354,0.0119396,0.020408379,0.000046192956],"about_ca_topic_score_codex":0.0019983286,"about_ca_topic_score_gemma":0.0045851567,"teacher_disagreement_score":0.012424885,"about_ca_system_score_codex":0.00024552114,"about_ca_system_score_gemma":0.00092873647,"threshold_uncertainty_score":0.04156536},"labels":[],"label_agreement":null},{"id":"W1560296461","doi":"","title":"Time Series Properties and Stochastic Forecasts: Some Econometrics of Mortality from the Canadian Laboratory","year":2001,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Nonparametric statistics; Econometrics; Parametric statistics; Series (stratigraphy); Interpretation (philosophy); Aggregate (composite); Parametric model; Stochastic modelling; Time series; Statistics; Computer science; Economics; Mathematics","score_opus":0.06805627457526982,"score_gpt":0.3052678030956875,"score_spread":0.2372115285204177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560296461","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":[0.3897361,0.07041456,0.34787494,0.060396492,0.0007282467,0.00044019576,0.0095863035,0.0006463295,0.12017676],"genre_scores_gemma":[0.89946604,0.031616334,0.03852171,0.00078052096,0.00042510132,0.00017952392,0.00289752,0.00009293642,0.026020313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923587,0.0002442979,0.00003241087,0.000069938,0.00030218164,0.00011530322],"domain_scores_gemma":[0.99663395,0.0021696936,0.00017413739,0.00015250975,0.0007585856,0.00011106108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033438618,0.0005892588,0.00055998977,0.0020227616,0.00150894,0.0021951683,0.0014153044,0.00095405744,0.0031334609],"category_scores_gemma":[0.017733922,0.00028807204,0.00065470376,0.0043565165,0.0016974428,0.0012635948,0.0007736952,0.0013372628,0.00023662872],"study_design_candidate":"simulation_or_modeling","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":[0.00006881931,0.000050148446,0.0113076335,0.00021895667,0.000060772803,0.00022212032,0.0010622619,0.24211378,0.00037897396,0.62521803,0.02160919,0.09768934],"study_design_scores_gemma":[0.000041571097,0.0000362564,0.037051477,0.00021238627,0.00007976662,0.000108831395,0.0011434506,0.62012345,0.00042652962,0.28701717,0.05356163,0.00019750997],"about_ca_topic_score_codex":0.96246254,"about_ca_topic_score_gemma":0.93152285,"teacher_disagreement_score":0.037537456,"about_ca_system_score_codex":0.01908339,"about_ca_system_score_gemma":0.013081549,"threshold_uncertainty_score":0.13846034},"labels":[],"label_agreement":null},{"id":"W1562373920","doi":"10.1080/10920277.2016.1161525","title":"Sarmanov Family of Bivariate Distributions for Multivariate Loss Reserving Analysis","year":2016,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Actua; Université Laval","funders":"","keywords":"Bivariate analysis; Joint probability distribution; Econometrics; Marginal distribution; Multivariate statistics; Portfolio; Property (philosophy); Multivariate normal distribution; Line of business; Mathematics; Actuarial science; Statistics; Economics; Finance; Random variable; Business model","score_opus":0.026657337553160045,"score_gpt":0.3350590078407152,"score_spread":0.30840167028755516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562373920","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":[0.006081556,0.00044434308,0.99130577,0.00019520159,0.00002887765,0.000036772664,0.0001666552,0.00018443618,0.0015563748],"genre_scores_gemma":[0.5941135,0.006467342,0.37927407,0.0006052835,0.0007061219,0.001268716,0.0020642863,0.00081668596,0.014683926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822515,0.0010173479,0.00007112444,0.00016945468,0.00036017565,0.00015670128],"domain_scores_gemma":[0.99106115,0.006107762,0.0007710066,0.00074750197,0.0010034707,0.00030908443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072832457,0.0013757547,0.001270208,0.0028917966,0.0008500479,0.0018128849,0.0015919518,0.001009422,0.004299898],"category_scores_gemma":[0.017841367,0.0006444277,0.0019000472,0.0025518578,0.0019124708,0.0029300093,0.001810791,0.0036986945,0.0015068401],"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":[0.000043696862,0.000046486068,0.0025466436,0.00006994736,0.00007412883,0.00024681186,0.00018130789,0.1360419,0.0011337241,0.83500606,0.0027565896,0.021852592],"study_design_scores_gemma":[0.000009254437,0.000020282834,0.0007743892,0.000029551426,0.000015163061,0.00011294671,0.000050744893,0.74309653,0.00031334363,0.25249946,0.0030486847,0.00002963341],"about_ca_topic_score_codex":0.0053808484,"about_ca_topic_score_gemma":0.0029712657,"teacher_disagreement_score":0.0072832457,"about_ca_system_score_codex":0.0013712295,"about_ca_system_score_gemma":0.0015885471,"threshold_uncertainty_score":0.038517892},"labels":[],"label_agreement":null},{"id":"W1562767132","doi":"10.1017/cbo9780511753855.003","title":"Models of Human Mortality","year":2006,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cover (algebra); Valuation (finance); Order (exchange); Actuarial science; Calculus (dental); Mathematics; Mathematical economics; Economics; Medicine; Engineering; Accounting; Finance","score_opus":0.04761521579515167,"score_gpt":0.2598381702888707,"score_spread":0.21222295449371903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562767132","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":[0.03131575,0.016032882,0.5378193,0.029865937,0.0021035771,0.00011837661,0.0030561686,0.0006953453,0.37899277],"genre_scores_gemma":[0.6575762,0.014979117,0.047571603,0.0026889453,0.0018094401,0.00056149607,0.0017555943,0.00022444953,0.2728332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996081,0.00020516959,0.000012202816,0.00005590344,0.00007963726,0.000039130726],"domain_scores_gemma":[0.999451,0.00028221562,0.000049849805,0.000062919855,0.00007222153,0.000081857084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009217625,0.00069451955,0.0006236322,0.0005144,0.0004132243,0.0017226803,0.0016155435,0.0015224897,0.017440768],"category_scores_gemma":[0.0021365597,0.00023921268,0.0007382062,0.00074635854,0.0010734427,0.002058252,0.0009998212,0.0015235926,0.002382686],"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":[0.000011432878,0.0000140767415,0.00023454323,0.000034317465,0.000022683886,0.00003719027,0.00010346219,0.044479657,0.00007114495,0.9403377,0.009178533,0.0054750997],"study_design_scores_gemma":[0.0000149345215,0.000020635995,0.00030287492,0.000029125797,0.000011958299,0.000044437078,0.00005147727,0.08708218,0.000023940229,0.88977855,0.022629593,0.000010240958],"about_ca_topic_score_codex":0.0040530865,"about_ca_topic_score_gemma":0.0044126953,"teacher_disagreement_score":0.017440768,"about_ca_system_score_codex":0.0014796131,"about_ca_system_score_gemma":0.00069829327,"threshold_uncertainty_score":0.0583452},"labels":[],"label_agreement":null},{"id":"W1563759807","doi":"10.1080/10920277.2005.10596227","title":"Mixture Gaussian Time Series Modeling of Long-Term Market Returns","year":2005,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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":"Econometrics; Stock exchange; Term (time); Economics; Log-normal distribution; Computer science; Mathematics; Statistics; Finance","score_opus":0.010209051078707784,"score_gpt":0.2707281966177344,"score_spread":0.2605191455390266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563759807","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":[0.1822762,0.0002745556,0.81430393,0.00036181323,0.000057264144,0.000054841465,0.0002565657,0.00040567984,0.0020091315],"genre_scores_gemma":[0.9682694,0.00030309692,0.026671018,0.000043776872,0.000051181323,0.00007208286,0.00028619485,0.00004495061,0.004258299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921286,0.00027503166,0.00003897591,0.00016491099,0.0002278184,0.00008031273],"domain_scores_gemma":[0.99729794,0.00169171,0.00043791637,0.00017507879,0.00031373778,0.0000836514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030570403,0.0007469164,0.00087882794,0.0011876181,0.00031359104,0.0012446156,0.0013961191,0.0013282456,0.001860985],"category_scores_gemma":[0.007718892,0.00046537406,0.001125637,0.0013343063,0.00091047015,0.0017770934,0.00059134443,0.00120054,0.00035063562],"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":[0.00005556831,0.00003435503,0.0021558597,0.00002082129,0.000046130393,0.000061660976,0.000076426906,0.9534725,0.00087220647,0.0360653,0.00029397738,0.0068451567],"study_design_scores_gemma":[0.0000024234364,0.00000614609,0.00041030286,0.0000019732613,0.0000040346886,0.00000815745,0.0000044733333,0.99553776,0.00007148234,0.0038650453,0.00008348131,0.0000047383824],"about_ca_topic_score_codex":0.011009074,"about_ca_topic_score_gemma":0.006031452,"teacher_disagreement_score":0.011009074,"about_ca_system_score_codex":0.0009211484,"about_ca_system_score_gemma":0.00047424654,"threshold_uncertainty_score":0.021889985},"labels":[],"label_agreement":null},{"id":"W1568411616","doi":"10.1007/s00180-006-0019-7","title":"Simplified estimation of multivariate duration models with unobserved heterogeneity","year":2006,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Multivariate statistics; Econometrics; Duration (music); Estimation; Curse of dimensionality; Economics; Maximum likelihood; Multivariate analysis; Simple (philosophy); Statistics; Mathematics","score_opus":0.03254177752904803,"score_gpt":0.3085778697139922,"score_spread":0.2760360921849442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568411616","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":[0.017946603,0.00021955675,0.98033834,0.00029901916,0.00002816516,0.000027206162,0.0003284124,0.00017544383,0.00063737767],"genre_scores_gemma":[0.5730414,0.0010377639,0.4144116,0.00021121469,0.0003983579,0.0005019265,0.0022708368,0.00024966954,0.007877315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977041,0.0014569216,0.00015280824,0.00028038275,0.00022608398,0.00017972855],"domain_scores_gemma":[0.97035235,0.026088782,0.0010087012,0.0016632915,0.00060677034,0.00028014331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062082456,0.0007323051,0.0020037307,0.0010195995,0.0004611293,0.0018801867,0.0033284652,0.0012783624,0.0066734976],"category_scores_gemma":[0.0315167,0.0012716219,0.0017275384,0.0018254494,0.00078567985,0.0022412871,0.002147411,0.0016943032,0.00063281954],"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":[0.00011985948,0.000045964294,0.0021915722,0.00008499139,0.000109970766,0.00014608413,0.000099614066,0.87996024,0.00025385217,0.086050026,0.0013764733,0.029561352],"study_design_scores_gemma":[0.000014078066,0.000007384617,0.00037154154,0.0000075045787,0.00001651857,0.00002166702,0.000008889736,0.96969014,0.00006790289,0.0294103,0.00037534314,0.000008750745],"about_ca_topic_score_codex":0.01483277,"about_ca_topic_score_gemma":0.014330947,"teacher_disagreement_score":0.01483277,"about_ca_system_score_codex":0.0011618901,"about_ca_system_score_gemma":0.0025111635,"threshold_uncertainty_score":0.032832682},"labels":[],"label_agreement":null},{"id":"W1574410237","doi":"10.1002/9781118445112.stat04399","title":"Mean Residual Lifetime","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Residual; Statistics; Residual risk; Reliability (semiconductor); Econometrics; Mathematics; Reliability engineering; Actuarial science; Economics; Engineering; Physics; Algorithm; Thermodynamics","score_opus":0.032420542244266715,"score_gpt":0.337171977036859,"score_spread":0.3047514347925923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574410237","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":[0.07570772,0.012951754,0.8530915,0.0023839364,0.0005988122,0.00007790839,0.0019554729,0.0009617068,0.052271277],"genre_scores_gemma":[0.92071444,0.0058876565,0.049762856,0.0005197417,0.00063993246,0.00017147092,0.0015574349,0.00069552305,0.020050993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99767977,0.00063010474,0.00013206371,0.0005390818,0.00077063276,0.00024831545],"domain_scores_gemma":[0.98133254,0.0096439775,0.0021969446,0.002588154,0.003793721,0.00044474803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004846755,0.00047448277,0.00086895376,0.0017520706,0.00059850846,0.0024895535,0.0014666442,0.0008458453,0.010404503],"category_scores_gemma":[0.036936942,0.0002269412,0.00077646645,0.0016632876,0.0015731009,0.004884636,0.0013729536,0.001749932,0.0017646933],"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":[0.00007596047,0.000028457514,0.0056358804,0.0002887668,0.00006392581,0.00012705757,0.00031527982,0.038764257,0.0013213839,0.854543,0.008936706,0.08989942],"study_design_scores_gemma":[0.000015073256,0.0001373806,0.0060864477,0.00031319773,0.00005888022,0.0011617668,0.00022724227,0.12928355,0.0023482281,0.81693643,0.04334754,0.00008425086],"about_ca_topic_score_codex":0.0014181188,"about_ca_topic_score_gemma":0.00066255155,"teacher_disagreement_score":0.010404503,"about_ca_system_score_codex":0.0016596288,"about_ca_system_score_gemma":0.0011669158,"threshold_uncertainty_score":0.03480649},"labels":[],"label_agreement":null},{"id":"W1574572923","doi":"10.7202/010088ar","title":"Stabilité des estimations de l’espérance de vie sans perte d’autonomie calculées au moyen de deux méthodes de construction de tables de survie","year":2004,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Humanities; Philosophy","score_opus":0.019656466596302158,"score_gpt":0.2848853495382055,"score_spread":0.26522888294190333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574572923","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":[0.107079275,0.0010866738,0.88513756,0.0003285504,0.00016779183,0.00020748001,0.0015040406,0.00081347756,0.0036751765],"genre_scores_gemma":[0.510269,0.0009204232,0.48044428,0.00011041702,0.00009853566,0.0008720344,0.0032430924,0.00049213826,0.0035500946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9750351,0.014825357,0.0015320673,0.0032947923,0.004788736,0.00052399293],"domain_scores_gemma":[0.8385861,0.13187903,0.0063414224,0.0102613205,0.012414672,0.00051752495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03333978,0.0011874614,0.0012400445,0.005975584,0.0010804402,0.0042965533,0.001308875,0.00081947044,0.0034840137],"category_scores_gemma":[0.15136984,0.00081434066,0.0024583172,0.004234922,0.0016341676,0.002321797,0.002348776,0.0028008642,0.0009469782],"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":[0.00091673236,0.00010076004,0.29862937,0.0011635204,0.0025400524,0.00020807341,0.006473326,0.11741341,0.0060498505,0.052651614,0.0039336714,0.5099196],"study_design_scores_gemma":[0.00015776668,0.0012866298,0.34390292,0.0013248968,0.0013685651,0.00088918203,0.005674761,0.49309483,0.02828875,0.07749354,0.045912474,0.0006056744],"about_ca_topic_score_codex":0.020518778,"about_ca_topic_score_gemma":0.024332572,"teacher_disagreement_score":0.03333978,"about_ca_system_score_codex":0.0022895792,"about_ca_system_score_gemma":0.0036169686,"threshold_uncertainty_score":0.17631972},"labels":[],"label_agreement":null},{"id":"W1576306301","doi":"10.3233/rda-2012-0087","title":"Efficient hedging for equity-linked life insurance contracts with stochastic interest rate","year":2013,"lang":"en","type":"article","venue":"Risk and Decision Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"","keywords":"Life insurance; Equity (law); Valuation (finance); Interest rate; Actuarial science; Stochastic game; Economics; Insurance policy; Econometrics; Financial economics; Microeconomics; Finance","score_opus":0.03863906529136878,"score_gpt":0.3419434431389947,"score_spread":0.30330437784762593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576306301","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":[0.14845781,0.0014044931,0.84630173,0.0005473582,0.000046140663,0.000090181165,0.000083223815,0.0000523391,0.0030166549],"genre_scores_gemma":[0.94497454,0.00094161025,0.0482657,0.00008556629,0.00008746121,0.00010409579,0.00018246482,0.000045948032,0.0053126453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997399,0.0016140488,0.00011167822,0.00028619877,0.00038014943,0.00020903733],"domain_scores_gemma":[0.9929548,0.005273429,0.00076850527,0.00025995282,0.0002687777,0.00047453464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077051236,0.0013557915,0.0023324424,0.0010393289,0.00060557766,0.002816324,0.0018023084,0.0026612266,0.0025158334],"category_scores_gemma":[0.015564851,0.0013066124,0.0014889522,0.001116136,0.0021062435,0.004215474,0.0025274698,0.0023694963,0.0001568474],"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":[0.00020319251,0.00016670937,0.0016538183,0.00015866596,0.00014370425,0.000328418,0.00021315398,0.75189775,0.0020295097,0.22548111,0.00050896173,0.01721504],"study_design_scores_gemma":[0.000035786616,0.00007362697,0.0002769552,0.00001589861,0.000022363383,0.000055744928,0.000023183535,0.93935347,0.00026706984,0.059554983,0.0003048507,0.000015998232],"about_ca_topic_score_codex":0.001036882,"about_ca_topic_score_gemma":0.000608371,"teacher_disagreement_score":0.0077051236,"about_ca_system_score_codex":0.0016585239,"about_ca_system_score_gemma":0.0013801745,"threshold_uncertainty_score":0.040749073},"labels":[],"label_agreement":null},{"id":"W1581372398","doi":"10.18452/8302","title":"Stock ownership decisions in DC pension plans","year":2003,"lang":"en","type":"book","venue":"edoc Publication server (Humboldt University of Berlin)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Stock (firearms); Pension; Diversification (marketing strategy); Business; Actuarial science; Weighting; Finance; Marketing; Engineering","score_opus":0.038839319261742514,"score_gpt":0.26903703791440453,"score_spread":0.23019771865266203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581372398","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":[0.9888953,0.000415593,0.0017087708,0.0003601188,0.000009854386,0.0000132791165,0.00019462891,0.0000070281394,0.008395461],"genre_scores_gemma":[0.9961653,0.00016854325,0.00022931765,0.000016293025,0.0000044817743,0.0000032791424,0.00006592769,0.0000016692507,0.0033452362],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998733,0.000039896768,0.0000063814014,0.000016683252,0.000027756152,0.00003590389],"domain_scores_gemma":[0.9996362,0.00014702136,0.00010538392,0.000017398108,0.000028534489,0.0000656025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043545724,0.00015696514,0.00014628921,0.0002812291,0.0002704271,0.00082692906,0.00019318094,0.0003154026,0.0030295972],"category_scores_gemma":[0.0017832791,0.00013952176,0.00014511151,0.0002499574,0.00027002,0.0005387207,0.00033601787,0.0003023755,0.0002450347],"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":[0.0011607063,0.00037297697,0.4564168,0.00010020873,0.00014202858,0.0024595363,0.0019902731,0.21666797,0.0047234423,0.14580262,0.008539829,0.1616237],"study_design_scores_gemma":[0.00008017565,0.0004935217,0.3878423,0.00013390309,0.00015923656,0.0011809532,0.003467271,0.44367492,0.004524178,0.13058683,0.027772758,0.00008392933],"about_ca_topic_score_codex":0.0075582643,"about_ca_topic_score_gemma":0.009092944,"teacher_disagreement_score":0.0075582643,"about_ca_system_score_codex":0.0008732254,"about_ca_system_score_gemma":0.0003593798,"threshold_uncertainty_score":0.015028536},"labels":[],"label_agreement":null},{"id":"W1582278691","doi":"10.1080/10920277.2010.10597597","title":"Developing Mortality Improvement Formulas","year":2010,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":44,"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 Waterloo","funders":"Society of Actuaries","keywords":"Annuity; Actuarial science; Life annuity; Pension; Valuation (finance); Longevity risk; Life insurance; Economics; Econometrics; Heuristic; Scale (ratio); Population; Mathematics; Finance; Geography; Demography; Sociology","score_opus":0.023535252163309504,"score_gpt":0.33035068552628777,"score_spread":0.3068154333629783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582278691","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":[0.007663559,0.0009642188,0.97071856,0.0009293651,0.00034012418,0.00011445277,0.00024922943,0.00032167198,0.018698694],"genre_scores_gemma":[0.31977427,0.0023878017,0.6604411,0.0007263178,0.0011616973,0.00055285706,0.000784274,0.00040566985,0.013766038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821043,0.0006184604,0.00014196604,0.00017254238,0.0007164674,0.00014011926],"domain_scores_gemma":[0.9921739,0.004630728,0.0005173417,0.00048983336,0.0020867242,0.00010146249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049052965,0.0008368016,0.0006373522,0.0023829842,0.00044698425,0.0012542568,0.0013322243,0.0009277588,0.0062231086],"category_scores_gemma":[0.027236512,0.00036811153,0.0008678313,0.0015458673,0.00057636225,0.0022985088,0.001562446,0.0021671206,0.0014458129],"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":[0.000027067197,0.000078640296,0.0025793288,0.00023622843,0.00003957357,0.00023143466,0.0001744663,0.27161375,0.0013710123,0.5326883,0.018181313,0.17277882],"study_design_scores_gemma":[0.000011069315,0.000027946338,0.00049037696,0.000094113144,0.000021208976,0.000098832636,0.00003890651,0.8194906,0.0009288182,0.16780443,0.010978918,0.000014840929],"about_ca_topic_score_codex":0.0032712359,"about_ca_topic_score_gemma":0.0028374298,"teacher_disagreement_score":0.0062231086,"about_ca_system_score_codex":0.0016047346,"about_ca_system_score_gemma":0.0014215065,"threshold_uncertainty_score":0.025942028},"labels":[],"label_agreement":null},{"id":"W1582486028","doi":"","title":"Multivariate stochastic analysis of a combination hybrid pension plan","year":2008,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"Simon Fraser University","keywords":"Salary; Pension; Inflation (cosmology); Multivariate statistics; Pension plan; Actuarial science; Plan (archaeology); Rate of return; Econometrics; Economics; Function (biology); Computer science; Finance; Statistics; Mathematics; Geography","score_opus":0.018283701672892533,"score_gpt":0.2575349332554493,"score_spread":0.23925123158255676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582486028","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":[0.55573416,0.00028973783,0.43706948,0.0005294084,0.000031428273,0.00005398233,0.00028546384,0.00015534482,0.005850991],"genre_scores_gemma":[0.9866649,0.00013740141,0.00843207,0.000020061258,0.000021019487,0.000035913716,0.00012894676,0.000023328841,0.0045363926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995505,0.00014363465,0.000015412666,0.00007008311,0.0001284435,0.00009188691],"domain_scores_gemma":[0.9986004,0.00074811425,0.00029634425,0.000056828732,0.00017411263,0.00012421512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016644931,0.0004900622,0.00063176436,0.000968918,0.00028637343,0.0009951001,0.0005281883,0.0004892528,0.0023388613],"category_scores_gemma":[0.0025665616,0.00030916583,0.0007610128,0.0006042162,0.0007974074,0.00064335624,0.0006966116,0.00074438076,0.00011417354],"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":[0.000047276735,0.000026894604,0.0025766783,0.000016699796,0.00004258927,0.000139117,0.000040563657,0.95692736,0.0012014392,0.034379784,0.00034942606,0.004252164],"study_design_scores_gemma":[0.0000018310681,0.000008267139,0.00068122946,0.0000011438453,0.0000049087184,0.000007401554,0.0000071578406,0.99718237,0.00006722954,0.0019593854,0.000074920266,0.0000041948874],"about_ca_topic_score_codex":0.010520992,"about_ca_topic_score_gemma":0.0050272737,"teacher_disagreement_score":0.010520992,"about_ca_system_score_codex":0.0013637915,"about_ca_system_score_gemma":0.00069271924,"threshold_uncertainty_score":0.020919502},"labels":[],"label_agreement":null},{"id":"W1583106676","doi":"10.1002/9780470061596.risk0356","title":"Longevity Risk and Life Annuities","year":2008,"lang":"en","type":"other","venue":"Encyclopedia of Quantitative Risk Analysis and Assessment","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"International Centre for Infectious Diseases; York University","funders":"","keywords":"Longevity; Longevity risk; Perspective (graphical); Argument (complex analysis); Actuarial science; Life insurance; Economics; Gerontology; Biology; Medicine; Computer science","score_opus":0.01963430985131492,"score_gpt":0.3380703255107148,"score_spread":0.3184360156593999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583106676","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":[0.017439654,0.16111656,0.011822913,0.03390714,0.0015800849,0.000046857505,0.0010435305,0.00009114117,0.7729522],"genre_scores_gemma":[0.5787295,0.22362868,0.0058103246,0.0043019047,0.006539758,0.00009411209,0.0008614947,0.000058841964,0.17997545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995178,0.00017163325,0.000021161633,0.00005789643,0.00019558672,0.000035995883],"domain_scores_gemma":[0.9979631,0.0010650663,0.00034042378,0.000101345715,0.00039172955,0.00013827569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007014902,0.0003658105,0.0003053897,0.001456993,0.00046633082,0.002531186,0.00036680576,0.0014063757,0.030217366],"category_scores_gemma":[0.0038153713,0.000071613926,0.00019801458,0.0020556669,0.0011463048,0.0013759552,0.0009949185,0.0015538883,0.0026474292],"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":[0.000025088131,0.000033919743,0.002146309,0.00023277964,0.000012309779,0.00014536765,0.0002997905,0.0013701745,0.00015433645,0.8761047,0.037132848,0.08234231],"study_design_scores_gemma":[0.0000059975573,0.00004938646,0.0062858392,0.0006282896,0.000013239985,0.0007256201,0.00048223315,0.0014714337,0.00016542779,0.605814,0.38433674,0.000021748298],"about_ca_topic_score_codex":0.0017658913,"about_ca_topic_score_gemma":0.0014664604,"teacher_disagreement_score":0.030217366,"about_ca_system_score_codex":0.0008653227,"about_ca_system_score_gemma":0.0007492247,"threshold_uncertainty_score":0.10108721},"labels":[],"label_agreement":null},{"id":"W1593096960","doi":"10.7202/010852ar","title":"La démographie des nonagénaires et des centenaires en Suisse","year":2005,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"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":"Political science; Geography; Humanities; Forestry; Art","score_opus":0.014412440498648723,"score_gpt":0.2918523612394875,"score_spread":0.2774399207408388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593096960","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":[0.96728224,0.005384131,0.0016158301,0.00067273626,0.000055091365,0.00012259092,0.007290591,0.000039143008,0.017537622],"genre_scores_gemma":[0.9749058,0.0040897964,0.0012153777,0.00017925582,0.000026448808,0.00012453427,0.0035436526,0.00001620627,0.01589894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935716,0.00012834898,0.000045481207,0.00011493778,0.00018734012,0.0001666487],"domain_scores_gemma":[0.99770147,0.00033644077,0.00040544628,0.00018444454,0.0011565366,0.00021568914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001126244,0.00036409494,0.00040525803,0.003946132,0.0012000541,0.0011811637,0.0005640185,0.0003296806,0.0059789415],"category_scores_gemma":[0.0029992315,0.0001706227,0.0004002906,0.0048714816,0.00056179165,0.00055306085,0.000683234,0.00046441262,0.0005273623],"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":[0.00013328707,0.000032402353,0.9358907,0.0004416403,0.00020574742,0.00023161744,0.009636771,0.0003306468,0.0017410351,0.0017164408,0.003303447,0.04633623],"study_design_scores_gemma":[0.0000012853354,0.00002771075,0.99014074,0.000067100096,0.000023175246,0.00006727515,0.0021486687,0.0001055361,0.0001419457,0.000050687842,0.0072175777,0.0000082445595],"about_ca_topic_score_codex":0.8444992,"about_ca_topic_score_gemma":0.9188705,"teacher_disagreement_score":0.8444992,"about_ca_system_score_codex":0.004538944,"about_ca_system_score_gemma":0.0055447537,"threshold_uncertainty_score":0.3128332},"labels":[],"label_agreement":null},{"id":"W1597454313","doi":"","title":"Population and society : essential readings","year":2012,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cohabitation; Fertility; Population; Demography; Demographic transition; Historical demography; Demographic analysis; Population growth; Total fertility rate; Sociology; Geography; History; Genealogy; Developed country; Family planning; Research methodology","score_opus":0.012205109143340907,"score_gpt":0.28304532724916487,"score_spread":0.270840218105824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597454313","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":[0.0005338028,0.17227462,0.0033553983,0.07584186,0.093294926,0.00036062443,0.004426242,0.00055592717,0.64935654],"genre_scores_gemma":[0.024301002,0.14738083,0.0047106813,0.027375584,0.10251498,0.0010151092,0.005366282,0.0007140844,0.6866213],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986613,0.00042224617,0.000086517626,0.00020314596,0.0005352405,0.00009153276],"domain_scores_gemma":[0.9974796,0.0009896231,0.00013305212,0.0003270759,0.0008485434,0.0002221001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013408329,0.0023937398,0.0016081487,0.004079748,0.0025996172,0.0061591943,0.0018636058,0.0028972395,0.1328652],"category_scores_gemma":[0.0073477253,0.0005522765,0.0008020358,0.0047988184,0.0027941098,0.0049640536,0.0028874811,0.004294023,0.05445418],"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":[0.000007962677,0.000017334594,0.0001248017,0.0002637772,0.0000046225427,0.00003439173,0.0004328127,0.00008979627,0.000056029487,0.0434248,0.9146412,0.040902372],"study_design_scores_gemma":[0.0000021735398,0.000007072199,0.0002623618,0.00041591513,0.0000016116833,0.000026939095,0.00020488467,0.000025481522,0.000009468549,0.016136426,0.98290414,0.0000034877653],"about_ca_topic_score_codex":0.0060535898,"about_ca_topic_score_gemma":0.0041555334,"teacher_disagreement_score":0.1328652,"about_ca_system_score_codex":0.00358764,"about_ca_system_score_gemma":0.00338897,"threshold_uncertainty_score":0.4444784},"labels":[],"label_agreement":null},{"id":"W1604902863","doi":"","title":"The overselling of population aging : apocalyptic demography, intergenerational challenges and social policy","year":2000,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":151,"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 ageing; Restructuring; Mythology; Population; Politics; Demography; Sociology; Demographic economics; Political science; Economics; History","score_opus":0.04711943358296961,"score_gpt":0.3551694008476146,"score_spread":0.308049967264645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604902863","genre_codex":"commentary","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":[0.04637888,0.08347844,0.0054345345,0.5872985,0.0060326355,0.000025751888,0.00023423893,0.00006821349,0.2710488],"genre_scores_gemma":[0.89732945,0.04076036,0.0013143213,0.017296335,0.0056222184,0.000047554964,0.000074264964,0.000060860515,0.03749457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99901783,0.0006458493,0.000015033123,0.0000635219,0.00014337656,0.00011436515],"domain_scores_gemma":[0.99745077,0.0016912803,0.00018021683,0.0001813369,0.00022217608,0.00027407205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020845542,0.00026465108,0.00026223483,0.0015549243,0.0050791646,0.005852937,0.0004944399,0.0016958443,0.006423996],"category_scores_gemma":[0.0043735947,0.00012864976,0.00013201026,0.0018408062,0.017220829,0.0057579502,0.0026795452,0.0036447016,0.00050874194],"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":[0.000009948991,0.000008436709,0.0007020644,0.000053329983,0.0000021303772,0.00009427465,0.020917311,0.00016514705,0.00007021716,0.9011619,0.062997356,0.013817808],"study_design_scores_gemma":[0.0000024649075,0.000008171317,0.001972962,0.0002876777,0.000002015094,0.0001190788,0.02650056,0.00024329031,0.00007537904,0.46804896,0.5027298,0.000009726073],"about_ca_topic_score_codex":0.010909806,"about_ca_topic_score_gemma":0.012300612,"teacher_disagreement_score":0.010909806,"about_ca_system_score_codex":0.005782055,"about_ca_system_score_gemma":0.0026759098,"threshold_uncertainty_score":0.041951954},"labels":[],"label_agreement":null},{"id":"W1617082656","doi":"10.1017/cbo9780511800146.010","title":"Pension mathematics","year":2009,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Pension; Salary; Valuation (finance); Notation; Pension plan; Actuarial science; Plan (archaeology); Function (biology); Scale (ratio); Economics; Computer science; Mathematics; Business; Finance; Arithmetic; Geography; Cartography","score_opus":0.030218352717594057,"score_gpt":0.23899912159480352,"score_spread":0.20878076887720948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1617082656","genre_codex":"other","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":[0.0052695526,0.04059673,0.14638725,0.0075694034,0.0025922463,0.000106053056,0.0013613256,0.0004868032,0.79563063],"genre_scores_gemma":[0.21249832,0.06497505,0.16965784,0.004172959,0.0050417194,0.00051426387,0.002946641,0.0005905474,0.5396026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99944705,0.00012879632,0.000054684275,0.000118211676,0.00021100097,0.000040227475],"domain_scores_gemma":[0.9996332,0.00012580128,0.000034540593,0.0000959138,0.00008484374,0.000025704987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089582347,0.00084823195,0.00064605643,0.0011473792,0.0014393369,0.0032441143,0.0008033702,0.00087448145,0.033364702],"category_scores_gemma":[0.0019841657,0.00033868905,0.0006911376,0.0014990389,0.0019467218,0.005376874,0.0016291621,0.0024966542,0.014890526],"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":[0.000002566485,0.0000050950866,0.000046214158,0.0000628994,0.0000028273419,0.000025420708,0.00009745516,0.00031989318,0.000091059715,0.9679882,0.013884228,0.017474137],"study_design_scores_gemma":[0.0000027646513,0.0000065311574,0.00013346727,0.000054618315,0.0000022128963,0.00011017368,0.000038368544,0.0006420386,0.00008119562,0.716193,0.28273,0.00000566697],"about_ca_topic_score_codex":0.0011202893,"about_ca_topic_score_gemma":0.0009378189,"teacher_disagreement_score":0.033364702,"about_ca_system_score_codex":0.002035433,"about_ca_system_score_gemma":0.0007620186,"threshold_uncertainty_score":0.111616075},"labels":[],"label_agreement":null},{"id":"W162601966","doi":"","title":"STOCHASTIC ANALYSIS OF AN INSURANCE PORTFOLIO","year":2002,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Portfolio; Actuarial science; Interest rate; Economics; Insurance policy; Econometrics; Business; Financial economics; Finance","score_opus":0.02960264496853029,"score_gpt":0.3043929267315743,"score_spread":0.27479028176304404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W162601966","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":[0.1212242,0.0037705852,0.8488622,0.0041427,0.00021397631,0.00011744431,0.00051023165,0.00019013489,0.020968542],"genre_scores_gemma":[0.9256489,0.0048634014,0.041002348,0.0003772124,0.0005965055,0.00026039858,0.00071244664,0.00010127641,0.02643762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981152,0.0008163141,0.00008907294,0.000268016,0.00048800802,0.0002234189],"domain_scores_gemma":[0.99596846,0.00256904,0.0005816469,0.00016698046,0.00040589276,0.0003079488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048159147,0.0010149166,0.001365939,0.0015912903,0.00065572606,0.0022826358,0.0011658086,0.0021700882,0.005055137],"category_scores_gemma":[0.012650439,0.000708339,0.0010746932,0.0012907032,0.0019400027,0.0027286096,0.0014814959,0.0018269541,0.00046452798],"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":[0.000028661734,0.00003651318,0.0019262878,0.00007958761,0.000100721256,0.00019597878,0.0000970095,0.33694154,0.001068001,0.65049404,0.0014798369,0.007551816],"study_design_scores_gemma":[0.000014215604,0.00003458161,0.0010362727,0.000025775154,0.000022464366,0.00006527738,0.000029136,0.78271043,0.00020174634,0.21415494,0.0016813694,0.000023733888],"about_ca_topic_score_codex":0.004492878,"about_ca_topic_score_gemma":0.0021570392,"teacher_disagreement_score":0.005055137,"about_ca_system_score_codex":0.0022157307,"about_ca_system_score_gemma":0.0013745545,"threshold_uncertainty_score":0.025469303},"labels":[],"label_agreement":null},{"id":"W1632830938","doi":"10.1111/j.1539-6975.2013.12015.x","title":"Pricing Standardized Mortality Securitizations: A Two‐Population Model With Transitory Jump Effects","year":2013,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":71,"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; University of Manitoba","funders":"","keywords":"Jump; Population; Economics; Econometrics; Mortality rate; Population model; Actuarial science; Demography","score_opus":0.00845896341644066,"score_gpt":0.2795769531343406,"score_spread":0.27111798971789997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1632830938","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":[0.4956752,0.0018340996,0.44381014,0.010038666,0.0007236344,0.00028746465,0.0027314818,0.00040478265,0.04449441],"genre_scores_gemma":[0.9535488,0.0009469002,0.009004517,0.00045388535,0.0002793312,0.00020644888,0.0005288094,0.000044001892,0.034987457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994629,0.00020149596,0.000024745445,0.00011091207,0.0000679574,0.00013200408],"domain_scores_gemma":[0.9977133,0.0012163842,0.00037603814,0.00009019924,0.00027006547,0.00033390356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016379973,0.0010916372,0.0016784526,0.00097563345,0.0006299573,0.0024101932,0.002825942,0.004100375,0.008773484],"category_scores_gemma":[0.0050697275,0.00066587556,0.0016966487,0.0010000331,0.001800488,0.002426097,0.0015450043,0.002712749,0.0008074913],"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":[0.00018976713,0.0002386699,0.0056884335,0.00009501573,0.00017444177,0.0008679804,0.00036241344,0.68939704,0.0014292083,0.28998888,0.0045929477,0.0069752303],"study_design_scores_gemma":[0.0000604285,0.000045987934,0.00072544155,0.000010940481,0.00003808231,0.00005968907,0.00005128564,0.9799129,0.000042287476,0.018435495,0.00059352774,0.000023930987],"about_ca_topic_score_codex":0.014662064,"about_ca_topic_score_gemma":0.00544193,"teacher_disagreement_score":0.014662064,"about_ca_system_score_codex":0.0014451713,"about_ca_system_score_gemma":0.0011996638,"threshold_uncertainty_score":0.029350221},"labels":[],"label_agreement":null},{"id":"W1646875978","doi":"10.5489/cuaj.1005","title":"Assessing life expectancy: our continuing challenge","year":2013,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Expectancy theory; Psychology; Medicine; Environmental health; Social psychology","score_opus":0.02489679889657638,"score_gpt":0.28120119683990474,"score_spread":0.2563043979433284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1646875978","genre_codex":"commentary","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":[0.020909643,0.17744522,0.023572534,0.7638542,0.0068863244,0.0001574713,0.0015129079,0.0002459241,0.005415674],"genre_scores_gemma":[0.44673768,0.24339138,0.13488773,0.11667342,0.05046331,0.0010390527,0.0022720206,0.0003478431,0.00418764],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.987879,0.005882423,0.0018234664,0.0013046985,0.0024830357,0.00062737934],"domain_scores_gemma":[0.84176606,0.109431945,0.005485204,0.003628686,0.028816357,0.010871756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057707418,0.0015599048,0.004726911,0.004849978,0.0034187182,0.00970606,0.0067380867,0.010325808,0.0055487803],"category_scores_gemma":[0.12923631,0.00094421324,0.0015558944,0.0034897747,0.0066263974,0.016709082,0.00722078,0.01571069,0.0015240185],"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":[0.00027935565,0.0009354619,0.15292409,0.0042469916,0.0008843672,0.00079279236,0.0056030806,0.0028665825,0.00065425737,0.028079268,0.29348475,0.5092489],"study_design_scores_gemma":[0.00018306873,0.00077250286,0.12943697,0.014436204,0.0007914164,0.0028933077,0.044089388,0.013910928,0.00046007676,0.42053726,0.37160644,0.00088247046],"about_ca_topic_score_codex":0.044346593,"about_ca_topic_score_gemma":0.061375998,"teacher_disagreement_score":0.057707418,"about_ca_system_score_codex":0.0052754777,"about_ca_system_score_gemma":0.016655875,"threshold_uncertainty_score":0.3051896},"labels":[],"label_agreement":null},{"id":"W1657465897","doi":"10.1016/j.econmod.2015.08.019","title":"Modeling longevity risk transfers as Nash bargaining problems: Methodology and insights","year":2015,"lang":"en","type":"article","venue":"Economic Modelling","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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 Waterloo; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Longevity risk; Longevity; Economics; Pension; Bargaining problem; Hedge; Nash equilibrium; Securitization; Actuarial science; Microeconomics; Finance","score_opus":0.15957184462241858,"score_gpt":0.32851020594051467,"score_spread":0.1689383613180961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1657465897","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":[0.07915714,0.0012577765,0.8925495,0.0025905622,0.00016282062,0.000090807596,0.00014063939,0.000065528126,0.023985121],"genre_scores_gemma":[0.89576775,0.0018719615,0.06474731,0.0003336209,0.00025717766,0.0003087083,0.000106365995,0.000069953916,0.036537126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989459,0.00065990875,0.000026570033,0.00010513463,0.00010935119,0.00015326218],"domain_scores_gemma":[0.99615663,0.0029678794,0.00035675027,0.000092482056,0.00020613712,0.00022017893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041575683,0.0010834938,0.0015558033,0.0012065661,0.00088377314,0.002738378,0.002396699,0.0033748846,0.0055049546],"category_scores_gemma":[0.009321988,0.0008630876,0.0012185791,0.0014428358,0.0024632777,0.0034927144,0.0018989253,0.002552505,0.00038994366],"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":[0.00001534171,0.00004833216,0.00048509898,0.000026996015,0.000026781383,0.000054219705,0.0001146911,0.7189717,0.00012489034,0.27696103,0.00063362555,0.002537225],"study_design_scores_gemma":[0.000013038564,0.000012469994,0.00010262806,0.000011546174,0.000011797146,0.000015850803,0.000067141904,0.85023755,0.000038730297,0.14884503,0.0006320317,0.000012130802],"about_ca_topic_score_codex":0.012888845,"about_ca_topic_score_gemma":0.009089361,"teacher_disagreement_score":0.012888845,"about_ca_system_score_codex":0.0029220614,"about_ca_system_score_gemma":0.0026181603,"threshold_uncertainty_score":0.025627673},"labels":[],"label_agreement":null},{"id":"W1662591990","doi":"10.1017/cbo9780511800146.013","title":"Emerging costs for equity-linked insurance","year":2009,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Equity (law); Actuarial science; Business; Profit (economics); Economics; Financial economics; Microeconomics; Computer science; Political science","score_opus":0.04573523096348679,"score_gpt":0.29539087495316646,"score_spread":0.24965564398967965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1662591990","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":[0.045139942,0.011293832,0.6453796,0.00649439,0.0005578765,0.00009889257,0.0002991111,0.0001567926,0.29057956],"genre_scores_gemma":[0.7928252,0.012865379,0.098955624,0.00082797697,0.0006135523,0.00026915016,0.00022907223,0.0001549352,0.09325896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918216,0.00022401959,0.000031942778,0.00009755371,0.0004075393,0.00005683126],"domain_scores_gemma":[0.9980584,0.0013794702,0.00012982394,0.00018138735,0.00018313507,0.00006782282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017683695,0.0005346648,0.00038859047,0.0008215651,0.00060960936,0.002070808,0.0010862314,0.001394753,0.008237703],"category_scores_gemma":[0.005998277,0.00032319053,0.00071513397,0.00084223936,0.002165082,0.0045199725,0.0013694788,0.0036017285,0.00058576406],"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":[0.000002156651,0.0000044786293,0.00005854862,0.000012425473,0.0000018117056,0.0000110143865,0.000022856797,0.004511805,0.000043691474,0.9880982,0.0005746374,0.0066583017],"study_design_scores_gemma":[0.0000024005412,0.000007487643,0.000117950105,0.00003469569,0.0000031428501,0.00003829553,0.000016942615,0.019550145,0.00008101545,0.9714238,0.008718724,0.000005226684],"about_ca_topic_score_codex":0.0010515066,"about_ca_topic_score_gemma":0.00072871364,"teacher_disagreement_score":0.008237703,"about_ca_system_score_codex":0.0024960535,"about_ca_system_score_gemma":0.0010088456,"threshold_uncertainty_score":0.02755779},"labels":[],"label_agreement":null},{"id":"W1716309778","doi":"10.1111/j.1539-6975.2013.12008.x","title":"Economic Pricing of Mortality‐Linked Securities: A Tâtonnement Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo; University of Manitoba","funders":"","keywords":"Population; Economics; Supply and demand; Arbitrage; Financial economics; Process (computing); Work (physics); Rational pricing; Business; Actuarial science; Microeconomics; Capital asset pricing model; Computer science","score_opus":0.014933926787781729,"score_gpt":0.2777997726276077,"score_spread":0.262865845839826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1716309778","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":[0.04994545,0.0012218677,0.9306716,0.0037331218,0.00022601354,0.00009786326,0.000075655546,0.000084393745,0.013944011],"genre_scores_gemma":[0.9175112,0.0011161759,0.06993851,0.00031784538,0.0005312459,0.000151036,0.000063538675,0.000052277803,0.010318049],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563676,0.0027526955,0.00017692863,0.0005331742,0.00068234804,0.00021804453],"domain_scores_gemma":[0.9898896,0.0073577636,0.001049227,0.0006441441,0.0005965682,0.00046272101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070254644,0.00095143676,0.0012798718,0.0018920475,0.00096221804,0.0038945337,0.0025104508,0.00363579,0.005589615],"category_scores_gemma":[0.033672757,0.000800941,0.0016063964,0.0014044123,0.005020185,0.007979482,0.0028384929,0.004371965,0.00027198964],"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":[0.000028601891,0.000041202118,0.0010547014,0.00003654806,0.000052509484,0.00015340919,0.00013021569,0.11211845,0.00029503775,0.8771172,0.0006007479,0.008371377],"study_design_scores_gemma":[0.000019230514,0.00003135664,0.00055566325,0.000022224418,0.00002005547,0.000059683818,0.00003382178,0.57273257,0.00016142594,0.42474467,0.0015864918,0.000032801236],"about_ca_topic_score_codex":0.0023757447,"about_ca_topic_score_gemma":0.0012931315,"teacher_disagreement_score":0.0070254644,"about_ca_system_score_codex":0.0025802143,"about_ca_system_score_gemma":0.0011873211,"threshold_uncertainty_score":0.037154675},"labels":[],"label_agreement":null},{"id":"W1753798782","doi":"10.25336/p61p53","title":"Regional disparities in Canadian adult and old-age mortality: A comparative study based on smoothed mortality ratio surfaces and age at death distributions","year":2013,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Demography; Mortality rate; Geography; Age groups; Cause of death; Medicine; Disease; Sociology","score_opus":0.10302095961099096,"score_gpt":0.36948721098068593,"score_spread":0.266466251369695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1753798782","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":[0.99341774,0.00070299866,0.00034049302,0.00009356997,0.0000047991985,0.000015737256,0.0039903983,0.000018906909,0.0014152493],"genre_scores_gemma":[0.9970823,0.00026603873,0.00032031658,0.0000112626985,0.0000025460265,0.0000049832806,0.002066215,0.0000060190896,0.00024031293],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992224,0.000083186605,0.000051280094,0.00014342958,0.0003052783,0.00019428539],"domain_scores_gemma":[0.9981559,0.00025244537,0.00035757603,0.000164526,0.0008662121,0.00020330086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012552728,0.00029763364,0.00037368803,0.0037515694,0.0013005709,0.0009994684,0.0008742648,0.00023541602,0.0016002846],"category_scores_gemma":[0.0044915625,0.00017802806,0.000667456,0.009335097,0.00062814425,0.00043881353,0.00088383886,0.00032208013,0.00013714799],"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":[0.000104527324,0.000014724284,0.9849213,0.000038069335,0.00012388387,0.00005683122,0.0012037661,0.0004831336,0.00015808763,0.00043177477,0.00068222673,0.011781567],"study_design_scores_gemma":[0.0000019282197,0.000008885626,0.99813,0.00000857596,0.000019803278,0.000030751762,0.0006858403,0.0004217522,0.000033079756,0.000034463566,0.0006165454,0.000008243858],"about_ca_topic_score_codex":0.988036,"about_ca_topic_score_gemma":0.9917623,"teacher_disagreement_score":0.012100305,"about_ca_system_score_codex":0.012100305,"about_ca_system_score_gemma":0.014008045,"threshold_uncertainty_score":0.087794304},"labels":[],"label_agreement":null},{"id":"W1759628964","doi":"","title":"Pricing Survivor Forwards and Swaps in Incomplete Markets Using Simulation Techniques","year":2012,"lang":"en","type":"article","venue":"CBS Research Portal (Copenhagen Business School)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université de Montréal; HEC Montréal","funders":"","keywords":"Financial economics; Economics; Computer science; Business","score_opus":0.13176570609688998,"score_gpt":0.4433441262572521,"score_spread":0.31157842016036214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1759628964","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":[0.1252215,0.00058167876,0.86805433,0.00070625026,0.000051552877,0.000057455913,0.00010503856,0.00012681143,0.005095397],"genre_scores_gemma":[0.8687796,0.0010565012,0.126043,0.00007867698,0.00007607657,0.00013346608,0.0001613071,0.00005461898,0.0036167584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900925,0.0006369669,0.0000529883,0.0000625059,0.00018375675,0.00005445683],"domain_scores_gemma":[0.9941373,0.0045300275,0.00064397935,0.00029746522,0.00023397575,0.00015723705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003666026,0.0007148999,0.0010236674,0.00083214685,0.00040892398,0.0016240034,0.0011256224,0.0015876406,0.002949483],"category_scores_gemma":[0.013538523,0.0005876743,0.0012059796,0.000772794,0.0012123493,0.003211045,0.00109055,0.0013296264,0.00016001062],"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":[0.000027685815,0.00001895069,0.00063610304,0.00002079267,0.000034943754,0.000053032985,0.00004068488,0.9117412,0.0002980363,0.08330718,0.00015400861,0.0036674691],"study_design_scores_gemma":[0.0000100856305,0.000011997331,0.000087038774,0.000004896554,0.000004933217,0.0000095533005,0.000006161725,0.9703667,0.000098004624,0.0291604,0.00023427565,0.000005906496],"about_ca_topic_score_codex":0.0036646312,"about_ca_topic_score_gemma":0.003457581,"teacher_disagreement_score":0.003666026,"about_ca_system_score_codex":0.0010041198,"about_ca_system_score_gemma":0.00089903985,"threshold_uncertainty_score":0.01938802},"labels":[],"label_agreement":null},{"id":"W1762321683","doi":"","title":"Applications of Bayesian Econometrics to Financial Economics","year":2005,"lang":"en","type":"dissertation","venue":"Lund University Publications (Lund University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Econometrics; Markov chain Monte Carlo; Portfolio; Estimator; Bayesian probability; Bayesian econometrics; Shrinkage estimator; Economics; Computer science; Bayesian inference; Statistics; Finance; Mathematics; Bayesian statistics; Bias of an estimator; Minimum-variance unbiased estimator","score_opus":0.012369717160859661,"score_gpt":0.2350436705386449,"score_spread":0.22267395337778523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1762321683","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":[0.007673822,0.018016817,0.90589595,0.014631248,0.00087660993,0.000087396,0.00026681577,0.0002606094,0.052290734],"genre_scores_gemma":[0.52298474,0.062478013,0.3672099,0.0040657516,0.0051737553,0.00065306533,0.0005629813,0.00030198426,0.036569823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962857,0.0023844317,0.0001598977,0.00022972011,0.00077763957,0.0001626465],"domain_scores_gemma":[0.9782628,0.019045789,0.00062356715,0.0006552801,0.0011436411,0.00026898386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007490224,0.00081372855,0.001137417,0.0031699091,0.0007146288,0.0028632814,0.0011404348,0.0018727937,0.008490982],"category_scores_gemma":[0.028304577,0.00082389155,0.0012857052,0.003277687,0.002479441,0.003349938,0.002751131,0.0027290764,0.00122227],"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":[0.000010431437,0.00003141615,0.0010450332,0.00013660226,0.00006297516,0.000055559492,0.00013255067,0.040923327,0.0000935468,0.900067,0.003980751,0.0534608],"study_design_scores_gemma":[0.0000114573695,0.000009674129,0.00059032696,0.000091812806,0.000009845554,0.00003600935,0.0000440261,0.06506888,0.000071658505,0.9182044,0.015842265,0.000019650746],"about_ca_topic_score_codex":0.0056167757,"about_ca_topic_score_gemma":0.0031723254,"teacher_disagreement_score":0.008490982,"about_ca_system_score_codex":0.0026608987,"about_ca_system_score_gemma":0.0017298342,"threshold_uncertainty_score":0.03961253},"labels":[],"label_agreement":null},{"id":"W1766564823","doi":"10.25336/p69k6x","title":"Back to the future: A review of forty years of population projections at Statistics Canada","year":2015,"lang":"en","type":"review","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Demographic statistics; Projections of population growth; Population; Population projection; Population statistics; Regional science; Geography; Demographic analysis; Fertility; Population growth; Statistics; Research methodology; Demography; Econometrics; Sociology; Economics; Mathematics","score_opus":0.09501216308650197,"score_gpt":0.40931233978327236,"score_spread":0.3143001766967704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1766564823","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":[0.00018317727,0.9940712,0.0002047282,0.0022055607,0.00033541836,0.000025686495,0.00142941,0.000018157429,0.0015266296],"genre_scores_gemma":[0.0011986528,0.99723744,0.0003030988,0.0004123187,0.00006871183,0.000018159619,0.00059246516,0.000005549347,0.000163578],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967198,0.00049234566,0.00057543366,0.0002280711,0.0017722382,0.00021207011],"domain_scores_gemma":[0.9859848,0.004449285,0.0009622258,0.00023503641,0.0077334563,0.0006351962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073572434,0.0015977139,0.0022925145,0.015689187,0.0014450434,0.003403562,0.0026777582,0.0010276366,0.0063806796],"category_scores_gemma":[0.020597702,0.0009014687,0.0019124846,0.031371966,0.0012988949,0.002745826,0.0015620154,0.002485066,0.0010844758],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011865939,0.000033299086,0.0023001188,0.11035021,0.00068794645,0.00020958263,0.00089334766,0.0019704464,0.00017344416,0.009304805,0.16554965,0.70840853],"study_design_scores_gemma":[0.000013883252,0.00002872578,0.0070651984,0.08853297,0.00080282544,0.00020616021,0.00059928995,0.0002004814,0.00015477928,0.0018483924,0.9004491,0.00009824165],"about_ca_topic_score_codex":0.7926286,"about_ca_topic_score_gemma":0.84415466,"teacher_disagreement_score":0.9766504,"about_ca_system_score_codex":0.023349596,"about_ca_system_score_gemma":0.13253404,"threshold_uncertainty_score":0.4171853},"labels":[],"label_agreement":null},{"id":"W1780116807","doi":"10.3233/rda-130099","title":"Valuation of finance/insurance contracts: Efficient hedging and stochastic interest rates modeling","year":2014,"lang":"en","type":"article","venue":"Risk and Decision Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Valuation (finance); Interest rate; Actuarial science; Economics; Financial economics; Business; Finance","score_opus":0.03142649339236644,"score_gpt":0.3219380355503289,"score_spread":0.2905115421579625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1780116807","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":[0.06605539,0.0016984264,0.92770004,0.0008142462,0.00005810704,0.00005504644,0.00008689273,0.000043901793,0.0034878973],"genre_scores_gemma":[0.9348707,0.0014519639,0.05848306,0.000106581225,0.00016971705,0.00009208911,0.0001445027,0.000035965215,0.0046453895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744046,0.0017084709,0.00011305519,0.0002735752,0.00030225635,0.0001620952],"domain_scores_gemma":[0.9948042,0.0036419143,0.0007480395,0.00023609081,0.00030722324,0.00026244012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006179637,0.0013391918,0.0024887577,0.0010705359,0.00046689349,0.00295764,0.001772098,0.0026696129,0.0016465223],"category_scores_gemma":[0.012299911,0.001243198,0.0016305487,0.0010696393,0.0022839557,0.0033483605,0.0016996824,0.0021421849,0.000117607466],"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":[0.000044964636,0.0000681562,0.0009672444,0.00007236105,0.00008024731,0.00011487279,0.00009750188,0.7854255,0.00055809174,0.20472056,0.00034890766,0.0075016855],"study_design_scores_gemma":[0.000010400047,0.000017355567,0.00013290609,0.000009408141,0.000012829564,0.000020605275,0.000008595801,0.96377236,0.00007657358,0.03573442,0.00019685828,0.000007670195],"about_ca_topic_score_codex":0.0027371154,"about_ca_topic_score_gemma":0.0012889397,"teacher_disagreement_score":0.006179637,"about_ca_system_score_codex":0.0015746129,"about_ca_system_score_gemma":0.00144485,"threshold_uncertainty_score":0.032681406},"labels":[],"label_agreement":null},{"id":"W1784973889","doi":"10.1002/9781118445112.stat04309.pub2","title":"Impact of Inflation and Interest on Aggregate Claims","year":2015,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Concordia University","funders":"","keywords":"Inflation (cosmology); Economics; Interest rate; Aggregate (composite); Monetary economics; Offset (computer science)","score_opus":0.07857118494943084,"score_gpt":0.39493036210892557,"score_spread":0.31635917715949474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1784973889","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":[0.9678853,0.0020993538,0.0020312078,0.0018447703,0.000067133784,0.000017410912,0.002636816,0.000070583155,0.023347357],"genre_scores_gemma":[0.99847513,0.00018289016,0.000084756924,0.00003812469,0.000047578615,0.0000028513728,0.0004775511,0.000007698251,0.0006834194],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99681085,0.0010116323,0.00019622498,0.00028565465,0.0014470911,0.00024852637],"domain_scores_gemma":[0.9696442,0.020758564,0.005480522,0.0010314353,0.002329586,0.0007556821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025491393,0.00012352278,0.00028901728,0.0013616062,0.00015992748,0.0018937682,0.00022729051,0.00032661197,0.004733614],"category_scores_gemma":[0.0236764,0.000084545136,0.00030003,0.001332244,0.00040092683,0.0006417041,0.000861194,0.0008839248,0.0006259901],"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":[0.0021465993,0.0002682095,0.8495964,0.00015894753,0.0005444444,0.0007707807,0.00070037635,0.021008251,0.0021880793,0.01756218,0.005963356,0.09909237],"study_design_scores_gemma":[0.000022202077,0.00016271613,0.9609276,0.00005545088,0.00011338657,0.00040512384,0.00032616148,0.023915334,0.00087854726,0.010350005,0.002809961,0.000033544122],"about_ca_topic_score_codex":0.0025152962,"about_ca_topic_score_gemma":0.0012809345,"teacher_disagreement_score":0.004733614,"about_ca_system_score_codex":0.0006518684,"about_ca_system_score_gemma":0.00029072296,"threshold_uncertainty_score":0.015835524},"labels":[],"label_agreement":null},{"id":"W1793470888","doi":"10.2139/ssrn.2561523","title":"Risk Management of Policyholder Behavior in Equity Linked Life Insurance","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Montréal; Université du Québec à Montréal; University of Waterloo","funders":"","keywords":"Life insurance; Actuarial science; Business; Equity (law); Political science","score_opus":0.03802928727680241,"score_gpt":0.3485042791468088,"score_spread":0.3104749918700064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1793470888","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":[0.99745315,0.000054578977,0.00082592136,0.00031037413,0.0000032581784,0.00000546759,0.000029521681,0.0000036666206,0.0013141559],"genre_scores_gemma":[0.9993316,0.000019933006,0.000106457024,0.0000152596,0.0000026659436,0.0000016405522,0.000015723615,8.635058e-7,0.000505899],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995426,0.00026330719,0.000019343317,0.000048692626,0.00004448753,0.00008163195],"domain_scores_gemma":[0.996011,0.0023128425,0.00084459875,0.00014250117,0.00017659554,0.00051247975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017142515,0.00012626479,0.00016610276,0.00043263394,0.0003625021,0.001333838,0.0002982785,0.00070697605,0.0035484403],"category_scores_gemma":[0.009687832,0.0001365338,0.00014610995,0.00028004966,0.00044925892,0.0007597506,0.00065974885,0.0005689204,0.00025973262],"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":[0.0006006001,0.0011530113,0.9405797,0.000019671594,0.000096559925,0.00032902855,0.0022499447,0.013371347,0.0024846639,0.0154281305,0.0006180752,0.023069425],"study_design_scores_gemma":[0.00002340054,0.00038870197,0.88774794,0.00001768054,0.00005485867,0.00019235114,0.004316897,0.07714588,0.0008953937,0.028290754,0.0008977493,0.000028467304],"about_ca_topic_score_codex":0.004539078,"about_ca_topic_score_gemma":0.0063914633,"teacher_disagreement_score":0.004539078,"about_ca_system_score_codex":0.00083793787,"about_ca_system_score_gemma":0.00043228932,"threshold_uncertainty_score":0.011870742},"labels":[],"label_agreement":null},{"id":"W1802179421","doi":"10.1017/cbo9781139208499","title":"Solutions Manual for Actuarial Mathematics for Life Contingent Risks","year":2012,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mathematics; Applied mathematics","score_opus":0.10497665231479789,"score_gpt":0.3080753538198546,"score_spread":0.20309870150505674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1802179421","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":[0.0011883792,0.0051060473,0.30206478,0.00779405,0.0039484086,0.0004302594,0.011573866,0.009587855,0.6583064],"genre_scores_gemma":[0.009331963,0.005444459,0.17830332,0.0018678189,0.0012044039,0.0005928259,0.014219825,0.0062576025,0.7827777],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871767,0.000116820935,0.00010511643,0.00013137549,0.0008751039,0.000053985957],"domain_scores_gemma":[0.99803454,0.00082192384,0.00005848672,0.00026087664,0.0007473738,0.000076747485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010826152,0.0013838363,0.0010344317,0.0024966425,0.00086402684,0.0028149877,0.001721804,0.0017845698,0.29825756],"category_scores_gemma":[0.005413227,0.00094584975,0.0010862191,0.00211455,0.0006548692,0.0038061175,0.0020533418,0.004152127,0.19890164],"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":[0.000012323127,0.000055821805,0.00009701166,0.00032423533,0.0000076064803,0.00007526431,0.00016920634,0.0014248936,0.0009717774,0.12320592,0.6569368,0.2167191],"study_design_scores_gemma":[0.000008321287,0.00001029324,0.00016953301,0.00009198216,0.0000035300166,0.00018683488,0.00002352549,0.0012451056,0.0004572272,0.053937417,0.9438537,0.000012496557],"about_ca_topic_score_codex":0.0012805583,"about_ca_topic_score_gemma":0.0020932457,"teacher_disagreement_score":0.29825756,"about_ca_system_score_codex":0.0012941671,"about_ca_system_score_gemma":0.0014780789,"threshold_uncertainty_score":0.99777097},"labels":[],"label_agreement":null},{"id":"W1810395884","doi":"","title":"Pension plan valuation and dynamic mortality tables","year":2007,"lang":"en","type":"article","venue":"DIAL (Catholic University of Leuven)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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":"Life table; Poisson regression; Pension; Actuarial science; Population; Poisson distribution; Valuation (finance); Mortality rate; Economics; Econometrics; Demography; Statistics; Finance; Mathematics","score_opus":0.03322727978926999,"score_gpt":0.29049000543505,"score_spread":0.25726272564578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1810395884","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":[0.26431173,0.0016401294,0.6777385,0.0016025124,0.00015582831,0.00026660415,0.006988861,0.0007810096,0.04651479],"genre_scores_gemma":[0.90922934,0.0007876181,0.08114354,0.000024828321,0.000048213697,0.0000911403,0.0024749315,0.00006206687,0.00613838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911433,0.0003227552,0.000060325678,0.00009733986,0.0003334337,0.000071743656],"domain_scores_gemma":[0.99813616,0.0011046341,0.00019928679,0.00018272836,0.0002890117,0.00008813804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023498936,0.0004140208,0.0003718956,0.0017129472,0.00030194022,0.0025478678,0.00087858515,0.0006208041,0.0061859563],"category_scores_gemma":[0.009926703,0.00022098803,0.00037548493,0.0020237302,0.00040187335,0.0018615555,0.00079813803,0.0005814457,0.00038326075],"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":[0.000096196214,0.00002943072,0.006456381,0.00004347986,0.000023030136,0.00025356442,0.0001223948,0.77926606,0.00049581996,0.14586581,0.0025457758,0.06480215],"study_design_scores_gemma":[0.000015522633,0.00003702034,0.0027571002,0.00003655148,0.000014694279,0.00011492513,0.000095488416,0.9313192,0.00044138322,0.05750975,0.0076378677,0.000020544978],"about_ca_topic_score_codex":0.010215359,"about_ca_topic_score_gemma":0.004854945,"teacher_disagreement_score":0.010215359,"about_ca_system_score_codex":0.001756911,"about_ca_system_score_gemma":0.0009303228,"threshold_uncertainty_score":0.020694077},"labels":[],"label_agreement":null},{"id":"W1816679487","doi":"10.25336/p65w28","title":"The Probabilistic Life Table and Its Applications to Canada","year":2015,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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":"Table (database); Probabilistic logic; Statistics; Life table; Sample (material); Variable (mathematics); Mathematics; Econometrics; Population; Demography; Computer science; Data mining; Sociology","score_opus":0.08491613987052238,"score_gpt":0.3518152688200497,"score_spread":0.26689912894952733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1816679487","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":[0.027971534,0.0059676324,0.8424537,0.010670798,0.0005764201,0.00055514934,0.011328389,0.0016085586,0.09886774],"genre_scores_gemma":[0.41225776,0.00691847,0.5569267,0.0008364072,0.0002471789,0.00048275013,0.0038826352,0.0003835076,0.018064607],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.997724,0.000833633,0.00010108468,0.00024303317,0.00093266356,0.00016548435],"domain_scores_gemma":[0.989823,0.004630894,0.0004727141,0.0006790763,0.004082015,0.00031246163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048976094,0.00041809882,0.000496495,0.002886567,0.0023047973,0.0018761167,0.0016433704,0.0006967325,0.010207474],"category_scores_gemma":[0.027023315,0.00037576965,0.0010063833,0.005595189,0.0015244924,0.0012842237,0.001413581,0.0013723974,0.0006249683],"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":[0.0000737638,0.000027476539,0.012931512,0.00017736811,0.00007169519,0.00031039846,0.000684165,0.12328706,0.00014254688,0.6873943,0.02956154,0.1453382],"study_design_scores_gemma":[0.000049058497,0.000035252437,0.013309405,0.00023306208,0.00006167308,0.0003788361,0.0005296832,0.3892716,0.00034553028,0.45193306,0.14371684,0.00013605933],"about_ca_topic_score_codex":0.90272146,"about_ca_topic_score_gemma":0.86066425,"teacher_disagreement_score":0.097278535,"about_ca_system_score_codex":0.02281893,"about_ca_system_score_gemma":0.033309966,"threshold_uncertainty_score":0.19570279},"labels":[],"label_agreement":null},{"id":"W1821957428","doi":"10.2139/ssrn.289551","title":"Mortality Derivatives and the Option to Annuitize","year":2001,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Actuarial science; Medicine; Economics","score_opus":0.014603706824298183,"score_gpt":0.30639883834224485,"score_spread":0.29179513151794667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1821957428","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":[0.71967816,0.015950738,0.032865565,0.07431701,0.00063608045,0.00005018196,0.0010919692,0.00017038587,0.15523992],"genre_scores_gemma":[0.9858122,0.0014588009,0.0006522413,0.00034819555,0.0002846476,0.000008647197,0.00008370819,0.000008865016,0.01134289],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99948287,0.00022265103,0.000033209042,0.00006166072,0.000101417616,0.000098116754],"domain_scores_gemma":[0.9948737,0.002727528,0.0012001679,0.00037773798,0.0002643144,0.00055646984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001892397,0.00027320205,0.0004673777,0.0006232749,0.00055397593,0.00224732,0.0006884368,0.0026239841,0.00957525],"category_scores_gemma":[0.014716164,0.00018771972,0.0003364798,0.00056550174,0.0019245698,0.002450249,0.0011007205,0.0031472985,0.00042023938],"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":[0.00061101775,0.00020795772,0.024998322,0.00008319511,0.000075972755,0.0009098701,0.00055928645,0.012037294,0.00048314882,0.8996018,0.0068856287,0.053546518],"study_design_scores_gemma":[0.00006533472,0.00010625828,0.013022758,0.000046618854,0.000045311797,0.0005879728,0.00029991328,0.015233651,0.00025864548,0.95782214,0.012475697,0.00003572585],"about_ca_topic_score_codex":0.0018096027,"about_ca_topic_score_gemma":0.0020619435,"teacher_disagreement_score":0.00957525,"about_ca_system_score_codex":0.0007581262,"about_ca_system_score_gemma":0.0008108566,"threshold_uncertainty_score":0.03203237},"labels":[],"label_agreement":null},{"id":"W1826957651","doi":"10.7202/010851ar","title":"L’évolution de la longévité à Okinawa, 1921-2000","year":2005,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":12,"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":"Humanities; Political science; Art","score_opus":0.007617854729949608,"score_gpt":0.26158850711406945,"score_spread":0.25397065238411987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1826957651","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":[0.9947813,0.0012658306,0.00012592938,0.00010318372,0.0000127483,0.000005486763,0.0008731608,0.000005121424,0.00282706],"genre_scores_gemma":[0.9945714,0.0011784155,0.00033023028,0.000027444588,0.000006228632,0.000019253153,0.00066807924,0.0000031970164,0.003195773],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998658,0.000017647128,0.0000112013,0.000040291485,0.000013345049,0.000051696974],"domain_scores_gemma":[0.9995203,0.00007542076,0.00019837682,0.000027694381,0.00011571394,0.00006255863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004068016,0.00034243055,0.00015212888,0.001635866,0.0010273933,0.00081944116,0.00039717468,0.0003483078,0.0022981754],"category_scores_gemma":[0.00068531564,0.00027371646,0.00024556185,0.0025722343,0.0005324249,0.000672077,0.00064298813,0.00044978235,0.00027849738],"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":[0.00029321588,0.0000415804,0.9354351,0.00028259086,0.00019129427,0.000402254,0.013990817,0.0004014115,0.0045362897,0.0010806839,0.0008873052,0.04245735],"study_design_scores_gemma":[0.0000012856423,0.000021786753,0.9934382,0.000031077285,0.000026018148,0.00004611464,0.0025308025,0.00007429771,0.00015646183,0.000028643064,0.0036385944,0.000006690341],"about_ca_topic_score_codex":0.16798168,"about_ca_topic_score_gemma":0.37200505,"teacher_disagreement_score":0.16798168,"about_ca_system_score_codex":0.002296242,"about_ca_system_score_gemma":0.0007244415,"threshold_uncertainty_score":0.33400786},"labels":[],"label_agreement":null},{"id":"W1827025678","doi":"10.25336/p64p4n","title":"Canadian mortality in perspective: a comparison with the United States and other developed countries","year":2002,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mortality rate; Demography; Context (archaeology); Developed country; Geography; Perspective (graphical); Population; Sociology; Mathematics","score_opus":0.07495402385832448,"score_gpt":0.35994608662943656,"score_spread":0.2849920627711121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1827025678","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":[0.7543507,0.033302356,0.0010535053,0.00891354,0.0005376744,0.00011998998,0.054198466,0.00014116232,0.14738251],"genre_scores_gemma":[0.97000575,0.012449412,0.0008890408,0.0005535157,0.00008339973,0.000035096513,0.012324213,0.000032933796,0.0036265964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99871635,0.00008401215,0.000069507456,0.00011173991,0.00065230264,0.00036612642],"domain_scores_gemma":[0.9977514,0.00009018789,0.00023728803,0.00005330487,0.0014934167,0.00037435212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093316217,0.00043581438,0.0004517669,0.010091878,0.0026550007,0.0019802707,0.0008170507,0.00030366136,0.0038758952],"category_scores_gemma":[0.0031725138,0.00014268306,0.00077735115,0.019385371,0.00043352114,0.000493556,0.0010872284,0.000537079,0.00025214467],"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":[0.0004228016,0.000051125513,0.85919166,0.00073913817,0.00046385676,0.0009617663,0.0031061121,0.0012883572,0.0005305234,0.011913708,0.027574074,0.093756944],"study_design_scores_gemma":[0.000013655841,0.00003092702,0.96730554,0.00014347173,0.00013224901,0.00028675236,0.0023754353,0.00031438284,0.00014861231,0.00030410066,0.028917974,0.000026912432],"about_ca_topic_score_codex":0.9903289,"about_ca_topic_score_gemma":0.99562067,"teacher_disagreement_score":0.030590307,"about_ca_system_score_codex":0.030590307,"about_ca_system_score_gemma":0.026837401,"threshold_uncertainty_score":0.22194928},"labels":[],"label_agreement":null},{"id":"W1832581181","doi":"","title":"Population Aging in Canada: Software for Exploring the Implications for the Labour Force and the Productive Capacity of the Economy","year":2005,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Scope (computer science); Projections of population growth; Population ageing; Immigration; Educational attainment; Population; Range (aeronautics); Economics; Demographic change; Population projection; Fertility; Demographic economics; Regional science; Geography; Economic growth; Computer science; Sociology; Engineering; Demography","score_opus":0.06641821774146038,"score_gpt":0.3260415558513702,"score_spread":0.2596233381099098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1832581181","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044739757,0.0010542162,0.3930412,0.0038763622,0.00033067676,0.0009249853,0.24061805,0.16796811,0.14744668],"genre_scores_gemma":[0.3053671,0.0035856653,0.4954258,0.0007755658,0.00010474848,0.002029068,0.123504974,0.012140545,0.057066575],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972135,0.000049546456,0.000016981705,0.000030846822,0.0001429217,0.000038396152],"domain_scores_gemma":[0.99871016,0.00044062902,0.000047588,0.00012888745,0.0005839405,0.000088716704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008420728,0.0008762873,0.00042381388,0.0015895192,0.0013865147,0.0014116438,0.001582021,0.00075394387,0.032281365],"category_scores_gemma":[0.0047829445,0.00061819324,0.0009817437,0.0026214847,0.00039331266,0.0011406911,0.0012421256,0.0007093043,0.004667071],"study_design_candidate":"simulation_or_modeling","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":[0.00026490155,0.00009463285,0.025874062,0.0006941752,0.00014618244,0.0003706475,0.00096714176,0.19657008,0.001145031,0.05183917,0.54003453,0.18199944],"study_design_scores_gemma":[0.00021263066,0.00003823472,0.014040159,0.00020395617,0.00007763497,0.00021375553,0.00038652765,0.596007,0.0017515402,0.024426866,0.36246738,0.000174345],"about_ca_topic_score_codex":0.88640845,"about_ca_topic_score_gemma":0.92381716,"teacher_disagreement_score":0.11359155,"about_ca_system_score_codex":0.010553088,"about_ca_system_score_gemma":0.01671615,"threshold_uncertainty_score":0.22852099},"labels":[],"label_agreement":null},{"id":"W183285359","doi":"10.1007/978-0-387-68477-2_8","title":"The Exponential Distribution","year":2007,"lang":"en","type":"book-chapter","venue":"Springer series in statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Exponential family; Natural exponential family; Gamma distribution; Mathematics; Exponential function; Exponential distribution; Exponential formula; Laplace distribution; Constant (computer programming); Hazard; Distribution (mathematics); Statistics; Phase-type distribution; Sobel operator; Statistical physics; Applied mathematics; Mathematical analysis; Computer science; Double exponential function; Physics; Artificial intelligence","score_opus":0.03245673760163911,"score_gpt":0.31596114901408956,"score_spread":0.2835044114124504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W183285359","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":[0.008179819,0.050498575,0.50959206,0.014093491,0.0043791244,0.00006508898,0.0008809172,0.0006934926,0.4116174],"genre_scores_gemma":[0.4042173,0.066547684,0.11251327,0.00777337,0.017437704,0.00054012815,0.0018937577,0.0011723321,0.38790447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893016,0.0003836756,0.000053978547,0.00026915324,0.00027817604,0.00008491102],"domain_scores_gemma":[0.9962708,0.002353882,0.00018575936,0.0004594257,0.0006184065,0.0001117134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018140967,0.0011009129,0.001129846,0.001977968,0.0008418871,0.0036610307,0.0010558406,0.002172555,0.01948893],"category_scores_gemma":[0.0139847305,0.00061486155,0.0008146197,0.0022310799,0.0033111463,0.006996278,0.0013849509,0.00496188,0.0081341555],"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":[0.0000045486117,0.000004748741,0.00009495744,0.00003172575,0.0000063134667,0.00003476784,0.00006314445,0.0006645752,0.00007120725,0.9711387,0.015015841,0.012869533],"study_design_scores_gemma":[0.000004576556,0.000004512841,0.00016735152,0.000044411237,0.0000060812627,0.00016795557,0.000026569109,0.0044080974,0.0000674061,0.936979,0.058112893,0.000011066202],"about_ca_topic_score_codex":0.0017391274,"about_ca_topic_score_gemma":0.00081409543,"teacher_disagreement_score":0.01948893,"about_ca_system_score_codex":0.0015409485,"about_ca_system_score_gemma":0.0010813881,"threshold_uncertainty_score":0.06519699},"labels":[],"label_agreement":null},{"id":"W1834697838","doi":"10.25336/p6mw42","title":"A Probability distribution for first birth interval","year":2006,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Interval (graph theory); Statistics; Probability distribution; Demography; Birth rate; Set (abstract data type); Econometrics; Mathematics; Population; Computer science; Fertility; Sociology","score_opus":0.06435455753983804,"score_gpt":0.3358254342038599,"score_spread":0.27147087666402187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834697838","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":[0.052978136,0.0011646575,0.9328941,0.0020822645,0.00016356006,0.00025518757,0.0014660821,0.0011139917,0.007882084],"genre_scores_gemma":[0.8634112,0.004045273,0.10428352,0.00038432766,0.0006860274,0.0010134863,0.003048352,0.0003864915,0.022741292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972811,0.0007862459,0.00012288519,0.00077337265,0.00062284723,0.0004135661],"domain_scores_gemma":[0.97735274,0.016426757,0.0022317471,0.0018349101,0.0016247026,0.0005291144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056775995,0.001205104,0.0016444582,0.004058125,0.0012393644,0.0037670867,0.005011097,0.003435997,0.01105779],"category_scores_gemma":[0.03449674,0.00089340174,0.0019029268,0.0028145195,0.0026278596,0.0048625623,0.0013832485,0.0039587566,0.0038071643],"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":[0.00021953158,0.00015490453,0.022743393,0.0002527398,0.00014165901,0.0010883247,0.0011380326,0.3408592,0.002889564,0.5751739,0.0071752886,0.048163466],"study_design_scores_gemma":[0.000067848,0.0002084368,0.007237921,0.00015703026,0.0000739066,0.0019893458,0.00021383318,0.8295632,0.00089525426,0.15144038,0.0079948455,0.00015794733],"about_ca_topic_score_codex":0.008485765,"about_ca_topic_score_gemma":0.002686715,"teacher_disagreement_score":0.01105779,"about_ca_system_score_codex":0.0022480204,"about_ca_system_score_gemma":0.0012805734,"threshold_uncertainty_score":0.036992013},"labels":[],"label_agreement":null},{"id":"W1836440821","doi":"10.2139/ssrn.2271259","title":"Optimal Retirement Tontines for the 21st Century: With Reference to Mortality Derivatives in 1693","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Actuarial science; Economics","score_opus":0.024002767820497446,"score_gpt":0.30640419392593177,"score_spread":0.28240142610543434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836440821","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":[0.85314864,0.021676615,0.0037441358,0.040179785,0.0006855571,0.000051326537,0.0037872286,0.000040772236,0.076685965],"genre_scores_gemma":[0.9872387,0.0023574715,0.00043959796,0.00036337858,0.00018963395,0.000015466869,0.00031048447,0.000008500822,0.009076801],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996238,0.000076889344,0.000030997206,0.000058354923,0.000054403765,0.00015551136],"domain_scores_gemma":[0.999595,0.00009015871,0.00009399669,0.000023349321,0.00006265549,0.00013487795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095113454,0.0002025392,0.0005300705,0.00081366394,0.0016383857,0.0025711944,0.0005029011,0.0016413719,0.0043676025],"category_scores_gemma":[0.0033090082,0.00012213428,0.00038641284,0.0012769683,0.00083601504,0.0012411659,0.0014281683,0.001942487,0.0002321273],"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":[0.00026219524,0.000103705126,0.03990205,0.00008680172,0.000049348775,0.00049202156,0.0024934986,0.0058452976,0.00014937854,0.90506995,0.015107699,0.030438134],"study_design_scores_gemma":[0.00007844946,0.00022886184,0.21751167,0.00032982204,0.00013643691,0.0004551774,0.0066468776,0.020519825,0.00043881196,0.63365257,0.11990118,0.0001002547],"about_ca_topic_score_codex":0.044413224,"about_ca_topic_score_gemma":0.094049275,"teacher_disagreement_score":0.044413224,"about_ca_system_score_codex":0.003986624,"about_ca_system_score_gemma":0.003005237,"threshold_uncertainty_score":0.08830941},"labels":[],"label_agreement":null},{"id":"W1852121144","doi":"10.25336/p66g6z","title":"Estimating the fertility level of Registered Indians in Canada: a challenging endeavour","year":2003,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Fertility; Total fertility rate; Population; Birth rate; Demography; Geography; Family planning; Research methodology; Sociology","score_opus":0.21941186386686376,"score_gpt":0.3659146233285321,"score_spread":0.14650275946166832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1852121144","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":[0.9469104,0.0052504777,0.0089315,0.0063435375,0.000084031795,0.00040012746,0.015052356,0.00016629207,0.016861394],"genre_scores_gemma":[0.9734684,0.0054920316,0.011507666,0.00032832442,0.00003431911,0.000093173425,0.004678752,0.000022943143,0.0043743723],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986179,0.00024579055,0.000081881415,0.00010550202,0.00069707335,0.00025176024],"domain_scores_gemma":[0.99672806,0.0005202313,0.0003326827,0.00012122908,0.0021208469,0.0001769122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022279348,0.00022097476,0.0003826361,0.0033830965,0.002438204,0.0012419636,0.0013602783,0.000286646,0.0010208017],"category_scores_gemma":[0.007943524,0.00015760057,0.000437462,0.008056316,0.00061103184,0.00052949943,0.0007615816,0.0006470966,0.00018091731],"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":[0.00005139569,0.000043901702,0.8524881,0.0003215981,0.00015229333,0.00023308714,0.003926562,0.005125521,0.00044402742,0.0065088454,0.007901605,0.12280303],"study_design_scores_gemma":[0.000008390295,0.000034844925,0.9691572,0.00015605545,0.00006817688,0.000094538205,0.006744476,0.006294198,0.00057748426,0.00093980023,0.015875934,0.00004893626],"about_ca_topic_score_codex":0.9965587,"about_ca_topic_score_gemma":0.99757546,"teacher_disagreement_score":0.030117966,"about_ca_system_score_codex":0.030117966,"about_ca_system_score_gemma":0.05233023,"threshold_uncertainty_score":0.21852219},"labels":[],"label_agreement":null},{"id":"W1866553204","doi":"10.1007/s10887-015-9117-0","title":"The longevity of famous people from Hammurabi to Einstein","year":2015,"lang":"en","type":"article","venue":"Journal of Economic Growth","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":56,"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":"Pontifícia Universidade Católica do Rio de Janeiro; Max-Planck-Institut für demografische Forschung; Universidade de São Paulo; Universidad de la República Uruguay; McGill University; Syddansk Universitet; Ministerio de Economía y Competitividad; Universität Zürich; Université Catholique de Louvain","keywords":"Longevity; Credence; Epoch (astronomy); Cohort; Human capital; Demography; Economics; History; Demographic economics; Gerontology; Economic growth; Statistics; Medicine; Sociology; Mathematics; Computer science","score_opus":0.02689993065413353,"score_gpt":0.29282575549182677,"score_spread":0.2659258248376932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866553204","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":[0.98913133,0.0026013516,0.000037058333,0.003026731,0.00015105639,0.0000043107616,0.00060119125,0.000004652198,0.0044422587],"genre_scores_gemma":[0.993396,0.0019679088,0.000050301576,0.0005172772,0.00016755378,0.0000056534955,0.0003094662,0.0000035379478,0.003582434],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998859,0.000021697502,0.000008668698,0.000016168793,0.000017251969,0.000050263032],"domain_scores_gemma":[0.99950945,0.00006553979,0.00012938175,0.000027039701,0.00011351443,0.00015501415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034582888,0.00014216347,0.00019808824,0.001184426,0.0017066379,0.000585555,0.00017740193,0.0005052636,0.0027857055],"category_scores_gemma":[0.0017978487,0.0000670361,0.0001516651,0.0010601267,0.0005535603,0.0008072051,0.00053383963,0.0006757321,0.00041384596],"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":[0.00045651512,0.00012819342,0.83132285,0.00019566878,0.00018472903,0.0026186975,0.05470914,0.00012257819,0.00082006596,0.008157261,0.02580833,0.07547594],"study_design_scores_gemma":[0.000008425835,0.00010233295,0.93105066,0.00008548731,0.000067342786,0.0017316951,0.01810408,0.00009161632,0.000117720505,0.0011992414,0.047406256,0.000035138935],"about_ca_topic_score_codex":0.016171077,"about_ca_topic_score_gemma":0.019974051,"teacher_disagreement_score":0.016171077,"about_ca_system_score_codex":0.0005720655,"about_ca_system_score_gemma":0.0004248016,"threshold_uncertainty_score":0.032153904},"labels":[],"label_agreement":null},{"id":"W1875256428","doi":"10.2143/ast.42.2.2182804","title":"Key Q-Duration: A Framework for Hedging Longevity Risk","year":2012,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":63,"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":"Longevity risk; Hedge; Longevity; Key (lock); Duration (music); Extension (predicate logic); Actuarial science; Matching (statistics); Basis risk; Measure (data warehouse); Construct (python library); Population; Econometrics; Economics; Computer science; Mathematics; Statistics; Medicine; Data mining; Biology; Computer security","score_opus":0.028161414668521328,"score_gpt":0.3217179683656514,"score_spread":0.2935565536971301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1875256428","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":[0.010221855,0.0005107864,0.9857605,0.00043972075,0.00007642483,0.000035989364,0.00011230306,0.00007426787,0.0027680958],"genre_scores_gemma":[0.58421516,0.00144145,0.40522328,0.00038517837,0.00044330824,0.00036795923,0.0003079071,0.0001342161,0.0074815727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970241,0.0015278241,0.00016802082,0.00054205,0.00056892016,0.00016899692],"domain_scores_gemma":[0.9928067,0.003798624,0.0010722034,0.0012332185,0.00069410895,0.0003951809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010043883,0.0012167861,0.00079219934,0.0017795447,0.0008101916,0.0028521898,0.0020395964,0.0020959876,0.004033849],"category_scores_gemma":[0.021162072,0.0006322842,0.001242386,0.0015097283,0.0024676707,0.005637031,0.0024440077,0.002828512,0.00041982217],"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":[0.00006691166,0.000054312957,0.0028660262,0.00007715416,0.00006715125,0.00012740397,0.00031938485,0.10904633,0.001203289,0.84169877,0.0010844809,0.043388855],"study_design_scores_gemma":[0.000028368017,0.00014736353,0.0023439075,0.000068344525,0.000031628857,0.00016610611,0.00009026693,0.37787506,0.00059195963,0.6107546,0.007824732,0.00007770438],"about_ca_topic_score_codex":0.0020104158,"about_ca_topic_score_gemma":0.0012495546,"teacher_disagreement_score":0.010043883,"about_ca_system_score_codex":0.0016461038,"about_ca_system_score_gemma":0.001189097,"threshold_uncertainty_score":0.05311781},"labels":[],"label_agreement":null},{"id":"W1880060489","doi":"10.25336/p6fc7g","title":"Demography of a man-made human catastrophe: The case of massive famine in Ukraine 1932-1933","year":2015,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":66,"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":"Harvard University","keywords":"Famine; Demography; Geography; Excess mortality; Rural population; Rural area; Socioeconomics; Population; Medicine; Economics; Sociology","score_opus":0.08176296857846417,"score_gpt":0.37914681269899136,"score_spread":0.2973838441205272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1880060489","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":[0.9959565,0.00026374275,0.00014420398,0.0004502446,0.000004365023,0.0000067178244,0.00034687083,0.0000059174668,0.0028214334],"genre_scores_gemma":[0.9995167,0.00014206822,0.00005003226,0.000014929576,0.000002649131,0.0000024454703,0.00008343861,0.0000015201497,0.00018626045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998406,0.00006506383,0.0000071882923,0.000018169434,0.000011861159,0.00005705259],"domain_scores_gemma":[0.9997414,0.000058515718,0.000065764834,0.000024661837,0.00004773455,0.00006180457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036389055,0.00014274172,0.00023104755,0.0011058231,0.00134649,0.00093644473,0.00038915046,0.00052075746,0.0016025435],"category_scores_gemma":[0.0010198618,0.00012573421,0.0003192733,0.001503525,0.001332845,0.00052924536,0.0011911023,0.00048257134,0.00012116754],"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":[0.0005598254,0.00013107657,0.83692765,0.00019134746,0.00027745182,0.016139526,0.05769595,0.025333995,0.003266487,0.026567107,0.0061862664,0.026723305],"study_design_scores_gemma":[0.0000067301594,0.00005437864,0.9643559,0.0000683648,0.000023294671,0.0016346243,0.021578245,0.0055253184,0.0001660898,0.0020680039,0.004484089,0.00003493188],"about_ca_topic_score_codex":0.22980191,"about_ca_topic_score_gemma":0.25030798,"teacher_disagreement_score":0.22980191,"about_ca_system_score_codex":0.0044728196,"about_ca_system_score_gemma":0.0007991311,"threshold_uncertainty_score":0.4569286},"labels":[],"label_agreement":null},{"id":"W1892769462","doi":"10.25336/p6c31t","title":"New Estimates of Aboriginal Fertility, 1966-1971 to 1996-2001","year":2004,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Fertility; Metis; Demography; Total fertility rate; Geography; Population; Birth rate; Research methodology; Family planning; Sociology","score_opus":0.058739902471569704,"score_gpt":0.4015254706780161,"score_spread":0.3427855682064464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892769462","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":[0.5186653,0.00919325,0.0085847685,0.0011295134,0.00036955107,0.0003708715,0.413657,0.00045601273,0.04757362],"genre_scores_gemma":[0.6835187,0.012830569,0.020872762,0.00017137438,0.00023876352,0.00067236775,0.2525238,0.00012092955,0.029050717],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99944884,0.00006408634,0.00007022577,0.000085585256,0.0002582475,0.000073057796],"domain_scores_gemma":[0.9972487,0.00017368139,0.00048002807,0.00014998567,0.0017681693,0.00017946347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015495038,0.00044146038,0.00030578708,0.0069603273,0.00073982845,0.0007648647,0.000671546,0.00012553272,0.0018923554],"category_scores_gemma":[0.0042026388,0.00031918107,0.0005420679,0.0054117665,0.00017917773,0.00064271153,0.0008349554,0.0005991197,0.0005729923],"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":[0.00021586168,0.000060125323,0.79988366,0.0010014476,0.0005876498,0.00029593476,0.011063501,0.0069248215,0.00062709284,0.0048077046,0.055261962,0.11927025],"study_design_scores_gemma":[0.000015311141,0.000027830778,0.9292751,0.00016000668,0.00009081267,0.00015357052,0.0013468813,0.0012025143,0.00024894168,0.00021693956,0.06723856,0.000023584334],"about_ca_topic_score_codex":0.8732817,"about_ca_topic_score_gemma":0.92376906,"teacher_disagreement_score":0.12671828,"about_ca_system_score_codex":0.00746041,"about_ca_system_score_gemma":0.0061932425,"threshold_uncertainty_score":0.25492907},"labels":[],"label_agreement":null},{"id":"W1892895908","doi":"10.1111/j.1467-9965.2006.00288.x","title":"ASSET ALLOCATION AND ANNUITY‐PURCHASE STRATEGIES TO MINIMIZE THE PROBABILITY OF FINANCIAL RUIN","year":2006,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":99,"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":"Economics; Optimal stopping; Annuity; Asset allocation; Consumption (sociology); Actuarial science; Investment strategy; Asset (computer security); Life annuity; Econometrics; Finance; Computer science; Financial economics; Portfolio; Pension","score_opus":0.01623561095969123,"score_gpt":0.2865203178059527,"score_spread":0.2702847068462615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892895908","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":[0.1457887,0.0011011085,0.8353829,0.0008213755,0.000038706854,0.00009966311,0.00008514871,0.0000846335,0.01659777],"genre_scores_gemma":[0.9157759,0.000860265,0.07441283,0.00015542582,0.000030131645,0.000121985344,0.00007219423,0.000057454108,0.008513798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996445,0.00015204397,0.000014840076,0.00006087298,0.00006401855,0.00006365723],"domain_scores_gemma":[0.9988493,0.0007507628,0.00018501651,0.00004926112,0.000097925215,0.00006782079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012579119,0.0006406466,0.00073989294,0.00048381655,0.00033732114,0.00112209,0.00091804704,0.0012565085,0.004176564],"category_scores_gemma":[0.0045843925,0.00038295944,0.0004386981,0.00027959718,0.0007780011,0.0016266822,0.00067247334,0.0011458773,0.00034248913],"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":[0.00006972071,0.00009702931,0.0016305315,0.000114828494,0.000048420803,0.0001412319,0.00018170041,0.78529257,0.0037094003,0.18154384,0.0014261326,0.025744649],"study_design_scores_gemma":[0.000021834983,0.0000953121,0.00051134254,0.000042002142,0.000023710241,0.00009787478,0.00006924131,0.9446808,0.0015097242,0.05137029,0.0015602952,0.000017521106],"about_ca_topic_score_codex":0.0015909927,"about_ca_topic_score_gemma":0.0014120647,"teacher_disagreement_score":0.004176564,"about_ca_system_score_codex":0.0009960306,"about_ca_system_score_gemma":0.0011087644,"threshold_uncertainty_score":0.013971984},"labels":[],"label_agreement":null},{"id":"W1893031497","doi":"","title":"Managing Longevity Risk in Defined Benefit Pension Plans","year":2012,"lang":"en","type":"article","venue":"Special Issues","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Longevity; Longevity risk; Life expectancy; Pension; Actuarial science; Business; Asset (computer security); Risk management; Investment (military); Economics; Finance; Gerontology; Political science; Medicine; Computer science; Population; Environmental health","score_opus":0.024408003821779007,"score_gpt":0.3111456096861128,"score_spread":0.28673760586433383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1893031497","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":[0.47789666,0.006257926,0.4253483,0.014911691,0.00037204623,0.00050167483,0.00025926906,0.00018755069,0.07426494],"genre_scores_gemma":[0.9704366,0.0010329376,0.024378378,0.00014299796,0.00007485647,0.00005868305,0.000057223377,0.000011764065,0.0038065806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99706835,0.0014495052,0.00013021322,0.0002495038,0.0006584881,0.00044389197],"domain_scores_gemma":[0.99758756,0.0011099091,0.000542849,0.00017529757,0.0002233353,0.00036105092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006389862,0.0005289753,0.0005739254,0.0009209193,0.0012269971,0.0044823103,0.0011991331,0.001926136,0.0020657976],"category_scores_gemma":[0.008951135,0.00042969198,0.00074649265,0.00066828565,0.0017280269,0.0036867703,0.0042805895,0.0020451644,0.00018049363],"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":[0.00015681711,0.00020671055,0.010215979,0.000109643996,0.000109961285,0.0007301598,0.002220125,0.1456774,0.0012070368,0.767017,0.0034032906,0.06894581],"study_design_scores_gemma":[0.00008445969,0.0004398401,0.0063085593,0.00027265598,0.00012503205,0.00037043972,0.002029012,0.21924907,0.0012912365,0.7413673,0.02836309,0.00009921543],"about_ca_topic_score_codex":0.0042320895,"about_ca_topic_score_gemma":0.0051612556,"teacher_disagreement_score":0.006389862,"about_ca_system_score_codex":0.0030375044,"about_ca_system_score_gemma":0.0036120673,"threshold_uncertainty_score":0.03379321},"labels":[],"label_agreement":null},{"id":"W1896597820","doi":"10.25336/p6kp4p","title":"Reflection on population forecasting: from predictions to prospective analysis","year":2003,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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 Alberta","funders":"","keywords":"Credibility; Predictability; Projection (relational algebra); Reflection (computer programming); Meaning (existential); Population projection; Epistemology; Population; Sociology; Computer science; Philosophy; Population growth; Statistics; Mathematics; Algorithm","score_opus":0.10253909360991681,"score_gpt":0.37968642716431633,"score_spread":0.2771473335543995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1896597820","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02036812,0.010984845,0.11932233,0.77012473,0.005653907,0.00005406997,0.0001846355,0.00015065428,0.07315676],"genre_scores_gemma":[0.90845984,0.015657613,0.035784345,0.022576574,0.0042791753,0.00012915806,0.000117147305,0.00022839174,0.01276784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98942417,0.007635977,0.00027020226,0.0006769848,0.0016093813,0.00038331765],"domain_scores_gemma":[0.96190125,0.029838623,0.0011052082,0.0020429678,0.004287843,0.00082409213],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023255385,0.00066333276,0.00048366943,0.0012393526,0.0028276825,0.010519121,0.002489294,0.004064772,0.0030198696],"category_scores_gemma":[0.080834754,0.0004980877,0.000557415,0.0013299071,0.025416568,0.017163062,0.0053558988,0.012540066,0.00056203024],"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":[0.000026343017,0.000019050642,0.0007313946,0.00007298478,0.000014455072,0.00015623186,0.017602287,0.0059562563,0.00011617798,0.91191417,0.035668965,0.027721696],"study_design_scores_gemma":[0.000008444143,0.00001785301,0.0002624354,0.00029703364,0.0000065352287,0.00006715857,0.010504202,0.0063796565,0.00023730054,0.87353396,0.1086544,0.00003106117],"about_ca_topic_score_codex":0.023018148,"about_ca_topic_score_gemma":0.016046837,"teacher_disagreement_score":0.9767446,"about_ca_system_score_codex":0.010171242,"about_ca_system_score_gemma":0.007891481,"threshold_uncertainty_score":0.12298775},"labels":[],"label_agreement":null},{"id":"W1899802988","doi":"10.25336/p6788z","title":"Abridged Life Tables for Registered Indians in Canada, 1976-1980 to 1996-2000","year":2004,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Aboriginal Affairs Northern Dev Canada; Statistics Canada","funders":"University of Alberta","keywords":"Life expectancy; Demography; Population; Geography; Confidence interval; Gerontology; Medicine; Sociology","score_opus":0.0779637261711755,"score_gpt":0.3462845983546376,"score_spread":0.2683208721834621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899802988","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":[0.19821694,0.002119735,0.0028343627,0.00036691708,0.00008522779,0.0002844171,0.77733606,0.00047164917,0.018284703],"genre_scores_gemma":[0.3607556,0.0037849257,0.006656538,0.0001849601,0.000051727926,0.00036323193,0.6116731,0.000092715956,0.016437141],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987123,0.000069572954,0.000118327036,0.0001180312,0.0008075975,0.00017423231],"domain_scores_gemma":[0.9918391,0.0006928753,0.0011117294,0.00039206858,0.005678746,0.00028545884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095409155,0.00045367022,0.00037717953,0.0060193464,0.00077343534,0.0011480488,0.0009704849,0.00014933817,0.0052762837],"category_scores_gemma":[0.006708335,0.00021110092,0.00054614915,0.01124657,0.00021489369,0.00033115264,0.00052770873,0.00047363978,0.0009638012],"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":[0.0006996855,0.00008690955,0.65781146,0.00096759986,0.0006034163,0.0003650047,0.002075533,0.018530209,0.00062764634,0.0056957663,0.13843171,0.17410511],"study_design_scores_gemma":[0.000021870012,0.000025559368,0.91482073,0.000115707255,0.000068495465,0.000115730865,0.00047560214,0.0022733863,0.00021587603,0.00022854107,0.08158779,0.00005073324],"about_ca_topic_score_codex":0.9791245,"about_ca_topic_score_gemma":0.98028255,"teacher_disagreement_score":0.02274585,"about_ca_system_score_codex":0.02274585,"about_ca_system_score_gemma":0.018031372,"threshold_uncertainty_score":0.16503346},"labels":[],"label_agreement":null},{"id":"W1899825239","doi":"10.1002/9781118445112.stat04357","title":"Options and Guarantees in Life Insurance","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Life insurance; Actuarial science; Context (archaeology); Payment; Cover (algebra); Insurance policy; Business; Variable (mathematics); Finance; Mathematics; Geography; Engineering","score_opus":0.032737837129122545,"score_gpt":0.33201677437524946,"score_spread":0.2992789372461269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899825239","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":[0.053270563,0.04876001,0.3199954,0.047755957,0.0019365349,0.0000934972,0.00085362233,0.00023557279,0.52709883],"genre_scores_gemma":[0.91796136,0.013998532,0.030174658,0.0018764153,0.0025297594,0.00017290858,0.00030481303,0.00011019232,0.0328713],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99576026,0.002101199,0.00027184337,0.00044892266,0.0011502611,0.0002676],"domain_scores_gemma":[0.9892842,0.007837485,0.0009956781,0.0005820478,0.00074581953,0.00055470865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050425804,0.0006859642,0.00077541906,0.0025256155,0.0017495912,0.0051349904,0.001069349,0.0036912533,0.013237071],"category_scores_gemma":[0.0145363435,0.00037664236,0.00085033354,0.0029883587,0.012447778,0.008109007,0.0029465242,0.006609094,0.001050686],"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":[0.0000050962285,0.0000028428858,0.000053951506,0.000014320887,0.0000021395206,0.000022479011,0.000045829198,0.0006591552,0.000013124726,0.9964169,0.00076551334,0.0019986585],"study_design_scores_gemma":[0.0000031685477,0.0000028558093,0.000042869735,0.000025305619,0.0000012217355,0.000014910792,0.000018020188,0.001366179,0.000011832803,0.9946984,0.0038117645,0.0000034405318],"about_ca_topic_score_codex":0.0026193857,"about_ca_topic_score_gemma":0.0011937614,"teacher_disagreement_score":0.013237071,"about_ca_system_score_codex":0.003517893,"about_ca_system_score_gemma":0.0015487574,"threshold_uncertainty_score":0.044282377},"labels":[],"label_agreement":null},{"id":"W1900345999","doi":"10.1016/j.frl.2015.10.004","title":"A DCC-GARCH multi-population mortality model and its applications to pricing catastrophic mortality bonds","year":2015,"lang":"en","type":"article","venue":"Finance research letters","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Bond; Autoregressive conditional heteroskedasticity; Economics; Mortality rate; Population; Econometrics; Financial economics; Demography; Medicine; Finance; Internal medicine; Volatility (finance)","score_opus":0.23000517011553004,"score_gpt":0.45580356600012223,"score_spread":0.2257983958845922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900345999","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":[0.2647384,0.003025942,0.71394134,0.0040091006,0.0004348719,0.00010290699,0.000652911,0.00042699548,0.012667511],"genre_scores_gemma":[0.9659416,0.0013370727,0.026426405,0.0002169442,0.00029350282,0.00007781776,0.0003271857,0.000041356616,0.0053380406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993284,0.00033440423,0.000029467772,0.00011116334,0.00011173633,0.00008494042],"domain_scores_gemma":[0.99668795,0.002096468,0.00044254313,0.00019365089,0.0003793251,0.00019994099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026050787,0.00094379316,0.0013399138,0.000904857,0.00060176133,0.0015907873,0.0020157641,0.002428301,0.0022140574],"category_scores_gemma":[0.00821564,0.00046511818,0.0011391259,0.0014904944,0.0010240859,0.001262122,0.0009108871,0.0025427663,0.0002281764],"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":[0.000037413465,0.000053882584,0.004220155,0.000048529884,0.000086302505,0.00041658606,0.00009377434,0.87032586,0.00049314153,0.113696165,0.0020130402,0.008515189],"study_design_scores_gemma":[0.000007936406,0.0000120959885,0.00042287374,0.000004260069,0.000014056558,0.00003713907,0.000012181957,0.9848261,0.000046409677,0.014280471,0.00032495148,0.000011634596],"about_ca_topic_score_codex":0.017706973,"about_ca_topic_score_gemma":0.00687161,"teacher_disagreement_score":0.017706973,"about_ca_system_score_codex":0.0014088362,"about_ca_system_score_gemma":0.001086453,"threshold_uncertainty_score":0.035207808},"labels":[],"label_agreement":null},{"id":"W1900844198","doi":"10.1111/j.1467-985x.2010.00684.x","title":"A New Look at Halley’s Life Table","year":2011,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada; University of Cambridge; Royal Society","keywords":"Table (database); Presentation (obstetrics); Context (archaeology); Outlier; Mathematics; Computer science; History; Statistics","score_opus":0.02445325463134244,"score_gpt":0.269093010632352,"score_spread":0.2446397560010096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900844198","genre_codex":"commentary","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":[0.009070078,0.11541779,0.035230998,0.5786572,0.0486329,0.00010839582,0.018827366,0.001030443,0.19302478],"genre_scores_gemma":[0.31623366,0.14676517,0.062136296,0.12938596,0.091717385,0.0004895381,0.022293413,0.0024142433,0.22856438],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99609923,0.0019023689,0.00037654783,0.00042870193,0.00096956105,0.00022363848],"domain_scores_gemma":[0.9810776,0.012646818,0.0009032297,0.000988059,0.0035334488,0.00085085083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048559927,0.00049372663,0.00072962535,0.006088177,0.0019832577,0.005685891,0.00080300245,0.0010386038,0.026566435],"category_scores_gemma":[0.04189866,0.0003382842,0.00043969764,0.00837949,0.0023638222,0.0077160583,0.0018471781,0.0041950825,0.0041517722],"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":[0.00004949304,0.000008910297,0.0011982429,0.00011173421,0.000014556423,0.0000725373,0.0011016063,0.00030008226,0.000024619887,0.22514662,0.6898323,0.082139336],"study_design_scores_gemma":[0.0000031992238,0.000012148705,0.0009212548,0.00019221677,0.000003884785,0.00007176659,0.0003358499,0.00012230247,0.00002825464,0.04635853,0.95192707,0.000023497529],"about_ca_topic_score_codex":0.01633163,"about_ca_topic_score_gemma":0.010501888,"teacher_disagreement_score":0.026566435,"about_ca_system_score_codex":0.003827766,"about_ca_system_score_gemma":0.0026322359,"threshold_uncertainty_score":0.088873625},"labels":[],"label_agreement":null},{"id":"W1902579649","doi":"10.3905/pa.2014.1.4.036","title":"Practical Applications of Alpha, Beta, and Now… Gamma","year":2014,"lang":"en","type":"article","venue":"Practical Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Advice (programming); Investment (military); Financial management; Alpha (finance); Retirement planning; Value (mathematics); Finance; Financial plan; Business; Actuarial science; Economics; Marketing; Computer science; Political science; Law","score_opus":0.031509390252452106,"score_gpt":0.380281940127565,"score_spread":0.3487725498751129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902579649","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":[0.029394943,0.026974598,0.38273662,0.12852792,0.010690612,0.00033926909,0.0010519513,0.0014703036,0.41881365],"genre_scores_gemma":[0.65993357,0.012487427,0.24880241,0.015421268,0.0047911564,0.0010019257,0.00038587966,0.00089884526,0.05627756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9837552,0.0093663195,0.0009335806,0.0016771486,0.0036763323,0.00059137313],"domain_scores_gemma":[0.9392768,0.039083473,0.0038828612,0.0053868545,0.010649669,0.0017203396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021951796,0.0017930622,0.00075521955,0.003481493,0.002545737,0.0071536964,0.001827783,0.0036311839,0.02264522],"category_scores_gemma":[0.11876595,0.0008243906,0.0010813983,0.0041450565,0.008106457,0.012031608,0.005079957,0.006149864,0.005422868],"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":[0.00020414179,0.00008023981,0.0099820215,0.0002972266,0.00005603325,0.00017842367,0.002650706,0.0038433566,0.0004512614,0.72581893,0.058261145,0.19817656],"study_design_scores_gemma":[0.000040033807,0.00014819822,0.0043338896,0.0005955672,0.000037120288,0.0004505707,0.0028380805,0.006829175,0.0006182058,0.81979316,0.16420503,0.00011099628],"about_ca_topic_score_codex":0.009130671,"about_ca_topic_score_gemma":0.005908052,"teacher_disagreement_score":0.02264522,"about_ca_system_score_codex":0.0046162326,"about_ca_system_score_gemma":0.003668148,"threshold_uncertainty_score":0.116093576},"labels":[],"label_agreement":null},{"id":"W1904138685","doi":"10.3978/j.issn.2078-6891.2011.024","title":"Increasing incidence in liver cancer in Canada, 1972-2006: Age-period-cohort analysis.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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":"Medicine; Incidence (geometry); Cohort; Demography; Cohort effect; Cohort study; Cancer; Cancer registry; Liver cancer; Internal medicine","score_opus":0.029581007994474502,"score_gpt":0.24793066126047225,"score_spread":0.21834965326599776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1904138685","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":[0.9593253,0.0051719965,0.0008546108,0.00044034113,0.00004770324,0.00015990407,0.032052774,0.000053139065,0.0018943036],"genre_scores_gemma":[0.9887983,0.0015583641,0.000934443,0.00009946772,0.000016234268,0.00005848705,0.0076603717,0.0000118765975,0.00086250424],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917907,0.00009149327,0.000055403158,0.0002097472,0.00024775145,0.00021658016],"domain_scores_gemma":[0.9980209,0.00012638586,0.00049364043,0.00017387756,0.00089976744,0.00028539542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015788106,0.0004195211,0.00043333732,0.0020762319,0.001156202,0.00086578587,0.0008891925,0.00037269204,0.001285292],"category_scores_gemma":[0.002230199,0.0003433905,0.0013174765,0.005429675,0.00039146424,0.0003194968,0.0006552756,0.0008140699,0.00018587966],"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":[0.000114250055,0.00001292901,0.9957071,0.000043098265,0.00023392521,0.000046346864,0.00009295908,0.00021056371,0.000060358187,0.000055954697,0.0008801798,0.0025424461],"study_design_scores_gemma":[0.000011924774,0.000019377538,0.99779534,0.000018875282,0.00015229308,0.0000728756,0.000106754276,0.00057498494,0.00003391989,0.000019943478,0.0011874768,0.0000062913077],"about_ca_topic_score_codex":0.9728943,"about_ca_topic_score_gemma":0.9751177,"teacher_disagreement_score":0.027105689,"about_ca_system_score_codex":0.014352307,"about_ca_system_score_gemma":0.020571463,"threshold_uncertainty_score":0.104133785},"labels":[],"label_agreement":null},{"id":"W1905738047","doi":"10.54782/001c.133103","title":"Consulting Industrial Meteorologiest","year":2012,"lang":"en","type":"article","venue":"The Journal of Weather Modification","topic":"Insurance, Mortality, Demography, Risk Management","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":"Environmental science","score_opus":0.12862474388397438,"score_gpt":0.3510061086204178,"score_spread":0.22238136473644343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905738047","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":[0.002102437,0.010289738,0.0034495948,0.0106637515,0.006552152,0.00017459402,0.0579876,0.006285742,0.9024943],"genre_scores_gemma":[0.008721347,0.0055765277,0.002533624,0.00077017565,0.001118247,0.000065241984,0.025760269,0.0014407594,0.9540138],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839157,0.00012364211,0.00013968861,0.00039779145,0.00078712736,0.00016010078],"domain_scores_gemma":[0.9955018,0.00038923646,0.00032127247,0.00067272503,0.0022833005,0.0008316641],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013761504,0.0014959053,0.0011150674,0.00464722,0.0013206708,0.0037394292,0.0015805298,0.0013735527,0.6449145],"category_scores_gemma":[0.0061063995,0.0006747928,0.00058200123,0.0067691,0.00043948583,0.0025359602,0.0014921856,0.0015787948,0.4841504],"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":[0.000051401617,0.000021924852,0.0011236501,0.00016105242,0.0000059015792,0.000070644215,0.000042834683,0.00008379069,0.00020317195,0.0012291699,0.85022604,0.14678046],"study_design_scores_gemma":[0.0000093366125,0.000005031463,0.0013905343,0.000040770457,0.0000027391038,0.00003800946,0.000035719215,0.00010173109,0.00009380322,0.0003560922,0.997922,0.0000042655397],"about_ca_topic_score_codex":0.029119479,"about_ca_topic_score_gemma":0.067631,"teacher_disagreement_score":0.6449145,"about_ca_system_score_codex":0.0020540848,"about_ca_system_score_gemma":0.00510658,"threshold_uncertainty_score":0.5064863},"labels":[],"label_agreement":null},{"id":"W1915269477","doi":"10.1136/tobaccocontrol-2015-052265","title":"The impact of cigarette smoking on life expectancy between 1980 and 2010: a global perspective","year":2015,"lang":"en","type":"article","venue":"Tobacco Control","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":97,"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 Toronto; St. Michael's Hospital; Centre for Global Health Research","funders":"World Health Organization","keywords":"Life expectancy; Demography; Medicine; Population; Developed country; Tobacco control; Mortality rate; China; Gerontology; Developing country; Environmental health; Public health; Geography; Economic growth; Surgery","score_opus":0.03610341522658411,"score_gpt":0.3456538458391345,"score_spread":0.3095504306125504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1915269477","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":[0.38040254,0.5334838,0.0014749356,0.04362467,0.0013417515,0.000028236642,0.01711037,0.00008292914,0.02245076],"genre_scores_gemma":[0.8376018,0.15438625,0.0005414793,0.0020831975,0.0013278299,0.000016115342,0.0034436644,0.0000119761335,0.00058766786],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997696,0.000071489834,0.000020189647,0.00004020402,0.000044274515,0.000054328015],"domain_scores_gemma":[0.9992537,0.00017636231,0.00023086704,0.000017001123,0.00018334565,0.00013878713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000816956,0.0004069696,0.00034186247,0.0020809933,0.00018581728,0.00087878574,0.00025439277,0.0005990906,0.0022396722],"category_scores_gemma":[0.001350949,0.00009201258,0.0007613926,0.002237934,0.00035184406,0.0009866172,0.00063545315,0.00084538554,0.00026442914],"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":[0.0003704313,0.000088360386,0.7524668,0.0026417668,0.0013424688,0.0004917656,0.0006667337,0.0032073632,0.0010426904,0.0049123294,0.016724322,0.21604492],"study_design_scores_gemma":[0.000011093508,0.00026066988,0.95557696,0.0016511863,0.00069477764,0.00066158525,0.000811589,0.0006557059,0.00021707952,0.0016527025,0.037778184,0.000028371958],"about_ca_topic_score_codex":0.011486012,"about_ca_topic_score_gemma":0.012940561,"teacher_disagreement_score":0.011486012,"about_ca_system_score_codex":0.0011560208,"about_ca_system_score_gemma":0.00082520803,"threshold_uncertainty_score":0.022838354},"labels":[],"label_agreement":null},{"id":"W1915991276","doi":"10.25336/p63w31","title":"Analysis of Life Histories: A State Space Approach","year":2001,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; State space; Markov chain; Space (punctuation); State (computer science); Markov model; Constant (computer programming); Theoretical computer science; Markov process; Data science; Programming language; Machine learning; Mathematics; Statistics","score_opus":0.06096893385393174,"score_gpt":0.3415866004914737,"score_spread":0.28061766663754195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1915991276","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":[0.0054207817,0.00024457226,0.9909965,0.00016453287,0.000013972615,0.000056896406,0.0005299708,0.00051037787,0.002062404],"genre_scores_gemma":[0.33126795,0.0012622384,0.6556147,0.00013894061,0.00011483931,0.00080443604,0.0032229573,0.00031552048,0.007258446],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878687,0.0006585489,0.00006303235,0.00022702044,0.00018137974,0.00008321809],"domain_scores_gemma":[0.9962715,0.0029456536,0.00022856674,0.00023623888,0.00022378468,0.00009420029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021493838,0.0008139986,0.0008302879,0.003610938,0.0006863216,0.002176574,0.001231929,0.0006215886,0.009975524],"category_scores_gemma":[0.007024876,0.00053802994,0.001630177,0.002445367,0.0009900668,0.002759992,0.0015673909,0.0013233274,0.0012017693],"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":[0.00015705997,0.00015956983,0.012299079,0.0003220431,0.00037957443,0.00013735106,0.0009942341,0.30788305,0.001415429,0.48722017,0.004666499,0.184366],"study_design_scores_gemma":[0.000017414439,0.00007318068,0.0028851833,0.000057070567,0.0000742507,0.00006652427,0.00019948444,0.6577208,0.00064494077,0.3282802,0.009936971,0.00004401483],"about_ca_topic_score_codex":0.009203255,"about_ca_topic_score_gemma":0.0063793617,"teacher_disagreement_score":0.009975524,"about_ca_system_score_codex":0.0011636574,"about_ca_system_score_gemma":0.0013307014,"threshold_uncertainty_score":0.03337145},"labels":[],"label_agreement":null},{"id":"W1930132352","doi":"10.7202/039992ar","title":"Conditions de vie durant l’enfance et longévité : évaluation d’une base de données créée à partir du recensement canadien de 1901 et de l’état civil québécois1","year":2010,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western University; Université de Montréal","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.01553773141330318,"score_gpt":0.28595794726023,"score_spread":0.2704202158469268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1930132352","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":[0.8281914,0.011851644,0.022191476,0.0025643485,0.00036096093,0.004289206,0.107825324,0.00041379206,0.022311905],"genre_scores_gemma":[0.8614193,0.005043589,0.04734581,0.0007171822,0.00013677555,0.0040508895,0.06812372,0.00021572263,0.01294705],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97220355,0.007176007,0.0027435676,0.0028846632,0.013655265,0.001336816],"domain_scores_gemma":[0.83672416,0.05570768,0.012664592,0.008045787,0.08424325,0.0026145156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041420884,0.001550179,0.0013389215,0.010948873,0.0021721595,0.004546608,0.0034429072,0.0012783458,0.004715423],"category_scores_gemma":[0.096791945,0.00095266534,0.0023009004,0.016376035,0.0014891309,0.0018853425,0.0026410788,0.0012243493,0.0011084445],"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":[0.0006028392,0.00023541648,0.8921814,0.0029872674,0.002518705,0.00039083284,0.012986095,0.0030107917,0.0020822412,0.0007565369,0.006513564,0.07573432],"study_design_scores_gemma":[0.00003341041,0.00024564035,0.966462,0.00093739183,0.000640406,0.00007795003,0.005012585,0.0017306386,0.0014997224,0.00014340732,0.023116127,0.0001006492],"about_ca_topic_score_codex":0.8391483,"about_ca_topic_score_gemma":0.8922628,"teacher_disagreement_score":0.16085172,"about_ca_system_score_codex":0.014202844,"about_ca_system_score_gemma":0.026431544,"threshold_uncertainty_score":0.32359797},"labels":[],"label_agreement":null},{"id":"W1945776626","doi":"10.29358/sceco.v0i19.251","title":"ANALYSIS OF THE MARRIAGES AND DIVORCES SEASONALITY IN ROMANIA COMPARED TO BACAU COUNTY DURING 2010-2013","year":2014,"lang":"en","type":"article","venue":"STUDIES AND SCIENTIFIC RESEARCHES ECONOMICS EDITION","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Seasonality; Quarter (Canadian coin); Demography; Seasonal adjustment; Geography; Statistics; Demographic economics; Mathematics; Economics; Sociology","score_opus":0.052135376030450924,"score_gpt":0.32837518341570776,"score_spread":0.27623980738525683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1945776626","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":[0.9985856,0.00020210433,0.000047137273,0.000029874396,0.0000035563626,0.0000049063765,0.0004218809,0.0000028815632,0.000702132],"genre_scores_gemma":[0.99882585,0.00017011186,0.00007124689,0.0000075436274,0.0000037494792,0.00000576611,0.0005711671,0.0000022697,0.0003422843],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998085,0.00004198355,0.000025972096,0.000042316948,0.00003120393,0.000050025108],"domain_scores_gemma":[0.9996749,0.000040150782,0.00013840961,0.000017953185,0.000077651785,0.00005088873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003562827,0.00008314319,0.00021170785,0.0009868363,0.00026004954,0.00034588657,0.00021407464,0.00008133786,0.0010303397],"category_scores_gemma":[0.0008878561,0.00009913457,0.00019851934,0.0015326929,0.00016402022,0.0001244536,0.00040619538,0.00018082453,0.00012041312],"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":[0.00006578593,0.000015982141,0.98912394,0.00003311785,0.00003251634,0.00019356537,0.0011595214,0.00019437283,0.00033284508,0.00016486645,0.00045471019,0.008228812],"study_design_scores_gemma":[6.3159945e-7,0.000010188299,0.9980457,0.000007821741,0.0000045480233,0.000080034304,0.0010623769,0.00016299583,0.00004063351,0.0000070265983,0.0005764407,0.000001598525],"about_ca_topic_score_codex":0.036617007,"about_ca_topic_score_gemma":0.06361356,"teacher_disagreement_score":0.036617007,"about_ca_system_score_codex":0.000579245,"about_ca_system_score_gemma":0.00039081636,"threshold_uncertainty_score":0.07280773},"labels":[],"label_agreement":null},{"id":"W1948242268","doi":"10.3968/j.css.1923669720120802.1085","title":"Solvency of Pension Reform: Issues and Challenges of the Accumulation Phase of Retirements in Nigeria/REFORME DE PENSION DE LA SOLVABILITE: ENJEUX ET DEFIS DE LA PHASE D'ACCUMULATION DES DEPART A LA RETRAITE AU NIGERIA","year":2012,"lang":"fr","type":"article","venue":"Canadian social science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Solvency; Salary; Pension; Welfare economics; Valuation (finance); Political science; Lease; Business; Economics; Accounting; Finance; Market liquidity; Law","score_opus":0.09482936259624941,"score_gpt":0.4137484457404565,"score_spread":0.3189190831442071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948242268","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":[0.99505097,0.00043459501,0.00008665183,0.0009511839,0.000009080398,0.000027060862,0.000020042646,4.6269435e-7,0.003419884],"genre_scores_gemma":[0.9987942,0.00025860436,0.000100421516,0.00003878923,0.000005413025,0.000010253001,0.000010163525,3.067749e-7,0.0007818524],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99834585,0.0008187546,0.00017071448,0.000094264724,0.00029282653,0.00027764429],"domain_scores_gemma":[0.9960586,0.0011285922,0.001902908,0.00010868609,0.0004641142,0.000337044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043988703,0.00011029057,0.00015054818,0.000740681,0.0014999692,0.0018386083,0.00026966393,0.00053019205,0.002299826],"category_scores_gemma":[0.0071936925,0.00011697689,0.00013502894,0.000678014,0.0012443627,0.0013161553,0.0013158239,0.00066372263,0.00009972156],"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":[0.00027771786,0.00027720717,0.7733513,0.00049160543,0.000022754552,0.0013275245,0.12347975,0.0005598567,0.0020661026,0.017211705,0.000944624,0.07999],"study_design_scores_gemma":[0.000009332001,0.0002548662,0.7499531,0.000477191,0.000012797798,0.00060206844,0.23595381,0.0005484392,0.0007719447,0.0020737804,0.00932533,0.000017310402],"about_ca_topic_score_codex":0.005766449,"about_ca_topic_score_gemma":0.011299282,"teacher_disagreement_score":0.005766449,"about_ca_system_score_codex":0.002078006,"about_ca_system_score_gemma":0.0026865439,"threshold_uncertainty_score":0.023263693},"labels":[],"label_agreement":null},{"id":"W1958236632","doi":"10.25336/p64s4p","title":"Population forecasting in Canada: conceptual and methodological developments","year":2001,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"University of Alberta","keywords":"Population projection; Population; Projections of population growth; Projection (relational algebra); Regional science; Econometrics; Value (mathematics); Geography; Computer science; Operations research; Population growth; Demography; Sociology; Mathematics; Machine learning","score_opus":0.273525627635989,"score_gpt":0.3938881632255497,"score_spread":0.12036253558956073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958236632","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":[0.0681389,0.17893769,0.35979933,0.18435247,0.0023058697,0.0005723,0.010115761,0.0009319867,0.19484574],"genre_scores_gemma":[0.5719167,0.17371486,0.23036313,0.0027378204,0.0013886372,0.00039225337,0.0035512059,0.00018794263,0.015747387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962261,0.0013931068,0.00021622713,0.00038851608,0.0013849541,0.00039111913],"domain_scores_gemma":[0.9922523,0.0030673088,0.0003997489,0.00036091323,0.003580298,0.00033943128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009089614,0.000809651,0.000575064,0.0053123864,0.0036430315,0.006703584,0.0026837664,0.0011994615,0.0019831916],"category_scores_gemma":[0.01813423,0.00047041144,0.00059042533,0.019098712,0.004091074,0.002463178,0.0016021675,0.0021171807,0.00027123658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027398524,0.00003584158,0.018642997,0.00081864116,0.00005210629,0.00016379585,0.0036113218,0.05861968,0.00015523178,0.61894464,0.020953527,0.27797475],"study_design_scores_gemma":[0.000019827883,0.000046766105,0.04730208,0.003192263,0.00011388243,0.00022600326,0.0093686655,0.20181222,0.000765064,0.32527813,0.41158733,0.00028779125],"about_ca_topic_score_codex":0.9855136,"about_ca_topic_score_gemma":0.97909695,"teacher_disagreement_score":0.06281921,"about_ca_system_score_codex":0.06281921,"about_ca_system_score_gemma":0.12614867,"threshold_uncertainty_score":0.45578742},"labels":[],"label_agreement":null},{"id":"W1960946677","doi":"10.3968/j.sms.1923845220120502.1986","title":"Modelling Adult Mortality in Nigeria: Ananalysis Based on the Lee-Carter Model","year":2012,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mortality rate; Demography; Government (linguistics); Public health; Child mortality; Index (typography); Pace; Geography; Medicine; Population; Sociology; Computer science","score_opus":0.1629688586995901,"score_gpt":0.40363060194162287,"score_spread":0.24066174324203277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1960946677","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":[0.8763564,0.0019063819,0.10560909,0.0011778595,0.00020628958,0.0003147095,0.0024610048,0.00025607107,0.011712195],"genre_scores_gemma":[0.9776284,0.0010662009,0.015880229,0.00010904733,0.000058024616,0.0002368539,0.0010014989,0.00003769488,0.0039820527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971384,0.00015112352,0.000014753682,0.000043329834,0.00002898637,0.000047898397],"domain_scores_gemma":[0.9990675,0.0005983909,0.000094379924,0.000031559608,0.00016256318,0.00004574408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239711,0.000566953,0.0006659774,0.0009337487,0.00045345252,0.0009858608,0.000882455,0.0008720497,0.0019046231],"category_scores_gemma":[0.0030278352,0.0002685274,0.0011490816,0.0007750117,0.00025629756,0.0005579101,0.0007298065,0.00079274835,0.00024076657],"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":[0.000099789286,0.00008063784,0.02608016,0.00009772032,0.00012252515,0.00026316437,0.00023681372,0.9568643,0.000504663,0.0045648827,0.0012016614,0.0098837055],"study_design_scores_gemma":[0.000011199699,0.00005371561,0.004197963,0.000026333268,0.000032448163,0.00005150599,0.00018755402,0.9925627,0.00011952601,0.0016720709,0.0010643479,0.000020678503],"about_ca_topic_score_codex":0.055880208,"about_ca_topic_score_gemma":0.036185075,"teacher_disagreement_score":0.055880208,"about_ca_system_score_codex":0.00088513753,"about_ca_system_score_gemma":0.001102264,"threshold_uncertainty_score":0.11110991},"labels":[],"label_agreement":null},{"id":"W1963693056","doi":"10.1007/s10985-011-9192-2","title":"Likelihood ratio procedures and tests of fit in parametric and semiparametric copula models with censored data","year":2011,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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 Waterloo; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Copula (linguistics); Econometrics; Parametric statistics; Multivariate statistics; Parametric model; Statistics; Computer science; Mathematics","score_opus":0.07996438307112196,"score_gpt":0.32528219362064204,"score_spread":0.24531781054952007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963693056","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":[0.032085028,0.000712126,0.964099,0.00043053518,0.000050570376,0.00012705199,0.00040314434,0.0005808873,0.0015116904],"genre_scores_gemma":[0.6147096,0.0013010825,0.3768041,0.00022430332,0.00038407897,0.001578807,0.0017903335,0.0007666415,0.002441117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95191294,0.042236082,0.0011896419,0.001705084,0.0023424043,0.00061387144],"domain_scores_gemma":[0.5196744,0.4537063,0.008225748,0.013357674,0.0037961856,0.0012397225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056411423,0.0017880338,0.0027735243,0.0054109558,0.00094977167,0.0030572992,0.004294557,0.0030998029,0.0088824555],"category_scores_gemma":[0.3628197,0.0010491492,0.0035273011,0.0042183334,0.0038490728,0.0073375315,0.003524758,0.004608756,0.0011649177],"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":[0.0017990172,0.00076695427,0.032010984,0.001071022,0.001981815,0.0013324777,0.0028386067,0.15657616,0.001590775,0.6004966,0.008732857,0.19080275],"study_design_scores_gemma":[0.00016876998,0.00055501115,0.008300854,0.00017499748,0.0002167872,0.0007269129,0.0007565732,0.5937841,0.00093934144,0.39118475,0.003062489,0.00012932283],"about_ca_topic_score_codex":0.0013394494,"about_ca_topic_score_gemma":0.00060277304,"teacher_disagreement_score":0.056411423,"about_ca_system_score_codex":0.0009929012,"about_ca_system_score_gemma":0.002096236,"threshold_uncertainty_score":0.29833573},"labels":[],"label_agreement":null},{"id":"W1964411117","doi":"10.1353/dem.2001.0036","title":"Evaluating the performance of the lee-carter method for forecasting mortality","year":2001,"lang":"en","type":"letter","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":522,"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":"National Institute on Aging","keywords":"Social security; Statistics; Econometrics; Demography; Economics; Mathematics; Sociology","score_opus":0.14287782532449375,"score_gpt":0.4053497976492753,"score_spread":0.2624719723247816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964411117","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":[0.64165163,0.005383705,0.32062897,0.005012568,0.0010721284,0.00063341844,0.001493654,0.0007080223,0.023415929],"genre_scores_gemma":[0.9112738,0.0011852417,0.083788775,0.00042381047,0.0002585067,0.00026350425,0.0006952056,0.00006364543,0.0020475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936807,0.00465153,0.00021696651,0.00024992062,0.0009962544,0.00020454712],"domain_scores_gemma":[0.9346217,0.057029806,0.0011052503,0.000986272,0.005748892,0.00050804706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016642159,0.0005333804,0.00059432356,0.002217285,0.00070419925,0.00085962884,0.0006763738,0.0011417136,0.0014315015],"category_scores_gemma":[0.064292476,0.00023083399,0.00047421624,0.0018553432,0.0004462324,0.001110341,0.0006980657,0.0010386078,0.00033374617],"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":[0.0029577839,0.00026158334,0.13830331,0.00031732387,0.00042252953,0.00029264207,0.00083857117,0.39582887,0.0021359087,0.020736352,0.016010353,0.42189485],"study_design_scores_gemma":[0.00015896054,0.0005077867,0.0147768445,0.00007783591,0.000048907033,0.00013086178,0.00033954263,0.9736814,0.0014474756,0.0045998185,0.0041276845,0.00010291471],"about_ca_topic_score_codex":0.026704034,"about_ca_topic_score_gemma":0.01744322,"teacher_disagreement_score":0.026704034,"about_ca_system_score_codex":0.0015783048,"about_ca_system_score_gemma":0.0012402696,"threshold_uncertainty_score":0.08801317},"labels":[],"label_agreement":null},{"id":"W1966616375","doi":"10.1080/10920277.2010.10597576","title":"Valuation of a Guaranteed Minimum Income Benefit","year":2010,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Annuity; Actuarial science; Valuation (finance); Economics; Life annuity; Payment; Stochastic game; Business; Microeconomics; Finance; Pension","score_opus":0.016973755439367317,"score_gpt":0.2977100865422832,"score_spread":0.2807363311029159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966616375","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":[0.54013556,0.001989245,0.35953677,0.0028494182,0.00023529862,0.00026195616,0.00094543205,0.00018996403,0.09385635],"genre_scores_gemma":[0.9829526,0.00026570738,0.01110274,0.00006910554,0.00005209002,0.00003901091,0.00007344204,0.000015946882,0.005429339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99819404,0.00073278096,0.000054721084,0.00019133391,0.00063007703,0.0001970176],"domain_scores_gemma":[0.996962,0.0016357034,0.00043660146,0.00037542975,0.00030822956,0.00028197418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034144954,0.0006673841,0.00071161747,0.00096812675,0.0004931728,0.0033763894,0.0012424788,0.0021696812,0.003761059],"category_scores_gemma":[0.009409021,0.00041960127,0.0009797548,0.00055831496,0.0012439715,0.003570034,0.0014348569,0.0020032036,0.00023435245],"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":[0.0003043991,0.00019626511,0.0037826349,0.00010814587,0.00013652809,0.0006690898,0.00032696055,0.27471662,0.008448187,0.67642546,0.0023587665,0.032526948],"study_design_scores_gemma":[0.00005034102,0.00050347485,0.004833722,0.0001133423,0.00008658191,0.0005777219,0.00019965287,0.6938567,0.0024412423,0.28760883,0.0096380925,0.00009028364],"about_ca_topic_score_codex":0.0009892635,"about_ca_topic_score_gemma":0.000571356,"teacher_disagreement_score":0.003761059,"about_ca_system_score_codex":0.002062825,"about_ca_system_score_gemma":0.0013451024,"threshold_uncertainty_score":0.018057823},"labels":[],"label_agreement":null},{"id":"W1967375836","doi":"10.1136/jech.2003.009522","title":"A framework for modelling differences in regional mortality over time","year":2004,"lang":"en","type":"article","venue":"Journal of Epidemiology & Community Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Manitoba Health; University of Manitoba","funders":"","keywords":"Poisson regression; Medicine; Demography; Population; Regression analysis; Statistics; Linear regression; Mortality rate; Linear model; Environmental health; Mathematics","score_opus":0.287463120104067,"score_gpt":0.46969246212098437,"score_spread":0.18222934201691737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967375836","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":[0.0078003444,0.00040335534,0.9873674,0.0010753466,0.00009077456,0.00015108626,0.0005152552,0.00019275708,0.0024036905],"genre_scores_gemma":[0.39579275,0.001190584,0.59340405,0.00037492017,0.00023417833,0.0023892922,0.0012279468,0.00021308733,0.005173149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98672163,0.010182293,0.00041303012,0.0013614807,0.0007848492,0.00053675537],"domain_scores_gemma":[0.98330414,0.012518266,0.0017823572,0.00091744214,0.0011328995,0.00034496334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02077958,0.0014656138,0.001361455,0.0028227768,0.0007674635,0.002789771,0.0056029544,0.002813025,0.005107338],"category_scores_gemma":[0.050474804,0.0010417948,0.003375746,0.0033697132,0.0027778298,0.0035181153,0.003952134,0.0029946207,0.0009614959],"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":[0.00008451105,0.000079064754,0.008401484,0.00018962163,0.00035295918,0.00035253225,0.0012427196,0.5324549,0.0002749124,0.43336987,0.0022729784,0.020924326],"study_design_scores_gemma":[0.000103709484,0.00020349666,0.0027292871,0.000115598246,0.00012949834,0.00021477138,0.0003616344,0.69215804,0.00010322834,0.29373863,0.010062112,0.00007996634],"about_ca_topic_score_codex":0.025590021,"about_ca_topic_score_gemma":0.015877912,"teacher_disagreement_score":0.025590021,"about_ca_system_score_codex":0.0030702048,"about_ca_system_score_gemma":0.0030771818,"threshold_uncertainty_score":0.109894216},"labels":[],"label_agreement":null},{"id":"W1968587921","doi":"10.1177/026455050104800213","title":"Discretionary Lifer Panels","year":2001,"lang":"en","type":"article","venue":"Probation Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humber College","funders":"","keywords":"Business; Psychology","score_opus":0.031680248003220744,"score_gpt":0.32408900657543455,"score_spread":0.2924087585722138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968587921","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":[0.20271657,0.0021380365,0.13417414,0.010166963,0.0021712296,0.0008687556,0.008011619,0.0030613975,0.6366913],"genre_scores_gemma":[0.71996117,0.00038190413,0.025084965,0.0017833393,0.001476907,0.00025229398,0.002690706,0.0002426976,0.24812607],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9950706,0.0020630837,0.00025540675,0.0007006735,0.0012899729,0.0006203173],"domain_scores_gemma":[0.98597485,0.0056440323,0.0010307533,0.0048678056,0.001497717,0.0009848155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057620304,0.0003415641,0.00064166496,0.00094845146,0.001182865,0.002611388,0.0014108011,0.0015158542,0.1056816],"category_scores_gemma":[0.018594472,0.0004471108,0.0005088654,0.0007583994,0.0004357857,0.0014204476,0.0018077804,0.0016689515,0.017511541],"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":[0.0020423264,0.0009246268,0.026189689,0.00025718918,0.0001730478,0.0011586965,0.00099584,0.015573019,0.007420719,0.10022257,0.22922811,0.6158142],"study_design_scores_gemma":[0.0010497341,0.0012782313,0.08029171,0.0002617855,0.00026470478,0.0020150272,0.000956927,0.12239486,0.010793061,0.22834018,0.55211043,0.0002433884],"about_ca_topic_score_codex":0.0010532571,"about_ca_topic_score_gemma":0.0032375848,"teacher_disagreement_score":0.1056816,"about_ca_system_score_codex":0.00060624356,"about_ca_system_score_gemma":0.0008941291,"threshold_uncertainty_score":0.35354018},"labels":[],"label_agreement":null},{"id":"W1971242360","doi":"10.1016/j.mcm.2008.10.014","title":"Valuation of contingent claims with mortality and interest rate risks","year":2008,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":46,"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":"Valuation (finance); Contingent valuation; Actuarial science; Interest rate; Mathematics; Econometrics; Economics; Finance; Willingness to pay; Microeconomics","score_opus":0.2615126896526627,"score_gpt":0.33749154922887037,"score_spread":0.07597885957620765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971242360","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":[0.52496654,0.0017363814,0.45943457,0.0020765944,0.00016245455,0.00007950893,0.00022200502,0.000117580115,0.01120443],"genre_scores_gemma":[0.9873979,0.00035157413,0.009205034,0.000024855806,0.00009763075,0.000019558092,0.000053604992,0.000014595345,0.002835335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985734,0.00093855197,0.000054064556,0.00008664245,0.00023367106,0.00011363916],"domain_scores_gemma":[0.99090403,0.007035606,0.0006950414,0.00035364245,0.00040176793,0.00060988835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044613476,0.00087598636,0.0008236427,0.0013262724,0.00040350808,0.004054995,0.0010752124,0.0022620792,0.002384372],"category_scores_gemma":[0.017865296,0.0005903775,0.0009518679,0.00095533003,0.0021610372,0.0038146207,0.001116497,0.0012299141,0.00016844351],"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":[0.00029958697,0.00013564364,0.0052524325,0.00008402857,0.00010271291,0.0011572689,0.00050689786,0.5344892,0.002951421,0.44313806,0.00085181685,0.01103085],"study_design_scores_gemma":[0.000021527809,0.000047287907,0.0009391216,0.000012796375,0.000021566399,0.00018613193,0.000064387896,0.8830065,0.00030275472,0.11500918,0.00037086045,0.000017813987],"about_ca_topic_score_codex":0.0012565707,"about_ca_topic_score_gemma":0.0005946664,"teacher_disagreement_score":0.0044613476,"about_ca_system_score_codex":0.0013214755,"about_ca_system_score_gemma":0.0007333736,"threshold_uncertainty_score":0.023594141},"labels":[],"label_agreement":null},{"id":"W1971966300","doi":"10.1016/j.matcom.2010.04.025","title":"Modeling old-age mortality risk for the populations of Australia and New Zealand: An extreme value approach","year":2010,"lang":"en","type":"article","venue":"Mathematics and Computers in Simulation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo; Actua","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life table; Demography; Mortality rate; Population; Table (database); Centenarian; Raw data; Geography; Statistics; Computer science; Mathematics; Sociology","score_opus":0.16568281853477548,"score_gpt":0.374494994165238,"score_spread":0.20881217563046253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971966300","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":[0.5564594,0.00045817404,0.4375526,0.0017187566,0.00008689587,0.000099626035,0.00035422997,0.000120119,0.0031501947],"genre_scores_gemma":[0.9800741,0.00024948115,0.01660401,0.000080334714,0.000038042075,0.00009251337,0.00018942618,0.0000241818,0.0026477901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992867,0.000364789,0.000037394846,0.00012138206,0.0000741932,0.00011549421],"domain_scores_gemma":[0.9981767,0.0011157271,0.000238414,0.00007438702,0.00019927,0.00019543477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022480513,0.0007263768,0.0013022244,0.000877602,0.0006357015,0.0017930024,0.0027893651,0.0017916459,0.0013022517],"category_scores_gemma":[0.007576829,0.00075619825,0.0015068909,0.00074046187,0.0013052,0.0017900084,0.0016283628,0.0020166172,0.00009941953],"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":[0.000020869007,0.000029553234,0.004515155,0.000008869267,0.000055689223,0.00007627402,0.00008253915,0.9868607,0.00008224935,0.006336063,0.00013053071,0.0018015876],"study_design_scores_gemma":[0.000006406542,0.000012173305,0.00075511093,0.000002577881,0.000012624743,0.000013008153,0.00003287275,0.99393785,0.000023777893,0.0051131067,0.00008359694,0.000006885734],"about_ca_topic_score_codex":0.08398856,"about_ca_topic_score_gemma":0.041132055,"teacher_disagreement_score":0.08398856,"about_ca_system_score_codex":0.0023504135,"about_ca_system_score_gemma":0.0017952798,"threshold_uncertainty_score":0.16699934},"labels":[],"label_agreement":null},{"id":"W1972116141","doi":"10.1016/j.annepidem.2014.05.006","title":"Mortality inequality in populations with equal life expectancy: Arriaga's decomposition method in SAS, Stata, and Excel","year":2014,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; Institut National de Santé Publique du Québec","funders":"","keywords":"Life expectancy; Demography; Medicine; Gerontology; Inequality; Population; Mortality rate; Environmental health; Mathematics; Sociology","score_opus":0.2502994331809021,"score_gpt":0.5065132554077527,"score_spread":0.25621382222685063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972116141","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056388855,0.00048067953,0.92340755,0.00050670264,0.00027678336,0.0014560643,0.0074278265,0.0014017089,0.008653895],"genre_scores_gemma":[0.33086088,0.0005540129,0.6489703,0.00021257982,0.0002543615,0.0061544576,0.0063749924,0.0010073504,0.0056110825],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9858601,0.009830791,0.0009024505,0.001432997,0.0013586241,0.00061502936],"domain_scores_gemma":[0.9679634,0.024536787,0.0012845283,0.003900544,0.0020087168,0.00030600323],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017024077,0.0008078634,0.0016454166,0.003726163,0.0005324033,0.0018344092,0.0013722484,0.0006157274,0.018197555],"category_scores_gemma":[0.057763807,0.00059627456,0.002814265,0.004611106,0.00072161434,0.0017228501,0.0028928157,0.0028393366,0.0014903097],"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":[0.00095522444,0.00063564617,0.12366141,0.0011745422,0.0028402058,0.00051506003,0.0040970016,0.030736119,0.0010000847,0.26722777,0.07897313,0.48818377],"study_design_scores_gemma":[0.00029798373,0.0006902905,0.17608549,0.0009329148,0.0013250726,0.00065260776,0.0038509613,0.48077703,0.0023528223,0.2462429,0.08658276,0.00020921553],"about_ca_topic_score_codex":0.010249424,"about_ca_topic_score_gemma":0.007577783,"teacher_disagreement_score":0.9829759,"about_ca_system_score_codex":0.000850639,"about_ca_system_score_gemma":0.0017669012,"threshold_uncertainty_score":0.090033054},"labels":[],"label_agreement":null},{"id":"W1972347043","doi":"10.1080/0032472032000137826","title":"Forecasting cohort incomplete fertility: A method and an application","year":2003,"lang":"en","type":"article","venue":"Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":22,"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 Victoria","funders":"","keywords":"Fertility; Cohort; Context (archaeology); Cohort effect; Econometrics; Total fertility rate; Statistics; Demography; Computer science; Geography; Mathematics; Population; Family planning; Sociology; Research methodology","score_opus":0.11627122385110618,"score_gpt":0.4245332306392132,"score_spread":0.30826200678810706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972347043","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":[0.009688813,0.00008932581,0.9891543,0.00022476604,0.000039680905,0.0000679231,0.00021631851,0.00021821792,0.0003006331],"genre_scores_gemma":[0.17105201,0.0003902879,0.826103,0.00009212819,0.0001684594,0.0003608446,0.0005632981,0.00006172578,0.0012082804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983329,0.00089305086,0.00009470421,0.00030394437,0.00031186786,0.00006339087],"domain_scores_gemma":[0.9874319,0.009462008,0.0006387933,0.001019562,0.0012279286,0.00021980291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070126154,0.0005737831,0.0010155941,0.0017899743,0.00059981504,0.0010542731,0.0014567398,0.0012551425,0.0015747441],"category_scores_gemma":[0.026814936,0.00045486563,0.0011110292,0.0020182445,0.00051188644,0.0014044036,0.0011839117,0.0013414391,0.00032326445],"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":[0.00017739525,0.00010114841,0.028305583,0.00014871883,0.00024020113,0.00036003639,0.000650925,0.5686844,0.0017040253,0.04240243,0.0041656336,0.35305953],"study_design_scores_gemma":[0.000023334615,0.000031461754,0.0012083664,0.000021539929,0.000017815128,0.0000740725,0.00005375144,0.9801398,0.00045328026,0.016182503,0.0017624544,0.000031551983],"about_ca_topic_score_codex":0.016452612,"about_ca_topic_score_gemma":0.013344226,"teacher_disagreement_score":0.016452612,"about_ca_system_score_codex":0.00080103526,"about_ca_system_score_gemma":0.001802275,"threshold_uncertainty_score":0.037086725},"labels":[],"label_agreement":null},{"id":"W1972766374","doi":"10.1080/02664763.2011.595399","title":"Applying a marginalized frailty model to competing risks","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph; Public Health Agency of Canada; Western University","funders":"","keywords":"Multivariate statistics; Econometrics; Martingale (probability theory); Multivariate analysis; Statistics; Proportional hazards model; Computer science; Breast cancer; Cluster analysis; Actuarial science; Mathematics; Medicine; Economics; Cancer","score_opus":0.1619227687976239,"score_gpt":0.366346027452832,"score_spread":0.2044232586552081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972766374","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":[0.006004988,0.00017627706,0.99270356,0.00024938764,0.000052456966,0.00006507669,0.00010195412,0.00010045008,0.0005457253],"genre_scores_gemma":[0.4332442,0.0013405951,0.556149,0.00049156183,0.00038142968,0.0009429813,0.0006364775,0.0001757477,0.006638028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950046,0.003340631,0.00017334412,0.0005369661,0.0006959395,0.00024861412],"domain_scores_gemma":[0.98701626,0.0101077,0.00060676655,0.0011309285,0.0008379225,0.0003004586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013501064,0.0012079047,0.001635968,0.0018495799,0.0004949426,0.0018324396,0.0041532037,0.0016944556,0.003978476],"category_scores_gemma":[0.034042254,0.00080167473,0.0031531367,0.0015609836,0.0016481698,0.001959231,0.0030185706,0.0035315056,0.00062790455],"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":[0.00015860781,0.00009986226,0.008265359,0.0002677315,0.0005381609,0.00068179396,0.00068861444,0.47601187,0.0009774368,0.45069164,0.0025539189,0.059064988],"study_design_scores_gemma":[0.000034946228,0.00011210812,0.000982985,0.00004720038,0.00007599408,0.0002117956,0.000055849134,0.8161114,0.00019988864,0.17990375,0.0022211808,0.00004287524],"about_ca_topic_score_codex":0.009052039,"about_ca_topic_score_gemma":0.0049586226,"teacher_disagreement_score":0.013501064,"about_ca_system_score_codex":0.0014433558,"about_ca_system_score_gemma":0.0024979275,"threshold_uncertainty_score":0.0714013},"labels":[],"label_agreement":null},{"id":"W1974040551","doi":"10.1142/s0219024908004816","title":"EFFICIENT HEDGING AND PRICING OF EQUITY-LINKED LIFE INSURANCE CONTRACTS ON SEVERAL RISKY ASSETS","year":2008,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life insurance; Equity (law); Hedge; Actuarial science; Imperfect; Economics; Maturity (psychological); Probabilistic logic; Expected utility hypothesis; Business; Financial economics; Microeconomics; Computer science","score_opus":0.015096483551329516,"score_gpt":0.29619045775817315,"score_spread":0.2810939742068436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974040551","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":[0.2541523,0.0006429553,0.7399694,0.0003416405,0.000031153257,0.000084281775,0.000047739883,0.000044832974,0.004685828],"genre_scores_gemma":[0.9670255,0.00029448327,0.030528504,0.000025797563,0.000022851687,0.00004591248,0.00003613165,0.000012360134,0.0020084274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989158,0.000564224,0.00005343756,0.00011506717,0.0002297406,0.00012171006],"domain_scores_gemma":[0.9975446,0.0016007958,0.00037344915,0.0001712611,0.00013826438,0.000171628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033311206,0.00084206805,0.0011009198,0.0006831787,0.00036814332,0.0015726892,0.0010030651,0.0013772558,0.0018615369],"category_scores_gemma":[0.010094193,0.0006876883,0.00073060044,0.0005897605,0.0015684337,0.0023973968,0.0013201055,0.0013135731,0.00011139414],"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":[0.000096317155,0.00008138449,0.0013765891,0.000054481196,0.00006574602,0.00023324996,0.00011793329,0.80787134,0.0024581836,0.16945453,0.0002161495,0.0179741],"study_design_scores_gemma":[0.000023373723,0.00007219167,0.0004244561,0.000011631506,0.00001669752,0.000042002346,0.000017130813,0.9392424,0.0006926291,0.059197437,0.0002475735,0.000012477344],"about_ca_topic_score_codex":0.0009758403,"about_ca_topic_score_gemma":0.00073150126,"teacher_disagreement_score":0.0033311206,"about_ca_system_score_codex":0.0013958194,"about_ca_system_score_gemma":0.0009296201,"threshold_uncertainty_score":0.017616868},"labels":[],"label_agreement":null},{"id":"W1975284204","doi":"10.1080/10920277.2007.10597478","title":"“An Extreme Value Analysis of Advanced Age Mortality Data,” Kathryn A. Watts, Debbie J. Dupuis, and Bruce L. Jones, October 2006","year":2007,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Division of Mathematical Sciences","keywords":"Value (mathematics); Mathematics; Statistics","score_opus":0.04198849460036662,"score_gpt":0.3534763958142764,"score_spread":0.3114879012139098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975284204","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":[0.023719223,0.013639731,0.8567545,0.084505394,0.00982602,0.0002822132,0.0040407577,0.0015142204,0.0057180286],"genre_scores_gemma":[0.21670642,0.016354648,0.70808476,0.017532451,0.021470888,0.00066723267,0.009527852,0.0010770377,0.008578793],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98560786,0.01123463,0.000572149,0.00039496296,0.0020523176,0.00013810478],"domain_scores_gemma":[0.95372975,0.036733814,0.0015246464,0.0028543072,0.004605088,0.0005524492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025387174,0.0009184845,0.0009587919,0.0034284405,0.0007443981,0.0026929164,0.0018197638,0.0015270159,0.0037611034],"category_scores_gemma":[0.107274026,0.0007141097,0.0019793324,0.0038073815,0.002106158,0.0035116738,0.0019238428,0.004176933,0.0014541813],"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":[0.00033900005,0.00014004031,0.038518474,0.00027703363,0.00065719755,0.00040262763,0.00035110823,0.027732601,0.0006218648,0.033337917,0.5874011,0.31022105],"study_design_scores_gemma":[0.00022314052,0.00034599032,0.07870897,0.0008253838,0.0003860797,0.0011748367,0.0011414454,0.31765288,0.0028550043,0.4480571,0.14827475,0.00035444126],"about_ca_topic_score_codex":0.0028035506,"about_ca_topic_score_gemma":0.006312115,"teacher_disagreement_score":0.025387174,"about_ca_system_score_codex":0.0005319166,"about_ca_system_score_gemma":0.0011772632,"threshold_uncertainty_score":0.13426179},"labels":[],"label_agreement":null},{"id":"W1975635071","doi":"10.1017/s0714980809990018","title":"Editorial: Realizing the Vision. The Canadian Longitudinal Study on Aging as a Strategic Initiative of the Canadian Institutes of Health Research","year":2009,"lang":"fr","type":"editorial","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institute of Aging; Canadian Institutes of Health Research; University of British Columbia","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.09815796744030741,"score_gpt":0.36751871421774,"score_spread":0.2693607467774326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975635071","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023699035,0.0030966906,0.00007548249,0.07990641,0.91628546,0.000024964515,0.00010356172,0.00003575224,0.00044796578],"genre_scores_gemma":[0.000397793,0.003838667,0.00014256692,0.068898596,0.9228353,0.000068369074,0.000083500745,0.00004349555,0.0036917345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.985499,0.002189063,0.0016647844,0.0019223737,0.0075185597,0.0012062367],"domain_scores_gemma":[0.92397416,0.032492787,0.003191068,0.001863936,0.031862855,0.0066151596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018947672,0.004097438,0.00506814,0.0058062742,0.0060266857,0.010205696,0.00845632,0.025947198,0.012624188],"category_scores_gemma":[0.088040136,0.0018882315,0.0037497233,0.0037710436,0.007743206,0.0075669037,0.0026404583,0.037593994,0.008722521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002275695,0.0000057433485,0.000028410157,0.00012698729,0.0000106673215,0.000022455966,0.000018213148,0.0000097108905,0.000012700127,0.000234452,0.9976554,0.0018524964],"study_design_scores_gemma":[0.00012529049,0.000029475492,0.0008112319,0.0013867654,0.000091294576,0.0001376891,0.00016349301,0.00015875934,0.00009911454,0.001232475,0.9957027,0.00006180977],"about_ca_topic_score_codex":0.03807696,"about_ca_topic_score_gemma":0.052270316,"teacher_disagreement_score":0.98820525,"about_ca_system_score_codex":0.011794765,"about_ca_system_score_gemma":0.018903794,"threshold_uncertainty_score":0.10020608},"labels":[],"label_agreement":null},{"id":"W1977141703","doi":"10.1080/10920277.2007.10597486","title":"Markov Aging Process and Phase-Type Law of Mortality","year":2007,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Type (biology); Process (computing); Law and economics; Law; Computer science; Political science; Economics; Geology; Programming language","score_opus":0.019967431597697333,"score_gpt":0.36392237044983655,"score_spread":0.34395493885213924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977141703","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":[0.122103184,0.00034351845,0.86666685,0.0011315625,0.00015102183,0.00009973959,0.00036055795,0.00023762991,0.008905917],"genre_scores_gemma":[0.9622774,0.00046075886,0.028312946,0.00020899579,0.00015305412,0.00025053628,0.0002783666,0.000033729084,0.008024218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994973,0.00018261772,0.000020460437,0.00010266341,0.00010704871,0.000089892514],"domain_scores_gemma":[0.99783796,0.0012746849,0.00037038513,0.00013304394,0.0002711737,0.000112728085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457721,0.00042028306,0.0006046653,0.00071007595,0.00050975743,0.00092757004,0.0011692435,0.0010296406,0.004990557],"category_scores_gemma":[0.004254785,0.0002333905,0.00075214915,0.0004216724,0.0013693575,0.0016677331,0.00055213465,0.0012605314,0.0005945754],"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":[0.000042634594,0.00006314002,0.004313642,0.000044736273,0.000037349655,0.00027029423,0.0002684097,0.21676898,0.0015593726,0.76981467,0.0015511556,0.005265692],"study_design_scores_gemma":[0.000037439382,0.00004449718,0.0008877834,0.000018768089,0.000016632775,0.00010256536,0.000032966105,0.85952365,0.00025411768,0.13778934,0.0012741735,0.00001814429],"about_ca_topic_score_codex":0.0038770407,"about_ca_topic_score_gemma":0.0020304567,"teacher_disagreement_score":0.004990557,"about_ca_system_score_codex":0.0009162312,"about_ca_system_score_gemma":0.00068032544,"threshold_uncertainty_score":0.016695023},"labels":[],"label_agreement":null},{"id":"W1978304766","doi":"10.1155/2010/423087","title":"Typologies of Extreme Longevity Myths","year":2010,"lang":"en","type":"article","venue":"Current Gerontology and Geriatrics Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal","funders":"National Institute on Aging","keywords":"Longevity; Mythology; Life expectancy; Medicine; Context (archaeology); Demography; Gerontology; History; Sociology; Population; Classics","score_opus":0.2895473985069168,"score_gpt":0.4899985850571692,"score_spread":0.2004511865502524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978304766","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":[0.89409703,0.005216017,0.02804158,0.015127111,0.00038468253,0.000124087,0.00024015286,0.000070425725,0.05669884],"genre_scores_gemma":[0.9966085,0.000496087,0.0018769501,0.00039321932,0.00010235527,0.000044096785,0.00008278081,0.0000069204516,0.00038909953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99180675,0.004483021,0.0008367439,0.0006847555,0.0017049839,0.0004837735],"domain_scores_gemma":[0.9626977,0.02076285,0.010040894,0.0035940614,0.0020775008,0.00082692225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008457923,0.00044619368,0.0003849785,0.005163252,0.002800738,0.0034923526,0.00095573196,0.0016182107,0.0016185482],"category_scores_gemma":[0.040408008,0.00020387843,0.0004645641,0.0024305936,0.016849622,0.005952048,0.0054371012,0.0019524994,0.00014948164],"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":[0.00023788594,0.00008993553,0.1939218,0.00042365832,0.00007983134,0.002087534,0.16488726,0.0010125354,0.0006511349,0.5649005,0.004769108,0.06693873],"study_design_scores_gemma":[0.000049650633,0.00017828304,0.08019393,0.0014220059,0.000066549845,0.010612275,0.16599047,0.0043263226,0.00076755526,0.68764347,0.048630174,0.000119318276],"about_ca_topic_score_codex":0.00046329526,"about_ca_topic_score_gemma":0.00042389103,"teacher_disagreement_score":0.008457923,"about_ca_system_score_codex":0.0017186244,"about_ca_system_score_gemma":0.0007849326,"threshold_uncertainty_score":0.044730365},"labels":[],"label_agreement":null},{"id":"W1978444120","doi":"10.2139/ssrn.2352572","title":"Optimal Surrender Policy for Variable Annuity Guarantees","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Annuity; Surrender; Actuarial science; Variable (mathematics); Economics; Econometrics; Business; Life annuity; Finance; Mathematics; Pension; Political science; Law","score_opus":0.011405317685692471,"score_gpt":0.29744355208694695,"score_spread":0.2860382344012545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978444120","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":[0.5040888,0.0029675025,0.41134593,0.014012792,0.0007103474,0.0004424333,0.0010968556,0.0011972592,0.06413808],"genre_scores_gemma":[0.9767526,0.0005042385,0.010525919,0.00030722027,0.00018958637,0.00007701103,0.00012339975,0.00007770309,0.011442361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982407,0.0006333858,0.0000717756,0.00029885635,0.00019799687,0.0005572373],"domain_scores_gemma":[0.9931906,0.004840299,0.0005562324,0.00032994137,0.00037132038,0.0007116268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004882416,0.0011761748,0.003473073,0.001006414,0.00084658025,0.0047171647,0.00219707,0.004536586,0.010234189],"category_scores_gemma":[0.017492348,0.001403231,0.0008997709,0.0006925011,0.0016813332,0.0034740153,0.0022835885,0.0047635594,0.0008201887],"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":[0.0016508612,0.00051510416,0.0017064976,0.00036160488,0.00014595312,0.00059254264,0.00030883486,0.6027669,0.0045249755,0.3405062,0.0112304045,0.035690125],"study_design_scores_gemma":[0.00030042944,0.00025246685,0.0013340476,0.00007541082,0.000078078,0.00012970471,0.00017567183,0.81995356,0.0009416568,0.1743925,0.0023183355,0.000048160484],"about_ca_topic_score_codex":0.0046202415,"about_ca_topic_score_gemma":0.0018785229,"teacher_disagreement_score":0.010234189,"about_ca_system_score_codex":0.0029260404,"about_ca_system_score_gemma":0.0039944234,"threshold_uncertainty_score":0.03423673},"labels":[],"label_agreement":null},{"id":"W1980037022","doi":"10.1007/bf03029465","title":"Sixteen years ofJapa: A content analysis of theJournal of the Australian Population Association","year":2000,"lang":"en","type":"article","venue":"Journal of the Australian Population Association","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Demography; Subject (documents); Population; Period (music); Association (psychology); Principal (computer security); Geography; Medicine; Library science; Psychology; Sociology; Archaeology; Art","score_opus":0.03740529332261644,"score_gpt":0.31167885952038016,"score_spread":0.2742735661977637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980037022","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":[0.9920874,0.0007412621,0.00022438283,0.0016102842,0.000076282886,0.00030308284,0.0013972352,0.000008809642,0.0035511325],"genre_scores_gemma":[0.99023026,0.0016461519,0.0015670756,0.0005791551,0.00008900387,0.0010032072,0.0028089515,0.000029066436,0.0020471339],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9933743,0.003088733,0.0011238302,0.00026175016,0.0016582261,0.00049321254],"domain_scores_gemma":[0.95436895,0.02226308,0.008231286,0.0014671985,0.0103046475,0.003364845],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009991326,0.00021957599,0.0006646712,0.0082600005,0.0031744747,0.0024388733,0.00088397186,0.0007169064,0.002139355],"category_scores_gemma":[0.056665037,0.00042006996,0.00079294597,0.01057231,0.0013099519,0.0024371627,0.0054790275,0.001399783,0.00029925464],"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":[0.00013884004,0.00030257946,0.7682409,0.0005991896,0.000097340984,0.00021213264,0.18789782,0.00005651545,0.0002859453,0.0010205806,0.0053593935,0.035788752],"study_design_scores_gemma":[0.000006187938,0.000052612926,0.92686456,0.00021783214,0.0000329898,0.00006605801,0.065284215,0.00013372583,0.00004350473,0.00013844168,0.007143324,0.000016563903],"about_ca_topic_score_codex":0.023846526,"about_ca_topic_score_gemma":0.051255073,"teacher_disagreement_score":0.99174,"about_ca_system_score_codex":0.0050470326,"about_ca_system_score_gemma":0.007964432,"threshold_uncertainty_score":0.052839875},"labels":[],"label_agreement":null},{"id":"W1980050495","doi":"10.1177/1741826710389361","title":"Time trends in cardiovascular and all-cause mortality in the ‘old’ and ‘new’ European Union countries","year":2011,"lang":"en","type":"article","venue":"European Journal of Cardiovascular Prevention & Rehabilitation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":60,"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":"Austrian Science Fund","keywords":"European union; Cause of death; Western europe; Disease; Geography; Medicine; Demography; International trade; Economics; Internal medicine","score_opus":0.042000797599002646,"score_gpt":0.2838796968756206,"score_spread":0.24187889927661793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980050495","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":[0.9696674,0.0195769,0.00071290496,0.00044564166,0.000116581985,0.000016066657,0.005879215,0.00004270474,0.0035424803],"genre_scores_gemma":[0.98817563,0.004041923,0.00071204157,0.00012444728,0.00008563347,0.000021146807,0.0064671077,0.00000997313,0.0003620578],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925274,0.00018407447,0.0001820115,0.00016515034,0.00011201049,0.00010407878],"domain_scores_gemma":[0.99819416,0.00026120938,0.00091389794,0.00011595541,0.00039061683,0.00012409818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015286694,0.00021522833,0.0003567655,0.002654766,0.00017576451,0.0007319563,0.0002999207,0.00032735016,0.0008692515],"category_scores_gemma":[0.002902538,0.00012225466,0.0006257294,0.0030262792,0.00021350327,0.00088957296,0.00051119825,0.0003108973,0.00015421626],"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":[0.00020617466,0.000023452823,0.9719722,0.0002757062,0.00061974867,0.00013114598,0.00031412108,0.000825332,0.00022123911,0.0005585607,0.0016092533,0.02324295],"study_design_scores_gemma":[0.000005770017,0.000041911386,0.9961643,0.0000674039,0.00006074192,0.00014575121,0.00022216883,0.00018228391,0.00008722937,0.000054616583,0.0029614025,0.000006361502],"about_ca_topic_score_codex":0.004199695,"about_ca_topic_score_gemma":0.0033524258,"teacher_disagreement_score":0.004199695,"about_ca_system_score_codex":0.00033835066,"about_ca_system_score_gemma":0.00018646724,"threshold_uncertainty_score":0.0083504915},"labels":[],"label_agreement":null},{"id":"W1980895071","doi":"10.1080/02664763.2010.516388","title":"A flexible parametric survival model which allows a bathtub-shaped hazard rate function","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":29,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weibull distribution; Bathtub; Log-normal distribution; Statistics; Mathematics; Parametric statistics; Hazard; Log-logistic distribution; Distribution (mathematics); Function (biology); Applied mathematics; Econometrics; Distribution fitting; Exponential distribution; Mathematical analysis; Geography","score_opus":0.05949770112114674,"score_gpt":0.29749862229911156,"score_spread":0.2380009211779648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980895071","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":[0.022685511,0.00032294207,0.97232515,0.00045619634,0.000098569275,0.0000884414,0.0005235187,0.00031014413,0.0031894795],"genre_scores_gemma":[0.7743011,0.0015137648,0.19547766,0.00037007532,0.00024483868,0.0009386217,0.0014052284,0.00023616794,0.025512407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891496,0.00045505067,0.00005409976,0.00019675099,0.00023664716,0.00014262607],"domain_scores_gemma":[0.996027,0.0024342297,0.00045262265,0.00048758628,0.0004337338,0.00016482628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003920177,0.00086588325,0.0010680213,0.0009811298,0.00047194844,0.001289437,0.0021437735,0.0012835066,0.005381037],"category_scores_gemma":[0.011082007,0.00042039042,0.0012117797,0.0014812745,0.0012399524,0.0020141427,0.0014974901,0.0025560607,0.0013142419],"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":[0.0002076809,0.00013650833,0.007130584,0.0002421039,0.00014718546,0.00088457315,0.0005220363,0.6088658,0.004281032,0.27910265,0.006476368,0.09200352],"study_design_scores_gemma":[0.00004177943,0.0002048235,0.0023552468,0.000058675585,0.00008255509,0.00093241094,0.000083738065,0.85315436,0.0006853315,0.13355786,0.008755132,0.000088119545],"about_ca_topic_score_codex":0.001973451,"about_ca_topic_score_gemma":0.0023736062,"teacher_disagreement_score":0.005381037,"about_ca_system_score_codex":0.0008296802,"about_ca_system_score_gemma":0.0015579602,"threshold_uncertainty_score":0.020732164},"labels":[],"label_agreement":null},{"id":"W1982360168","doi":"10.1007/s355-002-8328-x","title":"Equitable insurance premium schemes","year":2002,"lang":"en","type":"article","venue":"Social Choice and Welfare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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 Montréal","funders":"","keywords":"Equity (law); Partition (number theory); Public finance; Economics; Lorenz curve; Group (periodic table); Econometrics; Insurance premium; Actuarial science; Mathematics; Mathematical economics; Microeconomics; Inequality; Gini coefficient; Combinatorics; Economic inequality","score_opus":0.03232711827241993,"score_gpt":0.29603026103464647,"score_spread":0.26370314276222656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982360168","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":[0.41994715,0.002422529,0.11423184,0.029527036,0.00045835748,0.00045578502,0.0009165467,0.0004885548,0.43155214],"genre_scores_gemma":[0.9795094,0.00031099626,0.004335437,0.0009705274,0.00028874972,0.000097822274,0.00009793149,0.000022728867,0.014366396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9933348,0.0029180208,0.00024844203,0.0005026611,0.002047152,0.00094899203],"domain_scores_gemma":[0.9922998,0.0031425382,0.00094194093,0.0018800651,0.0011887427,0.0005468506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008957494,0.0003585156,0.0006948526,0.0017695525,0.0009842194,0.003160527,0.0011858841,0.0025121386,0.014122633],"category_scores_gemma":[0.03140167,0.0003147517,0.0003429598,0.0011003724,0.0016181968,0.0030036375,0.003374086,0.0019904238,0.00080989825],"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":[0.00023906768,0.0005396704,0.008342258,0.000056357676,0.000060107657,0.000080092155,0.00048555905,0.010938358,0.0005844752,0.87760264,0.0058931755,0.095178194],"study_design_scores_gemma":[0.00021668969,0.00020085792,0.014920246,0.000108870445,0.000077578065,0.00020230163,0.00034112285,0.024586204,0.0005239137,0.9319816,0.02681382,0.000026798913],"about_ca_topic_score_codex":0.0011757995,"about_ca_topic_score_gemma":0.0021950232,"teacher_disagreement_score":0.014122633,"about_ca_system_score_codex":0.0019582096,"about_ca_system_score_gemma":0.0021710962,"threshold_uncertainty_score":0.04737228},"labels":[],"label_agreement":null},{"id":"W1984099532","doi":"10.1080/08898480306715","title":"Immigration and the dependency ratio of a host population","year":2003,"lang":"en","type":"article","venue":"Mathematical Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Victoria","funders":"","keywords":"Dependency ratio; Immigration; Dependency (UML); Population; Context (archaeology); Demographic economics; Demography; Econometrics; Economics; Geography; Sociology; Computer science","score_opus":0.03519373083176848,"score_gpt":0.34615897708135557,"score_spread":0.3109652462495871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984099532","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":[0.9878385,0.000226348,0.0036460306,0.0002608825,0.000011780407,0.000006035952,0.00008181536,0.000013220558,0.007915492],"genre_scores_gemma":[0.99917835,0.00016024178,0.00019405814,0.000011118032,0.000011010268,0.0000024060394,0.000033011707,0.0000032256262,0.0004066607],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999736,0.00010167837,0.000014800783,0.00003923072,0.00004432495,0.00006392173],"domain_scores_gemma":[0.9969261,0.0012503293,0.0010927804,0.00019726167,0.00021320328,0.00032031132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078467303,0.00018987396,0.0002225778,0.00080231443,0.00041452292,0.0008805123,0.00030878317,0.00026013362,0.0038743233],"category_scores_gemma":[0.007760154,0.000092245915,0.00031326117,0.00043064987,0.0007030322,0.00087608234,0.0010291213,0.00052603165,0.00034785998],"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":[0.0006619885,0.00023416377,0.7305361,0.0001072897,0.00020006218,0.0026730548,0.0018755519,0.032268558,0.0054030623,0.16632901,0.002159415,0.05755173],"study_design_scores_gemma":[0.000088642206,0.0009346866,0.73750263,0.00011692851,0.00030170009,0.0067122034,0.002957496,0.14763,0.004203369,0.09109525,0.008350672,0.00010641447],"about_ca_topic_score_codex":0.0026887024,"about_ca_topic_score_gemma":0.0014188138,"teacher_disagreement_score":0.0038743233,"about_ca_system_score_codex":0.0006117754,"about_ca_system_score_gemma":0.00031500237,"threshold_uncertainty_score":0.012960911},"labels":[],"label_agreement":null},{"id":"W1984216158","doi":"10.1080/00324720215929","title":"Distinctive features of age-specific fertility profiles in the English-speaking world: Common patterns in Australia, Canada, New Zealand and the United States, 1970-98","year":2002,"lang":"en","type":"article","venue":"Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":50,"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":"Fertility; Demography; Ethnic group; Geography; Irish; Total fertility rate; Political science; Sociology; Population; Family planning; Research methodology; Linguistics; Law","score_opus":0.06593063073629275,"score_gpt":0.3257241302691602,"score_spread":0.25979349953286746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984216158","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":[0.9966654,0.0006829384,0.00007049428,0.000085009306,0.0000028108789,0.00001540062,0.0013224974,0.0000068065156,0.0011487015],"genre_scores_gemma":[0.9976928,0.00060133863,0.00008393093,0.000016796514,0.000002235304,0.0000054928537,0.0010877085,0.0000024558208,0.0005072708],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973744,0.000032134983,0.000024100444,0.000037113423,0.00006968238,0.00009946285],"domain_scores_gemma":[0.9988997,0.00013310375,0.00023897745,0.00005442183,0.00048032578,0.00019339165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059047964,0.00013180825,0.00028037853,0.0024499623,0.0009138437,0.00051361584,0.0003733241,0.00014006547,0.00080365926],"category_scores_gemma":[0.0023073985,0.00015612639,0.00023457356,0.0029475426,0.000685673,0.00020732885,0.00043977523,0.00027686733,0.000092300645],"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":[0.00009895086,0.000014020357,0.9738428,0.00005069435,0.000065663764,0.00028651173,0.0075520645,0.00032867971,0.00038860383,0.00048408835,0.00080691016,0.016081007],"study_design_scores_gemma":[8.0950065e-7,0.000004536872,0.9987721,0.0000046062614,0.00000444505,0.00006432096,0.00063395203,0.00006789041,0.000025659781,0.000015372461,0.00040303613,0.000003116913],"about_ca_topic_score_codex":0.9193034,"about_ca_topic_score_gemma":0.95136213,"teacher_disagreement_score":0.08069658,"about_ca_system_score_codex":0.003840385,"about_ca_system_score_gemma":0.003106194,"threshold_uncertainty_score":0.16234362},"labels":[],"label_agreement":null},{"id":"W1985683555","doi":"10.1007/s13385-012-0057-1","title":"Equity-linked products: evaluation of the dynamic hedging errors under stochastic mortality","year":2012,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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 Waterloo; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valuation (finance); Equity (law); Actuarial science; Econometrics; Economics; Replicating portfolio; Financial economics; Portfolio; Finance","score_opus":0.10007366647664193,"score_gpt":0.38622666322661725,"score_spread":0.2861529967499753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985683555","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":[0.93896973,0.000688802,0.056818407,0.00025492554,0.00008356435,0.00007273392,0.00042901328,0.00009571749,0.0025870788],"genre_scores_gemma":[0.99379,0.00011390119,0.0050692055,0.000017080005,0.000017287866,0.0000144404485,0.00025045374,0.00001630681,0.0007113626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883634,0.00061350147,0.00007861158,0.0001708052,0.00021897088,0.00008178739],"domain_scores_gemma":[0.9771639,0.01940913,0.00090342236,0.0009583107,0.0010729736,0.0004922873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011834297,0.00086163974,0.000966072,0.0010349182,0.0002357844,0.0019968734,0.00130222,0.0016732925,0.0022747666],"category_scores_gemma":[0.026388695,0.00036014736,0.0007542968,0.0007957447,0.00091102684,0.0020438556,0.0012798577,0.0009972954,0.00012428436],"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":[0.001123837,0.00013247327,0.011187709,0.000091543654,0.00015693215,0.00013593385,0.00011268398,0.9568156,0.00065920607,0.012589378,0.0003030158,0.01669171],"study_design_scores_gemma":[0.000035187924,0.00025243877,0.0034346487,0.000016481903,0.000058837657,0.000036474823,0.000028559796,0.99250185,0.00047608832,0.0030117284,0.00013228694,0.000015396248],"about_ca_topic_score_codex":0.00300904,"about_ca_topic_score_gemma":0.0013629029,"teacher_disagreement_score":0.011834297,"about_ca_system_score_codex":0.00085948926,"about_ca_system_score_gemma":0.00091852964,"threshold_uncertainty_score":0.06258649},"labels":[],"label_agreement":null},{"id":"W1987149948","doi":"10.1016/s0047-2727(99)00118-8","title":"The value of genetic information in the life insurance market","year":2000,"lang":"en","type":"article","venue":"Journal of Public Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":106,"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":"Deutscher Akademischer Austauschdienst","keywords":"Adverse selection; Economics; Incentive; Ex-ante; Life insurance; Value (mathematics); Microeconomics; Information asymmetry; Actuarial science; Private information retrieval; Selection (genetic algorithm); Value of information; Mathematical economics; Computer science","score_opus":0.014624350182147541,"score_gpt":0.24776714239554984,"score_spread":0.23314279221340228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987149948","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":[0.8825739,0.0028302781,0.017868489,0.039825156,0.0002067142,0.000050851104,0.0009308622,0.000105058,0.055608764],"genre_scores_gemma":[0.9965443,0.00042949495,0.00050432247,0.0003942224,0.0002253324,0.0000050893905,0.000045386947,0.00000838206,0.0018434634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99840975,0.00097650016,0.000049720613,0.00015251711,0.0001966586,0.00021486303],"domain_scores_gemma":[0.946931,0.04490965,0.0037440967,0.0014613639,0.00118863,0.0017652429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053081703,0.00033262043,0.0011071828,0.0015538764,0.0010201251,0.0056976867,0.0011342652,0.0052361186,0.010463228],"category_scores_gemma":[0.038386844,0.00054232427,0.0005483279,0.001130751,0.0041272207,0.0066106566,0.0011436663,0.002898481,0.00046750248],"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":[0.0020571498,0.0008029062,0.06009699,0.00020860438,0.0004009851,0.0024039051,0.00093204103,0.11124761,0.0023759531,0.75912344,0.008836257,0.05151424],"study_design_scores_gemma":[0.00025377233,0.00016753025,0.016608803,0.000050546903,0.00015335513,0.000330336,0.00058162294,0.11496831,0.00040268557,0.86400187,0.0023874005,0.00009374911],"about_ca_topic_score_codex":0.0029511296,"about_ca_topic_score_gemma":0.0028183481,"teacher_disagreement_score":0.010463228,"about_ca_system_score_codex":0.0022925853,"about_ca_system_score_gemma":0.0010264526,"threshold_uncertainty_score":0.035003006},"labels":[],"label_agreement":null},{"id":"W1992132077","doi":"10.1007/s13385-014-0098-8","title":"Sustainable retirement spending: the Czech case","year":2014,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cytodiagnostics (Canada)","funders":"Grantová Agentura České Republiky","keywords":"Economics; Czech; Rate of return; Econometrics; Asset allocation; Pension; Longevity risk; Investment (military); Asset (computer security); Portfolio; Volatility (finance); Investment strategy; Retirement planning; Geometric Brownian motion; Actuarial science; Financial economics; Microeconomics; Finance; Computer science","score_opus":0.023009923611213415,"score_gpt":0.2919752190649144,"score_spread":0.268965295453701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992132077","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":[0.9288423,0.0011886377,0.00083810923,0.0059102545,0.00008571081,0.000038007798,0.00046333636,0.000020559904,0.06261301],"genre_scores_gemma":[0.9977621,0.00022370096,0.00007845517,0.00012334259,0.000013606343,0.000007636966,0.00003793218,0.0000034755203,0.0017498187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985177,0.00018252472,0.0000720603,0.00010247178,0.00018617368,0.0009391831],"domain_scores_gemma":[0.9987424,0.00018256839,0.00023704441,0.00014324697,0.00017013562,0.0005246327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00167859,0.00024462107,0.0004797454,0.0012632122,0.0025680622,0.0038111485,0.00086634327,0.0015606328,0.0034018443],"category_scores_gemma":[0.0035181905,0.00023893025,0.0008871868,0.0014984602,0.0020512012,0.0012607024,0.0035744214,0.001695202,0.00018791582],"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":[0.0012082482,0.0005433823,0.14440769,0.00022581824,0.0003607297,0.008612932,0.002523841,0.02317486,0.0011700909,0.7601293,0.01390666,0.04373652],"study_design_scores_gemma":[0.00091904873,0.000690952,0.558335,0.000990754,0.00095184566,0.011865766,0.022263965,0.033025246,0.0017825343,0.22377546,0.14487687,0.0005225143],"about_ca_topic_score_codex":0.07533667,"about_ca_topic_score_gemma":0.101195596,"teacher_disagreement_score":0.07533667,"about_ca_system_score_codex":0.0043871286,"about_ca_system_score_gemma":0.005276504,"threshold_uncertainty_score":0.1497963},"labels":[],"label_agreement":null},{"id":"W1995142890","doi":"10.1016/j.insmatheco.2010.11.008","title":"An application of comonotonicity theory in a stochastic life annuity framework","year":2010,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Actuarial science; Life annuity; Annuity; Economics; Quantile; Econometrics; Present value; Payment; Pension; Finance","score_opus":0.010675923473016343,"score_gpt":0.28375454709506226,"score_spread":0.27307862362204594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995142890","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":[0.03170446,0.002397849,0.92048305,0.0042516612,0.0004093878,0.000029874755,0.000090758724,0.000071648865,0.04056126],"genre_scores_gemma":[0.8462976,0.004120338,0.12635717,0.0007845629,0.0015400652,0.00012216961,0.00011277117,0.00010857703,0.020556726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993011,0.0003859619,0.000034051674,0.000077326105,0.00013401576,0.000067584995],"domain_scores_gemma":[0.99789965,0.0012689567,0.0001900811,0.00017111696,0.00027421452,0.00019599806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002212044,0.00073136087,0.0009061974,0.0012198932,0.0010425821,0.0019295206,0.0013794033,0.0013722093,0.0048124627],"category_scores_gemma":[0.005975853,0.00035617503,0.0014583402,0.0013963664,0.0024118882,0.0033565816,0.0024672777,0.0020331172,0.00030116425],"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":[0.0000020391915,0.000005992677,0.00010808747,0.000007874514,0.000004841078,0.00003130514,0.00003779226,0.0052907793,0.00005175471,0.9929598,0.00027502206,0.0012246871],"study_design_scores_gemma":[0.0000036397573,0.0000074146183,0.00010697622,0.000007800077,0.0000056732806,0.0000251212,0.000024914962,0.05058349,0.000028474386,0.947504,0.0016964745,0.0000060141856],"about_ca_topic_score_codex":0.0036510746,"about_ca_topic_score_gemma":0.0029740555,"teacher_disagreement_score":0.0048124627,"about_ca_system_score_codex":0.0015416222,"about_ca_system_score_gemma":0.0013860678,"threshold_uncertainty_score":0.016099274},"labels":[],"label_agreement":null},{"id":"W1996420695","doi":"10.1017/s0714980800003676","title":"The Romanow Commission Report and Home Care","year":2003,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Commission; Action (physics); Content (measure theory); Internet privacy; Business; Computer science; Mathematics; Finance","score_opus":0.009958832234740266,"score_gpt":0.23813267352665984,"score_spread":0.2281738412919196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996420695","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":[0.020031491,0.035404846,0.0010927382,0.0795957,0.01218331,0.00032801143,0.08249756,0.0004862391,0.7683801],"genre_scores_gemma":[0.1221321,0.029638957,0.0024436705,0.015224393,0.0025733572,0.0007155025,0.06142988,0.00057463493,0.76526755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932121,0.00077509554,0.0005853418,0.0003906751,0.0036318214,0.0014049391],"domain_scores_gemma":[0.995353,0.00080741267,0.00053188676,0.0005363325,0.0019723922,0.00079889083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032359986,0.0004145083,0.0004884136,0.002989011,0.0014054914,0.0047044437,0.0013370545,0.0028936383,0.055560082],"category_scores_gemma":[0.013782499,0.00036500618,0.0006670458,0.005220772,0.00045927594,0.0017950679,0.0026549096,0.0032316502,0.009988043],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007206267,0.000036164238,0.006671786,0.00021762164,0.000022362714,0.00016034619,0.00028539554,0.000083912935,0.00006113074,0.038172904,0.9167227,0.03749359],"study_design_scores_gemma":[0.000015170672,0.000015175678,0.038323395,0.0004101415,0.000016782027,0.00012365085,0.0006217066,0.000085777545,0.0002132367,0.001586502,0.9585692,0.00001917758],"about_ca_topic_score_codex":0.109050184,"about_ca_topic_score_gemma":0.14421093,"teacher_disagreement_score":0.99437404,"about_ca_system_score_codex":0.0056259395,"about_ca_system_score_gemma":0.016943967,"threshold_uncertainty_score":0.21683091},"labels":[],"label_agreement":null},{"id":"W1996929291","doi":"10.1080/17442508.2013.859388","title":"A generalized pricing framework addressing correlated mortality and interest risks: a change of probability measure approach","year":2014,"lang":"en","type":"article","venue":"Stochastics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Valuation (finance); Econometrics; Measure (data warehouse); Interest rate; Actuarial science; Risk measure; Economics; Derivative (finance); Computer science; Mathematics; Financial economics; Finance; Data mining","score_opus":0.2691333990047604,"score_gpt":0.38025467213959085,"score_spread":0.11112127313483044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996929291","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":[0.019251341,0.00045000756,0.97659403,0.000581487,0.00009873474,0.000032061293,0.00003465697,0.000044888944,0.0029126846],"genre_scores_gemma":[0.76267123,0.0011307228,0.2244557,0.00031893636,0.0006826474,0.00019570238,0.000096240896,0.00007928513,0.010369611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871004,0.0006636515,0.00005013856,0.00015398761,0.00032182655,0.00010040923],"domain_scores_gemma":[0.99855417,0.0006429825,0.00017951256,0.0002012423,0.0002762244,0.00014592223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031249197,0.0009609716,0.0010908948,0.0010781926,0.0005190755,0.0019083759,0.0022924938,0.001865031,0.0029377078],"category_scores_gemma":[0.005424921,0.00044284618,0.0017176127,0.00096789707,0.0017826554,0.003191975,0.0016932429,0.0023488172,0.00022376321],"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":[0.00001855441,0.00004683112,0.00057209766,0.0000490089,0.000056462934,0.000285554,0.00010431737,0.1454785,0.0017967557,0.8402595,0.0008236141,0.010508723],"study_design_scores_gemma":[0.000013430405,0.00003905306,0.00033804763,0.0000087880235,0.000020161118,0.00011009304,0.000018112132,0.79691654,0.00020559045,0.20074451,0.0015639685,0.00002169604],"about_ca_topic_score_codex":0.0020818184,"about_ca_topic_score_gemma":0.0011329002,"teacher_disagreement_score":0.0031249197,"about_ca_system_score_codex":0.0015677387,"about_ca_system_score_gemma":0.0012725161,"threshold_uncertainty_score":0.016526341},"labels":[],"label_agreement":null},{"id":"W1997389624","doi":"10.1017/s1357321700004682","title":"The Importance of Year of Birth in Two-Dimensional Mortality Data","year":2006,"lang":"en","type":"article","venue":"British Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":78,"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":"Engineering and Physical Sciences Research Council","keywords":"Longevity; Demography; Longevity risk; Cohort; Cohort effect; Life table; Population; Pension; Actuarial science; Economics; Medicine; Gerontology; Finance","score_opus":0.02677408821620684,"score_gpt":0.32685034894167936,"score_spread":0.3000762607254725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997389624","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":[0.9023944,0.0031806033,0.07800469,0.0031142756,0.0003422028,0.00011294794,0.00807858,0.00040070646,0.004371595],"genre_scores_gemma":[0.99148047,0.00036623652,0.005305403,0.00011055436,0.00010992598,0.000014498216,0.0020591603,0.000042965603,0.00051078154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9878405,0.009455371,0.0006479645,0.0008946528,0.00080389506,0.00035764155],"domain_scores_gemma":[0.56808573,0.40761143,0.008789654,0.009938781,0.0036016002,0.001972803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029343106,0.00048716215,0.0008427154,0.0037759426,0.0008531677,0.003310929,0.0008975279,0.0015947365,0.0022732767],"category_scores_gemma":[0.11195428,0.00036261338,0.0012635135,0.004748349,0.0015096981,0.003373379,0.001521618,0.0020392858,0.00050822797],"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":[0.00088626274,0.00016942839,0.8810157,0.00022317865,0.0005321404,0.00042839372,0.0002381561,0.0779073,0.0009369561,0.004514955,0.0020177977,0.031129712],"study_design_scores_gemma":[0.000055866218,0.00048069388,0.5104576,0.00018636604,0.00036718554,0.00087780243,0.0006378848,0.46663693,0.001379553,0.014238012,0.0044777677,0.00020438482],"about_ca_topic_score_codex":0.008431538,"about_ca_topic_score_gemma":0.0064276336,"teacher_disagreement_score":0.029343106,"about_ca_system_score_codex":0.000812446,"about_ca_system_score_gemma":0.0008189872,"threshold_uncertainty_score":0.15518308},"labels":[],"label_agreement":null},{"id":"W1998942183","doi":"10.1139/f07-039","title":"Modelling length-at-age variability under irreversible growth","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Formalism (music); Mathematics; Coefficient of variation; Statistics; Statistical physics; Physics","score_opus":0.03790217705457887,"score_gpt":0.2638163614749962,"score_spread":0.22591418442041733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998942183","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":[0.27867293,0.00017505806,0.71759266,0.00030029225,0.00002998251,0.000024311785,0.00025441288,0.000173336,0.0027770547],"genre_scores_gemma":[0.9721077,0.0001673438,0.024381567,0.00003709306,0.00002022653,0.000047573503,0.0001233511,0.000036169928,0.003078882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996038,0.000108674125,0.000020447013,0.000115318406,0.00008647746,0.00006525358],"domain_scores_gemma":[0.998044,0.0011207261,0.0005099678,0.00013799443,0.00009954279,0.00008769453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015242675,0.00044718158,0.0004204317,0.0007390111,0.00033565977,0.00082758855,0.0015626863,0.0010139052,0.0010655111],"category_scores_gemma":[0.005637657,0.00041585695,0.0005383244,0.0005847121,0.0012859472,0.0012261395,0.00084506784,0.0008851072,0.00015652229],"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":[0.000009411492,0.000009626551,0.0031641796,0.000012661516,0.000013138274,0.0001059097,0.000065920685,0.9480892,0.0014999702,0.044336036,0.00009667284,0.0025973688],"study_design_scores_gemma":[0.0000028374043,0.00000860369,0.0007063953,0.000002628498,0.0000038448334,0.000028827975,0.000007839413,0.98443943,0.00024358692,0.014323984,0.00022474118,0.0000072191688],"about_ca_topic_score_codex":0.009956083,"about_ca_topic_score_gemma":0.008318575,"teacher_disagreement_score":0.009956083,"about_ca_system_score_codex":0.0014716556,"about_ca_system_score_gemma":0.0009287915,"threshold_uncertainty_score":0.019796252},"labels":[],"label_agreement":null},{"id":"W2000977401","doi":"10.1093/imaman/dpr018","title":"Markovian regime-switching market completion using additional Markov jump assets","year":2011,"lang":"en","type":"article","venue":"IMA Journal of Management Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Zhàng; China; Library science; Mathematics; Management; Mathematical economics; Economics; Political science; Computer science; Law","score_opus":0.05797535378134667,"score_gpt":0.3035448107146703,"score_spread":0.24556945693332366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000977401","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":[0.13558766,0.00019979467,0.8555172,0.00030804297,0.00006296419,0.0000777897,0.000119426484,0.00009084794,0.008036275],"genre_scores_gemma":[0.88124293,0.0003663723,0.10862626,0.0001185464,0.00017915087,0.00016779025,0.00024072465,0.000052536165,0.009005755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990089,0.0002951759,0.00007193696,0.00019520395,0.00028128113,0.00014744692],"domain_scores_gemma":[0.9964619,0.0015298224,0.0006189919,0.0005778692,0.00039396939,0.00041732515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003006379,0.00055504433,0.000937349,0.0008018532,0.00044709846,0.0015580998,0.0008459968,0.00086142134,0.0053630443],"category_scores_gemma":[0.0061182706,0.0003163202,0.00183579,0.0004974256,0.0016118676,0.0042781234,0.0016531022,0.0022421894,0.00034389718],"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":[0.000028217493,0.000046988203,0.00028533957,0.000030950392,0.000013783305,0.000097279306,0.000080303784,0.020671153,0.0012270442,0.9726523,0.00025552028,0.004611089],"study_design_scores_gemma":[0.00003526787,0.000107532054,0.0004260615,0.000017734441,0.000014975684,0.0001402707,0.000035277088,0.43446675,0.0012487291,0.56129193,0.0021871885,0.000028274442],"about_ca_topic_score_codex":0.00065562635,"about_ca_topic_score_gemma":0.00045689198,"teacher_disagreement_score":0.0053630443,"about_ca_system_score_codex":0.0007039537,"about_ca_system_score_gemma":0.0007991353,"threshold_uncertainty_score":0.017941177},"labels":[],"label_agreement":null},{"id":"W2002817476","doi":"10.1198/tech.2007.s493","title":"Life Time Data: Statistical Models and Methods","year":2007,"lang":"en","type":"article","venue":"Technometrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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 Windsor","funders":"","keywords":"Statistics; Computer science; Econometrics; Mathematics","score_opus":0.09794607587331645,"score_gpt":0.43873838874018734,"score_spread":0.3407923128668709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002817476","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":[0.020014826,0.00439047,0.9611127,0.0029585396,0.0006232837,0.0011851682,0.0067011993,0.0011908811,0.0018229607],"genre_scores_gemma":[0.3340542,0.008234747,0.62390673,0.001480641,0.0018872007,0.011774469,0.009845729,0.0006550107,0.008161326],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95601434,0.03618858,0.0017013402,0.0029113884,0.0025200425,0.0006643183],"domain_scores_gemma":[0.86542743,0.11719811,0.003729035,0.010141625,0.002747162,0.0007566099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051755942,0.0016775707,0.0031646083,0.0050527323,0.0009513135,0.004076357,0.0037370196,0.0029230968,0.008448662],"category_scores_gemma":[0.11817793,0.00081031595,0.0034443391,0.009735268,0.0026040385,0.0037660901,0.0026188653,0.004460365,0.0025802364],"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":[0.00069370854,0.00092431996,0.057528514,0.0021625215,0.0026612214,0.0004815445,0.0012115233,0.09771489,0.00045311014,0.36904427,0.050521888,0.41660246],"study_design_scores_gemma":[0.00029870862,0.00067560095,0.021189548,0.00083145004,0.0010568398,0.0007435697,0.0010933072,0.397061,0.0006431078,0.5293117,0.04689553,0.00019963784],"about_ca_topic_score_codex":0.0059811464,"about_ca_topic_score_gemma":0.004327853,"teacher_disagreement_score":0.051755942,"about_ca_system_score_codex":0.0022100285,"about_ca_system_score_gemma":0.0038858366,"threshold_uncertainty_score":0.2737149},"labels":[],"label_agreement":null},{"id":"W2006185018","doi":"10.2139/ssrn.2226369","title":"Portfolio Optimization under Solvency Constraints: A Dynamical Approach","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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":"","keywords":"Solvency; Portfolio optimization; Portfolio; Financial economics; Econometrics; Actuarial science; Computer science; Economics; Finance; Market liquidity","score_opus":0.00911610001382613,"score_gpt":0.25707448337925853,"score_spread":0.2479583833654324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006185018","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":[0.06297567,0.0019561998,0.8932461,0.0046146284,0.00024999984,0.00007488895,0.00026181297,0.000057898458,0.03656272],"genre_scores_gemma":[0.88263,0.002820164,0.08129644,0.0008927628,0.0006808142,0.00036776348,0.0003400358,0.00014360009,0.030828409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992131,0.00042580237,0.000031620246,0.00012473486,0.0001263791,0.00007835824],"domain_scores_gemma":[0.9957742,0.0029622817,0.00053705095,0.00013467632,0.0002728763,0.00031889146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020332963,0.0010444456,0.0015877329,0.0011245882,0.0006359318,0.0027477737,0.001812224,0.0035527672,0.0047888383],"category_scores_gemma":[0.011391039,0.001217735,0.0015524633,0.0009995188,0.002450474,0.0032210555,0.0024737043,0.0028175307,0.00031833624],"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":[0.000019847806,0.00004233238,0.00059154705,0.00005827484,0.00007428834,0.0001230067,0.00007083751,0.69525456,0.00034898412,0.29879433,0.001348403,0.0032734945],"study_design_scores_gemma":[0.000014547149,0.00001333439,0.00013119882,0.000011183998,0.000008226353,0.0000166146,0.0000149089055,0.9388527,0.000029504465,0.060320932,0.0005774397,0.000009424412],"about_ca_topic_score_codex":0.005072783,"about_ca_topic_score_gemma":0.003266142,"teacher_disagreement_score":0.005072783,"about_ca_system_score_codex":0.0012851169,"about_ca_system_score_gemma":0.0012363761,"threshold_uncertainty_score":0.016020298},"labels":[],"label_agreement":null},{"id":"W2007695757","doi":"10.1111/j.1095-8649.2001.tb00127.x","title":"Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods","year":2001,"lang":"en","type":"review","venue":"Journal of Fish Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography","funders":"","keywords":"Ageing; Scale (ratio); Statistics; Computer science; Quality (philosophy); Mathematics; Biology","score_opus":0.23547620133425323,"score_gpt":0.5020391156520508,"score_spread":0.2665629143177976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007695757","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":[0.00048005168,0.98561066,0.008651394,0.0011089959,0.0010227843,0.000060856084,0.00017284794,0.000050465515,0.0028419993],"genre_scores_gemma":[0.0063507087,0.97571224,0.013897063,0.0010709639,0.0010569928,0.00016778594,0.0002984079,0.000060416252,0.0013853138],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97750425,0.006310907,0.003919639,0.002774578,0.009219292,0.00027135675],"domain_scores_gemma":[0.9553963,0.030650066,0.0045052874,0.0018901777,0.0073102578,0.0002478189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025589647,0.0014501702,0.0032791127,0.0072719348,0.00086370617,0.0034941386,0.0025378354,0.002786193,0.0032249251],"category_scores_gemma":[0.032324027,0.0009640437,0.0017073229,0.006603517,0.004765038,0.004892922,0.0024984789,0.00311337,0.002479264],"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":[0.00011926367,0.000055662204,0.0016499961,0.054702632,0.00022664793,0.00021699764,0.0004958015,0.0011045348,0.003876861,0.016665738,0.017729647,0.9031561],"study_design_scores_gemma":[0.000016867105,0.00038484976,0.006600057,0.023640523,0.0004793229,0.002138877,0.00036040737,0.0006988805,0.006878112,0.014779837,0.94379485,0.00022747934],"about_ca_topic_score_codex":0.0044568474,"about_ca_topic_score_gemma":0.0038921302,"teacher_disagreement_score":0.025589647,"about_ca_system_score_codex":0.0025363355,"about_ca_system_score_gemma":0.0044733062,"threshold_uncertainty_score":0.13533258},"labels":[],"label_agreement":null},{"id":"W2007842692","doi":"10.1093/jhmas/jri003","title":"The Role of Morbidity in the Mortality Decline of the Nineteenth Century: Evidence from the Military Population at Gibraltar 1818-1899","year":2004,"lang":"en","type":"article","venue":"Journal of the History of Medicine and Allied Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Demography; Mortality rate; Medicine; Epidemiology; Epidemiological transition; Longevity; Disease; Population; Medical care; Gerontology; Environmental health; Emergency medicine; Surgery","score_opus":0.05586178729949639,"score_gpt":0.3138042267576564,"score_spread":0.25794243945816003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007842692","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":[0.9954,0.0015424576,0.00002332491,0.0006513325,0.000006621954,0.0000029465677,0.000177628,0.0000017267564,0.0021939331],"genre_scores_gemma":[0.99869186,0.00075314206,0.000019135583,0.00008154551,0.000018813205,0.0000018014961,0.00019460081,0.0000010189749,0.00023809884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995888,0.000121736666,0.000024712594,0.000046590816,0.00007251392,0.00014563484],"domain_scores_gemma":[0.99842215,0.0003703041,0.0006526692,0.000073426345,0.00033055074,0.0001509107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066246476,0.00016304907,0.00022548319,0.001990651,0.0009109457,0.0008432066,0.00054238294,0.00051733607,0.0018729861],"category_scores_gemma":[0.0036653378,0.00013198433,0.00017538399,0.0026749782,0.0013399011,0.0004560615,0.00084796845,0.0006455591,0.00025588638],"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":[0.00022312091,0.00003382026,0.96056616,0.00009533516,0.00004594202,0.0007127681,0.016458284,0.00008319794,0.00038217913,0.001106324,0.0008023385,0.019490512],"study_design_scores_gemma":[0.0000013206184,0.000019116924,0.997267,0.000019699095,0.000006028374,0.0000678205,0.0019582321,0.000019671725,0.000019810344,0.000039144696,0.0005800274,0.0000021725525],"about_ca_topic_score_codex":0.17946567,"about_ca_topic_score_gemma":0.22774911,"teacher_disagreement_score":0.17946567,"about_ca_system_score_codex":0.0016338201,"about_ca_system_score_gemma":0.0007504652,"threshold_uncertainty_score":0.3568421},"labels":[],"label_agreement":null},{"id":"W2010301849","doi":"10.1002/ajhb.20893","title":"Is there a trade‐off between fertility and longevity? A comparative study of women from three large historical databases accounting for mortality selection","year":2009,"lang":"en","type":"article","venue":"American Journal of Human Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Université du Québec à Chicoutimi; Western University","funders":"National Institute on Aging","keywords":"Longevity; Fertility; Selection (genetic algorithm); Demography; Database; Demographic economics; Geography; Gerontology; Medicine; Economics; Computer science; Sociology; Population","score_opus":0.08613255206924672,"score_gpt":0.3935735227038697,"score_spread":0.30744097063462295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010301849","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":[0.9938672,0.00050187594,0.0003630789,0.00006391212,0.0000029202292,0.000021753383,0.004816647,0.000007772121,0.00035469877],"genre_scores_gemma":[0.9889655,0.00028932065,0.0005226246,0.000049969894,0.00000999395,0.000041672727,0.009937275,0.000005678302,0.00017793958],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988507,0.0002889837,0.00014793793,0.00027340945,0.0002657669,0.00017325676],"domain_scores_gemma":[0.9952603,0.0015139887,0.001489372,0.0008427364,0.0006315802,0.00026216134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019276711,0.00021876747,0.00046670102,0.0024128223,0.00070232665,0.00067556143,0.00057949184,0.00025828305,0.0010923742],"category_scores_gemma":[0.005490477,0.00014277968,0.0002984694,0.004492605,0.00050053716,0.0004888894,0.00066608784,0.00019479771,0.00021744758],"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":[0.00008120782,0.000012632573,0.99380434,0.000023708268,0.00013668404,0.000048044305,0.00044687977,0.00004418723,0.00030565434,0.00006629994,0.00029323096,0.004737197],"study_design_scores_gemma":[0.0000057148654,0.000022364171,0.99855953,0.0000070226174,0.000041556494,0.000069034664,0.00033789605,0.00010078722,0.00009320796,0.000018076995,0.00074059254,0.000004165996],"about_ca_topic_score_codex":0.13026068,"about_ca_topic_score_gemma":0.26328662,"teacher_disagreement_score":0.13026068,"about_ca_system_score_codex":0.0010063148,"about_ca_system_score_gemma":0.0012232332,"threshold_uncertainty_score":0.25900495},"labels":[],"label_agreement":null},{"id":"W2010680053","doi":"10.1007/s10985-014-9302-z","title":"Diagnostic tools for bivariate accelerated life regression models","year":2014,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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 Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Univariate; Statistics; Goodness of fit; Bivariate data; Censoring (clinical trials); Mathematics; Regression analysis; Econometrics; Regression; Multivariate statistics","score_opus":0.12883621652253835,"score_gpt":0.3748461851185284,"score_spread":0.24600996859599006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010680053","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":[0.031932946,0.00073392526,0.96004456,0.0007812094,0.00009372506,0.0002474638,0.0021623815,0.0028719897,0.0011317839],"genre_scores_gemma":[0.4997065,0.0007895329,0.490634,0.00020002005,0.00023922924,0.0012127087,0.0050371024,0.00044463782,0.0017362182],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9934563,0.004634671,0.00060179614,0.00046702975,0.0006295666,0.0002106567],"domain_scores_gemma":[0.92015785,0.06864043,0.004207806,0.003304547,0.0029753135,0.0007141174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014348227,0.0016096997,0.001459991,0.0063558775,0.00049740594,0.0018813844,0.0019981929,0.0012086043,0.008227915],"category_scores_gemma":[0.12218235,0.000751272,0.0016948283,0.002535772,0.0004861557,0.0018035673,0.00287653,0.0024893377,0.0017834379],"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":[0.001201633,0.00095481257,0.21081054,0.00088725943,0.0011634299,0.0012027394,0.0005786869,0.10356555,0.0020643256,0.09421624,0.024564594,0.5587901],"study_design_scores_gemma":[0.00014757899,0.00022751722,0.011066452,0.0001612351,0.00023235819,0.00075518986,0.00021196927,0.8911667,0.0009118854,0.091493174,0.0035650213,0.000060957496],"about_ca_topic_score_codex":0.0022014587,"about_ca_topic_score_gemma":0.0017952834,"teacher_disagreement_score":0.014348227,"about_ca_system_score_codex":0.000498022,"about_ca_system_score_gemma":0.001830318,"threshold_uncertainty_score":0.07588154},"labels":[],"label_agreement":null},{"id":"W2010800461","doi":"10.1016/s0167-6687(02)00104-x","title":"A critique of fractional age assumptions","year":2002,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":27,"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":"Mathematics; Integer (computer science); Classification of discontinuities; Function (biology); Survival function; Range (aeronautics); Probability density function; Constant (computer programming); Applied mathematics; Statistics; Interpolation (computer graphics); Mathematical analysis; Survival analysis; Computer science","score_opus":0.038027321007682746,"score_gpt":0.28719452954424435,"score_spread":0.2491672085365616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010800461","genre_codex":"commentary","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":[0.043571964,0.011194976,0.1977938,0.4774795,0.0049672024,0.000064607695,0.0010821627,0.00028115712,0.26356465],"genre_scores_gemma":[0.84016,0.008919563,0.039625548,0.048153736,0.015910238,0.0002546473,0.00027520498,0.0002859221,0.04641512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962042,0.0012797425,0.00022722971,0.000649587,0.0013138291,0.0003255241],"domain_scores_gemma":[0.9730241,0.019423684,0.0015923384,0.0026704876,0.0024890923,0.0008003025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01177327,0.0005401362,0.0012387685,0.0019097396,0.0020555826,0.0029016314,0.002823762,0.00498238,0.01078818],"category_scores_gemma":[0.038991693,0.00042330672,0.0011211284,0.0013788155,0.013117752,0.00986222,0.0022324275,0.009074766,0.0020235714],"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":[0.0000093160115,0.000005357981,0.00019168566,0.00001116071,0.0000034177067,0.000013971298,0.00015102075,0.0001818922,0.000011171528,0.99233866,0.0046111313,0.00247121],"study_design_scores_gemma":[0.000011386843,0.0000039279957,0.00019520297,0.000023407461,0.00000381138,0.000048438244,0.000070567556,0.0006650358,0.00003036862,0.9828294,0.016112812,0.000005638251],"about_ca_topic_score_codex":0.005201183,"about_ca_topic_score_gemma":0.002380436,"teacher_disagreement_score":0.01177327,"about_ca_system_score_codex":0.002984622,"about_ca_system_score_gemma":0.0019482586,"threshold_uncertainty_score":0.062263787},"labels":[],"label_agreement":null},{"id":"W2011582768","doi":"10.1016/j.insmatheco.2013.03.013","title":"Actuarial applications of the linear hazard transform in mortality immunization","year":2013,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Hazard; Immunization; Hazard ratio; Statistics; Medicine; Mathematics; Immunology; Biology","score_opus":0.016332033086553017,"score_gpt":0.26231361089987854,"score_spread":0.24598157781332552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011582768","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":[0.031087691,0.0011475137,0.9540422,0.002482062,0.00026124265,0.00002094773,0.000113709655,0.00021605358,0.010628497],"genre_scores_gemma":[0.89031845,0.0022459084,0.081538826,0.00044091,0.0010471537,0.000076087475,0.00018493799,0.00016359553,0.023984112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990416,0.0005711368,0.00003441658,0.000084568135,0.00018496499,0.00008323995],"domain_scores_gemma":[0.9913051,0.0070460197,0.0004592906,0.00044457166,0.0005391351,0.0002059394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042178566,0.00043947974,0.00048741855,0.0013951296,0.00030871958,0.0010845255,0.0008350278,0.0008505385,0.0074037705],"category_scores_gemma":[0.021446276,0.00029401516,0.0005592278,0.0010332105,0.0013387685,0.0016959348,0.0013583661,0.0018123041,0.0004962651],"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":[0.000051440213,0.000099563615,0.0029624365,0.00005392102,0.000032523327,0.00014021558,0.00020558297,0.15409397,0.00062573014,0.7804234,0.0034660562,0.057845227],"study_design_scores_gemma":[0.000014208569,0.000029291652,0.00087136205,0.000019071824,0.000011794865,0.00010159294,0.00004084389,0.6673681,0.00042292624,0.32874343,0.0023612238,0.000016097776],"about_ca_topic_score_codex":0.0026472218,"about_ca_topic_score_gemma":0.001536724,"teacher_disagreement_score":0.0074037705,"about_ca_system_score_codex":0.0010466629,"about_ca_system_score_gemma":0.000992329,"threshold_uncertainty_score":0.024768054},"labels":[],"label_agreement":null},{"id":"W2013280852","doi":"10.3917/popu.1204.0683","title":"The Most Frequent Adult Length of Life in the Eighteenth Century: The Experience of the French-Canadians","year":2013,"lang":"fr","type":"article","venue":"Population (English Edition)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Humanities; Art; Political science","score_opus":0.010981637856652775,"score_gpt":0.23951269258814986,"score_spread":0.2285310547314971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013280852","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":[0.98693013,0.0033874996,0.000061734,0.0013087721,0.000047911053,0.000011814567,0.000734934,0.000004205148,0.007513063],"genre_scores_gemma":[0.9905475,0.0026366662,0.00010773053,0.00026890248,0.000016622173,0.000011164687,0.00030563687,0.000005200982,0.00610049],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9989882,0.00013302821,0.000036301986,0.000119520344,0.00026291455,0.00045995778],"domain_scores_gemma":[0.99783105,0.00021167475,0.00027049202,0.000038118484,0.0010866452,0.0005620509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014428783,0.00028826573,0.00046780202,0.0014229307,0.009022586,0.0025910547,0.0009027257,0.0007015263,0.0035409117],"category_scores_gemma":[0.0030363235,0.00023025608,0.0003981127,0.0027813073,0.0026300862,0.00093023194,0.0012059027,0.0009752686,0.00030598656],"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":[0.00014679645,0.00003979068,0.30788073,0.0002628949,0.000051314037,0.0007509118,0.6485553,0.000112040834,0.0005453999,0.002546753,0.0049209828,0.0341871],"study_design_scores_gemma":[0.0000055580786,0.00007529743,0.5143848,0.00032554913,0.00003123956,0.00050782337,0.41566882,0.000069837195,0.00012374672,0.00010119031,0.06863622,0.00006990843],"about_ca_topic_score_codex":0.98992515,"about_ca_topic_score_gemma":0.9945259,"teacher_disagreement_score":0.019802913,"about_ca_system_score_codex":0.019802913,"about_ca_system_score_gemma":0.022795714,"threshold_uncertainty_score":0.14368087},"labels":[],"label_agreement":null},{"id":"W2017034056","doi":"10.1080/10920277.2013.779917","title":"A Digital Picture of the Actuarial Research Community","year":2013,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Productivity; Quality (philosophy); Actuarial science; Field (mathematics); Psychology; Business; Economics; Economic growth","score_opus":0.04361134412412927,"score_gpt":0.3444788392854311,"score_spread":0.30086749516130185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017034056","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":[0.023989296,0.054802664,0.012165887,0.08453159,0.0052187834,0.00016797155,0.029911445,0.0027648096,0.7864475],"genre_scores_gemma":[0.27666175,0.1477429,0.041595556,0.02452682,0.012446617,0.0006668567,0.030441973,0.0023821436,0.4635353],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99843067,0.00033663944,0.00014082193,0.00023231345,0.0006935579,0.00016596219],"domain_scores_gemma":[0.9912537,0.002752839,0.0013869832,0.0011255105,0.001709856,0.0017710956],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002009049,0.00041765664,0.0003838882,0.015115231,0.0020168521,0.011638252,0.00071305863,0.001545955,0.07439455],"category_scores_gemma":[0.007988893,0.0003672764,0.00032836068,0.031120962,0.0017223074,0.01725744,0.00423298,0.0023902045,0.023991423],"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":[0.000116951844,0.000047014582,0.007729927,0.00060551474,0.000024121306,0.00031765652,0.003527693,0.00040342825,0.0013634441,0.11423773,0.4947451,0.37688148],"study_design_scores_gemma":[0.0000045060765,0.000008578075,0.0043418286,0.0001599945,0.0000031926277,0.00027397284,0.0011059927,0.000120023535,0.00007892006,0.005874822,0.988013,0.000015248993],"about_ca_topic_score_codex":0.002652888,"about_ca_topic_score_gemma":0.0037876589,"teacher_disagreement_score":0.98488474,"about_ca_system_score_codex":0.0012348056,"about_ca_system_score_gemma":0.0019036036,"threshold_uncertainty_score":0.2488746},"labels":[],"label_agreement":null},{"id":"W2017321829","doi":"10.1016/j.jtbi.2006.11.011","title":"Modeling human mortality using mixtures of bathtub shaped failure distributions","year":2006,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":77,"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":"Massey University","keywords":"Weibull distribution; Statistics; Gompertz function; Mathematics; Hazard; Life expectancy; Econometrics; Mortality rate; Reliability (semiconductor); Failure rate; Accelerated failure time model; Survival analysis; Demography; Biology; Medicine; Population; Ecology; Internal medicine","score_opus":0.02686604719784424,"score_gpt":0.3504633948582426,"score_spread":0.3235973476603984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017321829","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":[0.59702814,0.0006309743,0.3984874,0.0011729704,0.00014173135,0.0000765829,0.00048193158,0.0003245377,0.0016556702],"genre_scores_gemma":[0.9798869,0.00033070942,0.014095514,0.00010423243,0.000081277925,0.000110956746,0.00026631178,0.00005153044,0.005072546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882144,0.00063965993,0.00005063933,0.00022202589,0.00007906346,0.00018720079],"domain_scores_gemma":[0.98648876,0.010983917,0.00092414004,0.0004178988,0.0005642226,0.00062107324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00617605,0.0010943864,0.0019791326,0.0017962105,0.0008629419,0.0022124345,0.002804936,0.0035646202,0.0028627247],"category_scores_gemma":[0.01811865,0.0018975936,0.0019793701,0.0013553784,0.0021993364,0.003398894,0.0022896565,0.0025611133,0.00046388683],"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":[0.00015564884,0.00005374122,0.0059939176,0.000018874194,0.0000923473,0.00008428075,0.0001311375,0.9764654,0.00016891753,0.013612351,0.00032908563,0.0028942763],"study_design_scores_gemma":[0.000015780653,0.000017641718,0.0005925525,0.0000052304613,0.000014969761,0.000012522754,0.000020729969,0.99335575,0.000034697234,0.0058595915,0.000060229035,0.000010451764],"about_ca_topic_score_codex":0.016134681,"about_ca_topic_score_gemma":0.011856159,"teacher_disagreement_score":0.016134681,"about_ca_system_score_codex":0.0013713888,"about_ca_system_score_gemma":0.0008696976,"threshold_uncertainty_score":0.03266251},"labels":[],"label_agreement":null},{"id":"W2017859105","doi":"10.1016/j.insmatheco.2015.03.021","title":"A step-by-step guide to building two-population stochastic mortality models","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":55,"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 Manitoba; Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Population; Longevity risk; Population model; Econometrics; Divergence (linguistics); Bayes' theorem; Computer science; Process (computing); Longevity; Statistics; Mathematics; Bayesian probability; Artificial intelligence; Demography; Biology","score_opus":0.0460067272767507,"score_gpt":0.3264493624032728,"score_spread":0.2804426351265221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017859105","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":[0.00053946645,0.0007909953,0.9846534,0.0004192152,0.00018134221,0.0003900694,0.0028309347,0.0032395257,0.006954979],"genre_scores_gemma":[0.0040879813,0.0009955908,0.9840149,0.00032639477,0.00008094616,0.00091998925,0.0022201405,0.0009801966,0.006373841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945456,0.00024000484,0.000095369505,0.0000619508,0.00012345334,0.000024728135],"domain_scores_gemma":[0.9971609,0.0017657734,0.00008799845,0.0002773943,0.0005961094,0.000111864094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019596908,0.0019985142,0.001571683,0.0020399962,0.0010526131,0.002041316,0.005034078,0.0015263457,0.06685049],"category_scores_gemma":[0.009784033,0.0013152644,0.0021373965,0.0019255219,0.00072123314,0.0017801676,0.0022683148,0.004762282,0.035359137],"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":[0.00009293412,0.00049395626,0.0021813398,0.0018553546,0.00031235657,0.0014125487,0.0005960148,0.12781736,0.0053209225,0.37684175,0.20008104,0.28299442],"study_design_scores_gemma":[0.000092208895,0.000083119696,0.0007216982,0.0003040457,0.00008810902,0.000750984,0.00022207187,0.24734674,0.0022399372,0.40394348,0.34406355,0.00014404976],"about_ca_topic_score_codex":0.01232732,"about_ca_topic_score_gemma":0.022967808,"teacher_disagreement_score":0.06685049,"about_ca_system_score_codex":0.0008839349,"about_ca_system_score_gemma":0.0022985623,"threshold_uncertainty_score":0.22363716},"labels":[],"label_agreement":null},{"id":"W2019544745","doi":"10.1097/01.ede.0000158800.01170.36","title":"A Conversation With Lester Breslow","year":2005,"lang":"en","type":"article","venue":"Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","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":"National Cancer Institute","keywords":"Conversation; Psychology; Communication","score_opus":0.05341110214615076,"score_gpt":0.3631308260292551,"score_spread":0.30971972388310437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019544745","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":[0.0005420159,0.02122663,0.0006959263,0.89530396,0.049864072,0.000019829597,0.00011706354,0.000188108,0.03204241],"genre_scores_gemma":[0.012218997,0.016162585,0.0011719697,0.74277973,0.019569594,0.00008421853,0.00013131918,0.00045931427,0.20742226],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953755,0.0019679987,0.00019779474,0.0006626261,0.0012901865,0.00050580624],"domain_scores_gemma":[0.99123764,0.0029813056,0.00039895737,0.00033289916,0.0023510747,0.0026981798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053765597,0.0010935366,0.00087217847,0.0014357597,0.005974797,0.007859588,0.0015637737,0.007829475,0.05569345],"category_scores_gemma":[0.028775422,0.0006858396,0.00061321614,0.0012398467,0.00309651,0.010611472,0.004792426,0.016369363,0.028968489],"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":[0.000011249811,0.0000087954,0.000051991814,0.000026517266,0.0000018734492,0.00008101007,0.00062799297,0.000006043186,0.000050778854,0.002511209,0.9900794,0.0065430696],"study_design_scores_gemma":[0.000002739554,0.000006900466,0.000059050828,0.00010606851,0.000001305062,0.00016073936,0.00085039483,0.000010888224,0.0000435519,0.00072065904,0.99802893,0.000008708294],"about_ca_topic_score_codex":0.0059948773,"about_ca_topic_score_gemma":0.0077811736,"teacher_disagreement_score":0.05569345,"about_ca_system_score_codex":0.0031955452,"about_ca_system_score_gemma":0.004379591,"threshold_uncertainty_score":0.18631315},"labels":[],"label_agreement":null},{"id":"W2019742604","doi":"10.1287/inte.30.1.96.11617","title":"An Asset and Liability Management System for Towers Perrin-Tillinghast","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":95,"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 General-Tower (Canada)","funders":"","keywords":"Liability; Asset (computer security); Pension; Actuarial science; Business; Plan (archaeology); Investment (military); Asset management; Generator (circuit theory); Finance; Risk management; Risk analysis (engineering); Computer science; Power (physics); Computer security","score_opus":0.015810974103581617,"score_gpt":0.296733585176161,"score_spread":0.2809226110725794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019742604","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":[0.08327718,0.00072235946,0.6287456,0.0018595273,0.00025574438,0.0009518391,0.008246257,0.19129762,0.08464391],"genre_scores_gemma":[0.5690908,0.0008413575,0.3118722,0.0004474004,0.0002517398,0.0008511579,0.017363777,0.0059132376,0.093368374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919695,0.00016950144,0.000064516564,0.00020406679,0.0003151823,0.00004975536],"domain_scores_gemma":[0.99818426,0.0005725912,0.00018438583,0.00039097332,0.00044721985,0.00022056818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016918167,0.00047476127,0.00038515948,0.0012239092,0.0005200439,0.0015673267,0.0009458989,0.0005714619,0.04265499],"category_scores_gemma":[0.004144773,0.00044290678,0.00032269914,0.00074648374,0.00021587718,0.0019015165,0.0012325458,0.00072790886,0.009523398],"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":[0.0009975524,0.00047272412,0.012681384,0.00023513305,0.00008733957,0.0007928562,0.0007101541,0.078751564,0.014847381,0.029959613,0.22186644,0.6385978],"study_design_scores_gemma":[0.00024992842,0.00024484275,0.0056416695,0.00009332207,0.00006498599,0.00056488405,0.00011137663,0.66255724,0.011963152,0.01115565,0.307228,0.00012498346],"about_ca_topic_score_codex":0.004324331,"about_ca_topic_score_gemma":0.0033131863,"teacher_disagreement_score":0.04265499,"about_ca_system_score_codex":0.0010655827,"about_ca_system_score_gemma":0.0018100231,"threshold_uncertainty_score":0.14269519},"labels":[],"label_agreement":null},{"id":"W2021255504","doi":"10.1515/1557-4679.1419","title":"Testing the assumptions for the analysis of survival data arising from a prevalent cohort study with follow-up","year":2012,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"Medical Research Council; Health Canada; Pfizer Canada; University of Ottawa; Pfizer","keywords":"Censoring (clinical trials); Mathematics; Statistics; Truncation (statistics); Counting process; Event (particle physics); Survival analysis; Survival function; Econometrics; Independence (probability theory); Kaplan–Meier estimator","score_opus":0.11640586232519527,"score_gpt":0.3890585567505665,"score_spread":0.2726526944253712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021255504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07654383,0.0003005236,0.91696686,0.0013955774,0.00009131548,0.0013212048,0.0008084697,0.00014699652,0.0024251707],"genre_scores_gemma":[0.61625236,0.00060005806,0.36819318,0.0013662436,0.00028808953,0.008532129,0.0024980053,0.000097769014,0.0021721527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85309625,0.1084332,0.0077608936,0.0118636405,0.016324436,0.0025216222],"domain_scores_gemma":[0.23420024,0.71499354,0.0173418,0.028697625,0.0038351147,0.0009317098],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2432146,0.001400603,0.0024757737,0.0020591014,0.0016100523,0.0031304162,0.0076685343,0.0053350916,0.005362861],"category_scores_gemma":[0.55555314,0.0012285461,0.0045793275,0.002273003,0.010312621,0.0075558424,0.006381726,0.005625619,0.00091418024],"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":[0.0047369935,0.00089750096,0.18519206,0.0017641194,0.0025184161,0.0063439417,0.0057164608,0.11646518,0.0053474098,0.58055586,0.0027041924,0.087757915],"study_design_scores_gemma":[0.0009993325,0.0028664838,0.040211298,0.00065115246,0.000461414,0.002041833,0.0013735712,0.50594646,0.0057731206,0.43398747,0.0055362,0.0001516675],"about_ca_topic_score_codex":0.0016773177,"about_ca_topic_score_gemma":0.0008676974,"teacher_disagreement_score":0.7567854,"about_ca_system_score_codex":0.0016087673,"about_ca_system_score_gemma":0.0036459956,"threshold_uncertainty_score":0.93325114},"labels":[],"label_agreement":null},{"id":"W2025635898","doi":"10.1002/cjs.5540330308","title":"A copula-graphic estimator for the conditional survival function under dependent censoring","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":83,"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":"Mathematics; Copula (linguistics); Estimator; Statistics; Humanities; Econometrics; Philosophy","score_opus":0.038111437566028444,"score_gpt":0.29289331608484803,"score_spread":0.2547818785188196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025635898","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":[0.0038833898,0.00031151908,0.99363273,0.00019960258,0.000048710124,0.000051559302,0.00013349115,0.00020151987,0.0015374492],"genre_scores_gemma":[0.32062525,0.0012864725,0.6704828,0.0007511772,0.00024194994,0.00032469968,0.0014598749,0.00025214345,0.004575671],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99792695,0.0012362496,0.000052989737,0.00029696597,0.00037677385,0.00011015094],"domain_scores_gemma":[0.9925331,0.004623866,0.0006464626,0.0010484577,0.000964506,0.00018356783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058593606,0.00064143416,0.00091907807,0.0028779674,0.0004742445,0.0014280303,0.0015578304,0.0018223373,0.0029124082],"category_scores_gemma":[0.038041268,0.0004928546,0.0012117104,0.0023794104,0.0011832691,0.0016069509,0.0013650209,0.0022037495,0.0011547206],"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":[0.00008571683,0.00011900512,0.01634909,0.00036828802,0.00041986012,0.00033606417,0.00038241042,0.10218192,0.0034428036,0.5952883,0.018931007,0.26209548],"study_design_scores_gemma":[0.00008317991,0.0001571027,0.019060628,0.0002453031,0.00018301477,0.0010457304,0.00014582541,0.6006466,0.0022608521,0.35562414,0.02036486,0.00018276795],"about_ca_topic_score_codex":0.003020273,"about_ca_topic_score_gemma":0.0033761987,"teacher_disagreement_score":0.0058593606,"about_ca_system_score_codex":0.0010143168,"about_ca_system_score_gemma":0.0016216347,"threshold_uncertainty_score":0.03098762},"labels":[],"label_agreement":null},{"id":"W2025730037","doi":"10.1016/j.insmatheco.2005.08.007","title":"The impact of the determinants of mortality on life insurance and annuities","year":2005,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Longevity risk; Annuity; Actuarial science; Life insurance; Life annuity; Population; Business; Risk analysis (engineering); Longevity; Economics; Medicine; Environmental health; Gerontology; Finance; Pension","score_opus":0.027407647940484177,"score_gpt":0.30675602513792766,"score_spread":0.2793483771974435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025730037","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":[0.9703499,0.0032418426,0.0029655842,0.010732745,0.0000551451,0.000014041101,0.0005472201,0.000026483849,0.012066976],"genre_scores_gemma":[0.9971908,0.0008873156,0.00015077431,0.0000823221,0.00010634047,0.0000028904324,0.00009006074,0.000004600592,0.0014848075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885464,0.0006404607,0.000030195559,0.00007077435,0.00013844966,0.00026534955],"domain_scores_gemma":[0.974071,0.019418545,0.0024795483,0.0007864042,0.0010972236,0.0021472285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022380096,0.00027276456,0.00048384533,0.0012886254,0.00042631355,0.0019432026,0.0005193296,0.00090840703,0.009179311],"category_scores_gemma":[0.019840382,0.00022410974,0.00072285946,0.001188146,0.0015079337,0.0015795451,0.0010987034,0.0015863035,0.0005344134],"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":[0.00053740223,0.00040295985,0.87262875,0.00008043732,0.00042944623,0.00064980704,0.000579478,0.013752275,0.00069491216,0.082265995,0.002334161,0.025644338],"study_design_scores_gemma":[0.000027078286,0.000109932,0.9014536,0.000034625533,0.00024253002,0.00029923767,0.00068794074,0.02394355,0.00025427158,0.07084781,0.0020743357,0.000025095753],"about_ca_topic_score_codex":0.015019742,"about_ca_topic_score_gemma":0.010792455,"teacher_disagreement_score":0.015019742,"about_ca_system_score_codex":0.0009049088,"about_ca_system_score_gemma":0.0011843144,"threshold_uncertainty_score":0.030707896},"labels":[],"label_agreement":null},{"id":"W2025996078","doi":"10.1155/2010/726389","title":"Pricing Equity‐Indexed Annuities under Stochastic Interest Rates Using Copulas","year":2010,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Society of Actuaries","keywords":"Martingale (probability theory); Interest rate; Econometrics; Equity (law); Copula (linguistics); Actuarial science; Martingale pricing; Economics; Mathematics; Local martingale; Statistics; Finance","score_opus":0.09613789199123993,"score_gpt":0.3906623734816069,"score_spread":0.2945244814903669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025996078","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":[0.019444939,0.00016473963,0.9783829,0.00013136982,0.000021676775,0.000036371734,0.000033166776,0.00005172942,0.0017331665],"genre_scores_gemma":[0.71214986,0.00091882364,0.28278488,0.0001312829,0.00016101093,0.00021218286,0.00017328003,0.00017483339,0.0032938186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880207,0.0005364728,0.00006696442,0.000099432174,0.00039145903,0.000103628234],"domain_scores_gemma":[0.9958941,0.0025712736,0.0004967564,0.00028018467,0.00059186824,0.00016587098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049386695,0.0009454668,0.001075491,0.00097126793,0.00036476564,0.00205829,0.0014851431,0.0010619097,0.0021579457],"category_scores_gemma":[0.013109459,0.00070048944,0.0014150896,0.0008737672,0.0010342421,0.0025708654,0.0011169736,0.001436051,0.0002597948],"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":[0.000012965577,0.00003517083,0.0008073776,0.000034258992,0.00006189951,0.00013277524,0.00006154181,0.80872446,0.0010165416,0.18127157,0.00041055251,0.007430847],"study_design_scores_gemma":[0.0000024612061,0.000007833589,0.00010260872,0.00000494876,0.000005788164,0.000012388328,0.0000051277407,0.98080784,0.0001769943,0.018731892,0.00013666577,0.0000054601906],"about_ca_topic_score_codex":0.0023646296,"about_ca_topic_score_gemma":0.0017706183,"teacher_disagreement_score":0.0049386695,"about_ca_system_score_codex":0.0012013693,"about_ca_system_score_gemma":0.0014247024,"threshold_uncertainty_score":0.026118517},"labels":[],"label_agreement":null},{"id":"W2029306928","doi":"10.1016/j.juro.2011.12.121","title":"Re: Improved Prediction of Long-Term, Other Cause Mortality in Men With Prostate Cancer","year":2012,"lang":"en","type":"letter","venue":"The Journal of Urology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Medicine; Prostate cancer; Memoir; Gerontology; Cancer; Internal medicine; Art history; History","score_opus":0.02778221872631676,"score_gpt":0.3058331343624445,"score_spread":0.27805091563612777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029306928","genre_codex":"empirical","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":[0.323311,0.119723395,0.1497179,0.09697369,0.1377656,0.0024568771,0.08536667,0.007106492,0.077578396],"genre_scores_gemma":[0.61043066,0.033570662,0.15797485,0.011888573,0.05815081,0.0013278394,0.027734688,0.0013363479,0.09758558],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968676,0.0010923662,0.00024390098,0.0003534141,0.0013178863,0.00012485526],"domain_scores_gemma":[0.9906645,0.0031584485,0.0009442295,0.0006472683,0.00419973,0.00038590378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006242058,0.0011534496,0.0009582224,0.0035188142,0.00038227276,0.0019131828,0.0013943353,0.0011592673,0.012253533],"category_scores_gemma":[0.022807939,0.00029223974,0.0010466076,0.0016053842,0.00031388504,0.0014271615,0.00069129316,0.0013270855,0.006437331],"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":[0.001086966,0.00023803014,0.18522796,0.00089464354,0.00039916416,0.00015161835,0.0001282099,0.0013756435,0.0010838539,0.0008537042,0.5003937,0.3081666],"study_design_scores_gemma":[0.0007820155,0.0019774458,0.6642621,0.001785237,0.0016038956,0.005383879,0.0007797821,0.052349404,0.012001156,0.0077427225,0.25072807,0.0006042864],"about_ca_topic_score_codex":0.0042564357,"about_ca_topic_score_gemma":0.011199276,"teacher_disagreement_score":0.012253533,"about_ca_system_score_codex":0.0005984588,"about_ca_system_score_gemma":0.0007262345,"threshold_uncertainty_score":0.04099214},"labels":[],"label_agreement":null},{"id":"W2030651687","doi":"10.1016/j.insmatheco.2005.06.002","title":"Hedging guarantees in variable annuities under both equity and interest rate risks","year":2006,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Equity (law); Interest rate; Actuarial science; Economics; Jump; Embedded option; Variable (mathematics); Econometrics; Interest rate risk; Risk management; Monetary economics; Finance; Mathematics","score_opus":0.061309664645827176,"score_gpt":0.3131846313344105,"score_spread":0.25187496668858333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030651687","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":[0.7256528,0.0025481503,0.26084974,0.0023126947,0.00016959057,0.00002945584,0.00011781674,0.00014425699,0.008175572],"genre_scores_gemma":[0.9901766,0.00061401795,0.0053063342,0.000050387436,0.00008417924,0.000012466034,0.0000504741,0.000021439322,0.0036840339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871254,0.0004867885,0.00009817221,0.00019241874,0.0002798392,0.00023022656],"domain_scores_gemma":[0.9890435,0.006424216,0.0020078942,0.00084900076,0.0007666455,0.00090879115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061282385,0.0007313782,0.0014738974,0.00090553064,0.000649186,0.0032741886,0.0016479384,0.0021200841,0.0025181512],"category_scores_gemma":[0.02360537,0.00070902955,0.00095646887,0.0009536357,0.0028715641,0.0047200937,0.0021053224,0.0028224492,0.0001598601],"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":[0.00033584604,0.00008685364,0.0043324977,0.00009334205,0.000079944446,0.0004609336,0.0004353881,0.218168,0.0018560531,0.7561133,0.0010101666,0.017027598],"study_design_scores_gemma":[0.000047208596,0.00009749039,0.0020950758,0.000034218232,0.00004240995,0.00012697717,0.00012154207,0.4994275,0.00043567835,0.49681923,0.0007237009,0.000028961867],"about_ca_topic_score_codex":0.0022346452,"about_ca_topic_score_gemma":0.0012810646,"teacher_disagreement_score":0.0061282385,"about_ca_system_score_codex":0.0017372619,"about_ca_system_score_gemma":0.0010542781,"threshold_uncertainty_score":0.03240961},"labels":[],"label_agreement":null},{"id":"W2030816376","doi":"10.1016/j.jmva.2015.02.011","title":"A bivariate Gompertz–Makeham life distribution","year":2015,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of British Columbia","funders":"","keywords":"Mathematics; Gompertz function; Bivariate analysis; Applied mathematics; Context (archaeology); Distribution (mathematics); Limiting; Exponential function; Mathematical analysis; Statistics; Geography","score_opus":0.044647750517099793,"score_gpt":0.3409539441279859,"score_spread":0.2963061936108861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030816376","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":[0.35375378,0.002627953,0.60647815,0.0032625156,0.0002965599,0.00044155994,0.0021024845,0.0006709897,0.030366046],"genre_scores_gemma":[0.9600502,0.0013434457,0.02110099,0.0002766644,0.00023375104,0.0002706852,0.0012243392,0.0000913671,0.015408595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983302,0.0008612969,0.000064912405,0.0003111864,0.00020981776,0.00022264846],"domain_scores_gemma":[0.9908765,0.0058213626,0.0006246599,0.0012920401,0.0008639429,0.0005215233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00535404,0.000560313,0.00096700306,0.002728773,0.0008379798,0.0019236698,0.001343543,0.0012630979,0.017600011],"category_scores_gemma":[0.027209068,0.00031527216,0.0012075916,0.0020381922,0.0017851518,0.002357021,0.0017542327,0.0020490014,0.0024722998],"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":[0.00045935818,0.00026147816,0.059492495,0.0002405452,0.0003489863,0.0021120943,0.0031022562,0.034211982,0.0024782948,0.7419367,0.01617689,0.13917893],"study_design_scores_gemma":[0.00008300726,0.000625093,0.08751207,0.00027848384,0.00033875956,0.006303921,0.0036765253,0.378411,0.0018080707,0.47974083,0.040972218,0.0002500197],"about_ca_topic_score_codex":0.003042042,"about_ca_topic_score_gemma":0.0014195775,"teacher_disagreement_score":0.017600011,"about_ca_system_score_codex":0.0007442112,"about_ca_system_score_gemma":0.0006175484,"threshold_uncertainty_score":0.058877885},"labels":[],"label_agreement":null},{"id":"W2031037052","doi":"10.1002/rnc.1727","title":"An HMM approach for optimal investment of an insurer","year":2011,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Calgary","funders":"Australian Research Council","keywords":"Hamilton–Jacobi–Bellman equation; Hidden Markov model; Dynamic programming; Mathematical optimization; Stochastic control; Markov chain; Computer science; Optimal control; Investment (military); Bayesian probability; Mathematics; Artificial intelligence; Machine learning","score_opus":0.03824778654092933,"score_gpt":0.30764960784201106,"score_spread":0.26940182130108176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031037052","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":[0.019509628,0.00021464625,0.9749985,0.000758573,0.000038904625,0.000019122073,0.00011339684,0.00012425201,0.0042229914],"genre_scores_gemma":[0.8893965,0.0004545376,0.097072445,0.00018037546,0.00008501552,0.00015980532,0.00025649287,0.00008039365,0.012314371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930525,0.00025530832,0.00003445446,0.00017878089,0.00014142948,0.00008483386],"domain_scores_gemma":[0.99869615,0.000902865,0.00012658274,0.00006358337,0.00014611635,0.000064811975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014901614,0.0004278335,0.0010419869,0.00052278565,0.00041421226,0.0011172977,0.0011285825,0.0014794039,0.005684441],"category_scores_gemma":[0.0039984337,0.0006996685,0.0009791543,0.00039597304,0.0008565097,0.0013634993,0.0010013011,0.001567499,0.0004737836],"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":[0.000035949568,0.000021385002,0.0006111654,0.00003433513,0.00003447925,0.00010237183,0.000076918746,0.8582553,0.000913773,0.12935814,0.000858082,0.0096981125],"study_design_scores_gemma":[0.0000026988428,0.000005714622,0.00010448292,0.0000047371045,0.0000049577766,0.000005826633,0.0000044099584,0.9834818,0.0000858913,0.016076017,0.00021836229,0.0000051366333],"about_ca_topic_score_codex":0.012653852,"about_ca_topic_score_gemma":0.0071378974,"teacher_disagreement_score":0.012653852,"about_ca_system_score_codex":0.0018883016,"about_ca_system_score_gemma":0.0017055145,"threshold_uncertainty_score":0.025160432},"labels":[],"label_agreement":null},{"id":"W2031428813","doi":"10.1016/j.spl.2015.04.006","title":"On mixed <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>δ</mml:mi></mml:math>-shock models","year":2015,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Shock (circulatory); Mathematics; Scroll; Function (biology); Medicine; Theology; Philosophy","score_opus":0.02918441990647005,"score_gpt":0.2701730129454183,"score_spread":0.24098859303894823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031428813","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":[0.0020131266,0.00040760273,0.5498009,0.0036260511,0.0010325433,0.00028655073,0.16266346,0.08185044,0.1983194],"genre_scores_gemma":[0.0468527,0.0012500422,0.3684971,0.0030696024,0.00074561284,0.001608175,0.19676861,0.08017551,0.30103263],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989862,0.00035122075,0.00008369853,0.00014308693,0.00034362014,0.00009221196],"domain_scores_gemma":[0.9941327,0.0035684365,0.00017187753,0.0009833983,0.000969015,0.00017461451],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022224993,0.0014960172,0.0011091237,0.0017714865,0.00080406823,0.003896671,0.0038995058,0.001876834,0.54343754],"category_scores_gemma":[0.016587414,0.0011333978,0.002193741,0.0024515467,0.0003960878,0.004072566,0.0027115843,0.0026762597,0.26623598],"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":[0.00016202826,0.00011500465,0.00080678286,0.00033465502,0.00006243864,0.000139708,0.00014218497,0.012334011,0.0006167617,0.11849419,0.7948429,0.071949296],"study_design_scores_gemma":[0.00016397439,0.000034123204,0.000916702,0.00027155268,0.000037554502,0.00013711095,0.00010110169,0.07891352,0.0030812407,0.15436757,0.7618904,0.00008514719],"about_ca_topic_score_codex":0.022383742,"about_ca_topic_score_gemma":0.030824764,"teacher_disagreement_score":0.54343754,"about_ca_system_score_codex":0.0014839676,"about_ca_system_score_gemma":0.0019669544,"threshold_uncertainty_score":0.65123093},"labels":[],"label_agreement":null},{"id":"W2031728167","doi":"10.1080/10920277.2011.10597621","title":"Mortality Regimes and Pricing","year":2011,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Econometrics; Mortality rate; Markov chain; Economics; Population; Computer science; Statistics; Mathematics; Demography","score_opus":0.04208927077341051,"score_gpt":0.29999887690399163,"score_spread":0.2579096061305811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031728167","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":[0.7227067,0.0017633531,0.18064037,0.008147622,0.0003297445,0.00007969288,0.0013302283,0.0004876446,0.08451468],"genre_scores_gemma":[0.99477345,0.00026801,0.0018533219,0.00007065761,0.00008085336,0.000012472296,0.000108106084,0.000015788653,0.0028173323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999323,0.00025933664,0.000038568352,0.0001151222,0.0001517186,0.000112206006],"domain_scores_gemma":[0.9976439,0.0011036476,0.00068038225,0.0002399567,0.00015802584,0.00017408925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012227072,0.00021994037,0.00033378977,0.0007037883,0.00037626125,0.002106913,0.00072419125,0.0013672437,0.008414808],"category_scores_gemma":[0.009735299,0.00020478341,0.00051928876,0.00083026063,0.0010264672,0.0022762008,0.0007590197,0.0012362871,0.0005784715],"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":[0.000098653836,0.000091726186,0.02974062,0.00004726187,0.000069079666,0.0002799456,0.0003150955,0.09743524,0.0014907342,0.8356546,0.005084116,0.029692821],"study_design_scores_gemma":[0.000026177762,0.000052140444,0.016415527,0.00004255498,0.000016952159,0.00021470645,0.00014144764,0.36585206,0.0003911813,0.61100554,0.0058032563,0.00003846447],"about_ca_topic_score_codex":0.001933245,"about_ca_topic_score_gemma":0.0009578317,"teacher_disagreement_score":0.008414808,"about_ca_system_score_codex":0.0011909248,"about_ca_system_score_gemma":0.0004023216,"threshold_uncertainty_score":0.02815032},"labels":[],"label_agreement":null},{"id":"W2033445530","doi":"10.1136/bmj.g7168","title":"Mortality of first world war military personnel: comparison of two military cohorts","year":2014,"lang":"en","type":"article","venue":"BMJ","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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":"Massey University; King's College London; National Institute for Health and Care Research; University of Otago","keywords":"Cohort; Demography; Cause of death; Military personnel; Medicine; World War II; Gerontology; Cohort study; Quarter (Canadian coin); Disease; History; Law; Internal medicine; Political science","score_opus":0.03531003253236022,"score_gpt":0.35266772454206724,"score_spread":0.31735769200970704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033445530","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":[0.9987587,0.0003745296,0.00006652931,0.0000238069,0.0000111748,0.000025607686,0.0005224902,0.0000019670613,0.00021522486],"genre_scores_gemma":[0.9968305,0.00039156587,0.00009356793,0.000025780404,0.000026532662,0.000066148496,0.0023107985,0.0000030887268,0.00025207605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99900585,0.00026248026,0.00008610496,0.00028886434,0.00019459074,0.00016213916],"domain_scores_gemma":[0.9981243,0.00033256118,0.0007247729,0.00016582316,0.00026430193,0.0003883483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026967623,0.0005889629,0.0006169939,0.0017358961,0.0005069091,0.000711254,0.00081249897,0.0007409839,0.0017989622],"category_scores_gemma":[0.00553805,0.00033686866,0.0009140957,0.0006689261,0.00033383115,0.0007594708,0.001171038,0.0005043484,0.00026108552],"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":[0.005235835,0.00016075213,0.98923546,0.000067941306,0.0007628429,0.00009464904,0.00042492972,0.000098395874,0.0006106711,0.000048846814,0.00023756582,0.0030220093],"study_design_scores_gemma":[0.00012101829,0.0012210624,0.99748516,0.000020110383,0.000118491895,0.00012992125,0.00044044608,0.000113420305,0.000076226715,0.000027758497,0.00023789622,0.00000841349],"about_ca_topic_score_codex":0.008212365,"about_ca_topic_score_gemma":0.008590731,"teacher_disagreement_score":0.008212365,"about_ca_system_score_codex":0.0006890081,"about_ca_system_score_gemma":0.0005502113,"threshold_uncertainty_score":0.01632911},"labels":[],"label_agreement":null},{"id":"W2034102447","doi":"10.1016/j.insmatheco.2014.02.006","title":"Stochastic analysis of life insurance surplus","year":2014,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life insurance; Economics; Endowment policy; Portfolio; Econometrics; Endowment; Actuarial science; Lévy process; Economic surplus; Mathematics; Financial economics; Applied mathematics","score_opus":0.017439021262741453,"score_gpt":0.2545116527310223,"score_spread":0.23707263146828084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034102447","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":[0.38633472,0.005479813,0.5535052,0.014931496,0.00044339246,0.00011970724,0.0007462427,0.0003785646,0.038060844],"genre_scores_gemma":[0.9711556,0.0018069596,0.007608176,0.00035760028,0.0005118403,0.00007323593,0.00021308068,0.000097086275,0.01817647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884355,0.000522429,0.00004617334,0.00012686527,0.00023227348,0.00022861919],"domain_scores_gemma":[0.9911556,0.006151956,0.0008364366,0.00034267353,0.0007275075,0.0007858412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004398383,0.00070081,0.0017371668,0.0021946754,0.00080310844,0.0032357066,0.0019248868,0.0021174145,0.006676074],"category_scores_gemma":[0.015460396,0.0008455084,0.001142177,0.0013885874,0.0036139297,0.0042832387,0.0019654639,0.0022365341,0.0003399508],"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":[0.00002836731,0.000047661048,0.0009941987,0.000048397662,0.000043644945,0.00010100906,0.00010897899,0.09004463,0.00054521806,0.90373576,0.0018778208,0.0024243759],"study_design_scores_gemma":[0.000023636565,0.00001551745,0.0010728888,0.000022085387,0.00001839877,0.0000525491,0.00007207621,0.49867502,0.00007707734,0.4992546,0.0006908171,0.000025373041],"about_ca_topic_score_codex":0.009811517,"about_ca_topic_score_gemma":0.00686344,"teacher_disagreement_score":0.009811517,"about_ca_system_score_codex":0.004649091,"about_ca_system_score_gemma":0.002120629,"threshold_uncertainty_score":0.03373164},"labels":[],"label_agreement":null},{"id":"W2034184789","doi":"10.1016/j.jtbi.2010.04.008","title":"A population biological approach to the collective dynamics of countries undergoing demographic transition","year":2010,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demographic transition; Population; Population growth; Fertility; Productivity; Birth rate; Per capita income; Socioeconomic status; Per capita; Human capital; Economics; Total fertility rate; Metapopulation; Population size; Geography; Demographic economics; Demography; Economic growth; Family planning; Sociology; Biological dispersal","score_opus":0.011908074259142911,"score_gpt":0.28452164283599174,"score_spread":0.2726135685768488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034184789","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":[0.40073228,0.003176829,0.4877114,0.035148032,0.00049586646,0.00011772369,0.00032412988,0.000075274715,0.0722185],"genre_scores_gemma":[0.97396064,0.00121431,0.018383458,0.00045124165,0.0003997097,0.000082329076,0.000050333714,0.00001597967,0.0054419157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941874,0.00039664278,0.000016788032,0.000055206187,0.000055523305,0.000057176534],"domain_scores_gemma":[0.99774384,0.0015744371,0.00021142105,0.00012283814,0.00016419918,0.00018312607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019887555,0.00033592372,0.0005034704,0.0015999395,0.001225013,0.0024063622,0.0014360326,0.0014405964,0.006711978],"category_scores_gemma":[0.0055445824,0.00026281126,0.00072502124,0.0011340484,0.0031837756,0.003203683,0.0017918105,0.0017545635,0.00024534235],"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":[0.000010348042,0.000042692132,0.004573733,0.000031579002,0.000050580038,0.00017429599,0.0009969608,0.054181185,0.00019030305,0.93031836,0.0010398522,0.008390097],"study_design_scores_gemma":[0.00002262813,0.000046171685,0.0042421315,0.000028474618,0.00003097911,0.00018112891,0.0013546917,0.14221275,0.000030386354,0.84784365,0.0039883135,0.000018726167],"about_ca_topic_score_codex":0.0062066987,"about_ca_topic_score_gemma":0.004942861,"teacher_disagreement_score":0.006711978,"about_ca_system_score_codex":0.001645531,"about_ca_system_score_gemma":0.001070596,"threshold_uncertainty_score":0.022453845},"labels":[],"label_agreement":null},{"id":"W2034627894","doi":"10.1057/gpp.2011.21","title":"Economic Pricing of Mortality-linked Securities in the Presence of Population Basis Risk","year":2011,"lang":"en","type":"article","venue":"The Geneva Papers on Risk and Insurance Issues and Practice","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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 Waterloo","funders":"Ministry of Education, India; Natural Sciences and Engineering Research Council of Canada; Renmin University of China; Ministry of Earth Sciences","keywords":"Longevity risk; Hedge; Basis risk; Population; Actuarial science; Bond; Economics; Market liquidity; Pension; Business; Financial economics; Finance; Capital asset pricing model; Medicine; Biology; Environmental health","score_opus":0.03483506257510245,"score_gpt":0.31998749008208743,"score_spread":0.285152427506985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034627894","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":[0.72735053,0.00495938,0.20496404,0.014398452,0.0006458034,0.00012964568,0.00060032454,0.0002939755,0.046657894],"genre_scores_gemma":[0.9891928,0.00090130034,0.0029069425,0.00011174503,0.00034524797,0.000022248038,0.00009904641,0.000027428625,0.0063932585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973749,0.0014749292,0.0001249438,0.00020758962,0.0004501292,0.00036748254],"domain_scores_gemma":[0.98514694,0.010831062,0.0014980885,0.00076565496,0.0007555072,0.0010028601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058184066,0.00082501024,0.0020081028,0.0014531445,0.0007180038,0.008044922,0.0020881144,0.004716849,0.0042032795],"category_scores_gemma":[0.032516636,0.001070234,0.0012870615,0.0016224007,0.0032957005,0.008260814,0.0024055522,0.004685535,0.00032295365],"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":[0.0004672507,0.0001595261,0.0047792043,0.000091010246,0.00014596364,0.0011995294,0.00023676403,0.18424296,0.001007468,0.7951998,0.0029143242,0.009556133],"study_design_scores_gemma":[0.0000756619,0.00009522829,0.00258977,0.000032837597,0.000072847695,0.0002800815,0.00014759378,0.6703191,0.0003006583,0.3247359,0.0012881218,0.00006223552],"about_ca_topic_score_codex":0.0030532426,"about_ca_topic_score_gemma":0.002377064,"teacher_disagreement_score":0.008044922,"about_ca_system_score_codex":0.003126453,"about_ca_system_score_gemma":0.0014605118,"threshold_uncertainty_score":0.030771077},"labels":[],"label_agreement":null},{"id":"W2035335487","doi":"10.1007/s00484-002-0144-0","title":"Seasonal programming of adult longevity in Ukraine","year":2002,"lang":"en","type":"article","venue":"International Journal of Biometeorology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Longevity; Demography; Quarter (Canadian coin); Season of birth; Cause of death; Population; Ageing; Mortality rate; Birth rate; Gerontology; Medicine; Fertility; Geography; Disease; Internal medicine","score_opus":0.021953438581151175,"score_gpt":0.3158161824846912,"score_spread":0.29386274390354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035335487","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":[0.99952555,0.00010005019,0.00003965522,0.00002096022,0.0000014372273,6.3939854e-7,0.0001196111,0.0000032051344,0.00018888575],"genre_scores_gemma":[0.99971956,0.000040124825,0.000017374821,0.0000033819713,0.0000012461217,8.2802933e-7,0.00009494661,0.0000010028714,0.0001217014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999299,0.000011675382,0.000005709652,0.000017319642,0.000006242341,0.000029168325],"domain_scores_gemma":[0.999729,0.000055954373,0.00008061211,0.000022758517,0.000054657827,0.000057070814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017813333,0.00006108529,0.0001448393,0.0005478533,0.00019540454,0.00029052098,0.00014691422,0.00013594674,0.00060182327],"category_scores_gemma":[0.00054244214,0.00007148688,0.00014049967,0.0003336272,0.00013464695,0.00012432586,0.00034439404,0.00014297669,0.000068078836],"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":[0.00024198054,0.000042833824,0.9857717,0.00001811859,0.000063851556,0.00022756957,0.00083221047,0.00076181855,0.0026085658,0.00048411876,0.00029726897,0.008649817],"study_design_scores_gemma":[7.7265275e-7,0.000016708595,0.9991504,0.0000026470086,0.0000066275857,0.000055791745,0.00021094376,0.0002293381,0.00006388776,0.000045998593,0.00021563457,0.0000012198967],"about_ca_topic_score_codex":0.019848708,"about_ca_topic_score_gemma":0.019887256,"teacher_disagreement_score":0.019848708,"about_ca_system_score_codex":0.0004130034,"about_ca_system_score_gemma":0.00035651305,"threshold_uncertainty_score":0.03946632},"labels":[],"label_agreement":null},{"id":"W2035640238","doi":"10.1016/j.jkss.2011.03.004","title":"Discussion: Statistical models and methods for dependence in insurance data","year":2011,"lang":"en","type":"article","venue":"Journal of the Korean Statistical Society","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Mathematics; Econometrics; Statistical model; Statistics; Actuarial science; Economics","score_opus":0.11475019026191403,"score_gpt":0.4158700473085132,"score_spread":0.30111985704659916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035640238","genre_codex":"methods","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":[0.00595264,0.004033547,0.578995,0.39576465,0.009204247,0.0003158243,0.0010407292,0.00069475046,0.003998594],"genre_scores_gemma":[0.23824067,0.006047554,0.45378318,0.23142931,0.05187202,0.0029547669,0.0011333439,0.0016997169,0.012839393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.8769443,0.0982958,0.0059091193,0.005443933,0.012248394,0.0011584156],"domain_scores_gemma":[0.35765678,0.5875135,0.005746005,0.023746088,0.023701202,0.0016364102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15610045,0.0014187909,0.0017181204,0.002707604,0.0027235667,0.0063626333,0.006909994,0.008584877,0.007953849],"category_scores_gemma":[0.4510502,0.0009752087,0.005465733,0.0032545326,0.008807212,0.011650474,0.0030464444,0.018203365,0.0013637813],"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":[0.00020325766,0.0001594165,0.011616255,0.0009982457,0.0008638908,0.00062971725,0.001368715,0.012218755,0.0005102186,0.73943084,0.13854027,0.093460284],"study_design_scores_gemma":[0.0001679222,0.00009437791,0.0059786444,0.0011360056,0.0003607391,0.0013828253,0.0010330373,0.07727884,0.00142148,0.81799364,0.092934646,0.00021786029],"about_ca_topic_score_codex":0.0077407775,"about_ca_topic_score_gemma":0.0040095355,"teacher_disagreement_score":0.15610045,"about_ca_system_score_codex":0.004072558,"about_ca_system_score_gemma":0.008910461,"threshold_uncertainty_score":0.82554793},"labels":[],"label_agreement":null},{"id":"W2037092055","doi":"10.3917/popu.702.0315","title":"Familial and Environmental Influences on Longevity in Historical Quebec","year":2007,"lang":"fr","type":"article","venue":"Population (English Edition)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.014596787391643155,"score_gpt":0.26257872375323227,"score_spread":0.2479819363615891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037092055","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":[0.99424803,0.0007001341,0.00027583196,0.00025521213,0.000009056005,0.000007896206,0.001265237,0.000008944954,0.0032298057],"genre_scores_gemma":[0.9981851,0.00022264775,0.00009968625,0.000020451549,0.0000034497546,0.0000053937333,0.0002536483,0.0000024651604,0.0012071606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999772,0.00007189915,0.000009111255,0.000043668966,0.000030947216,0.00007241063],"domain_scores_gemma":[0.9986927,0.00034734828,0.00022382123,0.000102475075,0.00040844126,0.0002252192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067690614,0.0001548047,0.00019737952,0.0008992643,0.0015067023,0.000675081,0.0003432951,0.0002289245,0.0055442643],"category_scores_gemma":[0.0025814148,0.00011365692,0.00020454045,0.0014921856,0.0006531476,0.00024481307,0.0004113582,0.0002640462,0.00016605399],"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":[0.0000960567,0.000022068825,0.98070073,0.000035092868,0.00013175026,0.00022259983,0.0020549898,0.0033355665,0.00038359853,0.0013859149,0.0009800167,0.010651527],"study_design_scores_gemma":[0.0000024354863,0.000013473058,0.9961731,0.000022953507,0.000027795606,0.000023808241,0.0008142604,0.0009412135,0.000039022223,0.00010558855,0.0018305395,0.0000058312057],"about_ca_topic_score_codex":0.9634874,"about_ca_topic_score_gemma":0.9740805,"teacher_disagreement_score":0.036512613,"about_ca_system_score_codex":0.009037545,"about_ca_system_score_gemma":0.0035449788,"threshold_uncertainty_score":0.073455215},"labels":[],"label_agreement":null},{"id":"W2037351910","doi":"10.1007/s12062-009-9012-6","title":"Introducing the Journal of Population Ageing","year":2008,"lang":"en","type":"article","venue":"Journal of Population Ageing","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":60,"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":"Quarter (Canadian coin); Population ageing; Population; Maturity (psychological); Dominance (genetics); Geography; Ageing; Demography; Political science; Sociology; Medicine","score_opus":0.024853593007037825,"score_gpt":0.30298544720118464,"score_spread":0.2781318541941468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037351910","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006610129,0.14278618,0.0024162135,0.2307839,0.51867896,0.00013198551,0.0006098649,0.00045976424,0.103472054],"genre_scores_gemma":[0.01746195,0.20785959,0.003863575,0.12700287,0.44886145,0.00037141555,0.0011374945,0.0006703887,0.19277133],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9922082,0.0025042582,0.00081526994,0.0008772926,0.0028762755,0.0007187209],"domain_scores_gemma":[0.9719302,0.009718912,0.002153106,0.0019404928,0.0073881964,0.0068690763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008789027,0.0015146399,0.0013400428,0.004678486,0.0025028824,0.013239189,0.0024442493,0.008182641,0.07386242],"category_scores_gemma":[0.045873538,0.0005779546,0.00127628,0.003615096,0.0045570284,0.009474909,0.0071892156,0.012232387,0.034837604],"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":[0.000013249959,0.000039636445,0.0006101544,0.00076017185,0.00001433273,0.0001122213,0.0006335844,0.000035036734,0.00007217339,0.018515721,0.894843,0.084350824],"study_design_scores_gemma":[0.0000021800424,0.000010623447,0.00040452692,0.0005555637,0.000004129646,0.00017653899,0.00022691392,0.000011920707,0.000010126954,0.0026240596,0.9959675,0.000006040164],"about_ca_topic_score_codex":0.0016537847,"about_ca_topic_score_gemma":0.002826224,"teacher_disagreement_score":0.07386242,"about_ca_system_score_codex":0.0027085776,"about_ca_system_score_gemma":0.010419852,"threshold_uncertainty_score":0.2470944},"labels":[],"label_agreement":null},{"id":"W2038947255","doi":"10.3917/pope.702.0271","title":"Familial and Environmental Influences on Longevity in Historical Quebec","year":2007,"lang":"en","type":"article","venue":"Population (English Edition)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Political science; Humanities; Philosophy","score_opus":0.01336182846702365,"score_gpt":0.2668017184596767,"score_spread":0.25343988999265304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038947255","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":[0.9924373,0.0009976339,0.00006764826,0.0005502957,0.000017213468,0.0000055788,0.00090370787,0.0000054507104,0.00501529],"genre_scores_gemma":[0.99709237,0.00043093282,0.00004810306,0.000045659133,0.0000062377662,0.0000038880053,0.00026315113,0.000002975101,0.0021067387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997774,0.000044128286,0.000009793328,0.000043686945,0.000030015462,0.00009497375],"domain_scores_gemma":[0.9989967,0.0001204165,0.00018502193,0.00006830819,0.000335553,0.00029394694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004390832,0.00011847575,0.00014445808,0.0010944444,0.0027863518,0.00088307663,0.00052351813,0.0002961609,0.0048821615],"category_scores_gemma":[0.0015407196,0.000112374284,0.00016271329,0.002186404,0.0008665931,0.00034530755,0.0005755775,0.000376163,0.00019818987],"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":[0.00004031938,0.00001886386,0.9836189,0.000019730205,0.00004705006,0.00019436816,0.005008539,0.00020630976,0.00018216619,0.0011873875,0.0013130285,0.0081633255],"study_design_scores_gemma":[0.0000010457542,0.0000060679,0.99552864,0.000024147499,0.000009976378,0.000025022224,0.0021487004,0.00011074151,0.000019051085,0.000053131214,0.0020686071,0.0000048244174],"about_ca_topic_score_codex":0.981282,"about_ca_topic_score_gemma":0.9930697,"teacher_disagreement_score":0.018718004,"about_ca_system_score_codex":0.012305514,"about_ca_system_score_gemma":0.0048714033,"threshold_uncertainty_score":0.08928317},"labels":[],"label_agreement":null},{"id":"W2039389482","doi":"10.1007/s13524-014-0287-8","title":"Divergence in Age Patterns of Mortality Change Drives International Divergence in Lifespan Inequality","year":2014,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":102,"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":"National Institute on Aging; National Institutes of Health","keywords":"Life expectancy; Inequality; Demography; Divergence (linguistics); Longevity; Demographic economics; Population; Economics; Gerontology; Medicine; Sociology","score_opus":0.049407600460636275,"score_gpt":0.3306751406366866,"score_spread":0.2812675401760504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039389482","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":[0.9924027,0.00011849579,0.002350091,0.000168637,0.0000054348034,0.000004257267,0.00026257793,0.00000977089,0.004678024],"genre_scores_gemma":[0.9991819,0.000050264156,0.0003426002,0.00001640068,0.0000023405394,0.0000021569194,0.0001495841,0.0000031745055,0.00025160375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997143,0.00008911761,0.000017863511,0.000082853454,0.000034060176,0.000061778264],"domain_scores_gemma":[0.99906796,0.00025826364,0.0002643695,0.0001739667,0.00015006425,0.00008533899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073282665,0.00010879562,0.00019652939,0.000685168,0.0003467296,0.000744662,0.00019883152,0.00022510556,0.0022509294],"category_scores_gemma":[0.004050795,0.00007585505,0.00017288745,0.00076361385,0.00053903455,0.0006155886,0.0007821703,0.00041882182,0.00024137569],"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":[0.00010321508,0.00003795217,0.93783957,0.000030204279,0.00008134224,0.00018075637,0.004502353,0.0031526564,0.0023734828,0.01825536,0.0008845592,0.032558497],"study_design_scores_gemma":[0.000002744783,0.000022083881,0.98723674,0.000018543893,0.000013155597,0.00009809504,0.0015885988,0.0030750774,0.00035465197,0.005941541,0.00163827,0.000010542561],"about_ca_topic_score_codex":0.0070921364,"about_ca_topic_score_gemma":0.00913061,"teacher_disagreement_score":0.0070921364,"about_ca_system_score_codex":0.0005002243,"about_ca_system_score_gemma":0.0002220879,"threshold_uncertainty_score":0.014101744},"labels":[],"label_agreement":null},{"id":"W2039791170","doi":"","title":"Mortality statistics for the oldest-old","year":2000,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":29,"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":"Statistics; Demography; Geography; Mathematics; Sociology","score_opus":0.2987708441479337,"score_gpt":0.5909058921058483,"score_spread":0.2921350479579146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039791170","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":[0.19686401,0.022353608,0.0043210853,0.0022562935,0.0006205506,0.0001772833,0.7113702,0.0007907271,0.061246295],"genre_scores_gemma":[0.43171442,0.01715329,0.0046277046,0.00040955402,0.00026821016,0.00015573377,0.5235837,0.00008161387,0.022005828],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991677,0.000039313258,0.00006738832,0.00005095564,0.00051752524,0.00015716923],"domain_scores_gemma":[0.9969633,0.00015786264,0.00031212153,0.00011689431,0.002213557,0.00023624788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080909993,0.00028669904,0.00026652336,0.010441981,0.0006376551,0.0005022345,0.0005562816,0.00014029726,0.0028194338],"category_scores_gemma":[0.0038292264,0.00006727586,0.0003692834,0.008606654,0.00015896707,0.0002428952,0.0004536497,0.00047772157,0.000898297],"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":[0.00023611114,0.000053319225,0.5573752,0.0010225426,0.00024095223,0.00017851334,0.0011753466,0.003139834,0.0010545036,0.015844688,0.21915965,0.20051946],"study_design_scores_gemma":[0.000009449897,0.000040092473,0.80263036,0.00015620097,0.000056111065,0.00019991215,0.00031309685,0.0004828193,0.00039773734,0.00052356307,0.19516164,0.000028964128],"about_ca_topic_score_codex":0.8745684,"about_ca_topic_score_gemma":0.8843533,"teacher_disagreement_score":0.8745684,"about_ca_system_score_codex":0.004333954,"about_ca_system_score_gemma":0.0075638243,"threshold_uncertainty_score":0.25234056},"labels":[],"label_agreement":null},{"id":"W2039894932","doi":"10.1007/s00148-005-0229-2","title":"Time series analysis and stochastic forecasting: An econometric study of mortality and life expectancy","year":2005,"lang":"en","type":"article","venue":"Journal of Population Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Life expectancy; Econometrics; Nonparametric statistics; Parametric statistics; Interpretation (philosophy); Stochastic modelling; Aggregate (composite); Econometric model; Series (stratigraphy); Time series; Parametric model; Economics; Statistics; Computer science; Mathematics; Population; Demography; Sociology","score_opus":0.05190200454123763,"score_gpt":0.3132777123569885,"score_spread":0.26137570781575087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039894932","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":[0.84967756,0.0044179712,0.13430604,0.00547165,0.00009529639,0.000034105036,0.00020897927,0.000072429124,0.005715944],"genre_scores_gemma":[0.9892051,0.0017852567,0.0071138106,0.00007019077,0.0001439249,0.000015063326,0.000105477404,0.000020301299,0.0015407812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99926764,0.0005089025,0.000026234618,0.000058916707,0.0000703991,0.00006792144],"domain_scores_gemma":[0.96196234,0.036084026,0.0008399678,0.0003730754,0.0004818254,0.00025868585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004974303,0.0003952576,0.00063657574,0.0013311736,0.0004121509,0.001508359,0.0007606043,0.0013638968,0.0019742975],"category_scores_gemma":[0.03739684,0.00033072985,0.0009152902,0.0019435356,0.0012697061,0.002272256,0.00052182225,0.0015781771,0.00013289992],"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":[0.00018574885,0.00025433002,0.07270183,0.000106066494,0.00029785532,0.00047552795,0.0012895765,0.5773009,0.0007456019,0.2983184,0.0027312106,0.04559297],"study_design_scores_gemma":[0.000014797735,0.00003530508,0.012800171,0.000018913888,0.00005401823,0.00007564207,0.00029130498,0.90612394,0.00009204958,0.079793684,0.00067579903,0.000024355402],"about_ca_topic_score_codex":0.020607118,"about_ca_topic_score_gemma":0.012291353,"teacher_disagreement_score":0.020607118,"about_ca_system_score_codex":0.0011592093,"about_ca_system_score_gemma":0.0009294261,"threshold_uncertainty_score":0.04097432},"labels":[],"label_agreement":null},{"id":"W2041439812","doi":"10.5430/rwe.v5n2p135","title":"Relative Cohort Size and Fertility in Latin America and the Caribbean: A Panel Data Approach","year":2014,"lang":"en","type":"article","venue":"Research in World Economy","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fertility; Total fertility rate; Cohort; Demography; Economics; Cohort effect; Population; Panel data; Birth rate; Cohort study; Latin Americans; Demographic economics; Medicine; Econometrics; Family planning","score_opus":0.12271967776057349,"score_gpt":0.3732904558297433,"score_spread":0.25057077806916983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041439812","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":[0.9865739,0.0014754266,0.0062264064,0.0010025147,0.000031964755,0.00007813973,0.002949798,0.000031122472,0.0016307523],"genre_scores_gemma":[0.99345934,0.0008396177,0.002098778,0.00013524103,0.00002462382,0.00012785499,0.0021518406,0.0000068942895,0.0011557165],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824995,0.0012981376,0.00004918224,0.00015719837,0.00007971898,0.00016584877],"domain_scores_gemma":[0.99458236,0.0035543048,0.0009687629,0.00044974947,0.0002015467,0.00024322838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043726545,0.00026234135,0.0005947273,0.0011417717,0.00072256156,0.0010469835,0.00093597604,0.0008382861,0.0028040905],"category_scores_gemma":[0.006851476,0.00031414852,0.0011515202,0.0020991187,0.0004074391,0.00055958,0.0011214232,0.0010839135,0.00018472256],"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":[0.00019869015,0.00014231617,0.96752363,0.000076516524,0.0013499713,0.00039192772,0.0008851491,0.01238515,0.00031528488,0.004530892,0.001423743,0.010776714],"study_design_scores_gemma":[0.000087283894,0.00035620114,0.8786461,0.00022505542,0.0013792142,0.00026927082,0.0036731388,0.09622824,0.00034172373,0.0058515426,0.012863716,0.00007863672],"about_ca_topic_score_codex":0.12004189,"about_ca_topic_score_gemma":0.094306566,"teacher_disagreement_score":0.12004189,"about_ca_system_score_codex":0.0008759905,"about_ca_system_score_gemma":0.0007966574,"threshold_uncertainty_score":0.23868638},"labels":[],"label_agreement":null},{"id":"W2042025111","doi":"10.1109/bibmw.2008.4686221","title":"Comparing classification methods for predicting survival probabilities in the elderly","year":2008,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Receiver operating characteristic; Computer science; Affect (linguistics); Artificial intelligence; Statistical classification; Statistics; Machine learning; Frailty Index; Pattern recognition (psychology); Data mining; Mathematics; Medicine; Gerontology; Psychology","score_opus":0.17688963674463135,"score_gpt":0.41226005682270433,"score_spread":0.235370420078073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042025111","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":[0.8326686,0.0094658155,0.14431281,0.0018740483,0.0005768491,0.0007203121,0.0020704078,0.0016247819,0.0066864663],"genre_scores_gemma":[0.91939986,0.002181862,0.07446788,0.00019901009,0.00027206904,0.0004987644,0.0017590245,0.000104904146,0.0011166164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98929226,0.0062699905,0.0011250927,0.000781126,0.0021663413,0.00036525872],"domain_scores_gemma":[0.9135672,0.078002304,0.001728766,0.0019062464,0.0042283884,0.00056710385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026523003,0.0017981824,0.0014963761,0.007436122,0.00051679224,0.0016982445,0.0010353294,0.0016915544,0.0010768509],"category_scores_gemma":[0.055631436,0.0003173832,0.0014240681,0.003146459,0.00053098454,0.0019028154,0.0011394905,0.0014566752,0.0005592816],"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":[0.0049093133,0.001252985,0.2200539,0.0010843765,0.003082984,0.00017342049,0.00075893814,0.18633036,0.0018314541,0.0025908432,0.0057631535,0.57216823],"study_design_scores_gemma":[0.00031209187,0.0019422434,0.08813526,0.00029163284,0.00052541343,0.00025941056,0.0006274092,0.89635813,0.0024990027,0.0070651383,0.001801027,0.00018325032],"about_ca_topic_score_codex":0.0046250443,"about_ca_topic_score_gemma":0.0032055718,"teacher_disagreement_score":0.026523003,"about_ca_system_score_codex":0.0010357955,"about_ca_system_score_gemma":0.00087817543,"threshold_uncertainty_score":0.14026874},"labels":[],"label_agreement":null},{"id":"W2043508196","doi":"10.1080/10920277.2014.911108","title":"Applications of Mortality Durations and Convexities in Natural Hedges","year":2014,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Society of Actuaries","keywords":"Longevity risk; Portfolio; Econometrics; Convexity; Actuarial science; Matching (statistics); Jump; Economics; Volatility (finance); Life insurance; Mathematics; Statistics; Pension; Financial economics; Finance","score_opus":0.010284790604352266,"score_gpt":0.2943857046462392,"score_spread":0.2841009140418869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043508196","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":[0.16836165,0.0010838218,0.8195982,0.00028203658,0.000041018746,0.00007545855,0.0001293219,0.000083431536,0.010344991],"genre_scores_gemma":[0.9462415,0.0007102436,0.050787203,0.00004444631,0.00006462794,0.00005026715,0.00010921511,0.00003161038,0.0019609532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99926966,0.0002963164,0.00005331562,0.000106877866,0.00020403825,0.00006974875],"domain_scores_gemma":[0.9973694,0.001562388,0.00042212082,0.00027692525,0.0002078676,0.00016131261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577261,0.0007406715,0.00046103395,0.0010970639,0.00026551064,0.0010342839,0.0006889205,0.00047068103,0.0016496527],"category_scores_gemma":[0.009523309,0.00033139298,0.0008723642,0.0006501185,0.0010880718,0.0014641582,0.0014516673,0.0009929177,0.00011666375],"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":[0.00009119324,0.00006451354,0.0048889252,0.000069873946,0.000058223788,0.00017238245,0.0002472217,0.73194396,0.0028703734,0.20130944,0.00032330453,0.057960615],"study_design_scores_gemma":[0.000016195718,0.00013099222,0.004623313,0.0000373694,0.000031695767,0.00012628218,0.0000905493,0.87873286,0.0027939333,0.11168282,0.001697457,0.00003650966],"about_ca_topic_score_codex":0.001757289,"about_ca_topic_score_gemma":0.0008017351,"teacher_disagreement_score":0.002577261,"about_ca_system_score_codex":0.0012641354,"about_ca_system_score_gemma":0.00078418007,"threshold_uncertainty_score":0.013630033},"labels":[],"label_agreement":null},{"id":"W2044247228","doi":"10.1007/s11135-011-9464-7","title":"Frailty models with applications to the study of infant deaths on birth timing in Ghana and Kenya","year":2011,"lang":"en","type":"article","venue":"Quality & Quantity","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Infant mortality; Demography; Medicine; Sociology; Population","score_opus":0.17066789119575607,"score_gpt":0.38283425314911884,"score_spread":0.21216636195336278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044247228","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":[0.91004074,0.003044425,0.0786257,0.0043734824,0.00013577226,0.00025278833,0.0011758064,0.00026729322,0.0020840396],"genre_scores_gemma":[0.969192,0.0022006067,0.021485588,0.00019168544,0.00011617565,0.00030149048,0.00066822505,0.000078040604,0.005766088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798346,0.001494054,0.00007152222,0.00015151508,0.00005661673,0.00024276553],"domain_scores_gemma":[0.9667489,0.029504966,0.0016136344,0.0005442219,0.0005721548,0.0010159914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011924709,0.001434801,0.0026335472,0.0014731832,0.0018272018,0.0019599334,0.0027348916,0.0024972742,0.0057457634],"category_scores_gemma":[0.033559762,0.0010254673,0.0024436996,0.0023389081,0.001999121,0.0014873375,0.0031190596,0.0038729284,0.00038548675],"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":[0.00080527266,0.00054609723,0.13029878,0.00018889108,0.0009676127,0.0016807029,0.0019486698,0.77724856,0.00036603754,0.051889677,0.0034465266,0.0306131],"study_design_scores_gemma":[0.0003616012,0.00030806867,0.02853222,0.00012554083,0.00038330903,0.00027684818,0.0014534133,0.93249214,0.00014412461,0.03380528,0.0019962098,0.000121197736],"about_ca_topic_score_codex":0.20159964,"about_ca_topic_score_gemma":0.1708952,"teacher_disagreement_score":0.20159964,"about_ca_system_score_codex":0.003157716,"about_ca_system_score_gemma":0.0036157246,"threshold_uncertainty_score":0.40085244},"labels":[],"label_agreement":null},{"id":"W2044506606","doi":"10.1093/gerona/57.5.b202","title":"Fertility and Life Span: Late Children Enhance Female Longevity","year":2002,"lang":"en","type":"article","venue":"The Journals of Gerontology Series A","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":132,"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, Davis; National Institute on Aging; National Science Foundation","keywords":"Longevity; Life expectancy; Fertility; Demography; Offspring; Reproduction; Cohort; Senescence; Biology; Life span; Gerontology; Pregnancy; Population; Medicine; Ecology; Sociology; Evolutionary biology; Genetics","score_opus":0.05327388793792999,"score_gpt":0.32576242215803153,"score_spread":0.27248853422010155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044506606","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":[0.99788624,0.0009328566,0.000050267383,0.00011717415,0.0000018721911,0.0000017756764,0.0001798617,0.0000017573458,0.0008280628],"genre_scores_gemma":[0.99943084,0.00019747742,0.000032632677,0.00001309261,0.0000041380035,5.550825e-7,0.000065494714,4.5737414e-7,0.00025520576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998292,0.000043202166,0.000006899241,0.000027381497,0.000036068373,0.00005714298],"domain_scores_gemma":[0.9988732,0.00036339698,0.00044604996,0.0000628767,0.00009297148,0.00016150807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005867072,0.00012376648,0.00013769376,0.00052154926,0.00031860374,0.00039988288,0.00022138831,0.00031937618,0.0027506195],"category_scores_gemma":[0.0031221951,0.000078714984,0.0002160388,0.00051535456,0.0003592243,0.00020584327,0.00029643436,0.00021960594,0.00014839506],"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":[0.000090459274,0.000009502965,0.9953987,0.000007933939,0.00003317123,0.00007492804,0.00018472108,0.000029965568,0.00021223688,0.00011627392,0.00006370246,0.003778431],"study_design_scores_gemma":[9.571169e-7,0.000029546567,0.99948955,0.0000023040807,0.000011764856,0.000115855466,0.00008769575,0.00003301714,0.00003316037,0.000030968815,0.00016418274,0.0000010404946],"about_ca_topic_score_codex":0.07141358,"about_ca_topic_score_gemma":0.13870305,"teacher_disagreement_score":0.07141358,"about_ca_system_score_codex":0.00065122446,"about_ca_system_score_gemma":0.00061999995,"threshold_uncertainty_score":0.14199579},"labels":[],"label_agreement":null},{"id":"W2044600197","doi":"10.3109/09638280903168515","title":"On the interaction of disability and aging: Accelerated degradation models and their influence on projections of future care needs and costs for personal injury litigation","year":2009,"lang":"en","type":"article","venue":"Disability and Rehabilitation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University; Parkwood Institute","funders":"","keywords":"Scope (computer science); Computer science; Psychology; Cognitive psychology","score_opus":0.02230680500291725,"score_gpt":0.3186655457940196,"score_spread":0.29635874079110236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044600197","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":[0.67177945,0.0033873739,0.27287924,0.011057121,0.00014961167,0.0002776759,0.0008049924,0.00013448925,0.03953013],"genre_scores_gemma":[0.98355305,0.0012161884,0.011525863,0.00015650451,0.00004236167,0.00017898348,0.00012422285,0.000018828916,0.0031839728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635386,0.0027631435,0.00007678809,0.00020899341,0.00028358094,0.00031357355],"domain_scores_gemma":[0.9733496,0.02315025,0.0018646418,0.00036487254,0.0009100496,0.0003605718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009787965,0.00088227005,0.00089391734,0.0016676204,0.0006998392,0.0029773149,0.0012498291,0.0014806174,0.003930571],"category_scores_gemma":[0.030355504,0.000529641,0.0013985584,0.0014564293,0.0018636937,0.0027190866,0.0020696733,0.0016703026,0.0003321977],"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":[0.0001785002,0.000116201045,0.017858684,0.00014271738,0.000100596815,0.0005153158,0.00087732024,0.7495868,0.0001756879,0.21554081,0.00128071,0.013626685],"study_design_scores_gemma":[0.000038304694,0.000233459,0.011595916,0.0001280181,0.00011900178,0.0003664775,0.001184293,0.8333135,0.00015389461,0.15059692,0.0021967785,0.0000733627],"about_ca_topic_score_codex":0.015239014,"about_ca_topic_score_gemma":0.010891093,"teacher_disagreement_score":0.015239014,"about_ca_system_score_codex":0.0036635345,"about_ca_system_score_gemma":0.0021424997,"threshold_uncertainty_score":0.05176431},"labels":[],"label_agreement":null},{"id":"W2046606859","doi":"10.2139/ssrn.1335476","title":"Valuation of Mortality Risk via the Instantaneous Sharpe Ratio: Applications to Life Annuities","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Sharpe ratio; Valuation (finance); Economics; Actuarial science; Econometrics; Financial economics; Finance; Portfolio","score_opus":0.031883697519590644,"score_gpt":0.3055402629063182,"score_spread":0.27365656538672756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046606859","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":[0.03799184,0.009085979,0.93503475,0.0038489967,0.00025263708,0.00006519928,0.00011458468,0.00015613921,0.013449945],"genre_scores_gemma":[0.85150516,0.01590436,0.118343435,0.00040388707,0.0015434608,0.00012930323,0.00014293737,0.00016596196,0.0118615255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982615,0.0011745854,0.00006277477,0.00014503908,0.0002506935,0.000105309984],"domain_scores_gemma":[0.9834013,0.013500287,0.0011764781,0.0006533292,0.0007923375,0.00047619545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0099138245,0.001529171,0.0019422291,0.0025583752,0.0005431028,0.0041449466,0.0023727426,0.0032237468,0.004562957],"category_scores_gemma":[0.04150355,0.00079796574,0.0014382587,0.0026862796,0.004094707,0.0077270106,0.0024257184,0.005233149,0.00031536023],"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":[0.00004980111,0.00006315126,0.0017396676,0.0001074401,0.00007842111,0.00022889166,0.00022469454,0.15623543,0.000462451,0.81331027,0.0020417492,0.025458094],"study_design_scores_gemma":[0.000023349892,0.000038070408,0.0008629556,0.000034855635,0.00002979547,0.00012785751,0.00008651454,0.33549204,0.0002096958,0.66142493,0.0016315411,0.000038322152],"about_ca_topic_score_codex":0.0019294489,"about_ca_topic_score_gemma":0.0011140826,"teacher_disagreement_score":0.0099138245,"about_ca_system_score_codex":0.0020372062,"about_ca_system_score_gemma":0.0010009949,"threshold_uncertainty_score":0.052429914},"labels":[],"label_agreement":null},{"id":"W2046736888","doi":"10.1111/issg.12020","title":"Versicherungsmathematische <scp>B</scp>ilanzen für die <scp>B</scp>ewertung der <scp>N</scp>achhaltigkeit von <scp>R</scp>entensystemen der sozialen <scp>S</scp>icherheit","year":2013,"lang":"de","type":"article","venue":"Internationale Revue für Soziale Sicherheit","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Political science","score_opus":0.029695398477502917,"score_gpt":0.290517483006202,"score_spread":0.2608220845286991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046736888","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":[0.075132355,0.0013120141,0.86571646,0.0024094563,0.00024268571,0.0010990709,0.0013369399,0.00051488937,0.052236147],"genre_scores_gemma":[0.44581944,0.0016889848,0.5249473,0.0003398992,0.00010369114,0.0021499784,0.0014461644,0.00036619027,0.023138288],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9937972,0.0027322331,0.0004947602,0.0006852234,0.0019936776,0.00029690977],"domain_scores_gemma":[0.9782406,0.015102279,0.0014373616,0.0018619335,0.003102712,0.0002550415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010332096,0.0011036074,0.0005685553,0.00331983,0.00080738624,0.0033960151,0.0010235012,0.00092997396,0.02440712],"category_scores_gemma":[0.041520357,0.00048196022,0.002180908,0.002274456,0.0017260144,0.0028755849,0.002141342,0.002666333,0.0029804704],"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":[0.00044196672,0.0007541492,0.023339143,0.0022347462,0.0004459368,0.00026274865,0.001795765,0.12549533,0.009240013,0.3123065,0.00783499,0.5158487],"study_design_scores_gemma":[0.00031070856,0.00085724745,0.03749344,0.0018408393,0.00057736563,0.00040198478,0.0035658486,0.39674357,0.034687705,0.43602806,0.08719795,0.00029528912],"about_ca_topic_score_codex":0.006366179,"about_ca_topic_score_gemma":0.008982291,"teacher_disagreement_score":0.02440712,"about_ca_system_score_codex":0.0029018677,"about_ca_system_score_gemma":0.003530333,"threshold_uncertainty_score":0.08164996},"labels":[],"label_agreement":null},{"id":"W2048297174","doi":"10.1139/cjfas-2014-0193","title":"A better estimator of mortality rate from age-frequency data","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"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":"Estimator; Statistics; Confidence interval; Ratio estimator; Mathematics; Regression; Population; Linear regression; Econometrics; Autocorrelation; Sampling (signal processing); Regression analysis; Bias of an estimator; Computer science; Minimum-variance unbiased estimator; Demography","score_opus":0.05881564433423176,"score_gpt":0.29819028642207,"score_spread":0.23937464208783826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048297174","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":[0.05698401,0.0014900138,0.9388824,0.00029245464,0.00015586533,0.0000558958,0.000545046,0.00065661257,0.00093764695],"genre_scores_gemma":[0.48857048,0.0013894737,0.50317776,0.00039979842,0.00033157793,0.00022417646,0.0025567987,0.00026098947,0.00308898],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995395,0.0019198782,0.00039282258,0.0012992558,0.000870382,0.00012265057],"domain_scores_gemma":[0.9845722,0.008617345,0.0019994192,0.002076414,0.0025797018,0.00015502333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01036961,0.0006506462,0.0012336723,0.0023894564,0.00018462887,0.0011819792,0.0011666352,0.0011530085,0.002066585],"category_scores_gemma":[0.03866909,0.00033441567,0.0011523069,0.0015070525,0.0003382033,0.0025058505,0.0008351909,0.0015219019,0.00060082576],"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":[0.0004306488,0.0002305099,0.11572637,0.0010446324,0.0013487076,0.0002632172,0.0007971768,0.07246648,0.023183446,0.024476716,0.0057209507,0.75431126],"study_design_scores_gemma":[0.00025942936,0.0009869119,0.1770705,0.0006680755,0.00064221973,0.0017283378,0.00030120809,0.7273703,0.027586851,0.028165858,0.034619033,0.0006013018],"about_ca_topic_score_codex":0.003482051,"about_ca_topic_score_gemma":0.002235814,"teacher_disagreement_score":0.01036961,"about_ca_system_score_codex":0.00040968033,"about_ca_system_score_gemma":0.00047328576,"threshold_uncertainty_score":0.054840386},"labels":[],"label_agreement":null},{"id":"W2050859849","doi":"10.3917/pope.1204.0573","title":"The Most Frequent Adult Length of Life in the Eighteenth Century: The Experience of the French-Canadians","year":2012,"lang":"en","type":"article","venue":"Population (English Edition)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Humanities; Art","score_opus":0.013307375610114861,"score_gpt":0.2584638937925002,"score_spread":0.24515651818238532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050859849","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":[0.9722955,0.005655275,0.000047650905,0.004106594,0.00013526819,0.000011655548,0.00045238275,0.000006275333,0.017289437],"genre_scores_gemma":[0.9859243,0.0038514978,0.00008505473,0.00065458944,0.000030423866,0.000011110692,0.00015595873,0.0000074538475,0.009279526],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9986304,0.00016183789,0.000041788684,0.00011903694,0.0002728098,0.00077414577],"domain_scores_gemma":[0.99787927,0.00015042843,0.00024512436,0.000026299425,0.000854782,0.0008440943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014270894,0.0003068185,0.00048559142,0.0014008058,0.01720684,0.003484924,0.00116237,0.0010050304,0.004716258],"category_scores_gemma":[0.0024721106,0.0002780405,0.0003124141,0.0025727162,0.004510816,0.0015144374,0.0018534344,0.0016565527,0.0003717122],"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":[0.000077554534,0.000033069744,0.0850863,0.00017394892,0.00001929171,0.0006619577,0.88835365,0.0000344579,0.00038840962,0.003698837,0.004588388,0.01688419],"study_design_scores_gemma":[0.0000041807616,0.000054699838,0.15970631,0.00027566927,0.00001369596,0.00044861884,0.7610882,0.000025221685,0.000071474424,0.000115245944,0.07814822,0.000048461465],"about_ca_topic_score_codex":0.98518986,"about_ca_topic_score_gemma":0.9925937,"teacher_disagreement_score":0.025390884,"about_ca_system_score_codex":0.025390884,"about_ca_system_score_gemma":0.027886461,"threshold_uncertainty_score":0.1842246},"labels":[],"label_agreement":null},{"id":"W2051426763","doi":"10.2139/ssrn.2576798","title":"Assessing the Solvency Risk of Insurance Portfolios via a Continuous Time Cohort Model","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Solvency; Actuarial science; Risk model; Business; Econometrics; Economics; Finance; Market liquidity","score_opus":0.006781778018799928,"score_gpt":0.2740796704366113,"score_spread":0.2672978924178114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051426763","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":[0.9141049,0.00041682174,0.08092513,0.0015346254,0.00006484842,0.000096742064,0.0013110845,0.00010869143,0.0014372534],"genre_scores_gemma":[0.9883831,0.00031984784,0.0062613734,0.000065857384,0.00005391017,0.000068393914,0.00065039744,0.0000117238615,0.0041852403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864584,0.00059709506,0.000057381367,0.0003014191,0.000113054906,0.0002851719],"domain_scores_gemma":[0.97643197,0.018420376,0.0020526783,0.0009316484,0.00074404606,0.0014192853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009112453,0.000955188,0.0014965708,0.0019048561,0.0006560332,0.002681849,0.00247969,0.0026328694,0.0045578484],"category_scores_gemma":[0.02171516,0.00080448785,0.0017573937,0.0012568142,0.0010416072,0.0016491485,0.0017017584,0.0022188143,0.0005063622],"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":[0.0007487426,0.00057037466,0.18472506,0.000073119474,0.0008253994,0.0005126599,0.00032971284,0.76356393,0.00046700676,0.030734828,0.0015183149,0.015930766],"study_design_scores_gemma":[0.000046608387,0.00015139936,0.010122798,0.000012242332,0.000098727935,0.000056439945,0.00007632087,0.98292047,0.000060965984,0.006212274,0.00021912651,0.000022499944],"about_ca_topic_score_codex":0.055731818,"about_ca_topic_score_gemma":0.02392413,"teacher_disagreement_score":0.055731818,"about_ca_system_score_codex":0.0015048927,"about_ca_system_score_gemma":0.0023564245,"threshold_uncertainty_score":0.11081487},"labels":[],"label_agreement":null},{"id":"W2052216525","doi":"10.1111/isss.12010","title":"El balance actuarial como herramienta para evaluar la sostenibilidad de los sistemas de pensiones de la seguridad social","year":2013,"lang":"es","type":"article","venue":"Revista Internacional de Seguridad Social","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Balance (ability); Humanities; Political science; Geography; Art; Medicine","score_opus":0.01796955637079961,"score_gpt":0.357975764614113,"score_spread":0.3400062082433134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052216525","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":[0.68972486,0.011902667,0.23536372,0.0026160395,0.00027406562,0.00080930785,0.0052568964,0.0006051507,0.053447407],"genre_scores_gemma":[0.9292768,0.0038578415,0.05673838,0.00009810672,0.00011555575,0.00047791653,0.0011012756,0.00006608274,0.008268074],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99623495,0.0020208377,0.00027041626,0.00015270276,0.001201154,0.00011991699],"domain_scores_gemma":[0.99211544,0.0042971065,0.0013830671,0.00043290007,0.001569782,0.0002017088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0114137,0.00086616504,0.00076959137,0.0047864616,0.00054765696,0.0028008325,0.0004473969,0.00053986645,0.006894906],"category_scores_gemma":[0.019275103,0.0002627138,0.0007779322,0.002932582,0.0004359681,0.0018147695,0.0011961357,0.000550961,0.0011027267],"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":[0.0012733649,0.00023987582,0.2688524,0.0011976939,0.0005754506,0.00016306655,0.0034511695,0.03774199,0.009183979,0.020945672,0.004224225,0.652151],"study_design_scores_gemma":[0.00018646174,0.0027251886,0.65969366,0.0031859013,0.0010852517,0.00069128466,0.009109377,0.15560003,0.022892624,0.05211444,0.09239474,0.0003210033],"about_ca_topic_score_codex":0.0044188043,"about_ca_topic_score_gemma":0.004150317,"teacher_disagreement_score":0.0114137,"about_ca_system_score_codex":0.0009997274,"about_ca_system_score_gemma":0.0015410777,"threshold_uncertainty_score":0.0603621},"labels":[],"label_agreement":null},{"id":"W2052252118","doi":"10.1016/j.aap.2008.07.015","title":"Semi-parametric additive risk models: Application to injury duration study","year":2008,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"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 Saskatchewan","funders":"","keywords":"Proportional hazards model; Covariate; Additive model; Statistics; Multiplicative function; Survival analysis; Generalized additive model; Econometrics; Hazard; Hazard ratio; Regression analysis; Accelerated failure time model; Mixed model; Mathematics; Computer science; Confidence interval; Chemistry","score_opus":0.02381360732149325,"score_gpt":0.3345151867334882,"score_spread":0.31070157941199494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052252118","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":[0.09319965,0.00051124836,0.9035957,0.0003568531,0.000077158715,0.00018521541,0.0006808745,0.00045755584,0.00093585474],"genre_scores_gemma":[0.7446756,0.0009187902,0.24612658,0.00015746613,0.00015181718,0.0010498137,0.0011664936,0.0001880291,0.0055653723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931676,0.005277128,0.00031438694,0.0005379614,0.00047120906,0.00023183184],"domain_scores_gemma":[0.9420249,0.052279986,0.0016336646,0.002015528,0.0016152117,0.00043073646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020570429,0.001169063,0.0026353248,0.0016174075,0.0008545862,0.002294604,0.0042196396,0.0016360935,0.0036819358],"category_scores_gemma":[0.03849863,0.0008500905,0.0029758941,0.0022624948,0.0008298347,0.0015329826,0.002689412,0.0030981896,0.00064773706],"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":[0.0010695846,0.0007241231,0.02177636,0.00037902783,0.0012431844,0.00060546247,0.0012211388,0.8020249,0.000533042,0.057372898,0.0024936153,0.1105566],"study_design_scores_gemma":[0.000049726234,0.00017846785,0.0024348483,0.00002721319,0.00013826958,0.00013361579,0.00013914608,0.9703761,0.00013120499,0.02567208,0.000684935,0.000034473076],"about_ca_topic_score_codex":0.010985679,"about_ca_topic_score_gemma":0.011714205,"teacher_disagreement_score":0.020570429,"about_ca_system_score_codex":0.0009983173,"about_ca_system_score_gemma":0.0018222418,"threshold_uncertainty_score":0.10878813},"labels":[],"label_agreement":null},{"id":"W2053498219","doi":"10.1016/j.insmatheco.2008.10.005","title":"Editorial to the special issue on modeling and measurement of multivariate risk in insurance and finance","year":2008,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université Laval","funders":"","keywords":"Multivariate statistics; Actuarial science; Econometrics; Economics; Business; Mathematics; Statistics","score_opus":0.02754154638967685,"score_gpt":0.2544922538040683,"score_spread":0.22695070741439147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053498219","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006962145,0.0028017669,0.00030822775,0.029565493,0.9666536,0.000015435051,0.000078150544,0.000037555445,0.00047017608],"genre_scores_gemma":[0.00041765833,0.0012606628,0.00009614857,0.007429808,0.9888049,0.000013292395,0.000028222295,0.000019656507,0.0019296419],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99582374,0.0009046606,0.0007120438,0.0008354604,0.0014324639,0.00029161444],"domain_scores_gemma":[0.96078885,0.024091093,0.0018345615,0.00101902,0.009603668,0.0026628678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0083838105,0.005098764,0.0058898875,0.0039891624,0.0022284575,0.0067385337,0.00432751,0.01608043,0.011508095],"category_scores_gemma":[0.032393634,0.0016410329,0.005200509,0.0013990973,0.002544932,0.0045975414,0.0015162851,0.021206636,0.005090858],"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":[0.00006786692,0.000038078382,0.000096411335,0.0001887475,0.00006104298,0.00011602475,0.000013267265,0.00007465432,0.00006225853,0.0005025803,0.9956736,0.0031054113],"study_design_scores_gemma":[0.00032895253,0.00017281283,0.003293025,0.0009774895,0.00051371445,0.0008872085,0.00013358616,0.0028950074,0.00044588422,0.009459344,0.98079395,0.00009894633],"about_ca_topic_score_codex":0.0019330581,"about_ca_topic_score_gemma":0.0030380848,"teacher_disagreement_score":0.01608043,"about_ca_system_score_codex":0.0022769026,"about_ca_system_score_gemma":0.0017697975,"threshold_uncertainty_score":0.044338346},"labels":[],"label_agreement":null},{"id":"W2054127758","doi":"10.1016/j.insmatheco.2015.03.018","title":"Mortality modelling with regime-switching for the valuation of a guaranteed annuity option","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"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":"Annuity; Actuarial science; Valuation (finance); Life annuity; Economics; Valuation of options; Business; Econometrics; Finance; Pension","score_opus":0.1300792946984122,"score_gpt":0.3148422075839104,"score_spread":0.1847629128854982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054127758","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":[0.37355363,0.0021658167,0.6076881,0.004082175,0.0003353171,0.000101987964,0.0006236306,0.00023434884,0.011214906],"genre_scores_gemma":[0.97455853,0.000649276,0.0116938865,0.0001734287,0.00016008344,0.00008607677,0.00019240375,0.000048989943,0.012437414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998941,0.00055898353,0.000049879516,0.00015009342,0.00009403504,0.00020597772],"domain_scores_gemma":[0.99288356,0.005307907,0.0007295609,0.00022883833,0.0003221283,0.000528004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047605713,0.0011101648,0.002040337,0.0013180119,0.00083121844,0.0028701182,0.0027846752,0.004888343,0.005769254],"category_scores_gemma":[0.013497439,0.00094281405,0.0026237061,0.0009743608,0.002667647,0.0023662106,0.0021645639,0.0039499216,0.00037280525],"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":[0.0001328785,0.00008513718,0.0027620932,0.00006696081,0.00008279407,0.00020934928,0.00020564144,0.87968004,0.0008639089,0.11151447,0.0007174043,0.0036793856],"study_design_scores_gemma":[0.00000983163,0.0000123212585,0.00024279357,0.0000071685336,0.000012122649,0.000018456314,0.000014382457,0.9874034,0.00003469759,0.012092243,0.00014170805,0.000010896227],"about_ca_topic_score_codex":0.015023381,"about_ca_topic_score_gemma":0.0064022103,"teacher_disagreement_score":0.015023381,"about_ca_system_score_codex":0.0020170894,"about_ca_system_score_gemma":0.0014374931,"threshold_uncertainty_score":0.029871881},"labels":[],"label_agreement":null},{"id":"W2055154391","doi":"10.1016/j.cam.2013.02.013","title":"A comonotonicity-based valuation method for guaranteed annuity options","year":2013,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valuation (finance); Mathematics; Actuarial science; Annuity; Regular polygon; Econometrics; Life annuity; Mathematical economics; Economics; Finance; Pension","score_opus":0.03351786272698391,"score_gpt":0.34280132517400874,"score_spread":0.3092834624470248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055154391","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":[0.0046599326,0.00016751695,0.9938636,0.00016756362,0.000067102155,0.000025424333,0.000021059912,0.00005076656,0.0009770466],"genre_scores_gemma":[0.300911,0.0005484193,0.68908215,0.00026230502,0.00030926606,0.00024131186,0.00019763697,0.00019277904,0.00825512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991485,0.00046120712,0.00004606409,0.00010426651,0.00018098639,0.000058838483],"domain_scores_gemma":[0.9962373,0.0025773458,0.00012995265,0.00022272077,0.00056574395,0.00026693707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043443968,0.0005809036,0.0013906386,0.0010665028,0.0006770916,0.0016015245,0.0020770994,0.0016255799,0.00591904],"category_scores_gemma":[0.010436164,0.0005510965,0.0010799515,0.00089628197,0.001164255,0.0024192596,0.0024269833,0.002554759,0.0005174932],"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":[0.0001614268,0.00018515882,0.0011424075,0.00020439406,0.00008793528,0.00017253004,0.00024354848,0.3124781,0.0034941835,0.5440432,0.0040533105,0.13373387],"study_design_scores_gemma":[0.00001171898,0.000020358428,0.000058937723,0.000012325069,0.000007959325,0.000020500262,0.000007958027,0.9515675,0.00013704554,0.047366124,0.0007809808,0.000008499469],"about_ca_topic_score_codex":0.0021195426,"about_ca_topic_score_gemma":0.0023804428,"teacher_disagreement_score":0.00591904,"about_ca_system_score_codex":0.00093927246,"about_ca_system_score_gemma":0.0016416212,"threshold_uncertainty_score":0.022975683},"labels":[],"label_agreement":null},{"id":"W2055228949","doi":"10.1108/15265940510585789","title":"Diffusion models of insurer net worth: can one dimension suffice?","year":2005,"lang":"en","type":"article","venue":"The Journal of Risk Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","funders":"","keywords":"Laplace transform; Dimension (graph theory); Diffusion; Homogeneous; Computer science; Dimensional modeling; Distribution (mathematics); Mathematical optimization; Value (mathematics); Mathematics; Mathematical analysis","score_opus":0.0183361005300694,"score_gpt":0.25575389266887716,"score_spread":0.23741779213880776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055228949","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":[0.24217662,0.0022290146,0.70903784,0.011545314,0.00031932283,0.000152018,0.00056635635,0.00023515295,0.033738352],"genre_scores_gemma":[0.96794844,0.0013207662,0.020216454,0.00038870372,0.000114289534,0.00010661982,0.00010607812,0.000043350054,0.009755317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943894,0.00021889768,0.000030086076,0.00011295235,0.00010105633,0.000098041826],"domain_scores_gemma":[0.9977132,0.0011729123,0.00056399824,0.0001909261,0.00018029827,0.0001786163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018476336,0.00064187136,0.0009608865,0.00052672176,0.0005376926,0.0021395762,0.0018353505,0.0021536117,0.0032213908],"category_scores_gemma":[0.0073803845,0.0005257832,0.0010927962,0.0004974434,0.0017618043,0.0045774737,0.0013055302,0.002138673,0.00039123034],"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":[0.000039216575,0.000056032404,0.0026127375,0.000064951506,0.000035383808,0.0001691319,0.00026063054,0.5529022,0.000471884,0.43628722,0.0013848421,0.0057156375],"study_design_scores_gemma":[0.00002147949,0.000031666437,0.00058054505,0.00002639051,0.000014510428,0.0000683317,0.000072979055,0.8986275,0.00008208848,0.09904459,0.001407514,0.000022397953],"about_ca_topic_score_codex":0.008987314,"about_ca_topic_score_gemma":0.004366891,"teacher_disagreement_score":0.008987314,"about_ca_system_score_codex":0.0020190687,"about_ca_system_score_gemma":0.001045364,"threshold_uncertainty_score":0.017870009},"labels":[],"label_agreement":null},{"id":"W2057200769","doi":"10.1016/j.jspi.2009.03.003","title":"General frailty model and stochastic orderings","year":2009,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":34,"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":"Mathematics; Multiplicative function; Stochastic ordering; Stochastic modelling; Applied mathematics; Statistics; Mathematical analysis","score_opus":0.04300626722078061,"score_gpt":0.363052563037092,"score_spread":0.3200462958163114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057200769","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":[0.09853764,0.0013172174,0.88914865,0.0025529717,0.0001765277,0.0001064488,0.0017996796,0.00042153566,0.0059393616],"genre_scores_gemma":[0.83584666,0.0032068905,0.12046431,0.00073646655,0.00072458934,0.0005030834,0.0033833403,0.00034418402,0.034790557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970734,0.0012054483,0.00018056385,0.000608576,0.00044443045,0.00048762845],"domain_scores_gemma":[0.96572846,0.02516444,0.002806198,0.0029261718,0.0020938748,0.0012809777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010088387,0.0016275584,0.0038526086,0.003970889,0.0013060905,0.003507971,0.005349278,0.0037822493,0.0100556435],"category_scores_gemma":[0.04299151,0.0019355622,0.003910488,0.0044284496,0.00430019,0.0083245365,0.0026739202,0.005111013,0.0011273571],"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":[0.00008044318,0.00007201052,0.0034364937,0.000098552395,0.00014278454,0.00048156892,0.00022640768,0.22128756,0.00025709625,0.76319134,0.0025896977,0.008135987],"study_design_scores_gemma":[0.00004482852,0.000027823782,0.000884187,0.000023578543,0.000049945484,0.00017365023,0.00003496727,0.36717924,0.000046406527,0.6306036,0.0008964923,0.0000353117],"about_ca_topic_score_codex":0.022520635,"about_ca_topic_score_gemma":0.016038055,"teacher_disagreement_score":0.022520635,"about_ca_system_score_codex":0.0029258875,"about_ca_system_score_gemma":0.0029803752,"threshold_uncertainty_score":0.05335313},"labels":[],"label_agreement":null},{"id":"W2058550961","doi":"10.1007/s11294-013-9413-4","title":"Population Volatility in Large Counties in the United States","year":2013,"lang":"en","type":"article","venue":"International Advances in Economic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"Australian Government; Woodrow Wilson International Center for Scholars","keywords":"Volatility (finance); Economics; Population; Demographic economics; Econometrics; Environmental health; Medicine","score_opus":0.047177708095996085,"score_gpt":0.43678016454265717,"score_spread":0.3896024564466611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058550961","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":[0.99872357,0.00013193549,0.00006860565,0.00021324148,0.0000073134493,0.0000022246509,0.0005011295,0.000005502243,0.00034656472],"genre_scores_gemma":[0.99943477,0.00006848363,0.000022020382,0.000022116097,0.0000075923886,0.0000027272085,0.00035190937,0.0000013029254,0.00008894107],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975926,0.00007622125,0.000023291594,0.000052810614,0.000038766437,0.000049724],"domain_scores_gemma":[0.9989386,0.00032145812,0.00029517204,0.000063485124,0.000185433,0.00019582453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037355037,0.0001155676,0.00024058209,0.00089283683,0.00042264242,0.0008671548,0.00031212025,0.00039051022,0.00072105235],"category_scores_gemma":[0.0016231014,0.00017476032,0.00030475852,0.0014687225,0.00032463882,0.0005738271,0.0007546699,0.0004245273,0.00009390426],"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":[0.00006364248,0.000030067109,0.995697,0.0000060996435,0.000057344394,0.0000767874,0.00033249139,0.00087526283,0.0000883462,0.0002626138,0.00089596567,0.0016144309],"study_design_scores_gemma":[0.0000039302927,0.000012831675,0.9951062,0.000005566332,0.00001805056,0.00006200636,0.0014882414,0.0026914948,0.000036274694,0.00017161126,0.0003972362,0.000006473509],"about_ca_topic_score_codex":0.09627129,"about_ca_topic_score_gemma":0.14656276,"teacher_disagreement_score":0.09627129,"about_ca_system_score_codex":0.0009955439,"about_ca_system_score_gemma":0.0005588045,"threshold_uncertainty_score":0.1914218},"labels":[],"label_agreement":null},{"id":"W2059167404","doi":"10.1155/2010/813583","title":"Investigating Mortality Uncertainty Using the Block Bootstrap","year":2010,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Block (permutation group theory); Life expectancy; Econometrics; Mathematics; Risk model; Statistics; Demography; Population","score_opus":0.07911961873270416,"score_gpt":0.3644494018175526,"score_spread":0.2853297830848484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059167404","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":[0.17555763,0.0007545864,0.81975985,0.00034355887,0.000029781662,0.000036172372,0.00011084327,0.000093886294,0.0033138033],"genre_scores_gemma":[0.94735384,0.00053016667,0.051220074,0.0000605986,0.00006861884,0.000079335885,0.00014306359,0.000031289903,0.0005129577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971269,0.0019987172,0.000060558672,0.00016594016,0.0005123924,0.00013555269],"domain_scores_gemma":[0.96795267,0.0284983,0.0014010047,0.0010365829,0.0009043932,0.00020695725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007961464,0.00033123788,0.0007333946,0.001440069,0.00050669216,0.00078773656,0.0009287051,0.0007628247,0.0015032814],"category_scores_gemma":[0.03996964,0.00026172577,0.00054422,0.0008776694,0.0006909124,0.0017414246,0.0015360503,0.0009199774,0.00011470585],"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":[0.00043311281,0.000101764395,0.037408818,0.00018696356,0.0004144012,0.00040029257,0.0009986297,0.5379814,0.0028606842,0.298677,0.0014407585,0.11909615],"study_design_scores_gemma":[0.000014700096,0.00008751011,0.005775056,0.000028319671,0.00003349511,0.00007386785,0.000114676186,0.9158388,0.0008913908,0.0759335,0.0011771304,0.000031456264],"about_ca_topic_score_codex":0.003044086,"about_ca_topic_score_gemma":0.0013298721,"teacher_disagreement_score":0.007961464,"about_ca_system_score_codex":0.00046713452,"about_ca_system_score_gemma":0.0006038414,"threshold_uncertainty_score":0.04210478},"labels":[],"label_agreement":null},{"id":"W2059292772","doi":"10.6000/1929-6029.2014.03.01.2","title":"Modeling Survival After Diagnosis of a Specific Disease Based on Case Surveillance Data","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Parametric statistics; Disease; Life table; Population; Medicine; Survival analysis; Demography; Parametric model; Statistics; Mathematics; Environmental health; Pathology","score_opus":0.12379950754696019,"score_gpt":0.47064472328699547,"score_spread":0.3468452157400353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059292772","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":[0.83309853,0.0008643237,0.15972395,0.0008737222,0.00006504619,0.00030072322,0.0034450595,0.00024073185,0.0013879043],"genre_scores_gemma":[0.9805086,0.00039479064,0.015323923,0.000058634883,0.000053034273,0.00025270658,0.0023103165,0.000015378082,0.001082649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965473,0.0021069818,0.00019270745,0.0006769976,0.0002279925,0.00024811662],"domain_scores_gemma":[0.9695155,0.024789283,0.0031447383,0.001424763,0.00078145094,0.00034430748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011809654,0.0008523957,0.00095600466,0.001997996,0.00035004326,0.001039357,0.0017916935,0.0013096387,0.0011296157],"category_scores_gemma":[0.026506422,0.00062255113,0.0015563634,0.0015548638,0.00076068216,0.0011492568,0.0011883712,0.001080551,0.00023561115],"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":[0.00040751262,0.00026245232,0.12078015,0.00013322322,0.0004967307,0.00043547177,0.000388995,0.8423948,0.0006106548,0.008480969,0.0008101899,0.024798797],"study_design_scores_gemma":[0.000022161032,0.00011601931,0.011428811,0.0000135233395,0.00006427624,0.00010332659,0.00004639673,0.9853902,0.00014243739,0.0023503697,0.00030647026,0.000015969164],"about_ca_topic_score_codex":0.018717524,"about_ca_topic_score_gemma":0.011703677,"teacher_disagreement_score":0.018717524,"about_ca_system_score_codex":0.0012769847,"about_ca_system_score_gemma":0.00080275995,"threshold_uncertainty_score":0.06245613},"labels":[],"label_agreement":null},{"id":"W2060325779","doi":"10.3200/jrlp.138.4.293-302","title":"Achievement Age—Death Age Correlations Alone Cannot Provide Unequivocal Support for the Precocity—Longevity Hypothesis","year":2004,"lang":"en","type":"article","venue":"The Journal of Psychology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cape Breton University","funders":"","keywords":"Longevity; Psychology; Life expectancy; Expectancy theory; Demography; Developmental psychology; Gerontology; Social psychology; Medicine; Population","score_opus":0.0785691579896379,"score_gpt":0.3716436887925202,"score_spread":0.29307453080288226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060325779","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":[0.9793689,0.00038155794,0.009189625,0.0009225182,0.00009132038,0.000059552884,0.0005841504,0.00004386173,0.009358495],"genre_scores_gemma":[0.99821895,0.000052395353,0.0010501341,0.00008172647,0.000041240462,0.00001824808,0.00016034981,0.000005232911,0.00037178735],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99495244,0.0016546962,0.00047790966,0.0010592398,0.0014799947,0.00037584107],"domain_scores_gemma":[0.85044736,0.09811832,0.025962852,0.017813198,0.0055891634,0.0020691713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013122735,0.00039605194,0.0006919628,0.0011762449,0.00074818474,0.00096177903,0.00081603514,0.00052296254,0.007879129],"category_scores_gemma":[0.079413526,0.00025764754,0.0005821985,0.0007631785,0.0020695943,0.0019909285,0.0016942208,0.0011532437,0.0008641186],"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":[0.00072407606,0.00010813041,0.95858204,0.00010091866,0.00032877596,0.00016786373,0.00091256056,0.00069223164,0.0009503618,0.012015855,0.0007948913,0.024622353],"study_design_scores_gemma":[0.000053946438,0.0006324822,0.9771235,0.00003052066,0.0001688485,0.000694663,0.00089162297,0.0032172995,0.0015748328,0.012619875,0.0029628638,0.00002945721],"about_ca_topic_score_codex":0.00090832956,"about_ca_topic_score_gemma":0.0009029847,"teacher_disagreement_score":0.013122735,"about_ca_system_score_codex":0.00043068966,"about_ca_system_score_gemma":0.00066101924,"threshold_uncertainty_score":0.06940049},"labels":[],"label_agreement":null},{"id":"W2064093647","doi":"10.1063/1.4825900","title":"The problem of age determination in living individuals","year":2013,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Icon; Citation; Download; Computer science; World Wide Web; Information retrieval; Publishing; Filter (signal processing); Search engine optimization; Search engine; Art","score_opus":0.023096093018463684,"score_gpt":0.28367820009784456,"score_spread":0.2605821070793809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064093647","genre_codex":"commentary","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":[0.25596017,0.07393927,0.11035951,0.3639742,0.0057618003,0.00018260912,0.002692185,0.00019084745,0.18693945],"genre_scores_gemma":[0.94182146,0.01884992,0.011721714,0.0054258737,0.0038166684,0.00018975952,0.0004925824,0.00006794464,0.017614115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9939056,0.0029950626,0.00028150622,0.001176014,0.0010927535,0.0005490928],"domain_scores_gemma":[0.9749908,0.018222537,0.0025346905,0.001308162,0.0019624545,0.0009813139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007933748,0.00031526462,0.0011255726,0.0015074012,0.003286363,0.0021736203,0.0013519517,0.0032502043,0.006781313],"category_scores_gemma":[0.056275632,0.0004518554,0.0005491033,0.002015557,0.004867812,0.0038726584,0.0029478762,0.004372756,0.0008847551],"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":[0.00012016874,0.000056859593,0.027582226,0.0003004908,0.00008233125,0.0013938269,0.0033252116,0.004178683,0.00010537651,0.73671955,0.076930374,0.14920484],"study_design_scores_gemma":[0.00002843441,0.000052415275,0.013439453,0.00039090513,0.000043588763,0.003615783,0.0025646344,0.008737279,0.00018485366,0.92399484,0.046867747,0.00008016772],"about_ca_topic_score_codex":0.010145658,"about_ca_topic_score_gemma":0.0038563232,"teacher_disagreement_score":0.010145658,"about_ca_system_score_codex":0.0019271648,"about_ca_system_score_gemma":0.0016703733,"threshold_uncertainty_score":0.041958213},"labels":[],"label_agreement":null},{"id":"W2064839688","doi":"10.1177/0962280211406470","title":"A multi-state model for the analysis of changes in cognitive scores over a fixed time interval","year":2011,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; Capital District Health Authority; Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Covariate; Statistics; Poisson distribution; Multivariate statistics; Cognition; Mathematics; Econometrics; Survival function; Poisson regression; Survival analysis; Psychology; Medicine; Population; Psychiatry","score_opus":0.3759594243516181,"score_gpt":0.6005438283901415,"score_spread":0.22458440403852342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064839688","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":[0.013837005,0.0004936409,0.98128945,0.0007256889,0.0001712411,0.00014586502,0.0011387873,0.0002737375,0.0019244474],"genre_scores_gemma":[0.6425258,0.0022725863,0.31462866,0.0006959073,0.0005728014,0.004397007,0.0043623983,0.00021073634,0.03033409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739885,0.0012853158,0.00011053365,0.0007553597,0.00023989363,0.00020998697],"domain_scores_gemma":[0.99342805,0.005071379,0.0005611372,0.00039341717,0.00039310133,0.00015296254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066235675,0.0014203077,0.0019997729,0.0015732186,0.0008708448,0.002369777,0.0033235585,0.0024724314,0.008576163],"category_scores_gemma":[0.011430042,0.00093407306,0.0026803035,0.0019397046,0.0016877997,0.0030733983,0.0016227182,0.0038569642,0.0016462597],"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":[0.00021621857,0.00016964265,0.0070997104,0.00020388087,0.00048390197,0.00034929218,0.00040758174,0.7120817,0.00089079275,0.2570105,0.0024606308,0.018626245],"study_design_scores_gemma":[0.0000511694,0.00012118354,0.0014137309,0.000028096201,0.000102691294,0.00009449113,0.000044851724,0.9201855,0.000115711584,0.07481076,0.002983467,0.000048331123],"about_ca_topic_score_codex":0.013399089,"about_ca_topic_score_gemma":0.012095616,"teacher_disagreement_score":0.013399089,"about_ca_system_score_codex":0.0021956216,"about_ca_system_score_gemma":0.0023878466,"threshold_uncertainty_score":0.035029173},"labels":[],"label_agreement":null},{"id":"W2067104964","doi":"10.3138/cpp.37.2.183","title":"Age of Pension Eligibility, Gains in Life Expectancy, and Social Policy","year":2011,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Life expectancy; Baby boom; Pension; Social security; Old Age Security; Economics; Population; Population ageing; Demographic economics; Retirement age; Public policy; Income Support; Actuarial science; Labour economics; Birth rate; Economic growth; Demography; Fertility; Finance; Sociology","score_opus":0.07508140886328703,"score_gpt":0.3413024003677224,"score_spread":0.2662209915044354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067104964","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":[0.60374296,0.008022453,0.010570795,0.048113663,0.00047331429,0.00044120123,0.014724137,0.00022319354,0.31368834],"genre_scores_gemma":[0.9440983,0.004206838,0.0014216079,0.0005432684,0.00011638371,0.00006623671,0.0011910608,0.000019973286,0.048336346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999119,0.0001180626,0.000017799473,0.000060395723,0.00020273421,0.0004820844],"domain_scores_gemma":[0.99920696,0.00011962201,0.00013755732,0.000019113764,0.00017324681,0.00034359068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008942028,0.00053722825,0.00055126956,0.0016198123,0.0026979668,0.0028057417,0.001227936,0.0016933163,0.011160641],"category_scores_gemma":[0.0025459684,0.00029169582,0.0006936213,0.00201444,0.0020688265,0.0008116548,0.0014325299,0.0013946401,0.0005561779],"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":[0.00021507204,0.00030571182,0.15328677,0.00018731102,0.00023419583,0.0006875102,0.0017794035,0.17104639,0.00067450234,0.5976125,0.036282085,0.03768865],"study_design_scores_gemma":[0.0002451253,0.0004572644,0.43305835,0.00064737245,0.0007432617,0.000588462,0.004968026,0.20615299,0.0006116143,0.13233915,0.21986301,0.00032538245],"about_ca_topic_score_codex":0.96438897,"about_ca_topic_score_gemma":0.9738801,"teacher_disagreement_score":0.035611033,"about_ca_system_score_codex":0.031051666,"about_ca_system_score_gemma":0.032615975,"threshold_uncertainty_score":0.22529668},"labels":[],"label_agreement":null},{"id":"W2068949246","doi":"10.1109/sc.companion.2012.142","title":"Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk","year":2012,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"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":"Aggregate (composite); Computer science; Systematic risk; Reinsurance; Portfolio; Pipeline (software); Risk management; Econometrics; Actuarial science; Finance; Economics","score_opus":0.029090355161968295,"score_gpt":0.34922832193337033,"score_spread":0.32013796677140205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068949246","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":[0.16941158,0.00023800066,0.8180862,0.00031118412,0.00008987077,0.00016810995,0.00027166537,0.0015332233,0.009890024],"genre_scores_gemma":[0.7094318,0.00021358456,0.286977,0.000063395404,0.00003758934,0.00030573344,0.00031874672,0.00022464992,0.0024274157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996468,0.00012837307,0.000022823977,0.00004999937,0.00010984416,0.00004204647],"domain_scores_gemma":[0.99822825,0.0010485502,0.00017501146,0.00020768562,0.00025359952,0.00008691742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009913684,0.00066813634,0.00066206395,0.0006510082,0.0005017235,0.0008242999,0.0009574302,0.0007483988,0.0028533102],"category_scores_gemma":[0.0039520366,0.00047012637,0.0006936699,0.0008007059,0.0005734379,0.0008096818,0.0006909895,0.0007341796,0.0003099482],"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":[0.00003707646,0.000020622858,0.0009922336,0.000016957882,0.000019768926,0.000032229855,0.00002915298,0.98794043,0.00064919447,0.0044883317,0.00023324197,0.0055407644],"study_design_scores_gemma":[0.00000868683,0.0000050773347,0.000092596885,0.0000016043907,0.0000025992529,0.0000056765757,0.000004953469,0.99700934,0.00023419966,0.002442859,0.00019053048,0.0000019451288],"about_ca_topic_score_codex":0.009065034,"about_ca_topic_score_gemma":0.006090316,"teacher_disagreement_score":0.009065034,"about_ca_system_score_codex":0.0008737019,"about_ca_system_score_gemma":0.0012064425,"threshold_uncertainty_score":0.018024564},"labels":[],"label_agreement":null},{"id":"W2069556303","doi":"10.1111/j.1467-9892.2004.01898.x","title":"A Note on the Filtering for Some Time Series Models","year":2004,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Martingale (probability theory); Series (stratigraphy); Econometrics; Nonlinear system; Applied mathematics; Time series; Mathematics; Stochastic volatility; Martingale difference sequence; Computer science; Volatility (finance); Statistics; Geology","score_opus":0.016916938255524972,"score_gpt":0.2769240881223707,"score_spread":0.2600071498668457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069556303","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":[0.0034732542,0.005142091,0.9737501,0.0061342963,0.0027910392,0.000028419823,0.00014617495,0.00014707884,0.008387583],"genre_scores_gemma":[0.28158975,0.036522184,0.579544,0.013047214,0.03724991,0.00044251088,0.0012297803,0.0005834268,0.04979124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99637383,0.0014563964,0.00033905936,0.0006829392,0.0010086033,0.00013901241],"domain_scores_gemma":[0.98213977,0.014273757,0.0006949279,0.0014063255,0.0012840244,0.00020130642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008425393,0.0011805282,0.001586496,0.0015127728,0.0010425067,0.0022773675,0.0013916828,0.0034077421,0.004311446],"category_scores_gemma":[0.023635898,0.0005560115,0.0041931467,0.0027284005,0.0021915087,0.004276671,0.0015683076,0.0068590306,0.0010789422],"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":[0.0000620711,0.00007157324,0.0010209263,0.0004034089,0.0002169875,0.0009659164,0.00026230034,0.035764046,0.002092802,0.87162936,0.02090526,0.06660536],"study_design_scores_gemma":[0.000018375205,0.00006936383,0.00067874085,0.00012354054,0.00012253567,0.00035050703,0.000027232521,0.23955172,0.0009442229,0.72218394,0.035863675,0.00006610277],"about_ca_topic_score_codex":0.006963931,"about_ca_topic_score_gemma":0.003314323,"teacher_disagreement_score":0.008425393,"about_ca_system_score_codex":0.0016147178,"about_ca_system_score_gemma":0.0011808639,"threshold_uncertainty_score":0.044558287},"labels":[],"label_agreement":null},{"id":"W2075346150","doi":"10.1080/08898480109525500","title":"A non‐linear model of fecundability, postpartum amenorrhea, and sterility","year":2001,"lang":"en","type":"article","venue":"Mathematical Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"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":"Fertility; Statistics; Sterility; Standard deviation; Amenorrhea; Mathematics; Demography; Total fertility rate; Gynecology; Medicine; Population; Biology; Pregnancy; Family planning","score_opus":0.09552493980976431,"score_gpt":0.38931810466563627,"score_spread":0.2937931648558719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075346150","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":[0.33537874,0.0012062661,0.63929045,0.002529886,0.0001338367,0.00016143109,0.0019406765,0.00056556804,0.018793255],"genre_scores_gemma":[0.9497001,0.00071260583,0.019725885,0.00018363945,0.00009899805,0.00027724393,0.0007241271,0.000068045236,0.028509406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999108,0.00028115348,0.000034730056,0.00023740024,0.00013922501,0.000199454],"domain_scores_gemma":[0.9979918,0.0012121558,0.00033948853,0.00013307335,0.00018547785,0.00013791538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00200212,0.0008279991,0.0009970655,0.0009809331,0.00057731953,0.0014550816,0.0025440739,0.0014610535,0.0056893965],"category_scores_gemma":[0.006630281,0.0007179518,0.001505595,0.0011515366,0.001216728,0.0018762364,0.001068229,0.001394257,0.00083927513],"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":[0.00018314683,0.00015703612,0.015686471,0.00008066677,0.00018897044,0.0005409622,0.00042001047,0.8345171,0.0017447226,0.1277846,0.0013574418,0.017338911],"study_design_scores_gemma":[0.00004906944,0.000093059374,0.006144737,0.000014437248,0.00006928609,0.00034325515,0.00003836163,0.96090454,0.00019422985,0.03021694,0.0018977857,0.000034373836],"about_ca_topic_score_codex":0.030859016,"about_ca_topic_score_gemma":0.0154712945,"teacher_disagreement_score":0.030859016,"about_ca_system_score_codex":0.0024816603,"about_ca_system_score_gemma":0.002009424,"threshold_uncertainty_score":0.06135881},"labels":[],"label_agreement":null},{"id":"W2075762584","doi":"10.1080/10920277.2000.10595940","title":"Self-Annuitization and Ruin in Retirement","year":2000,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada; York University","keywords":"Life annuity; Longevity risk; Economics; Portfolio; Consumption (sociology); Actuarial science; Present value; Annuity; Econometrics; Pension; Finance","score_opus":0.0077983334369978105,"score_gpt":0.27072395303364355,"score_spread":0.26292561959664573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075762584","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":[0.95478266,0.00026082955,0.041114215,0.00012953118,0.000010319068,0.000013866502,0.00007584118,0.00005616507,0.0035565088],"genre_scores_gemma":[0.99848133,0.000030024303,0.0006528212,0.000005992778,0.0000025643837,0.0000031729392,0.000024385945,0.000004407795,0.000795324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996456,0.00011194632,0.000019368626,0.000067102905,0.000074268704,0.00008163561],"domain_scores_gemma":[0.99629694,0.002372508,0.0007351211,0.00028440973,0.00016893407,0.00014212157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001574301,0.00017284426,0.00024253929,0.0006025373,0.00022408902,0.00060265476,0.00047231023,0.00057311496,0.0024252464],"category_scores_gemma":[0.0084350435,0.00016521363,0.00032303526,0.00025470025,0.0008404432,0.00090372143,0.0007200965,0.00043960926,0.00014344454],"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":[0.00025609805,0.00010754449,0.13398975,0.00005297598,0.000066496395,0.0008999919,0.00053586566,0.7432544,0.0030473322,0.09526172,0.00075832574,0.02176944],"study_design_scores_gemma":[0.000007841801,0.00009041368,0.026747186,0.000024147492,0.000013330857,0.00043381727,0.00014371371,0.9439469,0.0023202538,0.025574263,0.00067375385,0.000024408775],"about_ca_topic_score_codex":0.0017388469,"about_ca_topic_score_gemma":0.0010217061,"teacher_disagreement_score":0.0024252464,"about_ca_system_score_codex":0.0005464361,"about_ca_system_score_gemma":0.0001904244,"threshold_uncertainty_score":0.008325815},"labels":[],"label_agreement":null},{"id":"W2076506722","doi":"10.1057/gpp.2015.9","title":"Towards a Large and Liquid Longevity Market: A Graphical Population Basis Risk Metric","year":2015,"lang":"en","type":"article","venue":"The Geneva Papers on Risk and Insurance Issues and Practice","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"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 Manitoba; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Hong Kong; University of Manitoba; University of Waterloo; Research Manitoba; Chinese University of Hong Kong; Society of Actuaries","keywords":"Longevity risk; Population; Metric (unit); Actuarial science; Hedge; Basis risk; Computer science; Pension; Risk analysis (engineering); Econometrics; Business; Economics; Finance; Marketing; Demography; Capital asset pricing model; Biology; Ecology","score_opus":0.02370928290403955,"score_gpt":0.3275619783382949,"score_spread":0.30385269543425536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076506722","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":[0.04192945,0.00028302515,0.94872653,0.002001279,0.00008148139,0.000051226434,0.00052840065,0.00037176991,0.006026795],"genre_scores_gemma":[0.76228034,0.0005079225,0.22980952,0.0005729683,0.0003442135,0.00020427669,0.0008016721,0.0002678933,0.0052112024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978295,0.0012984866,0.0000772171,0.0002894011,0.0003739741,0.00013140557],"domain_scores_gemma":[0.99196714,0.004524644,0.00091903115,0.0010448138,0.0009678489,0.00057639874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053330194,0.0007286519,0.0010222279,0.0021760117,0.000518494,0.0033121393,0.001999124,0.0019320024,0.005105236],"category_scores_gemma":[0.019229276,0.0004003887,0.0010049174,0.001528938,0.0015960969,0.0047828606,0.0033359097,0.0026633374,0.000658159],"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":[0.00013734656,0.00007109759,0.003664308,0.00007567376,0.00007704811,0.00012167408,0.00022264505,0.12270729,0.0011467077,0.80437165,0.006653409,0.060751136],"study_design_scores_gemma":[0.000019676916,0.000065068125,0.0014023901,0.00002462802,0.000019742327,0.00007501039,0.000047199497,0.54479975,0.000243608,0.4505799,0.0026940515,0.000029032546],"about_ca_topic_score_codex":0.002200403,"about_ca_topic_score_gemma":0.0014560125,"teacher_disagreement_score":0.0053330194,"about_ca_system_score_codex":0.0013626096,"about_ca_system_score_gemma":0.000843528,"threshold_uncertainty_score":0.028204024},"labels":[],"label_agreement":null},{"id":"W2078503503","doi":"10.12927/hcq.2013.22230","title":"Hospital Standardized Mortality Ratios: A Tale of Two Sites","year":2011,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Covenant Health","funders":"","keywords":"Medicine","score_opus":0.04229288203137771,"score_gpt":0.34848161053395693,"score_spread":0.3061887285025792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078503503","genre_codex":"commentary","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":[0.06940795,0.09337542,0.24755672,0.34136283,0.013848665,0.0015165755,0.0063479315,0.0046910313,0.22189291],"genre_scores_gemma":[0.54053473,0.039772194,0.32463032,0.024840867,0.008880159,0.0012707768,0.0027132719,0.0035810734,0.05377661],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9546777,0.027239632,0.0025911024,0.002488241,0.012219488,0.00078379543],"domain_scores_gemma":[0.9106989,0.052565955,0.004402919,0.0091159465,0.020018408,0.003197861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056446668,0.0011898902,0.0011250902,0.018345278,0.0027495723,0.012561045,0.0028333622,0.0019334318,0.004389891],"category_scores_gemma":[0.13149108,0.0007203776,0.0009862182,0.014385966,0.014042207,0.010749615,0.0067745913,0.00663804,0.0009734496],"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":[0.00028506378,0.00010849694,0.04216828,0.0005710127,0.0002941587,0.00042746402,0.01759011,0.0011773566,0.0005971653,0.38725656,0.13600866,0.41351563],"study_design_scores_gemma":[0.00012828883,0.00058293197,0.06120134,0.0018076681,0.00022806445,0.00089907006,0.026422072,0.004307547,0.0025729907,0.19215938,0.70915705,0.0005335981],"about_ca_topic_score_codex":0.0745987,"about_ca_topic_score_gemma":0.07202561,"teacher_disagreement_score":0.0745987,"about_ca_system_score_codex":0.012520955,"about_ca_system_score_gemma":0.008778723,"threshold_uncertainty_score":0.2985221},"labels":[],"label_agreement":null},{"id":"W2078511436","doi":"10.1002/cjs.5550360202","title":"Forecasting mortality rates via density ratio modeling","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"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":"Curtin University of Technology","keywords":"Econometrics; Semiparametric model; Series (stratigraphy); Conditional probability distribution; Statistics; Moment (physics); Extension (predicate logic); Computer science; Mathematics; Nonparametric statistics","score_opus":0.09095401882357076,"score_gpt":0.29463025859777336,"score_spread":0.2036762397742026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078511436","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":[0.1966136,0.0004978722,0.79912025,0.0007060901,0.000046348163,0.00005077124,0.0003471719,0.00080759625,0.001810388],"genre_scores_gemma":[0.9595327,0.0002504285,0.038584076,0.00006570156,0.000059475744,0.00006224819,0.00033392364,0.000029072318,0.0010824214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989298,0.00068241346,0.000039725048,0.00013864796,0.00015307771,0.00005636535],"domain_scores_gemma":[0.9904479,0.007951919,0.00060114067,0.0003177662,0.00053633546,0.00014509959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034544081,0.00043822706,0.0009121764,0.0015801408,0.00014620344,0.00085789413,0.0012962748,0.0009798408,0.0014287696],"category_scores_gemma":[0.018950593,0.0004219635,0.0007695613,0.00074029434,0.0004543902,0.0011092309,0.00073061406,0.0010862206,0.00034269132],"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":[0.00011257716,0.000074749645,0.00948091,0.000033591623,0.00006768328,0.000056166664,0.000043336597,0.9402005,0.00046445034,0.0152590675,0.0008959689,0.03331095],"study_design_scores_gemma":[0.0000029700113,0.0000071313075,0.00040145544,0.0000019494348,0.000003119606,0.000005839496,0.000002389874,0.9965605,0.000054780485,0.0028973997,0.00005938918,0.000003043315],"about_ca_topic_score_codex":0.0057568476,"about_ca_topic_score_gemma":0.0023567001,"teacher_disagreement_score":0.0057568476,"about_ca_system_score_codex":0.0005797385,"about_ca_system_score_gemma":0.00035824176,"threshold_uncertainty_score":0.018268824},"labels":[],"label_agreement":null},{"id":"W2080172164","doi":"10.1017/s1041610202008025","title":"Imputation of Missing Dates of Death or Institutionalization for Time-to-Event Analyses in the Canadian Study of Health and Aging","year":2001,"lang":"en","type":"article","venue":"International Psychogeriatrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; Health and Social Services Centre University Institute of Geriatrics of Sherbrooke","funders":"","keywords":"Institutionalisation; Missing data; Imputation (statistics); Event (particle physics); Demography; Population; Medicine; Gerontology; Statistics; Psychiatry; Sociology; Mathematics","score_opus":0.07619536295500424,"score_gpt":0.4421094625760345,"score_spread":0.3659140996210303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080172164","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":[0.4472136,0.0045324904,0.49774146,0.0043943035,0.0013941688,0.0027941351,0.035875697,0.0011929047,0.0048613283],"genre_scores_gemma":[0.72546923,0.0018783163,0.24747981,0.0006326511,0.0002739869,0.0017904432,0.018732332,0.00016301477,0.0035802189],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.96636224,0.018231072,0.0033608507,0.0021345427,0.007162857,0.002748451],"domain_scores_gemma":[0.9470921,0.025679441,0.007531939,0.012647435,0.0061275177,0.00092157215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05071783,0.00067151006,0.0017721015,0.003805532,0.0031748416,0.0016411861,0.0039513796,0.0011638042,0.0020808845],"category_scores_gemma":[0.12963755,0.0008881091,0.0022079076,0.008705864,0.0009578878,0.0008287466,0.0017289632,0.0023701638,0.00024774988],"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":[0.0022893713,0.00022500077,0.73690796,0.0008191771,0.0026466507,0.0012183745,0.0020787779,0.026471253,0.0010394796,0.02275787,0.023743657,0.17980243],"study_design_scores_gemma":[0.00058208575,0.00055599323,0.80506194,0.00089361536,0.0021367932,0.0009009302,0.0014381311,0.12045079,0.0041347276,0.027581185,0.035930496,0.00033327634],"about_ca_topic_score_codex":0.61637324,"about_ca_topic_score_gemma":0.6795529,"teacher_disagreement_score":0.38362676,"about_ca_system_score_codex":0.0061119846,"about_ca_system_score_gemma":0.024790937,"threshold_uncertainty_score":0.771772},"labels":[],"label_agreement":null},{"id":"W2080745002","doi":"10.2307/3552547","title":"Independence and Economic Security in Old Age","year":2001,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Independence (probability theory); Political science; Business; Development economics; Economics; Mathematics","score_opus":0.0188668617137556,"score_gpt":0.2885423142759772,"score_spread":0.2696754525622216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080745002","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":[0.0073391953,0.63225275,0.00077098253,0.07248889,0.026163504,0.00009814622,0.002236989,0.000052339758,0.25859728],"genre_scores_gemma":[0.09107881,0.6184138,0.000945372,0.011575616,0.02925545,0.00017393532,0.002795714,0.00005266162,0.2457087],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9997633,0.000047738282,0.000014078905,0.000026703516,0.00009403978,0.000054178767],"domain_scores_gemma":[0.9994374,0.00013198887,0.000046330642,0.000018431307,0.00021521939,0.00015060155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005424274,0.00046199915,0.00034961486,0.0018207445,0.0013375074,0.0021338288,0.0004233479,0.0009261341,0.022951758],"category_scores_gemma":[0.0015679834,0.0001513496,0.00023543957,0.0019117004,0.0009991255,0.0013927786,0.0012334496,0.001942169,0.0033605848],"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":[0.000038625072,0.000045145833,0.0040296065,0.00062235247,0.000008075881,0.0001497631,0.0015566603,0.00018241061,0.00009008631,0.06213947,0.7649539,0.16618386],"study_design_scores_gemma":[0.000002491866,0.000019453528,0.015093697,0.00093181804,0.000003593688,0.00011674154,0.00074805133,0.000025700438,0.000033597233,0.00691967,0.9760953,0.0000098224],"about_ca_topic_score_codex":0.038648333,"about_ca_topic_score_gemma":0.03342709,"teacher_disagreement_score":0.9613517,"about_ca_system_score_codex":0.0025264025,"about_ca_system_score_gemma":0.0026332529,"threshold_uncertainty_score":0.07684678},"labels":[],"label_agreement":null},{"id":"W2082993001","doi":"10.1111/j.0006-341x.2001.00197.x","title":"Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Ministry of Health, British Columbia","keywords":"Interaction; Simple (philosophy); Computer science; Econometrics; Test (biology); Random effects model; Statistics; Mathematics; Medicine","score_opus":0.053828481904653155,"score_gpt":0.32136784621170883,"score_spread":0.26753936430705566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082993001","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":[0.7588504,0.0014984489,0.23617165,0.0005868735,0.00008381877,0.0001588218,0.0004266695,0.00025569758,0.0019675149],"genre_scores_gemma":[0.9711349,0.00014482952,0.027942719,0.000117665986,0.00004404121,0.00007571431,0.000185113,0.000025237732,0.00032980953],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.88052374,0.10375448,0.0021993343,0.007696521,0.0039288523,0.0018969757],"domain_scores_gemma":[0.6801876,0.29265156,0.011682521,0.012211278,0.0019293806,0.0013375942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.069495335,0.0010202297,0.0025064049,0.004801805,0.001575266,0.002308177,0.0023343428,0.0030774157,0.0015367563],"category_scores_gemma":[0.14735702,0.00091440236,0.0034037705,0.005319267,0.0047692005,0.002659512,0.0036645702,0.0019078109,0.00029971034],"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":[0.0015460803,0.00024230963,0.8800918,0.00036791616,0.0035268064,0.0013018762,0.0012618566,0.02924343,0.0040446026,0.0122446595,0.0004036239,0.065725215],"study_design_scores_gemma":[0.00014370437,0.0026168628,0.7886015,0.00010806971,0.0031012543,0.0021490334,0.0013309121,0.13964392,0.004570604,0.054687504,0.0026860253,0.00036063418],"about_ca_topic_score_codex":0.0053233635,"about_ca_topic_score_gemma":0.006473529,"teacher_disagreement_score":0.069495335,"about_ca_system_score_codex":0.00091868185,"about_ca_system_score_gemma":0.0010228915,"threshold_uncertainty_score":0.36753088},"labels":[],"label_agreement":null},{"id":"W2083018738","doi":"10.2105/ajph.2009.160341","title":"Understanding the Rapid Increase in Life Expectancy in South Korea","year":2010,"lang":"en","type":"article","venue":"American Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":120,"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 Institutes of Health Research","keywords":"Life expectancy; Medicine; Demography; Mortality rate; Disease; Cause of death; Longevity; Gerontology; Diabetes mellitus; Environmental health; Population; Surgery; Internal medicine","score_opus":0.09694154307073732,"score_gpt":0.3420999229288051,"score_spread":0.24515837985806777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083018738","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":[0.99629104,0.0008960442,0.00071570376,0.0004637148,0.00000631463,0.000007914356,0.0005011201,0.0000052076944,0.0011130588],"genre_scores_gemma":[0.99913555,0.0003059334,0.00025002682,0.000042605912,0.000004675466,0.0000041844705,0.00016601253,0.0000011437579,0.00008992076],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998646,0.00003723294,0.000015337993,0.0000344116,0.0000162332,0.000032226297],"domain_scores_gemma":[0.9994425,0.00009277083,0.00030542913,0.000025029536,0.00008753021,0.000046586483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005524039,0.00015965963,0.00010160222,0.0008705553,0.00010900444,0.00037687205,0.00016613587,0.0001544364,0.0009666995],"category_scores_gemma":[0.0012760352,0.00011007519,0.00018411556,0.00059083797,0.00016772549,0.00079113414,0.00053187914,0.00025232142,0.000078138],"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":[0.00005942886,0.000017830902,0.9646856,0.00015339063,0.00007380477,0.00031191699,0.0012745579,0.0014469405,0.0022230633,0.00073476555,0.0005060467,0.028512679],"study_design_scores_gemma":[0.0000015535567,0.000029760975,0.9967097,0.000019408248,0.000012962125,0.00017466591,0.0007505756,0.00072582276,0.0001758142,0.0003379788,0.0010574113,0.000004375487],"about_ca_topic_score_codex":0.005514784,"about_ca_topic_score_gemma":0.01061369,"teacher_disagreement_score":0.005514784,"about_ca_system_score_codex":0.00043434056,"about_ca_system_score_gemma":0.00030323377,"threshold_uncertainty_score":0.010965347},"labels":[],"label_agreement":null},{"id":"W2083569754","doi":"10.1016/j.insmatheco.2009.09.012","title":"Mortality risk modeling: Applications to insurance securitization","year":2009,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Longevity risk; Securitization; Annuity; Actuarial science; Jump; Life insurance; Econometrics; Cash flow; Longevity; Economics; Mortality rate; Cash; Life annuity; Pension; Demography; Finance; Medicine","score_opus":0.029047603305508884,"score_gpt":0.2981360623999476,"score_spread":0.26908845909443874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083569754","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":[0.029988326,0.0025402005,0.95615274,0.0043111765,0.00022261232,0.000032471813,0.00016287566,0.0001613659,0.006428185],"genre_scores_gemma":[0.79590756,0.0074920123,0.17055023,0.0007175275,0.0012639696,0.00022331867,0.00032728442,0.0001985565,0.02331946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946755,0.00029874576,0.000028557875,0.00007379386,0.00008333805,0.00004804446],"domain_scores_gemma":[0.99613696,0.0028151134,0.00032025468,0.00012568329,0.0003570308,0.00024490253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002597836,0.0011060543,0.0014588255,0.0011489629,0.00085448107,0.0023407305,0.0016860843,0.0023869993,0.0031372446],"category_scores_gemma":[0.0100934785,0.0005416478,0.0011724443,0.0016902721,0.0013678089,0.0020523758,0.0014686867,0.0022051926,0.0004237783],"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":[0.000020206247,0.00007340449,0.004190201,0.000057223326,0.00006160956,0.00014031748,0.0002578199,0.68074524,0.00030178268,0.2882402,0.0033355367,0.022576455],"study_design_scores_gemma":[0.000005457099,0.00000587272,0.00028392635,0.000012058086,0.000010108169,0.000030236128,0.000043417036,0.8773212,0.000047384186,0.12108388,0.0011473927,0.000009025803],"about_ca_topic_score_codex":0.013179899,"about_ca_topic_score_gemma":0.011462338,"teacher_disagreement_score":0.013179899,"about_ca_system_score_codex":0.0016105964,"about_ca_system_score_gemma":0.0018406064,"threshold_uncertainty_score":0.026206374},"labels":[],"label_agreement":null},{"id":"W2084824207","doi":"10.1111/j.1744-6163.1996.tb00512.x","title":"Closing of a Psychiatric Intensive Care Unit: A Manifestation of Lost Values","year":2009,"lang":"en","type":"article","venue":"Perspectives In Psychiatric Care","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto; Laurentian University","funders":"","keywords":"Intensive care unit; Psychiatry; Closing (real estate); Unit (ring theory); Intensive care; Medicine; Value (mathematics); Psychology; Nursing; Intensive care medicine; Political science","score_opus":0.019052054188777617,"score_gpt":0.34946183701160183,"score_spread":0.3304097828228242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084824207","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":[0.93279755,0.001971284,0.0007453144,0.044139847,0.00042046877,0.00005100731,0.000082025676,0.000029105999,0.01976345],"genre_scores_gemma":[0.9949772,0.0008774376,0.0002721552,0.002067061,0.00011646623,0.0000065145327,0.00001722362,0.000012087892,0.0016537915],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99866295,0.00034543796,0.000088552915,0.00008569969,0.00044850705,0.00036882734],"domain_scores_gemma":[0.99496776,0.0010304722,0.0018115896,0.00015406773,0.0007311973,0.0013048485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012103583,0.00024663183,0.00023601673,0.00059654086,0.008776752,0.0023249458,0.0012564673,0.0019765696,0.0026650552],"category_scores_gemma":[0.007832917,0.00023966447,0.00028131274,0.0009993135,0.0056929444,0.0013754057,0.002179882,0.004362898,0.00016101524],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013138611,0.00020779476,0.1620182,0.0006596034,0.00011002037,0.20563248,0.5113224,0.0004933191,0.0033235112,0.011830369,0.043010253,0.061260726],"study_design_scores_gemma":[0.000027926451,0.00015712697,0.11409071,0.00095238315,0.00006874314,0.10187332,0.7051994,0.0006115057,0.0009513157,0.004140917,0.071818255,0.000108389446],"about_ca_topic_score_codex":0.10772967,"about_ca_topic_score_gemma":0.23606631,"teacher_disagreement_score":0.10772967,"about_ca_system_score_codex":0.0110949585,"about_ca_system_score_gemma":0.014125567,"threshold_uncertainty_score":0.2142052},"labels":[],"label_agreement":null},{"id":"W2088549373","doi":"10.1016/s0167-6687(03)00129-x","title":"The Gerber–Shiu discounted penalty function in the stationary renewal risk model","year":2003,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":58,"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":"Penalty method; Risk model; Poisson distribution; Mathematics; Function (biology); Applied mathematics; Mathematical economics; Econometrics; Mathematical optimization; Statistics","score_opus":0.018446860236516177,"score_gpt":0.260285069900934,"score_spread":0.2418382096644178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088549373","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":[0.21766639,0.0048100348,0.7306172,0.010327386,0.00054089964,0.000067383386,0.00031746167,0.00022449756,0.035428762],"genre_scores_gemma":[0.92555434,0.0020068637,0.023860024,0.00037867244,0.00039319528,0.000079336736,0.00017944508,0.00010134442,0.047446836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99913305,0.0003624085,0.000037769085,0.00012714457,0.00016620301,0.00017352503],"domain_scores_gemma":[0.99720275,0.0014648978,0.00027580318,0.00018957935,0.00037497195,0.00049208844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032823607,0.0010039342,0.0023698988,0.0013187809,0.0010107923,0.0029229189,0.0027261195,0.0037177345,0.0042874627],"category_scores_gemma":[0.010296277,0.0006723535,0.0010640357,0.0011369176,0.003735239,0.004460208,0.0018643431,0.0034143394,0.00053959223],"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":[0.00005350663,0.000036072262,0.00046279354,0.00005297124,0.000032007083,0.0001325185,0.000118394775,0.11779953,0.00048555934,0.8741132,0.00165407,0.0050593717],"study_design_scores_gemma":[0.00001937675,0.000018233579,0.0003044787,0.000019473564,0.000018521368,0.00006452459,0.000032416734,0.586509,0.00009770073,0.41175368,0.0011356361,0.000027037193],"about_ca_topic_score_codex":0.0052559534,"about_ca_topic_score_gemma":0.0029866803,"teacher_disagreement_score":0.0052559534,"about_ca_system_score_codex":0.002452391,"about_ca_system_score_gemma":0.0022583671,"threshold_uncertainty_score":0.017793417},"labels":[],"label_agreement":null},{"id":"W2089365578","doi":"10.1002/sim.3295","title":"The power of testing a semi‐parametric shared gamma frailty parameter in failure time data","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Queen's University","funders":"","keywords":"Estimator; Nonparametric statistics; Statistics; Parametric statistics; Mathematics; Hazard; Power (physics); Parametric model; Function (biology); Econometrics; Computer science; Physics","score_opus":0.08350817376501589,"score_gpt":0.3597535548972013,"score_spread":0.2762453811321854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089365578","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":[0.45698887,0.0011324735,0.53385025,0.0018627613,0.00013592733,0.00025938387,0.00041454248,0.00024648328,0.005109299],"genre_scores_gemma":[0.973545,0.00019594183,0.025184283,0.00022861129,0.00007957755,0.00021610862,0.00022334256,0.000042720538,0.00028447772],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95322275,0.0372512,0.00146093,0.0035799239,0.0036185237,0.0008665962],"domain_scores_gemma":[0.35020456,0.6264243,0.00691948,0.013491618,0.002163079,0.0007969029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11421949,0.00084977056,0.002668709,0.0023108942,0.0006795767,0.0023593013,0.0024335934,0.003894262,0.0030155987],"category_scores_gemma":[0.38327876,0.0005401176,0.002782798,0.0017623261,0.0059980806,0.005070969,0.0033381712,0.0024763707,0.00042843525],"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":[0.008041472,0.00084257394,0.24260192,0.0015026027,0.0041773613,0.002130589,0.002497312,0.26192334,0.010744009,0.107345834,0.0031829474,0.35501003],"study_design_scores_gemma":[0.00093260186,0.003695101,0.08242059,0.00052930583,0.0009306676,0.002340564,0.0009705944,0.5963546,0.008900999,0.3004875,0.0022070748,0.00023053265],"about_ca_topic_score_codex":0.000686525,"about_ca_topic_score_gemma":0.00024133333,"teacher_disagreement_score":0.11421949,"about_ca_system_score_codex":0.0008699342,"about_ca_system_score_gemma":0.0013017607,"threshold_uncertainty_score":0.6040576},"labels":[],"label_agreement":null},{"id":"W2091113334","doi":"10.1002/cjs.5540330309","title":"Accelerated life regression modelling of dependent bivariate time-to-event data","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Mathematics; Statistics; Event (particle physics); Physics","score_opus":0.15602601224913537,"score_gpt":0.3282356958564785,"score_spread":0.17220968360734312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091113334","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":[0.026473563,0.0003055536,0.9714694,0.00037312633,0.00008963859,0.00010902457,0.00024021426,0.00023584797,0.0007037092],"genre_scores_gemma":[0.6212735,0.0012967419,0.36517045,0.00036618777,0.00025609435,0.0015517711,0.0019480245,0.0002303486,0.007906884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9859454,0.010854225,0.0004238159,0.0012491337,0.0010114317,0.00051599124],"domain_scores_gemma":[0.9151362,0.0694788,0.0052550165,0.0058493624,0.0033910917,0.00088959234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027664289,0.0013486505,0.0018020878,0.0017867967,0.0005165516,0.0015854441,0.0037050978,0.0017255979,0.004060442],"category_scores_gemma":[0.10349577,0.0009482535,0.0029980151,0.0018797499,0.0014677626,0.0021758915,0.002307426,0.004040794,0.0008192608],"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":[0.00049000356,0.00024515734,0.019207945,0.0003455456,0.00064200145,0.00067815976,0.00094476115,0.6532335,0.001102096,0.24727619,0.0026619863,0.07317265],"study_design_scores_gemma":[0.000047779806,0.00014580142,0.0022041744,0.000039952713,0.00006333044,0.00012819908,0.000052524916,0.9353763,0.0002960067,0.059812326,0.001799228,0.000034384513],"about_ca_topic_score_codex":0.004224898,"about_ca_topic_score_gemma":0.0029647776,"teacher_disagreement_score":0.027664289,"about_ca_system_score_codex":0.0011215978,"about_ca_system_score_gemma":0.0014852535,"threshold_uncertainty_score":0.14630449},"labels":[],"label_agreement":null},{"id":"W2092569563","doi":"10.1086/671712","title":"Paying the Piper: The High Cost of Funerals in South Africa","year":2008,"lang":"en","type":"article","venue":"Economic Development and Cultural Change","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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":"National Institute on Aging; Wellcome Trust; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Princeton University","keywords":"Quarter (Canadian coin); Demographic economics; Per capita; Economics; Socioeconomics; Demography; Geography; Labour economics; Population; Sociology","score_opus":0.10089005850804883,"score_gpt":0.27427720948018847,"score_spread":0.17338715097213964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092569563","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":[0.99824214,0.00018096334,0.00009134029,0.00059733,0.0000031006084,0.000008500015,0.00014091651,0.0000011149316,0.0007345796],"genre_scores_gemma":[0.9993237,0.00017818352,0.00005579291,0.000032218264,0.0000040179784,0.0000049687333,0.0000780468,7.7466984e-7,0.00032238013],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994777,0.00018481043,0.000023639363,0.000050266375,0.00007231596,0.00019134842],"domain_scores_gemma":[0.9971413,0.0010587866,0.0013143152,0.00006689102,0.000109552544,0.00030911402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007922884,0.00014629618,0.00025020537,0.0008028525,0.0012928427,0.001361909,0.0006196603,0.00088061177,0.004057977],"category_scores_gemma":[0.00778589,0.00017870973,0.00024043968,0.0014932425,0.0007919276,0.0014116842,0.0014678604,0.0011024805,0.00020066103],"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":[0.00014633882,0.00016788008,0.96201277,0.00008385831,0.00006787744,0.0019210282,0.009684095,0.0035802294,0.00020412823,0.003946029,0.0016271947,0.0165585],"study_design_scores_gemma":[0.000012101318,0.00009935859,0.9415521,0.00009959424,0.000030500098,0.00076555955,0.044095203,0.007342541,0.0001295233,0.0023159094,0.0035349827,0.00002263525],"about_ca_topic_score_codex":0.04727199,"about_ca_topic_score_gemma":0.085721985,"teacher_disagreement_score":0.04727199,"about_ca_system_score_codex":0.002097572,"about_ca_system_score_gemma":0.0010032863,"threshold_uncertainty_score":0.093993664},"labels":[],"label_agreement":null},{"id":"W2093395080","doi":"10.1016/j.jbankfin.2014.04.019","title":"Optimal portfolio selection with life insurance under inflation risk","year":2014,"lang":"en","type":"article","venue":"Journal of Banking & Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Economics; Bond; Hedge; Life insurance; Portfolio; Risk premium; Volatility (finance); Inflation (cosmology); Econometrics; Index (typography); Interest rate; Martingale (probability theory); Monetary economics; Financial economics; Actuarial science; Finance; Mathematics","score_opus":0.00928415868215113,"score_gpt":0.2542361625596884,"score_spread":0.24495200387753727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093395080","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":[0.55902153,0.0013407894,0.4250248,0.0037118448,0.00013801569,0.00014702584,0.0002836653,0.00020809859,0.010124265],"genre_scores_gemma":[0.97832423,0.00035582916,0.014989393,0.00013208138,0.00013269662,0.000050725554,0.00011065267,0.00002393237,0.005880355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874675,0.0007546246,0.00004809078,0.00016647212,0.00011405999,0.0001699818],"domain_scores_gemma":[0.99407107,0.0047382317,0.00045010468,0.00016505593,0.0002315679,0.00034397657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032663483,0.0006159026,0.002042859,0.0008520713,0.00032582702,0.0018345551,0.0010654853,0.0018519546,0.0033389835],"category_scores_gemma":[0.014465116,0.0009510598,0.0006910639,0.00087010296,0.0008223292,0.0018568124,0.0013198779,0.00089203997,0.00030113183],"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":[0.0005105415,0.00024460495,0.009274521,0.00014356646,0.00028663018,0.00047680736,0.0001446403,0.9103476,0.001129732,0.045464076,0.0018712984,0.030106068],"study_design_scores_gemma":[0.00009913882,0.00011624631,0.0016720019,0.000010456652,0.000042247222,0.00007208363,0.00003768146,0.96611774,0.0001690303,0.031428583,0.00022023455,0.000014631695],"about_ca_topic_score_codex":0.00204328,"about_ca_topic_score_gemma":0.001098854,"teacher_disagreement_score":0.0033389835,"about_ca_system_score_codex":0.0009316964,"about_ca_system_score_gemma":0.0010374745,"threshold_uncertainty_score":0.01727432},"labels":[],"label_agreement":null},{"id":"W2093695277","doi":"10.1080/10920277.2004.10596126","title":"Efficient Gain and Loss Amortization and Optimal Funding in Pension Plans","year":2004,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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":"Amortization; Pension; Asset (computer security); Economics; Amortizing loan; Actuarial science; Context (archaeology); Econometrics; Investment (military); Rate of return; Stochastic control; Finance; Mathematics; Computer science; Optimal control; Loan","score_opus":0.013861936592355292,"score_gpt":0.2821910403048427,"score_spread":0.2683291037124874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093695277","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":[0.44678897,0.0003521313,0.5484908,0.00030409568,0.000028685448,0.00013509938,0.00003477327,0.0001365487,0.0037288638],"genre_scores_gemma":[0.9620951,0.00009273109,0.036644578,0.000019204736,0.000010889426,0.000049393555,0.000017994218,0.000016731845,0.0010532851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980964,0.0010836624,0.00008725931,0.00013950077,0.00033612197,0.00025709014],"domain_scores_gemma":[0.99521637,0.0030378357,0.00057778927,0.00052742765,0.000386812,0.00025388878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005623443,0.00049083045,0.00064557075,0.0008182833,0.00040297743,0.0009725856,0.00082209136,0.00055522984,0.0015246883],"category_scores_gemma":[0.012795903,0.00033961618,0.0004617942,0.0004751617,0.001202756,0.0014483581,0.001427822,0.0006732258,0.00014277124],"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":[0.0002518628,0.000105934945,0.0020800652,0.00003836867,0.00006852837,0.000076443524,0.00016103378,0.87120867,0.0029895834,0.063484795,0.00036433272,0.059170343],"study_design_scores_gemma":[0.000039665312,0.00014814448,0.00072391244,0.0000119422375,0.000021766555,0.000040023406,0.0000408202,0.95890856,0.002069943,0.03755081,0.00043089566,0.000013438676],"about_ca_topic_score_codex":0.0021637443,"about_ca_topic_score_gemma":0.0010391334,"teacher_disagreement_score":0.005623443,"about_ca_system_score_codex":0.0012754495,"about_ca_system_score_gemma":0.00082196837,"threshold_uncertainty_score":0.029739976},"labels":[],"label_agreement":null},{"id":"W2093959889","doi":"10.1016/j.gaceta.2007.10.002","title":"La mortalidad evitable y no evitable: distribución geográfica en áreas pequeñas de España (1990–2001)","year":2009,"lang":"es","type":"article","venue":"Gaceta Sanitaria","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"","keywords":"Geography; Demography; Mortality rate; Distribution (mathematics); Medicine","score_opus":0.011551842922319151,"score_gpt":0.3058619675639846,"score_spread":0.29431012464166545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093959889","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":[0.9926547,0.001590903,0.00024123171,0.00009362667,0.000008924743,0.0000091557085,0.0036189368,0.000016873071,0.0017657577],"genre_scores_gemma":[0.9935514,0.0012554365,0.00028451215,0.000020422842,0.000014893129,0.000020048084,0.004006387,0.0000043531945,0.0008426026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981993,0.000044976056,0.00002269569,0.000057641286,0.000030673724,0.000024176356],"domain_scores_gemma":[0.9990865,0.0002617133,0.0003709529,0.00004935724,0.0001810886,0.000050352664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007586075,0.0002801271,0.00022079218,0.001829858,0.0000972317,0.00041462865,0.00031064573,0.00026888886,0.0012185449],"category_scores_gemma":[0.001689606,0.00009356969,0.0003395037,0.001894814,0.00018514805,0.00023857741,0.00043570306,0.00016106346,0.00015901453],"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":[0.00029881753,0.000025976933,0.979722,0.00011063178,0.00016463961,0.00015663124,0.00038814876,0.0012479118,0.00020329368,0.00024956497,0.0009965098,0.016435985],"study_design_scores_gemma":[0.000013671276,0.000025655874,0.99784315,0.00003338309,0.000035126533,0.00009470624,0.000272465,0.0003864927,0.00002902241,0.00008313225,0.0011791144,0.000004011589],"about_ca_topic_score_codex":0.06134416,"about_ca_topic_score_gemma":0.04196976,"teacher_disagreement_score":0.06134416,"about_ca_system_score_codex":0.0006262353,"about_ca_system_score_gemma":0.00030697123,"threshold_uncertainty_score":0.12197417},"labels":[],"label_agreement":null},{"id":"W2096560961","doi":"10.1037/a0023426","title":"Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study.","year":2011,"lang":"en","type":"article","venue":"Developmental Psychology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":150,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Aging","keywords":"Cohort; Life expectancy; Cohort effect; Psychology; Demography; Gerontology; Longitudinal study; Cognition; Cohort study; Life course approach; Population; Cognitive decline; Life span; Developmental psychology; Medicine; Dementia; Disease; Psychiatry","score_opus":0.1342174586502982,"score_gpt":0.39559880965838934,"score_spread":0.2613813510080911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096560961","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":[0.9967535,0.0007251199,0.00024339116,0.000308253,0.00003412347,0.0000181899,0.0011169945,0.00000685961,0.00079351896],"genre_scores_gemma":[0.998162,0.00025048762,0.00021094138,0.000054413496,0.000012810657,0.000019205303,0.0007989369,0.0000027441504,0.00048843556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995407,0.00023142874,0.000031205625,0.000089740264,0.000042945812,0.00006404481],"domain_scores_gemma":[0.99644876,0.0012728616,0.0009918768,0.00042308593,0.0002751524,0.0005882919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030804835,0.00029246326,0.00019448982,0.0012280997,0.00061340985,0.0006926902,0.00044559673,0.0005280542,0.0015320188],"category_scores_gemma":[0.0070769917,0.00021256242,0.0006128673,0.0010470907,0.00035293153,0.0006807412,0.00063104916,0.0010048341,0.00013243048],"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":[0.00012712934,0.00004529052,0.99705935,0.0000067485726,0.00018986111,0.00008350961,0.00045902657,0.00008491105,0.000050515682,0.00022366263,0.00040068408,0.0012693169],"study_design_scores_gemma":[0.000007556046,0.000084705294,0.99814296,0.000020460888,0.00009664676,0.000064386826,0.00040803626,0.00049492676,0.00002740569,0.00018977675,0.0004580973,0.0000050922977],"about_ca_topic_score_codex":0.056366224,"about_ca_topic_score_gemma":0.083011486,"teacher_disagreement_score":0.056366224,"about_ca_system_score_codex":0.00039694985,"about_ca_system_score_gemma":0.00045272135,"threshold_uncertainty_score":0.11207628},"labels":[],"label_agreement":null},{"id":"W2097941154","doi":"10.25336/p6fs5v","title":"Using cohort change ratios to estimate life expectancy in populations with negligible migration: A new approach","year":2012,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Life expectancy; Census; Econometrics; Censoring (clinical trials); Table (database); Statistics; Demographic analysis; Point estimation; Contrast (vision); Point (geometry); Computer science; Demography; Population; Mathematics; Sociology; Data mining; Artificial intelligence","score_opus":0.2570312184924722,"score_gpt":0.43400294439875214,"score_spread":0.17697172590627996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097941154","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":[0.04476047,0.002236424,0.94526047,0.00085165154,0.00039233494,0.00041748263,0.0008611249,0.00065140467,0.0045687226],"genre_scores_gemma":[0.33474126,0.0021641166,0.65708727,0.00028205407,0.00047542684,0.0004129678,0.0006424687,0.000138323,0.0040561557],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965798,0.0015393174,0.0002511355,0.00057118206,0.0009300654,0.00012839951],"domain_scores_gemma":[0.9941182,0.002992013,0.00049029593,0.00067660457,0.0015649227,0.00015799688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008031444,0.0007943166,0.0009107991,0.0067570377,0.0005689413,0.0013202657,0.002053899,0.00049105135,0.0016514363],"category_scores_gemma":[0.02198452,0.00031898366,0.0015185709,0.0029572798,0.00071878737,0.0014575967,0.0014082983,0.0010561881,0.00028251123],"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":[0.00022770367,0.00018965988,0.16477698,0.00043483815,0.0012434955,0.00060925796,0.0019508641,0.02840051,0.0033690394,0.06508812,0.0068147425,0.72689486],"study_design_scores_gemma":[0.0002401348,0.0008984729,0.29009038,0.00037594457,0.0013700952,0.0027173988,0.0021756643,0.545569,0.0045621586,0.069506474,0.08188775,0.0006065598],"about_ca_topic_score_codex":0.119759776,"about_ca_topic_score_gemma":0.12393522,"teacher_disagreement_score":0.119759776,"about_ca_system_score_codex":0.0021602497,"about_ca_system_score_gemma":0.0023606822,"threshold_uncertainty_score":0.23812538},"labels":[],"label_agreement":null},{"id":"W2098062766","doi":"10.1002/sim.822","title":"Simultaneous modelling of operative mortality and long‐term survival after coronary artery bypass surgery","year":2001,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; Simon Fraser University; University of Alberta","funders":"Division of Mathematical Sciences; Heart and Stroke Foundation of Canada","keywords":"Covariate; Poisson regression; Coronary artery bypass surgery; Proportional hazards model; Medicine; Survival analysis; Bypass surgery; Accelerated failure time model; Artery; Poisson distribution; Regression analysis; Term (time); Surgery; Cardiology; Internal medicine; Statistics; Mathematics; Population","score_opus":0.05708445331418451,"score_gpt":0.3571871711612406,"score_spread":0.3001027178470561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098062766","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":[0.6422361,0.000854877,0.34807676,0.0025842402,0.00011017334,0.00015118152,0.0014959337,0.0002999045,0.0041907313],"genre_scores_gemma":[0.9821181,0.00043056748,0.010180166,0.000056263445,0.00005004246,0.00024627981,0.0005478645,0.00002880823,0.006341817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978054,0.0011940178,0.00009392429,0.0003466712,0.00021279791,0.00034717095],"domain_scores_gemma":[0.9910863,0.006610396,0.0011788104,0.0003655627,0.00035455753,0.000404285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00417192,0.0010003996,0.0011260202,0.00084956293,0.0005201431,0.0016471173,0.0021884057,0.0020112263,0.0023379682],"category_scores_gemma":[0.01574837,0.00087472313,0.0019147323,0.0011256128,0.0013964808,0.0015608149,0.0028963138,0.0019385038,0.0003916712],"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":[0.0005565972,0.00023113929,0.035214413,0.00014298866,0.00035627352,0.00058423285,0.001126419,0.89828545,0.0019893534,0.04442648,0.00063869834,0.016448002],"study_design_scores_gemma":[0.000051704046,0.00028100648,0.014098812,0.000028400085,0.00012011994,0.00013028282,0.00014759738,0.9518325,0.0004886496,0.031725008,0.0010545684,0.000041414747],"about_ca_topic_score_codex":0.009488487,"about_ca_topic_score_gemma":0.009606617,"teacher_disagreement_score":0.009488487,"about_ca_system_score_codex":0.0014234993,"about_ca_system_score_gemma":0.0023247502,"threshold_uncertainty_score":0.022063494},"labels":[],"label_agreement":null},{"id":"W2099121581","doi":"10.1080/10920277.2001.10595951","title":"Impacts on Economic Security Programs of Rapidly Shifting Demographics","year":2001,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Baby boom; Life expectancy; Social security; Pension; Boom; Population; Old Age Security; Government (linguistics); Demographics; Business; Productivity; Population ageing; Economics; Labour economics; Health care; Economic growth; Birth rate; Fertility; Finance; Market economy; Medicine; Engineering","score_opus":0.017876673462148066,"score_gpt":0.29717009206751066,"score_spread":0.2792934186053626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099121581","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":[0.9086591,0.0022439254,0.0012476397,0.016587395,0.00042356222,0.00018870867,0.00041921964,0.000051768937,0.07017881],"genre_scores_gemma":[0.9933781,0.0013718378,0.00032128312,0.0012451804,0.00019757882,0.000046462606,0.00011855572,0.000005657007,0.003315401],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99780625,0.0011895684,0.00004414835,0.00006923079,0.00035596997,0.00053483306],"domain_scores_gemma":[0.9962727,0.0013928233,0.00077514234,0.00012602306,0.00065519253,0.00077805517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027237958,0.00023851306,0.00011351709,0.0010392878,0.0006919671,0.0006866254,0.0004715929,0.0006210423,0.0073266095],"category_scores_gemma":[0.0070274672,0.0000676921,0.0003273998,0.00047026147,0.00056421047,0.0005995367,0.0017966254,0.0006072538,0.0003739845],"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":[0.0008544022,0.0028292984,0.25641263,0.00042922847,0.000103583654,0.0018317685,0.002731168,0.016914422,0.002684385,0.111178644,0.026173908,0.57785654],"study_design_scores_gemma":[0.00017327169,0.0061875302,0.7685661,0.0013382396,0.00013692287,0.0011097704,0.011833679,0.008735507,0.0042550163,0.02688136,0.1706855,0.0000971198],"about_ca_topic_score_codex":0.0034490563,"about_ca_topic_score_gemma":0.0051412596,"teacher_disagreement_score":0.0073266095,"about_ca_system_score_codex":0.001408242,"about_ca_system_score_gemma":0.0016518524,"threshold_uncertainty_score":0.024509907},"labels":[],"label_agreement":null},{"id":"W2100244627","doi":"10.1080/08898480.2011.540173","title":"Modelling Deceleration in Senescent Mortality","year":2011,"lang":"en","type":"article","venue":"Mathematical Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":36,"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","funders":"","keywords":"Mortality rate; Demography; Cohort; Gerontology; Statistics; Econometrics; Medicine; Economics; Mathematics; Sociology","score_opus":0.31633522314603163,"score_gpt":0.408388313478529,"score_spread":0.09205309033249737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100244627","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":[0.7924263,0.0011776539,0.1982372,0.0005613072,0.00014332257,0.0000712833,0.0012699312,0.00039453025,0.0057184957],"genre_scores_gemma":[0.9882582,0.00036061823,0.0067567034,0.000048203183,0.000034619865,0.00005297437,0.00039086715,0.00004827642,0.0040494455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996979,0.0001019803,0.000017906677,0.00008309232,0.000034347016,0.000064809145],"domain_scores_gemma":[0.99792045,0.0012686617,0.0003460404,0.00010157137,0.00018000822,0.00018323134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013089796,0.0007410043,0.00070810004,0.0009848662,0.00022969455,0.0009806247,0.001452728,0.0014113346,0.0018622935],"category_scores_gemma":[0.0071243555,0.00039946052,0.00092710793,0.0005995704,0.0006448273,0.00066603516,0.0008640239,0.0008182255,0.00032264713],"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":[0.00007164461,0.000030318131,0.006378017,0.00005102885,0.00003585332,0.00012433124,0.00009560384,0.97831386,0.0009261843,0.009046236,0.00047574306,0.0044510723],"study_design_scores_gemma":[0.000007931919,0.000021917926,0.0012139558,0.000007692022,0.0000089998875,0.00002847921,0.000013244063,0.99582505,0.00011944087,0.0024100572,0.0003351828,0.000008139813],"about_ca_topic_score_codex":0.012404265,"about_ca_topic_score_gemma":0.0055209254,"teacher_disagreement_score":0.012404265,"about_ca_system_score_codex":0.0009865208,"about_ca_system_score_gemma":0.0006242977,"threshold_uncertainty_score":0.024664104},"labels":[],"label_agreement":null},{"id":"W2100843150","doi":"10.1111/1475-6773.12403","title":"The Impact of Improved Population Life Expectancy in Survival Trend Analyses of Specific Diseases","year":2015,"lang":"en","type":"article","venue":"Health Services Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Ottawa Hospital; University of Ottawa","funders":"University of Ottawa","keywords":"Life expectancy; Medicine; Demography; Population; Survival analysis; Mortality rate; Relative survival; Medical diagnosis; Gerontology; Environmental health; Internal medicine; Pathology","score_opus":0.20383456949158785,"score_gpt":0.5363243875824955,"score_spread":0.3324898180909076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100843150","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":[0.9198621,0.0081544,0.05996041,0.0031824159,0.00036893087,0.00026928194,0.003065661,0.0002071664,0.004929746],"genre_scores_gemma":[0.9840095,0.0009985062,0.013438755,0.00015388569,0.00015477835,0.000081713966,0.0008203066,0.000039861137,0.00030281884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9751874,0.019688806,0.00154349,0.0010829752,0.0019701568,0.00052718475],"domain_scores_gemma":[0.8844551,0.0916524,0.01266408,0.0049727536,0.005666231,0.0005895469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051700868,0.00050181145,0.00062466343,0.0021175728,0.00033517784,0.00126688,0.0005473306,0.00043826987,0.0015146681],"category_scores_gemma":[0.14088528,0.00021918317,0.0023592205,0.0042047845,0.0006088925,0.0027796214,0.0010854919,0.0010061124,0.00014060717],"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":[0.000564108,0.0000715108,0.90851194,0.0005309553,0.00242462,0.00014263579,0.000580873,0.006409338,0.00038394076,0.0015374006,0.0008474356,0.0779952],"study_design_scores_gemma":[0.000041061132,0.0014763243,0.9548221,0.00043771524,0.002545143,0.00039700637,0.000641415,0.029987158,0.0011677449,0.0030977505,0.005331332,0.000055191962],"about_ca_topic_score_codex":0.009828161,"about_ca_topic_score_gemma":0.018274147,"teacher_disagreement_score":0.051700868,"about_ca_system_score_codex":0.00095361343,"about_ca_system_score_gemma":0.001929549,"threshold_uncertainty_score":0.2734236},"labels":[],"label_agreement":null},{"id":"W2102453338","doi":"","title":"Data Validation and Measurement of Cohort Mortality among Centenarians in Quebec (Canada) According to Ethnic Origin","year":2008,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Life expectancy; Demography; Ethnic group; Cohort; Mortality rate; Longevity; Gerontology; Medicine; Population; Sociology; Internal medicine","score_opus":0.1437644230952529,"score_gpt":0.34403205665257164,"score_spread":0.20026763355731875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102453338","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":[0.50771123,0.37323937,0.013067615,0.005574611,0.0016115074,0.0034117533,0.07250227,0.00027264364,0.022609001],"genre_scores_gemma":[0.8070188,0.13224933,0.011445026,0.0011545515,0.00022234739,0.0019701077,0.03823438,0.00010236121,0.0076031457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.991115,0.0020084816,0.0008753237,0.00081940816,0.004765011,0.00041674197],"domain_scores_gemma":[0.92611325,0.011345979,0.003354313,0.0027606485,0.055619407,0.00080634357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021687405,0.00067209586,0.001030648,0.005535328,0.001720231,0.0021359143,0.0026902272,0.00061510416,0.0019078656],"category_scores_gemma":[0.04570655,0.00044636984,0.0011992141,0.010369057,0.0010931909,0.00051278016,0.0007265561,0.0006489491,0.00052849576],"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":[0.0002892984,0.000068482375,0.78352755,0.0033580696,0.0015457643,0.00007748082,0.001976172,0.00040580292,0.00040212704,0.00067180325,0.018445006,0.18923245],"study_design_scores_gemma":[0.000029488936,0.00007856048,0.9699299,0.0035487604,0.0009910284,0.00007579111,0.0010303115,0.000341136,0.0004062428,0.000060611383,0.02346327,0.000044942495],"about_ca_topic_score_codex":0.9649255,"about_ca_topic_score_gemma":0.97835404,"teacher_disagreement_score":0.035074472,"about_ca_system_score_codex":0.01862331,"about_ca_system_score_gemma":0.04390661,"threshold_uncertainty_score":0.13512218},"labels":[],"label_agreement":null},{"id":"W2103475226","doi":"10.1111/j.1475-4991.2008.00272.x","title":"LIFETIMES OF MACHINERY AND EQUIPMENT: EVIDENCE FROM DUTCH MANUFACTURING","year":2008,"lang":"en","type":"article","venue":"Review of Income and Wealth","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":60,"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":"Weibull distribution; Econometrics; Asset (computer security); Stock (firearms); Capital asset; Economics; Service (business); Manufacturing; Business; Statistics; Finance; Engineering; Computer science; Economy; Mathematics; Marketing","score_opus":0.0293045639611985,"score_gpt":0.3239343820497526,"score_spread":0.2946298180885541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103475226","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":[0.9586412,0.02555175,0.0009785134,0.00025928664,0.000027010921,0.000015527228,0.0074664378,0.000005191243,0.007055173],"genre_scores_gemma":[0.974195,0.015792323,0.00027364262,0.000071884155,0.00002539396,0.000020197773,0.0077716773,0.000008520639,0.0018412876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99907184,0.0001452277,0.00015727273,0.0002092089,0.0003250368,0.00009133125],"domain_scores_gemma":[0.99579465,0.0011155566,0.0020040434,0.00020690978,0.00073241384,0.00014647396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011497819,0.00016033501,0.00032587608,0.0017651796,0.00020407916,0.00065545546,0.00047642732,0.00026964853,0.0017894879],"category_scores_gemma":[0.0063363872,0.00014561535,0.00041107638,0.0050356355,0.00024997146,0.00066681736,0.0005559082,0.00023786195,0.00030877755],"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":[0.00022857409,0.00003273191,0.92315674,0.0010693378,0.00033831614,0.0008238683,0.0028146284,0.0009961114,0.00037501828,0.0016520409,0.0034569874,0.06505571],"study_design_scores_gemma":[0.0000036462623,0.000037377697,0.9850513,0.00023485016,0.00008756331,0.00033444064,0.0014487553,0.00040503868,0.00021045248,0.00019310584,0.01198363,0.000009907192],"about_ca_topic_score_codex":0.08902463,"about_ca_topic_score_gemma":0.10427272,"teacher_disagreement_score":0.08902463,"about_ca_system_score_codex":0.0008273938,"about_ca_system_score_gemma":0.0007112449,"threshold_uncertainty_score":0.17701292},"labels":[],"label_agreement":null},{"id":"W2104081209","doi":"10.48550/arxiv.1101.1796","title":"Properties of a Stochastic Model for Life Table Data: Exploring Life Expectancy Limits","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Life expectancy; Gompertz function; Vitality; Econometrics; Table (database); Population; Life table; Stochastic modelling; Statistics; Demography; Mathematics; Computer science; Sociology; Biology","score_opus":0.49565823681238685,"score_gpt":0.25461531947707267,"score_spread":0.2410429173353142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104081209","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":[0.18993552,0.000800006,0.8019574,0.0022710792,0.00005520262,0.00011618264,0.0009518062,0.00041396913,0.0034987004],"genre_scores_gemma":[0.94813293,0.0010047132,0.044259373,0.00056365057,0.00015701781,0.00035942454,0.0019456231,0.00023076008,0.003346488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957579,0.0022815673,0.00019102573,0.00077276526,0.00061931653,0.00037745424],"domain_scores_gemma":[0.9053524,0.07852013,0.0076680398,0.00331025,0.0032333324,0.0019158074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018037822,0.0010356486,0.0025875177,0.0030978671,0.0011053903,0.0036302716,0.0033640715,0.0026650755,0.0029102352],"category_scores_gemma":[0.09879887,0.0011596974,0.0020743506,0.0019265714,0.003429648,0.0064222123,0.0026754402,0.0037623527,0.00042280462],"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":[0.00012329394,0.000088854766,0.01743728,0.0002106861,0.00021710817,0.00046344026,0.0009537822,0.5320085,0.0009053657,0.43661237,0.0016964765,0.009282849],"study_design_scores_gemma":[0.00001668344,0.000045879875,0.0015149481,0.000044911114,0.00001858891,0.00011634347,0.000075934986,0.87209487,0.0001448578,0.12532686,0.0005676466,0.00003251438],"about_ca_topic_score_codex":0.008098888,"about_ca_topic_score_gemma":0.0029929942,"teacher_disagreement_score":0.018037822,"about_ca_system_score_codex":0.0023938457,"about_ca_system_score_gemma":0.001357832,"threshold_uncertainty_score":0.095394254},"labels":[],"label_agreement":null},{"id":"W2105525533","doi":"10.4054/demres.2011.24.34","title":"A Dynamic Extension of the Period Life Table","year":2011,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Period (music); Table (database); Life table; Allowance (engineering); Demography; Economics; Operations management; Computer science; Sociology; Population; Philosophy; Database","score_opus":0.11384514530773898,"score_gpt":0.37900855932050953,"score_spread":0.26516341401277055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105525533","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":[0.014952423,0.001761547,0.92144394,0.0019931993,0.00045730264,0.00014221358,0.0041079363,0.00042685214,0.05471452],"genre_scores_gemma":[0.5721619,0.005962572,0.35589772,0.00091738335,0.0016215441,0.00056985725,0.0046687755,0.00053967716,0.057660483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987649,0.00045675808,0.00006712939,0.0002944196,0.00029596963,0.000120811004],"domain_scores_gemma":[0.9956151,0.0023607549,0.00043320522,0.0007322542,0.00056735927,0.0002913938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003011308,0.00043107022,0.0005127703,0.001327851,0.0006956374,0.0025621562,0.0017138688,0.0007911088,0.025157126],"category_scores_gemma":[0.012325612,0.00045955795,0.0011297754,0.002348928,0.00076505204,0.0051082666,0.0015011587,0.0020014541,0.00285448],"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":[0.00007018991,0.000019008645,0.0016768323,0.00006416648,0.000033081145,0.00014004252,0.00016351018,0.03120405,0.00025234066,0.9208711,0.007735885,0.03776987],"study_design_scores_gemma":[0.000031000425,0.00006297616,0.001512273,0.000063749874,0.000042154985,0.0004204833,0.00004501605,0.11851036,0.00017683323,0.7982709,0.08082374,0.00004047044],"about_ca_topic_score_codex":0.0037979828,"about_ca_topic_score_gemma":0.002407038,"teacher_disagreement_score":0.025157126,"about_ca_system_score_codex":0.0013734329,"about_ca_system_score_gemma":0.0013609586,"threshold_uncertainty_score":0.08415896},"labels":[],"label_agreement":null},{"id":"W2106053216","doi":"10.2307/3315932","title":"Interval censoring: Model characterizations for the validity of the simplified likelihood","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":60,"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":"Censoring (clinical trials); Mathematics; Statistics; Maximum likelihood; Likelihood function; Estimator; Nonparametric statistics; Equivalence (formal languages); Applied mathematics; Econometrics; Discrete mathematics","score_opus":0.059633291311302634,"score_gpt":0.29453476309242455,"score_spread":0.23490147178112192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106053216","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":[0.01704404,0.00049539097,0.9766325,0.0009955979,0.00004316777,0.00008447164,0.000260121,0.00013428323,0.0043104007],"genre_scores_gemma":[0.8297074,0.00161864,0.15908135,0.00095531833,0.00057802495,0.0008669646,0.001211751,0.00028428138,0.00569629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9838393,0.010226459,0.00080078706,0.0014978888,0.0027569921,0.0008786056],"domain_scores_gemma":[0.7406625,0.22679463,0.012878131,0.011724386,0.006292434,0.0016479181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028797515,0.001055288,0.002418957,0.0028924511,0.00085360516,0.0035549002,0.004262649,0.002403788,0.006303549],"category_scores_gemma":[0.17947884,0.00096228597,0.0023313453,0.0026768784,0.006698816,0.007607,0.00480907,0.0054961797,0.0010010998],"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":[0.00012036899,0.00005290428,0.0027151767,0.00017776378,0.00009159086,0.00026433,0.0005233195,0.11910408,0.0004497447,0.8609272,0.0019327691,0.013640676],"study_design_scores_gemma":[0.000049179864,0.00004847959,0.0011965671,0.000081093785,0.00004007877,0.0001677451,0.00007146367,0.43422592,0.00026634574,0.562118,0.0016915039,0.00004359921],"about_ca_topic_score_codex":0.004196039,"about_ca_topic_score_gemma":0.0015372151,"teacher_disagreement_score":0.028797515,"about_ca_system_score_codex":0.0025207396,"about_ca_system_score_gemma":0.0019137905,"threshold_uncertainty_score":0.15229768},"labels":[],"label_agreement":null},{"id":"W2106562467","doi":"10.7202/039991ar","title":"Application de l’analyse des séries chronologiques à la projection d’effectifs de population scolaire par la méthode des composantes","year":2010,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Humanities; Mathematics; Philosophy","score_opus":0.011714209478168413,"score_gpt":0.28207900336468106,"score_spread":0.27036479388651263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106562467","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":[0.012421333,0.00022367174,0.98497134,0.00014969226,0.00009429967,0.000056245735,0.00026756575,0.00023797338,0.0015778209],"genre_scores_gemma":[0.26537293,0.0018501151,0.72208923,0.0001280144,0.00039107478,0.00091448816,0.0009895706,0.00042198977,0.007842604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980394,0.0010697308,0.00009154854,0.00034101136,0.00038893608,0.000069441594],"domain_scores_gemma":[0.9905131,0.007503961,0.0004133414,0.00087233953,0.0005919755,0.00010522458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005576338,0.0009641245,0.0006020574,0.0025990943,0.00056204846,0.001296949,0.00076165376,0.0005389264,0.006625591],"category_scores_gemma":[0.023394832,0.00039371426,0.0017218932,0.0020388393,0.0008105186,0.0012754264,0.0007421457,0.0021374605,0.0011587391],"study_design_candidate":"simulation_or_modeling","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":[0.00014987703,0.00009317515,0.03536291,0.00038750295,0.00068234187,0.00034205368,0.0016909414,0.18528993,0.006823084,0.27497673,0.0028173367,0.49138418],"study_design_scores_gemma":[0.000070135226,0.00020900783,0.036657874,0.00023199484,0.00020966785,0.0007874427,0.0006157605,0.73843265,0.010437321,0.13674341,0.07544673,0.00015807508],"about_ca_topic_score_codex":0.014003692,"about_ca_topic_score_gemma":0.008540073,"teacher_disagreement_score":0.9859963,"about_ca_system_score_codex":0.0006555223,"about_ca_system_score_gemma":0.001363661,"threshold_uncertainty_score":0.029490888},"labels":[],"label_agreement":null},{"id":"W2108584434","doi":"10.25336/p6z88g","title":"Modeling and Projection of Age and Sex Specific Marriage Rates","year":2000,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Projection (relational algebra); Demography; Geography; Sociology; Mathematics; Algorithm","score_opus":0.06037514756090169,"score_gpt":0.3384911177321485,"score_spread":0.27811597017124684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108584434","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":[0.8963597,0.0005132015,0.07710558,0.001102653,0.0000832714,0.0001576822,0.009530612,0.0010169175,0.014130409],"genre_scores_gemma":[0.97467834,0.00028895482,0.01753612,0.000046532896,0.000011718227,0.000073524025,0.0026494497,0.0000570382,0.0046583563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974686,0.000059285136,0.000011420106,0.00005720725,0.00004577968,0.00007943562],"domain_scores_gemma":[0.9991273,0.000373853,0.00005620685,0.00005435077,0.00031002963,0.00007821564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009044313,0.00048686087,0.0005869248,0.0010602524,0.0011241843,0.0012494362,0.0019170977,0.000941438,0.0033123174],"category_scores_gemma":[0.002924366,0.00071454095,0.001107196,0.0018038651,0.00045864133,0.00051909155,0.00044623137,0.00081995,0.00048387534],"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":[0.00003567778,0.000027986536,0.01405577,0.00002220741,0.000037553913,0.00004389028,0.00008715249,0.9741461,0.00016859446,0.0037534777,0.0010446496,0.006576947],"study_design_scores_gemma":[0.0000074628283,0.0000062593417,0.0043527614,0.000007070011,0.0000143282405,0.000012063804,0.000060095896,0.99399215,0.0000987791,0.0008334753,0.0006023068,0.000013157251],"about_ca_topic_score_codex":0.91986674,"about_ca_topic_score_gemma":0.8819148,"teacher_disagreement_score":0.08013326,"about_ca_system_score_codex":0.007086168,"about_ca_system_score_gemma":0.009497629,"threshold_uncertainty_score":0.1612103},"labels":[],"label_agreement":null},{"id":"W2108890792","doi":"10.1590/s1135-57272010000300005","title":"El coste de mortalidad asociado al consumo de tabaco en España","year":2010,"lang":"es","type":"article","venue":"Revista Española de Salud Pública","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Association Canadienne des Technologues en Radiation Médicale","keywords":"Medicine; Humanities; Philosophy","score_opus":0.008984696572011718,"score_gpt":0.33599322209420907,"score_spread":0.32700852552219734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108890792","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":[0.96639055,0.0063784784,0.005460007,0.0047278567,0.000079385216,0.00007637096,0.008075753,0.00005149753,0.008760142],"genre_scores_gemma":[0.98524415,0.0032615429,0.001393983,0.0003343486,0.000041153573,0.00005167015,0.0029901825,0.000007166322,0.006675828],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961996,0.00016860309,0.00002350236,0.00006107838,0.00007680221,0.000049951657],"domain_scores_gemma":[0.99914837,0.00039941122,0.00019186929,0.000049087743,0.00016000838,0.000051219307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012556274,0.0003068396,0.0003519828,0.00048694163,0.00018098945,0.0008925944,0.0004787139,0.00071647234,0.0054520434],"category_scores_gemma":[0.002695093,0.00016547297,0.0008178304,0.001261488,0.00019865793,0.0004966141,0.0006174098,0.0004925052,0.0003757014],"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":[0.0021837268,0.00031108165,0.7655032,0.0011730384,0.0016339073,0.0003787349,0.00046243623,0.0932974,0.0029434087,0.014894415,0.007573763,0.10964476],"study_design_scores_gemma":[0.0003111268,0.0015509271,0.8949658,0.0007712809,0.0013187481,0.00043668484,0.0013796395,0.06705765,0.0017630449,0.014222507,0.01615677,0.00006570321],"about_ca_topic_score_codex":0.1064063,"about_ca_topic_score_gemma":0.06460819,"teacher_disagreement_score":0.1064063,"about_ca_system_score_codex":0.002600865,"about_ca_system_score_gemma":0.0022578225,"threshold_uncertainty_score":0.2115739},"labels":[],"label_agreement":null},{"id":"W2109144923","doi":"10.25336/p6js65","title":"Cohort Working Life Tables for Older Canadians","year":2010,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Human Resources and Skills Development Canada","keywords":"Life expectancy; Cohort; Life table; Demography; Table (database); Gerontology; Cohort effect; Statistics; Psychology; Medicine; Population; Sociology; Mathematics; Database; Computer science","score_opus":0.04560246168035859,"score_gpt":0.3467427472892173,"score_spread":0.3011402856088587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109144923","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":[0.0067411554,0.0012529504,0.005062247,0.0005480952,0.00019869745,0.0007192513,0.94810313,0.0009311576,0.036443196],"genre_scores_gemma":[0.050352402,0.0031199048,0.023751222,0.0004304448,0.00009740033,0.00149843,0.8836128,0.00036988637,0.03676756],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99855083,0.00011938237,0.0001838908,0.00014740854,0.0007130194,0.00028546093],"domain_scores_gemma":[0.989931,0.0009219728,0.0006808391,0.00086914585,0.007111702,0.00048537384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002235631,0.000526659,0.0004681951,0.009568568,0.0017052557,0.0016410735,0.0013096593,0.00033657427,0.04715965],"category_scores_gemma":[0.012583918,0.00034756344,0.00092833844,0.013786759,0.00018997156,0.00065914297,0.0006870083,0.0008058118,0.005823572],"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":[0.00012338019,0.000036232876,0.029227888,0.0006607238,0.00012820825,0.00010715571,0.0005469255,0.0026890729,0.00015297673,0.013398317,0.84880096,0.10412825],"study_design_scores_gemma":[0.000050957504,0.000023950726,0.12321382,0.0003576977,0.00006729509,0.00015734069,0.00040081813,0.0016860483,0.00012230832,0.0022528223,0.8715912,0.00007570074],"about_ca_topic_score_codex":0.9518409,"about_ca_topic_score_gemma":0.95472544,"teacher_disagreement_score":0.048159122,"about_ca_system_score_codex":0.012509564,"about_ca_system_score_gemma":0.0311704,"threshold_uncertainty_score":0.15776473},"labels":[],"label_agreement":null},{"id":"W2109353468","doi":"10.1177/1403494815577459","title":"Leaving Sweden behind: Gains in life expectancy in Canada","year":2015,"lang":"en","type":"article","venue":"Scandinavian Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal; Institut National de Santé Publique du Québec","funders":"","keywords":"Life expectancy; Demography; Population; Population ageing; Gerontology; Medicine; Ageing; Geography; Sociology","score_opus":0.16383140260247597,"score_gpt":0.3751322285722894,"score_spread":0.21130082596981342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109353468","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":[0.9575117,0.008785908,0.00026379336,0.0057096616,0.00012427085,0.000037582897,0.008443767,0.000036339745,0.019086896],"genre_scores_gemma":[0.9948009,0.0019683284,0.00017702945,0.0002815997,0.000014542359,0.000007927321,0.0014340357,0.0000069039615,0.00130875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99924755,0.000034898105,0.000032326727,0.00007313439,0.0002461335,0.0003661151],"domain_scores_gemma":[0.9979127,0.000088212226,0.00023088661,0.00003562404,0.00095467205,0.00077784574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005896678,0.00019216738,0.00031365114,0.0016965437,0.0029886758,0.0015870475,0.00079695205,0.0004097182,0.0023774833],"category_scores_gemma":[0.0029720399,0.00010065831,0.00043385415,0.0036457584,0.0005461921,0.00050341664,0.0011253324,0.00085726415,0.00018238956],"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":[0.00017401499,0.000050507457,0.91815865,0.00016189847,0.000111315116,0.00053467654,0.0038527988,0.00052731944,0.000251499,0.0018359247,0.010221579,0.06411979],"study_design_scores_gemma":[0.000006880673,0.000017706448,0.98862505,0.0001374309,0.000042520467,0.00014227835,0.0025558344,0.00041967398,0.00009222716,0.00025345557,0.007686528,0.000020390105],"about_ca_topic_score_codex":0.99118936,"about_ca_topic_score_gemma":0.995777,"teacher_disagreement_score":0.030338872,"about_ca_system_score_codex":0.030338872,"about_ca_system_score_gemma":0.03457593,"threshold_uncertainty_score":0.2201249},"labels":[],"label_agreement":null},{"id":"W2111949477","doi":"10.1017/s1748499512000061","title":"A Semi-Markov Multiple State Model for Reverse Mortgage Terminations","year":2012,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Prepayment of loan; Equity (law); Home equity; Valuation (finance); Mortgage insurance; Business; Actuarial science; Shared appreciation mortgage; Economics; Finance; Insurance policy; Key person insurance","score_opus":0.1075538967808854,"score_gpt":0.4050389435953712,"score_spread":0.2974850468144858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111949477","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":[0.33381215,0.0006177531,0.6418378,0.0037577671,0.00013971482,0.00021902447,0.0020893055,0.00044143686,0.01708502],"genre_scores_gemma":[0.97356004,0.0002808451,0.011465521,0.000111883906,0.000051332263,0.000228246,0.0005876224,0.000033788547,0.013680716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894625,0.00039626434,0.000048132784,0.00019210874,0.0001485266,0.0002688051],"domain_scores_gemma":[0.99454415,0.003805063,0.00070399314,0.00015923822,0.0004408738,0.00034666513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002794742,0.0008028758,0.0016232722,0.001009499,0.000780221,0.00231507,0.0024197602,0.0025347073,0.010269276],"category_scores_gemma":[0.0057755955,0.00087134494,0.0016074475,0.00086434174,0.0018543766,0.002176575,0.0015445035,0.0026300524,0.00080253923],"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":[0.00010585795,0.00005764104,0.0024989268,0.000032638854,0.00003540381,0.00023489019,0.00012332067,0.9305362,0.0004155184,0.06340502,0.0006815057,0.0018730588],"study_design_scores_gemma":[0.000019343417,0.000016449096,0.00025076914,0.000006008999,0.000008729195,0.00001493333,0.000018751325,0.99197644,0.000036130234,0.00748015,0.00016304902,0.000009229131],"about_ca_topic_score_codex":0.03459343,"about_ca_topic_score_gemma":0.022698354,"teacher_disagreement_score":0.03459343,"about_ca_system_score_codex":0.0027011198,"about_ca_system_score_gemma":0.002146186,"threshold_uncertainty_score":0.06878418},"labels":[],"label_agreement":null},{"id":"W2112723050","doi":"10.1007/978-3-319-65433-1_7","title":"Estimating the Goodman, Keyfitz and Pullum Kinship Equations: An Alternative Procedure","year":2017,"lang":"en","type":"book-chapter","venue":"Demographic research monographs","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Kinship; Population; Applied mathematics; Fertility; Mathematics; Calculus (dental); Econometrics; Demography; Sociology; Anthropology","score_opus":0.1695742448272643,"score_gpt":0.42345550568462537,"score_spread":0.25388126085736107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112723050","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":[0.0070311753,0.00016307984,0.99053407,0.00022669524,0.000042308264,0.000064072956,0.00020368444,0.00017966708,0.0015552449],"genre_scores_gemma":[0.060931947,0.00032683244,0.93191326,0.00014108379,0.00006713419,0.00021455546,0.000345033,0.000100619305,0.005959516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847966,0.0006403186,0.00010775379,0.00035815933,0.0003537855,0.0000603702],"domain_scores_gemma":[0.9971699,0.001966407,0.00013450698,0.00030099053,0.00037962798,0.00004847999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004189722,0.00084261183,0.0011097387,0.0023391328,0.0006016266,0.001824724,0.0026399072,0.0014774863,0.007503028],"category_scores_gemma":[0.01680259,0.00056901935,0.0013366307,0.0021628395,0.0007500134,0.002568452,0.0017373709,0.0024776808,0.0016976965],"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":[0.00012683397,0.00017451018,0.010003656,0.0003417304,0.00038457083,0.00035380854,0.001002566,0.08873629,0.0033664566,0.39988998,0.0064709024,0.48914862],"study_design_scores_gemma":[0.00007631644,0.00012918866,0.0055296537,0.000120861165,0.00014920947,0.00060785824,0.00028012792,0.62318105,0.0033568433,0.3408888,0.025556125,0.00012397238],"about_ca_topic_score_codex":0.0070166746,"about_ca_topic_score_gemma":0.009893658,"teacher_disagreement_score":0.007503028,"about_ca_system_score_codex":0.0008248204,"about_ca_system_score_gemma":0.0020287326,"threshold_uncertainty_score":0.025100172},"labels":[],"label_agreement":null},{"id":"W2113053846","doi":"10.1017/s0515036100013404","title":"Guaranteed Annuity Options","year":2003,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":127,"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":"Life annuity; Annuity; Interest rate; Actuarial science; Solvency; Life insurance; Economics; Business; Finance; Pension","score_opus":0.01850134186072535,"score_gpt":0.289132554037539,"score_spread":0.2706312121768137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113053846","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":[0.035440512,0.007815465,0.31776237,0.0057979324,0.0011818198,0.00026864343,0.0029782818,0.003521252,0.6252337],"genre_scores_gemma":[0.5760047,0.004605269,0.092008084,0.0009639939,0.0007353534,0.00033966952,0.0030303285,0.00062192563,0.32169068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99863225,0.00030202474,0.00009113397,0.00010412293,0.00073453033,0.00013583],"domain_scores_gemma":[0.9976739,0.00064527424,0.0002528204,0.0005906093,0.0005560264,0.00028134199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017807913,0.00038094746,0.0003174988,0.00096250133,0.0008499897,0.0025776692,0.0016699774,0.001646153,0.062241524],"category_scores_gemma":[0.008160115,0.00026664155,0.0004935001,0.0010061896,0.0006271444,0.0029287322,0.0018942254,0.0014637545,0.011260704],"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":[0.00027866813,0.0001021653,0.0009692629,0.00013993964,0.000022980495,0.00035195387,0.0002570592,0.007324779,0.0015044872,0.73788613,0.05645462,0.19470799],"study_design_scores_gemma":[0.00011497584,0.0001405414,0.0010854537,0.00017411886,0.000024346178,0.001163388,0.00009720313,0.022005437,0.0020376556,0.25230968,0.7207945,0.000052644893],"about_ca_topic_score_codex":0.0005842207,"about_ca_topic_score_gemma":0.0007142731,"teacher_disagreement_score":0.062241524,"about_ca_system_score_codex":0.00065109535,"about_ca_system_score_gemma":0.00077723304,"threshold_uncertainty_score":0.20821863},"labels":[],"label_agreement":null},{"id":"W2114833782","doi":"10.1111/j.1539-6975.2012.01508.x","title":"Managing Capital Market and Longevity Risks in a Defined Benefit Pension Plan","year":2013,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":42,"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 Manitoba","funders":"","keywords":"Longevity risk; Downside risk; Asset allocation; Pension; Hedge; Asset (computer security); Longevity; Actuarial science; Plan (archaeology); Business; Risk management; Economics; Capital market; Pension plan; Finance; Portfolio; Computer science","score_opus":0.01991385905656408,"score_gpt":0.27366052023474197,"score_spread":0.2537466611781779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114833782","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":[0.45294705,0.0018246225,0.5072243,0.0045671477,0.0001667762,0.000215047,0.00053550035,0.00017341998,0.03234614],"genre_scores_gemma":[0.97630125,0.00038169292,0.012444753,0.00007444624,0.000032162276,0.00009962445,0.00006353603,0.000016474138,0.010586062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942833,0.00027047528,0.00001538752,0.00008160222,0.000089108435,0.000115049814],"domain_scores_gemma":[0.9992536,0.00031099693,0.00016896825,0.000037482798,0.0000685935,0.00016036008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016871168,0.0009297884,0.0008558942,0.0005418063,0.0006002119,0.0020851812,0.0013323595,0.00216254,0.003402006],"category_scores_gemma":[0.002531735,0.0005392002,0.0006839503,0.00043387027,0.0012462373,0.0019631307,0.0012014307,0.0015173561,0.00020032209],"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":[0.000069211026,0.00006672637,0.0008567084,0.000034318946,0.00003745281,0.00018664911,0.000072233226,0.9090474,0.00079442014,0.08437603,0.00061712187,0.0038415883],"study_design_scores_gemma":[0.00003723805,0.000092873044,0.00049865653,0.00001681958,0.000025580453,0.00004746246,0.000048869944,0.9721371,0.00015062037,0.025995627,0.0009341102,0.000015011378],"about_ca_topic_score_codex":0.0057556164,"about_ca_topic_score_gemma":0.0035839977,"teacher_disagreement_score":0.0057556164,"about_ca_system_score_codex":0.002201685,"about_ca_system_score_gemma":0.0018085961,"threshold_uncertainty_score":0.015974462},"labels":[],"label_agreement":null},{"id":"W2115041626","doi":"10.2143/ast.39.1.2038060","title":"Uncertainty in Mortality Forecasting: An Extension to the Classical Lee-Carter Approach","year":2009,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extension (predicate logic); Econometrics; Probabilistic logic; Confidence interval; Goodness of fit; Probabilistic forecasting; Interval (graph theory); Model selection; Measure (data warehouse); Statistics; Mathematics; Actuarial science; Economics; Computer science; Data mining","score_opus":0.06422932624758866,"score_gpt":0.3183700133588789,"score_spread":0.25414068711129023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115041626","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":[0.109069675,0.0013229362,0.8806822,0.0018311576,0.00014748324,0.00008246302,0.00032082418,0.00012087491,0.0064223553],"genre_scores_gemma":[0.95256406,0.0007117068,0.044378337,0.0002168293,0.00026697156,0.00008657838,0.00015602444,0.000027623808,0.0015917544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715686,0.0015291732,0.00010408755,0.00039580415,0.0005724065,0.00024171418],"domain_scores_gemma":[0.98389006,0.013070404,0.0010578115,0.0005098299,0.0011834494,0.00028841203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009353791,0.0005840994,0.0012083863,0.0018807746,0.00077040144,0.0015779171,0.0022241678,0.0012929413,0.0020519316],"category_scores_gemma":[0.028921297,0.0004471807,0.001099949,0.002106061,0.0011451882,0.0022804562,0.0016034769,0.001608007,0.00015228374],"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":[0.00007046646,0.000032456534,0.005119114,0.000056734803,0.00010825625,0.00016015343,0.00018374993,0.8953901,0.00020402168,0.07134904,0.000987734,0.026338203],"study_design_scores_gemma":[0.0000063138655,0.000021782826,0.0006755104,0.000017293733,0.000014714476,0.00002217406,0.000026348776,0.97171336,0.00006212307,0.026965387,0.00045499802,0.000019992895],"about_ca_topic_score_codex":0.023879629,"about_ca_topic_score_gemma":0.013567541,"teacher_disagreement_score":0.023879629,"about_ca_system_score_codex":0.0016683183,"about_ca_system_score_gemma":0.0014280621,"threshold_uncertainty_score":0.04946816},"labels":[],"label_agreement":null},{"id":"W2116000777","doi":"","title":"Pricing Catastrophic Mortality Bonds Using State Space Models","year":2013,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Econometrics; Bond; State space; Autoregressive integrated moving average; Mortality rate; Longevity risk; Outlier; Economics; Statistics; Mathematics; Demography; Time series; Finance","score_opus":0.05533855328847497,"score_gpt":0.3305860081590666,"score_spread":0.2752474548705916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116000777","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":[0.2783318,0.00063442724,0.71238977,0.0010898209,0.00007960373,0.00007579631,0.00035845206,0.00025062152,0.0067898137],"genre_scores_gemma":[0.9811476,0.0004142691,0.012323922,0.000056851648,0.000057774527,0.00008265352,0.00022337167,0.000027128337,0.0056662955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993425,0.00029692217,0.000031573232,0.0001215589,0.000117665644,0.00008973762],"domain_scores_gemma":[0.9968906,0.002199433,0.00043799556,0.000095858486,0.00022308946,0.00015298785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018917533,0.00083456567,0.0010375486,0.00072306104,0.00036667657,0.0022346654,0.0012850607,0.0018215481,0.004109383],"category_scores_gemma":[0.00694787,0.0005657163,0.0010900996,0.00075115566,0.0008735354,0.0019388583,0.0010628089,0.0016588769,0.00026554783],"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":[0.000039013343,0.000027273833,0.0018330521,0.000017249326,0.000031923948,0.000094348165,0.00004474425,0.94964194,0.000249985,0.04483497,0.00035620786,0.002829166],"study_design_scores_gemma":[0.0000035896012,0.0000069183384,0.00016433593,0.0000019085476,0.0000036247513,0.000006481494,0.0000051943066,0.99277496,0.000025212885,0.00692532,0.000078574274,0.0000038419494],"about_ca_topic_score_codex":0.008573508,"about_ca_topic_score_gemma":0.0047692615,"teacher_disagreement_score":0.008573508,"about_ca_system_score_codex":0.0011042652,"about_ca_system_score_gemma":0.00086019706,"threshold_uncertainty_score":0.017047226},"labels":[],"label_agreement":null},{"id":"W2118641598","doi":"10.1080/10920277.2001.10595987","title":"Actuarial Modeling with MCMC and BUGs","year":2001,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":113,"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":"Markov chain Monte Carlo; Gibbs sampling; Computer science; Bayesian probability; Suite; Software; Documentation; Bayesian inference; Variety (cybernetics); Inference; Econometrics; Data mining; Artificial intelligence; Mathematics; Programming language","score_opus":0.015853101498521698,"score_gpt":0.27467807416811063,"score_spread":0.2588249726695889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118641598","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":[0.0024065685,0.00019967399,0.9943124,0.00035422892,0.000043348646,0.00002592117,0.00011872167,0.0009617134,0.0015773617],"genre_scores_gemma":[0.16289169,0.0005461819,0.8316856,0.0002871205,0.00015564401,0.0003797657,0.00041434757,0.0007749001,0.0028648279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944642,0.0037618885,0.00022880253,0.00038133154,0.0010016747,0.00016215742],"domain_scores_gemma":[0.96746963,0.027223228,0.0013711661,0.0024289622,0.0012480293,0.00025903343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010063441,0.00097402115,0.0011808481,0.0025921464,0.0009600679,0.0024799292,0.002341559,0.0015087697,0.010914011],"category_scores_gemma":[0.054188024,0.0012700151,0.0012904997,0.0019071184,0.0018899759,0.0023174754,0.0020964055,0.0031372923,0.0014366385],"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":[0.00005924751,0.00004259246,0.0024970747,0.00012336155,0.00013973082,0.00011868498,0.00018157519,0.5092345,0.00027648956,0.41100252,0.0061180815,0.07020617],"study_design_scores_gemma":[0.000015203156,0.000007018791,0.00018393323,0.00003071576,0.000013863386,0.000027500577,0.000009275475,0.8645296,0.0001782633,0.13157168,0.0034192263,0.000013587518],"about_ca_topic_score_codex":0.009313009,"about_ca_topic_score_gemma":0.0077940356,"teacher_disagreement_score":0.010914011,"about_ca_system_score_codex":0.0012496618,"about_ca_system_score_gemma":0.0015181241,"threshold_uncertainty_score":0.053221166},"labels":[],"label_agreement":null},{"id":"W2119624633","doi":"10.1017/s1474747203001306","title":"A risk management approach to the pricing of a single equity-linked contract","year":2003,"lang":"en","type":"article","venue":"Journal of Pensions Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Laval","funders":"","keywords":"Diversification (marketing strategy); Equity (law); Volatility (finance); Economics; Bond; Financial economics; Microeconomics; Business; Actuarial science; Finance; Marketing","score_opus":0.030409953689871563,"score_gpt":0.2743228378990281,"score_spread":0.24391288420915655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119624633","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":[0.027668446,0.0004381643,0.9635091,0.00083479716,0.00011760524,0.000054507917,0.00003808025,0.00004114026,0.007297998],"genre_scores_gemma":[0.71095425,0.000944753,0.27340573,0.00027821204,0.0005573365,0.00023179296,0.0000707506,0.00007457986,0.013482602],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893755,0.0005236179,0.000038274906,0.00014091561,0.000268892,0.00009086255],"domain_scores_gemma":[0.9984255,0.0009156471,0.00016239894,0.00013632468,0.00017663324,0.00018356093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034153229,0.0010942578,0.0010959363,0.0011769241,0.0010166401,0.0030334177,0.0024944772,0.0025168387,0.006954918],"category_scores_gemma":[0.006661522,0.00060220057,0.001824963,0.0009523423,0.0021989578,0.003536123,0.0018930717,0.0031983654,0.00039498584],"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":[0.0000318718,0.00008453692,0.00042373317,0.00003750956,0.00004050907,0.00020501755,0.00013221687,0.45571998,0.001130064,0.5308122,0.00057917763,0.010803259],"study_design_scores_gemma":[0.000012109403,0.00003390567,0.00007864097,0.000010914172,0.000011114978,0.000039852468,0.000015513291,0.86063373,0.00012221074,0.13853396,0.00049305253,0.000015063109],"about_ca_topic_score_codex":0.0018268296,"about_ca_topic_score_gemma":0.0015119697,"teacher_disagreement_score":0.006954918,"about_ca_system_score_codex":0.0016219444,"about_ca_system_score_gemma":0.0017358343,"threshold_uncertainty_score":0.023266494},"labels":[],"label_agreement":null},{"id":"W2120185573","doi":"10.1017/asb.2015.6","title":"MODELING DEPENDENCE BETWEEN LOSS TRIANGLES WITH HIERARCHICAL ARCHIMEDEAN COPULAS","year":2015,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"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 du Québec à Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Copula (linguistics); Line of business; Property insurance; Tail dependence; Econometrics; Independence (probability theory); Computer science; Line (geometry); Mathematics; Mathematical economics; Business model; Actuarial science; Economics; Statistics; Insurance policy; General insurance","score_opus":0.05682800852378835,"score_gpt":0.30650356723663547,"score_spread":0.2496755587128471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120185573","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":[0.34423938,0.0012467689,0.6456,0.0014050021,0.000112461006,0.00023849195,0.0017952728,0.00035814656,0.0050044395],"genre_scores_gemma":[0.9636838,0.0005179094,0.029594747,0.00020233463,0.00010975697,0.00018923909,0.0011831917,0.00010882166,0.0044102585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962555,0.0018784214,0.00014549345,0.0008385407,0.00035342757,0.00052858546],"domain_scores_gemma":[0.9768861,0.01644586,0.003358244,0.0013226868,0.0012597597,0.0007272076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009645287,0.0014465194,0.0023164616,0.002714456,0.0010547865,0.0028072458,0.003690706,0.0023374588,0.005480389],"category_scores_gemma":[0.02502552,0.0016128869,0.0025940335,0.003127846,0.0024183495,0.0028072444,0.0025698082,0.004091824,0.00083371956],"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":[0.000120251556,0.00013234536,0.019681502,0.00006754101,0.00025573984,0.000523879,0.000321688,0.8605393,0.00043509694,0.10766757,0.0027415915,0.007513487],"study_design_scores_gemma":[0.00001092296,0.000019897683,0.0019545597,0.000011187388,0.000019196388,0.000023640443,0.00004109227,0.97844714,0.000049799386,0.019057184,0.00035421376,0.000011163536],"about_ca_topic_score_codex":0.04056594,"about_ca_topic_score_gemma":0.02159522,"teacher_disagreement_score":0.04056594,"about_ca_system_score_codex":0.0030603309,"about_ca_system_score_gemma":0.0012620228,"threshold_uncertainty_score":0.08065963},"labels":[],"label_agreement":null},{"id":"W2120556742","doi":"10.1111/j.1539-6975.2005.00124.x","title":"<scp>The Implied Longevity Yield: A Note on Developing an Index for Life Annuities</scp>","year":2005,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Workplace Safety & Insurance Board; York University","funders":"","keywords":"Life annuity; Deferral; Economics; Actuarial science; Pension; Index (typography); Longevity; Yield (engineering); Longevity risk; Econometrics; Finance; Gerontology; Medicine","score_opus":0.03116195847269139,"score_gpt":0.3225341775593875,"score_spread":0.29137221908669614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120556742","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":[0.19105743,0.0026912128,0.6625677,0.012111254,0.000905892,0.00070849166,0.031948972,0.0022110802,0.09579804],"genre_scores_gemma":[0.69897145,0.0016567261,0.27227202,0.00042759156,0.0005286087,0.00026045833,0.011426354,0.0003157415,0.014141062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987478,0.00027251654,0.00010351772,0.000088734436,0.0007282141,0.00005926883],"domain_scores_gemma":[0.9946485,0.0007131252,0.0007201011,0.0006339045,0.0031333568,0.00015094514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002361007,0.0004120256,0.00024093007,0.0028179968,0.00044806098,0.0021463833,0.00079769624,0.00041830592,0.0028303433],"category_scores_gemma":[0.016698314,0.00016879673,0.00025367754,0.0032882504,0.0005241405,0.0021041557,0.0008190661,0.001028012,0.0011189508],"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":[0.00010401206,0.00010297279,0.19884168,0.0003250295,0.000089982495,0.00026626542,0.000599525,0.09026183,0.0071180454,0.19199695,0.113275476,0.39701825],"study_design_scores_gemma":[0.000013188085,0.0002544902,0.37983125,0.00032405954,0.00002959601,0.00064943667,0.00042009313,0.25139356,0.01732757,0.07126077,0.27829754,0.00019847637],"about_ca_topic_score_codex":0.05701406,"about_ca_topic_score_gemma":0.0478802,"teacher_disagreement_score":0.05701406,"about_ca_system_score_codex":0.0028378062,"about_ca_system_score_gemma":0.0025357467,"threshold_uncertainty_score":0.1133644},"labels":[],"label_agreement":null},{"id":"W2120970002","doi":"10.3968/j.css.1923669720080403.002","title":"Global Crisis in Fertility Theory: What Went Wrong?","year":2009,"lang":"en","type":"article","venue":"Canadian social science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fertility; Population; Sociology; Humanities; Positive economics; Ethnology; Economics; Philosophy; Demography","score_opus":0.013999798056223634,"score_gpt":0.306774207903383,"score_spread":0.29277440984715936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120970002","genre_codex":"commentary","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":[0.02505355,0.10389403,0.014105051,0.8052003,0.0044117374,0.000026110572,0.00024336025,0.00006379725,0.047002006],"genre_scores_gemma":[0.7701865,0.10230589,0.008512196,0.09886822,0.009667696,0.00013752413,0.00030474854,0.00012593748,0.009891331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973423,0.0017152039,0.00008015076,0.0003284173,0.00035808276,0.00017590917],"domain_scores_gemma":[0.993028,0.004884681,0.0003891812,0.0004813376,0.00093742006,0.00027927657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010878156,0.00044502516,0.000905752,0.0017393817,0.0015981452,0.0029545366,0.0019702043,0.0034940573,0.004357338],"category_scores_gemma":[0.014062225,0.00022467884,0.0007829104,0.0023281001,0.017219933,0.009042189,0.002927386,0.006713364,0.00063408737],"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":[0.000027453687,0.00001791,0.0034584366,0.00022215517,0.000032917917,0.00011781402,0.001897292,0.0008372806,0.000036001253,0.93545955,0.015958581,0.04193473],"study_design_scores_gemma":[0.000022264245,0.00003344306,0.0033417619,0.0005710896,0.000021606324,0.00013050578,0.0038642683,0.0010206397,0.000083391096,0.91829044,0.07259617,0.000024347637],"about_ca_topic_score_codex":0.00565248,"about_ca_topic_score_gemma":0.0031835216,"teacher_disagreement_score":0.010878156,"about_ca_system_score_codex":0.0035373939,"about_ca_system_score_gemma":0.0021941743,"threshold_uncertainty_score":0.057529926},"labels":[],"label_agreement":null},{"id":"W2121052251","doi":"10.1017/s1748499500000051","title":"Unit-Linked Life Insurance Contracts with Lapse Rates Dependent on Economic Factors","year":2006,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":18,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Life insurance; Economics; Volatility (finance); Context (archaeology); Surrender; Unit (ring theory); Asset (computer security); Financial market; Product (mathematics); Financial economics; Actuarial science; Microeconomics; Econometrics; Finance; Computer science","score_opus":0.053297578695461444,"score_gpt":0.342307097806776,"score_spread":0.28900951911131456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121052251","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":[0.81174135,0.00049358496,0.17989762,0.000566774,0.00006690944,0.00008742847,0.00020686275,0.00018530771,0.006754273],"genre_scores_gemma":[0.9933431,0.00012351865,0.0039347545,0.000025377769,0.000020607142,0.00003041086,0.000046389338,0.00001045513,0.0024652719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880826,0.0005117012,0.000065260756,0.00017880723,0.00020073605,0.00023521906],"domain_scores_gemma":[0.98995835,0.0052601863,0.002549431,0.000722127,0.0004728669,0.0010370434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004504767,0.00073293695,0.000915917,0.0010255905,0.00044320198,0.0022548651,0.0016821986,0.0023532116,0.0059039504],"category_scores_gemma":[0.017065782,0.0005383516,0.0010611088,0.0007906247,0.002622849,0.0027534557,0.0015321734,0.002598507,0.00047465178],"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":[0.0003500935,0.0002987104,0.011130761,0.00007687387,0.00011023821,0.0008832945,0.0003415553,0.70448655,0.003402443,0.26539922,0.0007224509,0.012797775],"study_design_scores_gemma":[0.000050500865,0.0002103288,0.002395153,0.000018954104,0.000032101772,0.00018471868,0.00006608286,0.9373426,0.000644659,0.058443937,0.00057194894,0.00003887502],"about_ca_topic_score_codex":0.0019526874,"about_ca_topic_score_gemma":0.00089026784,"teacher_disagreement_score":0.0059039504,"about_ca_system_score_codex":0.001508119,"about_ca_system_score_gemma":0.00054639287,"threshold_uncertainty_score":0.023823798},"labels":[],"label_agreement":null},{"id":"W2122351741","doi":"10.1017/s071498081500001x","title":"Modelling the Age Dynamics of Chronic Health Conditions: Life-Table-Consistent Transition Probabilities and their Application","year":2015,"lang":"fr","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Population; Incidence (geometry); Cohort; Demography; Mathematics; Path (computing); Statistics; Medicine; Econometrics; Computer science; Environmental health","score_opus":0.023372672836352436,"score_gpt":0.23964864881339448,"score_spread":0.21627597597704204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122351741","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":[0.0985001,0.00052969216,0.8948012,0.0013104707,0.00011957283,0.00018606034,0.0011572548,0.00035535626,0.003040367],"genre_scores_gemma":[0.8806392,0.0007478182,0.109161,0.00024347106,0.00013211563,0.0004908813,0.0010384428,0.00009942214,0.0074476027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985769,0.00078190706,0.00006845618,0.00028999842,0.00013945867,0.00014330343],"domain_scores_gemma":[0.98141265,0.016100451,0.0010889545,0.00042526194,0.0006248845,0.00034774886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00509599,0.0007818566,0.0014110738,0.0013971094,0.0006082466,0.0018558203,0.0027049182,0.0025653313,0.0051905354],"category_scores_gemma":[0.029043602,0.0009286737,0.0015563027,0.0014423172,0.0014508879,0.0025211712,0.0013070941,0.002557762,0.0005380556],"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":[0.0000496883,0.00003408461,0.0042064777,0.00003009055,0.00004300065,0.00010518872,0.00016067643,0.94216335,0.00010088472,0.047756195,0.0005180682,0.004832352],"study_design_scores_gemma":[0.0000106871885,0.000010512503,0.0003613892,0.000009994404,0.000010768318,0.000027423017,0.000020769388,0.97282606,0.00003488889,0.026359601,0.0003165406,0.000011320737],"about_ca_topic_score_codex":0.03636608,"about_ca_topic_score_gemma":0.018560043,"teacher_disagreement_score":0.03636608,"about_ca_system_score_codex":0.0019330536,"about_ca_system_score_gemma":0.0019934608,"threshold_uncertainty_score":0.07230878},"labels":[],"label_agreement":null},{"id":"W212308795","doi":"","title":"The Survival of Exchange-Listed Hedge Funds","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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":"Hedge fund; Business; Global assets under management; Passive management; Proportional hazards model; Actuarial science; Fund of funds; Institutional investor; Econometrics; Economics; Finance; Statistics; Market liquidity; Mathematics; Corporate governance","score_opus":0.015265462084631495,"score_gpt":0.2982257575887859,"score_spread":0.2829602955041544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W212308795","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":[0.9988539,0.00023674466,0.00012165645,0.00007756109,0.0000024835838,0.00000132696,0.00017918838,0.0000025811028,0.00052447413],"genre_scores_gemma":[0.9992188,0.00010961435,0.000042863623,0.0000092366045,0.0000059097692,0.000001443965,0.00022002913,6.733106e-7,0.0003912588],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997615,0.00005073748,0.000023512952,0.000036296424,0.0000444666,0.000083470586],"domain_scores_gemma":[0.99541485,0.0007824347,0.0028039177,0.00014720425,0.00034506447,0.0005065525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011915779,0.0001088884,0.00016448647,0.0007641984,0.00022316749,0.00059837557,0.0001671243,0.00031747943,0.0015320785],"category_scores_gemma":[0.0056811026,0.00004876134,0.00016006097,0.0004075502,0.00021172318,0.00063673046,0.00044392946,0.00025586784,0.00032148344],"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":[0.0002530108,0.00003165515,0.974434,0.000021209886,0.000041415686,0.00017191686,0.0006855067,0.0008687706,0.00068204664,0.0012337555,0.0003486307,0.021228172],"study_design_scores_gemma":[0.000006412235,0.00020328457,0.99428624,0.000015802714,0.000027557871,0.0003716933,0.00060357276,0.0013711415,0.0005350551,0.0008816833,0.0016848304,0.000012646402],"about_ca_topic_score_codex":0.002002003,"about_ca_topic_score_gemma":0.0020249768,"teacher_disagreement_score":0.002002003,"about_ca_system_score_codex":0.00035667297,"about_ca_system_score_gemma":0.00021401363,"threshold_uncertainty_score":0.006301701},"labels":[],"label_agreement":null},{"id":"W2125048715","doi":"10.4236/ti.2011.22010","title":"Asset Allocation, Time Diversification and Portfolio Optimization for Retirement","year":2011,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Diversification (marketing strategy); Portfolio; Portfolio optimization; Asset allocation; Economics; Downside risk; Bond; Rate of return on a portfolio; Stock (firearms); Investment portfolio; Bootstrapping (finance); Financial economics; Econometrics; Business; Finance","score_opus":0.032142744405646274,"score_gpt":0.2663592759600898,"score_spread":0.23421653155444352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125048715","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":[0.88497144,0.00073287566,0.11212633,0.0002972499,0.000011840389,0.00007243909,0.00017891254,0.000042951746,0.001566034],"genre_scores_gemma":[0.9830724,0.00022646904,0.016001105,0.000014533819,0.000009496488,0.00004566098,0.00014981627,0.000005304326,0.00047517734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993036,0.00044180776,0.000030160309,0.00008464421,0.000077150755,0.000062662024],"domain_scores_gemma":[0.99721056,0.0020116137,0.00044131023,0.000114873255,0.00014319104,0.00007838443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00341024,0.00038886804,0.0006271307,0.0008026268,0.00021712377,0.00052910007,0.00033284313,0.00054025406,0.00092072453],"category_scores_gemma":[0.009088776,0.00024037,0.00057050487,0.0006845069,0.00032802214,0.0007994408,0.00042625424,0.00057468016,0.00008899169],"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":[0.00021757394,0.00012436518,0.03131068,0.00004923353,0.000122031495,0.00010449422,0.0000895019,0.9217241,0.0010683611,0.012522092,0.00039525807,0.032272402],"study_design_scores_gemma":[0.00002130334,0.00013014332,0.012772574,0.000011209532,0.00003431311,0.000033408498,0.000041190895,0.9770033,0.00046211886,0.009148994,0.00033050895,0.000010934691],"about_ca_topic_score_codex":0.0022997404,"about_ca_topic_score_gemma":0.0017621642,"teacher_disagreement_score":0.00341024,"about_ca_system_score_codex":0.00083751965,"about_ca_system_score_gemma":0.0006919106,"threshold_uncertainty_score":0.018035233},"labels":[],"label_agreement":null},{"id":"W2125583907","doi":"10.5539/ass.v6n12p206","title":"Research on Aging of Population and the Sustainable Development","year":2010,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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":"Population ageing; Sustainable development; Pension; Population; Economic growth; Development economics; Business; Developed country; Economics; Aged population; Key (lock); Political science; Sociology; Finance; Demography; Computer science","score_opus":0.03043608695979506,"score_gpt":0.38736849860334155,"score_spread":0.3569324116435465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125583907","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":[0.05249532,0.48069116,0.017629312,0.17578302,0.004055491,0.00007446737,0.00033771322,0.00006688593,0.26886663],"genre_scores_gemma":[0.45408913,0.509183,0.0064189318,0.0061108037,0.0026703267,0.000061391016,0.0001555184,0.000019643427,0.021291357],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914205,0.0004003136,0.0000485323,0.00015553768,0.00017383846,0.000079762984],"domain_scores_gemma":[0.9965222,0.0021185284,0.00036764488,0.00017965781,0.00061603583,0.00019587373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024199018,0.00045689804,0.00042035978,0.0018928176,0.0010755705,0.0025313846,0.0004452804,0.0012950012,0.0061086803],"category_scores_gemma":[0.0037887066,0.00011483291,0.00034520958,0.0031772116,0.0042225416,0.005768833,0.0012063191,0.0016426586,0.0006374117],"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":[0.000015775518,0.00008696467,0.009830983,0.0008817152,0.00004435312,0.00022007823,0.00533364,0.0010742221,0.00030536644,0.79662615,0.018414412,0.16716634],"study_design_scores_gemma":[0.0000064908445,0.00012261092,0.018168624,0.001859184,0.00004377931,0.00049224845,0.00942526,0.0010069654,0.00047971532,0.5273954,0.4409633,0.000036369787],"about_ca_topic_score_codex":0.004837867,"about_ca_topic_score_gemma":0.005432704,"teacher_disagreement_score":0.0061086803,"about_ca_system_score_codex":0.0023636264,"about_ca_system_score_gemma":0.0033468858,"threshold_uncertainty_score":0.020435512},"labels":[],"label_agreement":null},{"id":"W2125854305","doi":"10.1111/j.1467-9965.2006.00267.x","title":"CLASSICAL AND IMPULSE STOCHASTIC CONTROL FOR THE OPTIMIZATION OF THE DIVIDEND AND RISK POLICIES OF AN INSURANCE FIRM","year":2006,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":145,"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":"Stochastic control; Dividend; Bankruptcy; Impulse control; Dividend policy; Bellman equation; Optimization problem; Economics; Actuarial science; Payment; Mathematical optimization; Time horizon; Control (management); Optimal control; Mathematical economics; Mathematics; Finance","score_opus":0.01017256837691073,"score_gpt":0.26534133773120716,"score_spread":0.25516876935429644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125854305","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":[0.11051793,0.0021017967,0.86608356,0.00297725,0.000220937,0.00006584656,0.00013229855,0.00011482344,0.017785566],"genre_scores_gemma":[0.9554454,0.0013509211,0.02698671,0.00021492167,0.00018958977,0.00016952048,0.00008817618,0.00004433286,0.015510412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993411,0.00029144433,0.000019035067,0.00008735321,0.00014618324,0.00011488749],"domain_scores_gemma":[0.99726,0.001981027,0.00031636018,0.00005327018,0.00021916638,0.00017011524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024303857,0.0010851445,0.00091995636,0.0009298105,0.0005647202,0.001903059,0.00090117025,0.001780148,0.0036712566],"category_scores_gemma":[0.0062580323,0.0006255583,0.0009932327,0.00068000006,0.0032736312,0.0013830516,0.0014092193,0.0017880644,0.00019319566],"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":[0.000040673996,0.000047472156,0.00042860926,0.00007395875,0.000047956448,0.0000741355,0.000065150154,0.7828405,0.00066564244,0.21174076,0.0005743012,0.003400894],"study_design_scores_gemma":[0.000014219866,0.00002086972,0.00016094229,0.000011273357,0.000011659076,0.000007881075,0.000018871024,0.9610296,0.000116633855,0.038333435,0.00026376956,0.000010819801],"about_ca_topic_score_codex":0.013359196,"about_ca_topic_score_gemma":0.0073686815,"teacher_disagreement_score":0.013359196,"about_ca_system_score_codex":0.0038459566,"about_ca_system_score_gemma":0.0027654055,"threshold_uncertainty_score":0.02790451},"labels":[],"label_agreement":null},{"id":"W2125951325","doi":"10.1080/10920277.2006.10597419","title":"An Extreme Value Analysis Of Advanced Age Mortality Data","year":2006,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University; HEC Montréal","funders":"","keywords":"Generalized Pareto distribution; Extreme value theory; Generalized extreme value distribution; Statistics; Pareto distribution; Demography; Econometrics; Mathematics; Sociology","score_opus":0.049155409252723284,"score_gpt":0.35254059564459944,"score_spread":0.30338518639187617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125951325","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":[0.3644324,0.0002762293,0.63062084,0.00065740896,0.00005424912,0.00015023684,0.001185233,0.00041703414,0.0022063912],"genre_scores_gemma":[0.93118274,0.00014599168,0.06632953,0.00010115162,0.00007283816,0.00011646999,0.001356196,0.000037982496,0.0006571926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99460983,0.0035380358,0.000229,0.0005332967,0.0008712645,0.00021859877],"domain_scores_gemma":[0.96676284,0.025927363,0.0023640394,0.0020860655,0.0024318474,0.0004278733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011151651,0.00047868406,0.0007606666,0.004032405,0.00045056205,0.0015287531,0.00079315633,0.0007438737,0.0014234675],"category_scores_gemma":[0.032843076,0.00017465338,0.0010069472,0.0030338215,0.00089996756,0.0012404657,0.0011151519,0.0011556613,0.00022325157],"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":[0.00044529483,0.0004381809,0.25174257,0.0003162056,0.00073223887,0.0011696692,0.00096982124,0.4805508,0.005801564,0.09041247,0.0063775713,0.16104355],"study_design_scores_gemma":[0.00002145545,0.00024533606,0.05019085,0.000056817815,0.000041322673,0.0002784968,0.0003271929,0.88945824,0.0013823555,0.055686496,0.0022522593,0.000059122158],"about_ca_topic_score_codex":0.001994483,"about_ca_topic_score_gemma":0.0011208155,"teacher_disagreement_score":0.011151651,"about_ca_system_score_codex":0.0007158561,"about_ca_system_score_gemma":0.0007556123,"threshold_uncertainty_score":0.058976293},"labels":[],"label_agreement":null},{"id":"W2126278898","doi":"10.1002/asmb.2009","title":"Statistical learning for variable annuity policyholder withdrawal behavior","year":2014,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"Annuity; Actuarial science; Economics; Stock (firearms); Profit (economics); Variable (mathematics); Econometrics; Business; Life annuity; Microeconomics; Finance; Engineering; Mathematics; Pension","score_opus":0.024375993456422246,"score_gpt":0.29270177087955923,"score_spread":0.268325777423137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126278898","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":[0.3954516,0.00036948692,0.6013046,0.00090645155,0.000040871484,0.000079505444,0.00023444694,0.00023762918,0.0013753716],"genre_scores_gemma":[0.9791199,0.00015799004,0.017809274,0.000082999875,0.0000333082,0.00006444395,0.00025562174,0.000023482877,0.0024530024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989561,0.0005428663,0.00004022482,0.0002125964,0.00013344019,0.00011471362],"domain_scores_gemma":[0.98365027,0.012962252,0.001581589,0.00056650705,0.0008975513,0.0003418161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00552346,0.0006449611,0.00095890736,0.00085545104,0.00031850897,0.0008056773,0.0011457546,0.0011533881,0.0022393344],"category_scores_gemma":[0.019716464,0.00032620307,0.00058728067,0.00062004104,0.001078734,0.0012616279,0.000678742,0.0017870596,0.00031629],"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":[0.000110898414,0.00013968335,0.012621963,0.00003233906,0.00006371763,0.00007699928,0.000079634265,0.93933475,0.00067960995,0.021050317,0.0006967675,0.0251133],"study_design_scores_gemma":[0.0000014391942,0.00000852768,0.00034760314,0.0000016763022,0.0000020178547,0.000003250985,0.000003056652,0.99702173,0.000047879603,0.00253085,0.000029636754,0.0000022852082],"about_ca_topic_score_codex":0.0058742175,"about_ca_topic_score_gemma":0.0039094333,"teacher_disagreement_score":0.0058742175,"about_ca_system_score_codex":0.0012534276,"about_ca_system_score_gemma":0.0010037328,"threshold_uncertainty_score":0.029211164},"labels":[],"label_agreement":null},{"id":"W2126914149","doi":"","title":"Human Capital, Asset Allocation, and Life Insurance","year":2008,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Bequest; Life insurance; Asset allocation; Human capital; Portfolio; Economics; Actuarial science; Asset (computer security); Basis risk; Economic capital; Financial economics; Microeconomics; Capital asset pricing model; Computer science","score_opus":0.04094150479409817,"score_gpt":0.33837411466066325,"score_spread":0.29743260986656506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126914149","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":[0.82056177,0.0075128325,0.05786531,0.009554423,0.00008833651,0.000102492464,0.00029001434,0.00005090335,0.1039739],"genre_scores_gemma":[0.9954377,0.0008779441,0.0016079458,0.00008689127,0.000020447726,0.00001068867,0.000018949762,0.0000019193799,0.0019375754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99954176,0.00022999312,0.00001395509,0.000041990326,0.00006411553,0.00010827604],"domain_scores_gemma":[0.9984504,0.0008183766,0.00040619355,0.00005916671,0.00008090139,0.00018485305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011842827,0.00027606313,0.00027941974,0.0006163072,0.00045669972,0.0016409961,0.00038239962,0.0009777555,0.0039961366],"category_scores_gemma":[0.003620383,0.000105658335,0.00019413947,0.00068755524,0.0019618268,0.0011954949,0.00085216056,0.0007125995,0.00015703445],"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":[0.00011419035,0.00014522763,0.05358617,0.00008229765,0.00008186313,0.00052461424,0.0004743051,0.13346635,0.00072166463,0.7436074,0.0025541594,0.064641826],"study_design_scores_gemma":[0.000038832437,0.00021524185,0.053490788,0.000123709,0.000038915925,0.0003831886,0.0010091492,0.09082766,0.0007929659,0.84019387,0.012846146,0.000039463674],"about_ca_topic_score_codex":0.003179393,"about_ca_topic_score_gemma":0.0038258992,"teacher_disagreement_score":0.0039961366,"about_ca_system_score_codex":0.0018192884,"about_ca_system_score_gemma":0.000859,"threshold_uncertainty_score":0.013368428},"labels":[],"label_agreement":null},{"id":"W2127737754","doi":"10.25336/p6vs46","title":"Support Vector Machines as tools for mortality graduation","year":2012,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Graduation (instrument); Support vector machine; Nonparametric statistics; Parametric statistics; Computer science; Variety (cybernetics); Machine learning; Statistics; Econometrics; Artificial intelligence; Mathematics","score_opus":0.16807662891173014,"score_gpt":0.43620575391979816,"score_spread":0.268129125008068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127737754","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":[0.031149201,0.00585822,0.9536216,0.0012773406,0.00025272366,0.00012219496,0.0004180443,0.002357171,0.00494355],"genre_scores_gemma":[0.48145542,0.0027729142,0.51018685,0.00020976407,0.00048426457,0.00027280606,0.0008061518,0.00018647028,0.0036253247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944951,0.003035793,0.0004409906,0.0003732498,0.0014971152,0.00015769739],"domain_scores_gemma":[0.98247945,0.012722609,0.001498637,0.000920612,0.001965489,0.00041310405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007517676,0.00092195225,0.0009888079,0.005380011,0.00038187447,0.002099665,0.0012673163,0.0011384804,0.0024231363],"category_scores_gemma":[0.037011243,0.00030346715,0.0005704508,0.0035739138,0.0009379047,0.0022825338,0.0017791257,0.001988779,0.00091583014],"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":[0.00015490105,0.00019298884,0.013392197,0.00036741226,0.0001466817,0.00014338306,0.0005772879,0.12105423,0.00093396613,0.0687725,0.009612693,0.7846517],"study_design_scores_gemma":[0.000037277743,0.00020972501,0.0073130294,0.00027356297,0.000038508777,0.00019848887,0.00042514334,0.8186372,0.0022142685,0.15318762,0.017366508,0.00009867766],"about_ca_topic_score_codex":0.001189847,"about_ca_topic_score_gemma":0.0008401358,"teacher_disagreement_score":0.007517676,"about_ca_system_score_codex":0.00068521505,"about_ca_system_score_gemma":0.0007349072,"threshold_uncertainty_score":0.03975779},"labels":[],"label_agreement":null},{"id":"W2129441270","doi":"10.1186/1742-5573-7-8","title":"Population attributable fraction: comparison of two mathematical procedures to estimate the annual attributable number of deaths","year":2010,"lang":"en","type":"article","venue":"Epidemiologic Perspectives & Innovations","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Government of Canada; Public Health Ontario; University of Toronto; Public Health Agency of Canada; University of Ottawa","funders":"","keywords":"Attributable risk; Fraction (chemistry); Statistics; Population; Mathematics; Medicine","score_opus":0.04832235422932598,"score_gpt":0.4610671162361549,"score_spread":0.4127447620068289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129441270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014076339,0.0031414877,0.97543395,0.00078175287,0.00031750268,0.0006042583,0.00048140428,0.0004341101,0.004729226],"genre_scores_gemma":[0.16368823,0.0034079412,0.82663184,0.00046430706,0.00031641798,0.0018882068,0.00074406574,0.0002728544,0.0025860327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9617247,0.029452775,0.001533832,0.0015233804,0.0055390336,0.00022630431],"domain_scores_gemma":[0.88744366,0.09555997,0.004958231,0.0057703205,0.0060289237,0.00023894434],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03848698,0.0012253748,0.0010234305,0.008325883,0.00055126514,0.0018956286,0.0024357196,0.0016101749,0.004250817],"category_scores_gemma":[0.13199085,0.00037811178,0.0020240094,0.003260831,0.0017257566,0.0028354914,0.0020822443,0.001278285,0.0008127561],"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":[0.00058467063,0.00025656552,0.035288393,0.0015334135,0.0018763639,0.00014070192,0.0008460661,0.07103898,0.0012469406,0.20950471,0.008561728,0.6691215],"study_design_scores_gemma":[0.00085681205,0.0020855968,0.1107258,0.0028141625,0.0020564066,0.0034281209,0.0019663433,0.45944393,0.011997924,0.31274325,0.09082415,0.0010575773],"about_ca_topic_score_codex":0.0021084133,"about_ca_topic_score_gemma":0.0019047128,"teacher_disagreement_score":0.96151304,"about_ca_system_score_codex":0.0016424953,"about_ca_system_score_gemma":0.0023435962,"threshold_uncertainty_score":0.20354098},"labels":[],"label_agreement":null},{"id":"W2129448803","doi":"10.5539/jsd.v4n5p264","title":"Another Look at Rectangularization: Variability of Age at Death within a Given Population","year":2011,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Demography; Survival analysis; Mortality rate; Population; Table (database); Statistics; Mathematics; Computer science","score_opus":0.024780849309045574,"score_gpt":0.2597858880971032,"score_spread":0.23500503878805762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129448803","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":[0.91891515,0.0005739632,0.07564155,0.0006686272,0.000023420946,0.000030821153,0.00027403768,0.000100322635,0.0037720092],"genre_scores_gemma":[0.997695,0.000088156994,0.0019043356,0.00002800818,0.000011745548,0.000005227233,0.00006917728,0.000013139756,0.00018522052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998973,0.00046853948,0.000034614302,0.00033225596,0.00011189437,0.00007966717],"domain_scores_gemma":[0.9829836,0.011575524,0.0022881078,0.0020431108,0.0007445484,0.0003651375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033645465,0.00019299221,0.00046305137,0.0010242601,0.00033721278,0.0009401683,0.0007191494,0.00065469346,0.00198901],"category_scores_gemma":[0.023230053,0.00016434694,0.00057278917,0.0012611353,0.0019469758,0.0016968942,0.0009234226,0.0011793993,0.00011485186],"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":[0.0008278527,0.00011533993,0.6083103,0.00022601012,0.0006516833,0.0017653253,0.00661004,0.21851708,0.021492016,0.07713631,0.0013006245,0.063047394],"study_design_scores_gemma":[0.000025133493,0.0009834209,0.65021026,0.00010956074,0.00016841975,0.0020848087,0.0040212,0.2327866,0.0048564062,0.09968975,0.0047878427,0.00027658042],"about_ca_topic_score_codex":0.0023539471,"about_ca_topic_score_gemma":0.0010667635,"teacher_disagreement_score":0.0033645465,"about_ca_system_score_codex":0.0006007808,"about_ca_system_score_gemma":0.00027453213,"threshold_uncertainty_score":0.017793655},"labels":[],"label_agreement":null},{"id":"W2133600798","doi":"10.1002/cjs.11146","title":"Positive quadrant dependence testing and constrained copula estimation","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":13,"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":"Fonds Wetenschappelijk Onderzoek","keywords":"Copula (linguistics); Resampling; Econometrics; Nonparametric statistics; Null hypothesis; Statistics; Statistical hypothesis testing; Mathematics; Parametric statistics; Computer science","score_opus":0.031046009042065156,"score_gpt":0.28596230233988784,"score_spread":0.25491629329782267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133600798","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":[0.12434203,0.00022203708,0.8721284,0.0002902814,0.000027990773,0.00019724746,0.00022533782,0.00028498517,0.0022817445],"genre_scores_gemma":[0.9071576,0.00008856125,0.091610044,0.00008383532,0.00003585098,0.00022286814,0.0003673259,0.000086002525,0.00034790803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97285265,0.021587813,0.0007129453,0.002039472,0.0021964787,0.0006105179],"domain_scores_gemma":[0.6773492,0.29129547,0.010616798,0.01342158,0.0058024186,0.0015145828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025350295,0.0010493356,0.0016999949,0.0033312438,0.0008404972,0.001868102,0.002508801,0.0013129133,0.006421695],"category_scores_gemma":[0.20818785,0.0005144949,0.001804651,0.003074807,0.0037403426,0.0028578723,0.0026094737,0.002151773,0.0004995579],"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":[0.0013899013,0.00050452596,0.07415354,0.00048448212,0.0014860845,0.0020118912,0.0010271688,0.42538,0.00585297,0.2742553,0.0045566633,0.20889753],"study_design_scores_gemma":[0.00007240866,0.00024854197,0.012273714,0.000063682994,0.000051501036,0.0002999347,0.00013680107,0.87426203,0.0019084539,0.10975818,0.0008616838,0.00006311079],"about_ca_topic_score_codex":0.0034823783,"about_ca_topic_score_gemma":0.0012819805,"teacher_disagreement_score":0.025350295,"about_ca_system_score_codex":0.00090493896,"about_ca_system_score_gemma":0.0012511754,"threshold_uncertainty_score":0.13406676},"labels":[],"label_agreement":null},{"id":"W2135944108","doi":"10.1016/j.annepidem.2010.03.006","title":"A Multiphase Method for Estimating Cohort Effects in Age-Period Contingency Table Data","year":2010,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Columbia College","funders":"National Institute on Drug Abuse; National Institute on Aging; National Institute on Alcohol Abuse and Alcoholism","keywords":"Medicine; Cohort; Demography; Cohort effect; Confidence interval; Interpretability; Homicide; Statistics; Relative risk; Cohort study; Pediatrics; Poison control; Injury prevention; Mathematics; Medical emergency; Internal medicine; Computer science","score_opus":0.17914620108907273,"score_gpt":0.49962492357902655,"score_spread":0.3204787224899538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135944108","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":[0.008276812,0.000096733944,0.9901765,0.00003748245,0.000039354203,0.0002975058,0.00049075467,0.00046283126,0.00012194556],"genre_scores_gemma":[0.057386532,0.00010589305,0.9391337,0.000042355663,0.000049615126,0.0011833938,0.0013836542,0.000121571975,0.0005932943],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9777789,0.018690778,0.0007826882,0.0014010608,0.0011174093,0.00022901635],"domain_scores_gemma":[0.84875274,0.13288045,0.0026105812,0.012722987,0.0025528714,0.00048029193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034508474,0.0008819267,0.00189207,0.0035585049,0.0009971609,0.0013463815,0.002969335,0.0011117767,0.0071154363],"category_scores_gemma":[0.088189885,0.0016269402,0.0033808625,0.0030799636,0.0004998339,0.0016294809,0.0018370409,0.002269107,0.00088757946],"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":[0.002380626,0.0009176114,0.06662682,0.0007556301,0.006291879,0.00043930905,0.00076145056,0.116211265,0.00390689,0.047994994,0.007938229,0.74577534],"study_design_scores_gemma":[0.0006885531,0.0010479904,0.023248002,0.00016300335,0.0010217119,0.0008130783,0.00013896804,0.9152665,0.0022897627,0.04630239,0.008856201,0.000163877],"about_ca_topic_score_codex":0.0035740447,"about_ca_topic_score_gemma":0.004525533,"teacher_disagreement_score":0.034508474,"about_ca_system_score_codex":0.0004185632,"about_ca_system_score_gemma":0.0018960272,"threshold_uncertainty_score":0.18250048},"labels":[],"label_agreement":null},{"id":"W2136644815","doi":"10.1109/acc.2007.4283152","title":"Insurance Claims Modulated by a Hidden Marked Point Process","year":2007,"lang":"en","type":"article","venue":"Proceedings of the ... American Control Conference/Proceedings of the American Control Conference","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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 Calgary","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Hidden Markov model; Markov chain; Poisson distribution; Hidden semi-Markov model; Markov model; Computer science; Variable-order Markov model; Markov process; Point process; Markov property; Econometrics; Continuous-time Markov chain; Estimator; Particle filter; Cox process; Filter (signal processing); Mathematics; Mathematical optimization; Applied mathematics; Statistics; Poisson process; Artificial intelligence; Machine learning","score_opus":0.009164499566207273,"score_gpt":0.2636490404936768,"score_spread":0.2544845409274695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136644815","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":[0.1721655,0.0002413128,0.8249995,0.00054604874,0.000085338055,0.000050827217,0.00020109209,0.00024806682,0.0014622725],"genre_scores_gemma":[0.95778537,0.00040642856,0.03532255,0.00009341101,0.000106480315,0.00008106322,0.0002456056,0.00003298893,0.005925974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987459,0.00039785102,0.000061140665,0.00031886977,0.00032320898,0.00015292493],"domain_scores_gemma":[0.9926272,0.0047725798,0.0012153081,0.00059927785,0.0005927103,0.00019295112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042407415,0.0008200571,0.0013070705,0.0012389377,0.0005082156,0.0013342476,0.0020851882,0.002232078,0.0024911347],"category_scores_gemma":[0.011194841,0.0008361815,0.0015114481,0.0010575524,0.002476061,0.0030134087,0.0012325653,0.0023420334,0.00040750534],"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":[0.00022590291,0.00008130954,0.00432374,0.000102026155,0.00010915672,0.00055088213,0.00032726832,0.7056957,0.005029767,0.2679455,0.0007218728,0.014886959],"study_design_scores_gemma":[0.000018112869,0.000033573713,0.0007704444,0.000007632725,0.00001805933,0.00004633477,0.00001798323,0.95845884,0.00065543153,0.039699458,0.0002507762,0.000023460927],"about_ca_topic_score_codex":0.0035161683,"about_ca_topic_score_gemma":0.0025279436,"teacher_disagreement_score":0.0042407415,"about_ca_system_score_codex":0.0012344868,"about_ca_system_score_gemma":0.00079151377,"threshold_uncertainty_score":0.02242744},"labels":[],"label_agreement":null},{"id":"W2137606295","doi":"10.7202/007411ar","title":"Compression de la mortalité et rectangularisation de la courbe de survie au Québec au cours du XXe siècle","year":2004,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Physics; Humanities; Art","score_opus":0.008108519657400672,"score_gpt":0.28499536983941726,"score_spread":0.2768868501820166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137606295","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":[0.95373267,0.0024380544,0.004652774,0.0016462095,0.00012974459,0.0001281197,0.002542724,0.0001691456,0.034560602],"genre_scores_gemma":[0.981729,0.0012760912,0.0017526739,0.0002133083,0.000019782023,0.00006873588,0.0008438841,0.00001765243,0.014078866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935776,0.00012229622,0.00003060264,0.00011445743,0.00023790954,0.00013694685],"domain_scores_gemma":[0.9979358,0.00027966316,0.00047623916,0.00011348107,0.00099322,0.00020163044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006593227,0.00034924294,0.00025673435,0.00086936494,0.0010353561,0.00090915954,0.00047386764,0.00034553526,0.006287095],"category_scores_gemma":[0.0025727262,0.00016631863,0.00032088847,0.0012876593,0.0009719728,0.00025102973,0.00053299585,0.0006989461,0.0004463645],"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":[0.0013947802,0.0002056277,0.6560484,0.00062955625,0.00025256554,0.001003395,0.007037237,0.013983937,0.03254815,0.007861937,0.012436344,0.26659805],"study_design_scores_gemma":[0.000022809694,0.0005829286,0.95941615,0.00012545429,0.000056134777,0.00022793224,0.0025016002,0.0019644762,0.0051846993,0.00026245424,0.029594863,0.0000605215],"about_ca_topic_score_codex":0.8222044,"about_ca_topic_score_gemma":0.89152277,"teacher_disagreement_score":0.17779559,"about_ca_system_score_codex":0.008234065,"about_ca_system_score_gemma":0.008672357,"threshold_uncertainty_score":0.35768527},"labels":[],"label_agreement":null},{"id":"W2138407064","doi":"10.1111/j.1539-6975.2006.00194.x","title":"<scp>Killing the Law of Large Numbers: Mortality Risk Premiums and the Sharpe Ratio</scp>","year":2006,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":86,"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":"Sharpe ratio; Stylized fact; Economics; Portfolio; Hazard ratio; Stock (firearms); Econometrics; Systematic risk; Risk premium; Capital asset pricing model; Actuarial science; Financial economics; Mathematics; Statistics; Confidence interval","score_opus":0.009229328043893752,"score_gpt":0.2730810355334207,"score_spread":0.26385170748952697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138407064","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":[0.22501384,0.031660143,0.59133774,0.024483515,0.0010815074,0.000063342115,0.0002059448,0.00042234565,0.12573175],"genre_scores_gemma":[0.975087,0.004451111,0.012815295,0.00089320546,0.0014089439,0.000025020672,0.000036451067,0.000053037405,0.005229929],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99937063,0.00025162078,0.00003216185,0.00008320609,0.00019532791,0.00006706852],"domain_scores_gemma":[0.9954722,0.0028669592,0.0007273117,0.00026935042,0.0004793967,0.00018474212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027435264,0.00045432392,0.00043527046,0.0012185519,0.00039922222,0.002704971,0.0005553333,0.0015889042,0.002800746],"category_scores_gemma":[0.011976114,0.00025322638,0.00059392577,0.0007805898,0.0031630713,0.0037162213,0.00091085985,0.0020878778,0.00033024498],"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":[0.000016314572,0.000020855356,0.00095096015,0.000049300852,0.00001862996,0.00019874443,0.00010850383,0.009090345,0.0011067392,0.97592723,0.0024460303,0.0100663705],"study_design_scores_gemma":[0.0000053550957,0.000023297061,0.001691424,0.000036144567,0.000012789462,0.00024008972,0.00004249256,0.04914833,0.0005216449,0.9447315,0.0035263586,0.00002056492],"about_ca_topic_score_codex":0.0011676286,"about_ca_topic_score_gemma":0.00048481257,"teacher_disagreement_score":0.002800746,"about_ca_system_score_codex":0.0011103277,"about_ca_system_score_gemma":0.00038101486,"threshold_uncertainty_score":0.01450932},"labels":[],"label_agreement":null},{"id":"W2139453286","doi":"10.1111/issr.12008","title":"Actuarial balance sheets as a tool to assess the sustainability of social security pension systems","year":2013,"lang":"en","type":"article","venue":"International Social Security Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Balance sheet; Social security; Balance (ability); Sustainability; Pension; Financial security; Pension system; Actuarial science; Business; Economics; Environmental economics; Finance; Medicine","score_opus":0.023615971269614948,"score_gpt":0.35674666263605154,"score_spread":0.3331306913664366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139453286","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":[0.11593427,0.04804142,0.6754995,0.005241944,0.0023178877,0.0009103497,0.0059613576,0.0014245079,0.1446689],"genre_scores_gemma":[0.7822564,0.019442812,0.1857884,0.00023803752,0.00086153706,0.00087159494,0.0020860357,0.0001501997,0.0083049955],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98424757,0.010207465,0.0009151165,0.0002837544,0.0042178943,0.0001281731],"domain_scores_gemma":[0.96534395,0.019508181,0.0056914617,0.002595404,0.006523103,0.000338033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035064865,0.0012480493,0.00066258945,0.01643301,0.00062094023,0.0037688657,0.0010942229,0.00078980427,0.0055621313],"category_scores_gemma":[0.05011621,0.00041642683,0.00077832444,0.008125539,0.001242025,0.004104053,0.0019041536,0.0012345716,0.0008793582],"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":[0.00034683093,0.00014651395,0.019263612,0.0018638959,0.00081027474,0.00020812028,0.0014742956,0.1201574,0.0014397413,0.34137505,0.021851897,0.49106234],"study_design_scores_gemma":[0.00009388879,0.0011393903,0.061436933,0.0062228683,0.0006330043,0.00060389366,0.004208572,0.17719372,0.0056903055,0.49977323,0.24253811,0.0004661422],"about_ca_topic_score_codex":0.0012374729,"about_ca_topic_score_gemma":0.0010107119,"teacher_disagreement_score":0.035064865,"about_ca_system_score_codex":0.0013938939,"about_ca_system_score_gemma":0.0012607883,"threshold_uncertainty_score":0.18544292},"labels":[],"label_agreement":null},{"id":"W2140026356","doi":"10.25336/p65p73","title":"An Evaluation of the Pearsonian Type I Curve of Fertility for Aboriginal Populations in Canada, 1996 to 2001","year":2008,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Fertility; Total fertility rate; Demography; Geography; Birth rate; Census; Population; Statistics; Mathematics; Sociology; Family planning; Research methodology","score_opus":0.1889349842205765,"score_gpt":0.4382346206132467,"score_spread":0.2492996363926702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140026356","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":[0.8469007,0.0037066916,0.08269004,0.0028051913,0.0002070383,0.00081603223,0.010234835,0.0017612994,0.05087821],"genre_scores_gemma":[0.9812996,0.00077416573,0.010783603,0.00019086387,0.000029250947,0.0001622947,0.0045338124,0.00022783084,0.001998646],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98809344,0.0048053977,0.0005388936,0.0018676812,0.0038834475,0.000811132],"domain_scores_gemma":[0.9039377,0.047919024,0.009797769,0.009932107,0.027041672,0.0013716428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036213513,0.00065078394,0.0006839712,0.0034892205,0.001225606,0.0021105884,0.0027432186,0.0010935778,0.0021651273],"category_scores_gemma":[0.15161271,0.0003709518,0.0020134863,0.005712531,0.0019544286,0.0013844103,0.0016451002,0.0013686541,0.00049627287],"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":[0.0004954904,0.000034402212,0.7945233,0.00022222215,0.00043428355,0.00030905736,0.0024231565,0.120444514,0.00019709094,0.01896756,0.008274437,0.053674497],"study_design_scores_gemma":[0.000050566632,0.0004376889,0.7295415,0.00035607175,0.00024045528,0.0008970624,0.0033670045,0.2349601,0.0007112208,0.009038715,0.02023953,0.00016016646],"about_ca_topic_score_codex":0.50629073,"about_ca_topic_score_gemma":0.2438738,"teacher_disagreement_score":0.49370927,"about_ca_system_score_codex":0.012567445,"about_ca_system_score_gemma":0.01286069,"threshold_uncertainty_score":0.9932336},"labels":[],"label_agreement":null},{"id":"W2141671486","doi":"","title":"SPENDING RETIREMENT ON PLANET VULCAN: The Impact of Longevity Risk Aversion on Optimal Withdrawal Rates","year":2010,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":86,"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":"Treasury; Risk aversion (psychology); Economics; Consumption (sociology); Longevity risk; Inflation (cosmology); Investment (military); Order (exchange); Bond; Longevity; Financial market; Expected utility hypothesis; Actuarial science; Monetary economics; Financial economics; Pension; Finance","score_opus":0.016559151935333933,"score_gpt":0.33346343416966184,"score_spread":0.3169042822343279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141671486","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":[0.9718574,0.00041403135,0.019620681,0.00090591074,0.000019269593,0.000016631577,0.000093505565,0.000024939704,0.0070476043],"genre_scores_gemma":[0.99703145,0.00017636963,0.0016404596,0.00004756028,0.000007660264,0.0000069855932,0.000019879484,0.0000048776647,0.001064833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967754,0.00016931746,0.0000139177355,0.000039645896,0.000023628198,0.00007593939],"domain_scores_gemma":[0.9979826,0.0011380423,0.00044797585,0.00011546719,0.00010045759,0.00021553933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017180812,0.00028494277,0.00034919416,0.0002848955,0.0003026313,0.0010597773,0.00034910164,0.0005980531,0.0022437135],"category_scores_gemma":[0.006398226,0.00025756672,0.00032077287,0.00018730243,0.0005281231,0.00068686984,0.00068793737,0.00083746255,0.00016224882],"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":[0.0021730552,0.0006781372,0.083994575,0.0002489824,0.00028615107,0.0008269517,0.0010872097,0.4973187,0.012724647,0.3086542,0.0022747735,0.0897326],"study_design_scores_gemma":[0.00016814328,0.0009001053,0.071859814,0.0001493751,0.00019217367,0.0004299479,0.0011735337,0.7245989,0.0035580695,0.1932725,0.0036064212,0.00009101871],"about_ca_topic_score_codex":0.0024923768,"about_ca_topic_score_gemma":0.0026977526,"teacher_disagreement_score":0.0024923768,"about_ca_system_score_codex":0.00087774923,"about_ca_system_score_gemma":0.0006540633,"threshold_uncertainty_score":0.009086132},"labels":[],"label_agreement":null},{"id":"W2141893259","doi":"10.25336/p65c9r","title":"Survival advantage of siblings and spouses of centenarians in 20th-century Quebec","year":2013,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Longevity; Demography; Cohort; Gerontology; Biology; Medicine; Sociology","score_opus":0.03349886344610397,"score_gpt":0.3307907380165326,"score_spread":0.2972918745704286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141893259","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":[0.9993432,0.00012282217,0.000010386593,0.00003381638,0.0000023842413,0.0000018855652,0.00017223695,6.780859e-7,0.00031264263],"genre_scores_gemma":[0.999233,0.00009005518,0.000018640863,0.000020688763,0.0000013879815,0.0000014326102,0.00015638585,7.0929025e-7,0.00047760492],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997904,0.0000296903,0.000010093372,0.00004713927,0.000036858284,0.00008580487],"domain_scores_gemma":[0.99942374,0.00005294006,0.00016210708,0.000036245354,0.00016360318,0.00016132655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036422373,0.00021347823,0.0002245418,0.0008578537,0.0018050744,0.00061108137,0.00035222498,0.00030897898,0.0022558984],"category_scores_gemma":[0.0013977534,0.00014977112,0.00023650241,0.000980365,0.0005704005,0.00027654864,0.0005504757,0.00026358842,0.00014413419],"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":[0.00013135505,0.00002207282,0.993109,0.000006949019,0.00002918153,0.00025294535,0.0023068571,0.00004569509,0.00051223324,0.00009388909,0.0002955779,0.0031941656],"study_design_scores_gemma":[0.000004015879,0.00002580708,0.99745756,0.000008803114,0.000011275245,0.00015069087,0.0018303819,0.00007786013,0.0000647227,0.00002046028,0.00034168796,0.000006699119],"about_ca_topic_score_codex":0.91302776,"about_ca_topic_score_gemma":0.9629549,"teacher_disagreement_score":0.08697224,"about_ca_system_score_codex":0.0058571226,"about_ca_system_score_gemma":0.002410645,"threshold_uncertainty_score":0.17496884},"labels":[],"label_agreement":null},{"id":"W2142853400","doi":"10.1136/heartjnl-2012-301828","title":"Cardiovascular disease mortality in the Americas: current trends and disparities","year":2012,"lang":"en","type":"article","venue":"Heart","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":77,"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":"Medicine; Demography; Mortality rate","score_opus":0.04744313641787379,"score_gpt":0.3450717139747678,"score_spread":0.29762857755689404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142853400","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":[0.7800136,0.17160282,0.001131466,0.024529187,0.0004599535,0.00005641716,0.0085994685,0.000094370116,0.013512662],"genre_scores_gemma":[0.9527541,0.04108814,0.0010840325,0.0012605801,0.00070955115,0.000027152759,0.0025653199,0.000008640677,0.00050255825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996031,0.00007813655,0.000055416203,0.00009230121,0.000102031714,0.00006913001],"domain_scores_gemma":[0.99898213,0.00015531685,0.00042402247,0.00003793321,0.00027450832,0.00012596957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010653274,0.000202704,0.00036237252,0.0013884895,0.00035731553,0.0010665867,0.00029329286,0.0004928018,0.0013029346],"category_scores_gemma":[0.0015190858,0.000106298445,0.00031357846,0.0027712318,0.00038381226,0.0012444722,0.00063672836,0.0005471292,0.00018342937],"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":[0.00005858772,0.0000695536,0.920237,0.00047725305,0.00016173733,0.00009443587,0.00078082137,0.00022891231,0.00033442068,0.0015697349,0.0049667396,0.071020864],"study_design_scores_gemma":[0.000005127815,0.00004849288,0.9878314,0.0004138523,0.000064274434,0.00020296642,0.0017197648,0.00036112557,0.000048836595,0.00086737564,0.008429288,0.0000075287353],"about_ca_topic_score_codex":0.02080488,"about_ca_topic_score_gemma":0.033275153,"teacher_disagreement_score":0.02080488,"about_ca_system_score_codex":0.00073576777,"about_ca_system_score_gemma":0.00101285,"threshold_uncertainty_score":0.04136759},"labels":[],"label_agreement":null},{"id":"W2142993902","doi":"10.1186/1478-7547-1-6","title":"PopMod: a longitudinal population model with two interacting disease states","year":2003,"lang":"en","type":"article","venue":"Cost Effectiveness and Resource Allocation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"Erasmus Universiteit Rotterdam; Universiteit Leiden; World Health Organization","keywords":"Life expectancy; Population; Population health; Medicine; Disease; Health services research; Public health; Gerontology; Environmental health","score_opus":0.02269602199634658,"score_gpt":0.3223167446390418,"score_spread":0.29962072264269524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142993902","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":[0.043536685,0.0011893212,0.899998,0.0039395858,0.00052336976,0.00045235513,0.02502993,0.0019412275,0.02338949],"genre_scores_gemma":[0.5816888,0.0032732384,0.33921185,0.0020568494,0.00053727167,0.0042437147,0.020479586,0.00047715692,0.048031524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993747,0.00035750293,0.000032002237,0.00010470957,0.00006512928,0.00006590668],"domain_scores_gemma":[0.99849224,0.0010041472,0.00013449871,0.00010938015,0.00017107869,0.00008869371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002353449,0.00073346,0.0009257111,0.00074035744,0.00060146576,0.0015912373,0.002355343,0.00151992,0.012213061],"category_scores_gemma":[0.0057503036,0.0005545885,0.001607133,0.0011706565,0.00045983423,0.0013977727,0.0014898884,0.0017118255,0.0017084935],"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":[0.00027374914,0.0001323383,0.013147735,0.00018067502,0.0003038594,0.00047792893,0.00032208284,0.72446644,0.00039634435,0.20050919,0.022883512,0.03690605],"study_design_scores_gemma":[0.00017066534,0.000098168,0.001329079,0.000044871605,0.00011480417,0.00023062104,0.000058683447,0.9078383,0.00014580418,0.06302252,0.02689619,0.000050405688],"about_ca_topic_score_codex":0.024914134,"about_ca_topic_score_gemma":0.017783314,"teacher_disagreement_score":0.024914134,"about_ca_system_score_codex":0.0011049338,"about_ca_system_score_gemma":0.0022524463,"threshold_uncertainty_score":0.049538255},"labels":[],"label_agreement":null},{"id":"W2143421159","doi":"10.1080/10920277.2007.10597438","title":"The Lee-Carter Model for Forecasting Mortality, Revisited","year":2007,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":50,"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 Waterloo","funders":"","keywords":"Outlier; Econometrics; Index (typography); Statistical model; Statistics; Probabilistic logic; Time series; Series (stratigraphy); Anomaly detection; Computer science; Mathematics; Data mining; Biology","score_opus":0.06126692100696645,"score_gpt":0.3482603790701845,"score_spread":0.286993458063218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143421159","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":[0.10922488,0.0038900066,0.87420756,0.0036645813,0.0003706889,0.00010027005,0.0010597261,0.0003140446,0.0071681924],"genre_scores_gemma":[0.91565657,0.002895762,0.07238715,0.00042796446,0.00041003266,0.00019116231,0.0006597166,0.00007960423,0.007292109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992094,0.00044325204,0.000028258917,0.00012528959,0.00010621018,0.000087519715],"domain_scores_gemma":[0.99737096,0.001728786,0.00027999445,0.00013876152,0.00034918607,0.00013226556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032572837,0.00057551416,0.0006916068,0.0010449258,0.00042123385,0.0010678087,0.0019996036,0.0013379055,0.0022488837],"category_scores_gemma":[0.013170142,0.00030103995,0.00068247295,0.0015084709,0.0007223692,0.0018440167,0.000703846,0.0015349677,0.00042779208],"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":[0.00013208247,0.00003619243,0.014085486,0.00010244654,0.00009498006,0.00021096737,0.00022338533,0.8296058,0.00048906676,0.11838081,0.0041139177,0.032524873],"study_design_scores_gemma":[0.0000098940145,0.000030875734,0.0011109632,0.00002070283,0.000012653707,0.000045132365,0.00004251844,0.9659722,0.00008069151,0.031264152,0.0013859764,0.000024264937],"about_ca_topic_score_codex":0.019278092,"about_ca_topic_score_gemma":0.015136038,"teacher_disagreement_score":0.019278092,"about_ca_system_score_codex":0.0010129213,"about_ca_system_score_gemma":0.0010939285,"threshold_uncertainty_score":0.038331747},"labels":[],"label_agreement":null},{"id":"W2143979304","doi":"10.1111/j.1539-6975.2010.01394.x","title":"<scp>Canonical Valuation of Mortality‐Linked Securities</scp>","year":2010,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":40,"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":"Valuation (finance); Security market; Actuarial science; Longevity risk; Economics; Financial economics; Econometrics; Business; Pension; Finance","score_opus":0.025508061337265645,"score_gpt":0.31448241586477393,"score_spread":0.2889743545275083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143979304","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":[0.18728542,0.0008321457,0.78307664,0.0022038177,0.00022389574,0.00011017355,0.00033863328,0.00018035072,0.025748888],"genre_scores_gemma":[0.9565209,0.00073143136,0.03932326,0.00007878336,0.0002216856,0.0000509801,0.00021475258,0.000048261463,0.002809986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986865,0.00084030355,0.000044000506,0.000119720586,0.00024304219,0.000066431996],"domain_scores_gemma":[0.9936666,0.0027645114,0.0011700912,0.00074528466,0.0011137932,0.0005396796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004182091,0.00054086285,0.0005016417,0.0014748998,0.0004265241,0.003111427,0.00084930426,0.000731655,0.0056961766],"category_scores_gemma":[0.020009939,0.00028238815,0.0006078314,0.0015030912,0.0032053615,0.005426789,0.0018315747,0.0016331979,0.00034130595],"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":[0.000031668867,0.000030916795,0.002987166,0.000034877838,0.000027456774,0.000059290407,0.00006427117,0.03969364,0.00031644307,0.943345,0.001643224,0.011765975],"study_design_scores_gemma":[0.000007675292,0.00004380402,0.002377453,0.00002987995,0.000008601167,0.000079992795,0.000076066455,0.38156864,0.00039379127,0.6136386,0.0017504214,0.000025034895],"about_ca_topic_score_codex":0.0015988658,"about_ca_topic_score_gemma":0.0012107902,"teacher_disagreement_score":0.0056961766,"about_ca_system_score_codex":0.0013008205,"about_ca_system_score_gemma":0.0009815459,"threshold_uncertainty_score":0.022117257},"labels":[],"label_agreement":null},{"id":"W2144494932","doi":"10.1007/s10522-006-9073-3","title":"Health decline, aging and mortality: how are they related?","year":2007,"lang":"en","type":"article","venue":"Biogerontology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":74,"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":"National Institute on Aging; National Institutes of Health; Heart and Stroke Foundation of Canada","keywords":"Index (typography); Frailty Index; Hazard; Human health; Proportional hazards model; Gerontology; Demography; Medicine; Environmental health; Biology; Internal medicine; Computer science; Ecology","score_opus":0.03591310338283212,"score_gpt":0.3486949266205168,"score_spread":0.3127818232376847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144494932","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":[0.31583238,0.5164909,0.0029222781,0.13785696,0.0025862039,0.00015224626,0.0013404113,0.00009091291,0.022727827],"genre_scores_gemma":[0.84032905,0.14172581,0.0014566216,0.008062477,0.0057017785,0.00009982255,0.00043493757,0.000025386758,0.0021640745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99699795,0.0017137248,0.00017690477,0.00035138126,0.0004346152,0.00032552914],"domain_scores_gemma":[0.98235875,0.010154373,0.0034518333,0.0005780099,0.0020645822,0.0013924347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004928741,0.0007745624,0.0019526362,0.0035983042,0.00093576015,0.00410807,0.00096997747,0.0045890277,0.009969224],"category_scores_gemma":[0.027138103,0.0005880573,0.001102836,0.0053584347,0.003820752,0.005152706,0.002062768,0.0028169546,0.00060762523],"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":[0.0006085082,0.0005563327,0.8225505,0.0011170784,0.0016395921,0.00070336094,0.0026516328,0.00094465405,0.00018881552,0.027249193,0.0065359934,0.13525447],"study_design_scores_gemma":[0.000060028953,0.0002814739,0.94181406,0.000949757,0.00074729935,0.00058182137,0.005266414,0.0011860437,0.00005240554,0.041157793,0.007822788,0.000080141675],"about_ca_topic_score_codex":0.020573104,"about_ca_topic_score_gemma":0.02036758,"teacher_disagreement_score":0.020573104,"about_ca_system_score_codex":0.0016286977,"about_ca_system_score_gemma":0.002596454,"threshold_uncertainty_score":0.040906727},"labels":[],"label_agreement":null},{"id":"W2147580046","doi":"10.3402/gha.v7.23574","title":"The development and experience of epidemiological transition theory over four decades: a systematic review","year":2014,"lang":"en","type":"review","venue":"Global Health Action","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":185,"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 Global Health Research","funders":"","keywords":"Epidemiological transition; Pace; Population; Public health; Positive economics; Development economics; Medicine; Sociology; Geography; Demography; Economics","score_opus":0.11167845338049791,"score_gpt":0.47868902801016644,"score_spread":0.3670105746296685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147580046","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":[0.0010333143,0.99730515,0.00021164877,0.0007547908,0.00013834487,0.000068629124,0.00014420589,0.000003381315,0.0003405735],"genre_scores_gemma":[0.017741669,0.97997004,0.00072448194,0.0009501491,0.00010420091,0.00024826822,0.0001824696,0.0000051798197,0.000073495736],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.979699,0.00877373,0.006501629,0.0016937988,0.0028215367,0.0005103163],"domain_scores_gemma":[0.8879136,0.09096077,0.0127633605,0.0014670279,0.006104321,0.0007910121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026197348,0.00091553567,0.004280305,0.019591348,0.00080693705,0.005279433,0.0022989549,0.0024830927,0.002787794],"category_scores_gemma":[0.09976763,0.0012946522,0.004039541,0.021973228,0.001781238,0.008095407,0.0031399599,0.0022792935,0.00023904532],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022787956,0.00003728577,0.004750004,0.77808416,0.006003486,0.00042894136,0.0059239906,0.00041168768,0.0001331426,0.0063839857,0.0056846365,0.19193077],"study_design_scores_gemma":[0.000046533056,0.000090041234,0.006203343,0.93111813,0.008748867,0.00072091166,0.0025818513,0.000120498145,0.000074484386,0.0019954797,0.048257478,0.00004231295],"about_ca_topic_score_codex":0.007633402,"about_ca_topic_score_gemma":0.016815137,"teacher_disagreement_score":0.026197348,"about_ca_system_score_codex":0.006889599,"about_ca_system_score_gemma":0.018852206,"threshold_uncertainty_score":0.13854647},"labels":[],"label_agreement":null},{"id":"W2147765990","doi":"10.1142/s0217590810004048","title":"AN ACTUARIAL APPROACH TO ASSESSING PERSONAL INJURY COMPENSATIONS IN SINGAPORE: THEORY AND PRACTICE","year":2010,"lang":"en","type":"article","venue":"The Singapore Economic Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personal injury; Lump sum; Actuarial science; Discounting; Plaintiff; Payment; Economics; Human settlement; Value (mathematics); Law; Finance; Engineering; Computer science; Political science","score_opus":0.03373630248001568,"score_gpt":0.38504825028273953,"score_spread":0.35131194780272385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147765990","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":[0.3079039,0.0053660288,0.6005134,0.004274709,0.0002798338,0.0008971247,0.001032061,0.00048985693,0.079243116],"genre_scores_gemma":[0.91531503,0.0033546363,0.0761788,0.00013856064,0.00011566581,0.00026932504,0.00030053145,0.000034037126,0.0042933975],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99078137,0.0063926713,0.0007104434,0.00042337173,0.0015171187,0.00017500014],"domain_scores_gemma":[0.9775547,0.014962027,0.0031027254,0.0011785211,0.002679575,0.00052244245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018576425,0.0006965824,0.0007469278,0.007971597,0.0008270823,0.0036206103,0.0018338846,0.0009848633,0.006071298],"category_scores_gemma":[0.04529871,0.00052404014,0.00059992424,0.0046790214,0.001704862,0.0027137117,0.002571937,0.00111262,0.00074317685],"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":[0.00014160539,0.00028289872,0.14973722,0.00050437317,0.00034492248,0.00066195027,0.0047933185,0.2762119,0.00058905093,0.19891205,0.008275472,0.35954523],"study_design_scores_gemma":[0.000028815919,0.00035191257,0.09004097,0.0005938216,0.00011893252,0.0006024894,0.0048131375,0.7511519,0.00084895047,0.12851955,0.02275248,0.0001769563],"about_ca_topic_score_codex":0.012007141,"about_ca_topic_score_gemma":0.010187024,"teacher_disagreement_score":0.018576425,"about_ca_system_score_codex":0.0030183238,"about_ca_system_score_gemma":0.002006011,"threshold_uncertainty_score":0.0982427},"labels":[],"label_agreement":null},{"id":"W2148830585","doi":"10.3905/jor.2014.2.2.099","title":"Mortality Plateaus and the Pricing of Longevity Insurance","year":2014,"lang":"en","type":"article","venue":"The Journal of Retirement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Annuity; Longevity risk; Life annuity; Longevity; Economics; Life insurance; Volatility (finance); Popularity; Actuarial science; Life table; Value (mathematics); Present value; Financial economics; Pension; Finance; Political science","score_opus":0.02145062405887676,"score_gpt":0.29967213653524705,"score_spread":0.2782215124763703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148830585","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":[0.81219774,0.007715317,0.09257401,0.014764236,0.0004581144,0.000040112252,0.00013911883,0.00018829976,0.07192314],"genre_scores_gemma":[0.99593323,0.00060104224,0.0007142598,0.00009931445,0.00018846948,0.0000055397427,0.000015861408,0.000008617187,0.0024337359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995358,0.00021886169,0.000020073012,0.000044990345,0.00010563364,0.00007465635],"domain_scores_gemma":[0.9963988,0.0021858823,0.0006718812,0.00013861185,0.00028557712,0.00031915907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001749896,0.00031139475,0.00031897664,0.0006213188,0.00049693463,0.0027912,0.000812218,0.0015699601,0.004099472],"category_scores_gemma":[0.0140351,0.0002225674,0.00040780593,0.00049305975,0.0021417416,0.0032332833,0.0013524231,0.0020322604,0.00026798082],"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":[0.00033729651,0.00008791923,0.0194983,0.00008647498,0.000048259364,0.00150032,0.0009373531,0.07426275,0.0023146905,0.8518712,0.004419883,0.044635586],"study_design_scores_gemma":[0.000030407378,0.0002256068,0.020368585,0.000106424275,0.000022130997,0.0004438661,0.00074923405,0.3035014,0.0006344998,0.6691453,0.0047126105,0.000060031398],"about_ca_topic_score_codex":0.0018740855,"about_ca_topic_score_gemma":0.0013062939,"teacher_disagreement_score":0.004099472,"about_ca_system_score_codex":0.0013700912,"about_ca_system_score_gemma":0.00052892463,"threshold_uncertainty_score":0.013714075},"labels":[],"label_agreement":null},{"id":"W2149263046","doi":"10.71781/12861","title":"La démographie des centenaires québécois : validation des âges au décès, mesure de la mortalité et composante familiale de la longévité","year":2010,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Humanities; Political science; Geography; Art","score_opus":0.007244322910459741,"score_gpt":0.2318759331187463,"score_spread":0.22463161020828656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149263046","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":[0.9472514,0.004860061,0.0028982912,0.0013069898,0.000075988435,0.00017876344,0.02958426,0.00005995945,0.013784259],"genre_scores_gemma":[0.9725954,0.0026884878,0.0022389302,0.00019115578,0.000021208078,0.0001812993,0.009598456,0.000017284685,0.012467765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945325,0.00010456303,0.00006075544,0.00012949892,0.00016020593,0.000091691436],"domain_scores_gemma":[0.99614406,0.0004581475,0.0004867815,0.00022317427,0.0023989833,0.00028883293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001583541,0.00040369586,0.00036474667,0.0048273867,0.0018887474,0.0012288691,0.00068813405,0.00038411227,0.004482948],"category_scores_gemma":[0.0042377384,0.00021508268,0.00043067473,0.006622331,0.00062365807,0.0006244795,0.0007049086,0.00050057104,0.0005115738],"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":[0.000058404094,0.00001251849,0.9576697,0.00034175458,0.00020969934,0.00016582652,0.010382579,0.00040249567,0.00084372907,0.0009388774,0.004590218,0.024384191],"study_design_scores_gemma":[0.0000011770018,0.000012947545,0.9872645,0.00012404924,0.000033322634,0.000041570827,0.003848841,0.00017189236,0.00014130626,0.00005963191,0.00828924,0.000011449599],"about_ca_topic_score_codex":0.95194274,"about_ca_topic_score_gemma":0.9717557,"teacher_disagreement_score":0.048057258,"about_ca_system_score_codex":0.0063211955,"about_ca_system_score_gemma":0.0071837553,"threshold_uncertainty_score":0.09668052},"labels":[],"label_agreement":null},{"id":"W2149445560","doi":"","title":"Living to age 100 in Canada in 2000","year":2000,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Demography; Statistics; Maximum likelihood; Cohort; Mortality rate; Goodness of fit; Standard error; Geography; Mathematics; Sociology","score_opus":0.012910559658339158,"score_gpt":0.2679483640391879,"score_spread":0.2550378043808488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149445560","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":[0.15988421,0.0074714352,0.00043158577,0.0041310103,0.00065617176,0.00023652763,0.77015764,0.00034527815,0.056686066],"genre_scores_gemma":[0.4162075,0.015573622,0.0017198558,0.0040775836,0.00019592691,0.00032269632,0.4900372,0.000106301515,0.07175925],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99929845,0.00001450925,0.000043401556,0.000089889894,0.0003009107,0.0002529],"domain_scores_gemma":[0.99884856,0.000024735635,0.00012391739,0.000021609296,0.0006204034,0.0003607113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000432852,0.00057691487,0.0004703812,0.003131117,0.0021965038,0.0013245979,0.0013618979,0.00051145314,0.010092516],"category_scores_gemma":[0.00170237,0.00031159434,0.0006245507,0.008014799,0.00031206434,0.0007232367,0.0008353688,0.0011402175,0.002697436],"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":[0.00027463256,0.00010873038,0.41913512,0.0008164943,0.00012630051,0.00077571644,0.001264977,0.0007335771,0.00030300574,0.0035777583,0.48525932,0.08762445],"study_design_scores_gemma":[0.00004160493,0.00002639171,0.8378132,0.00035420185,0.000059709288,0.0003580532,0.0013424556,0.0006360642,0.00012749941,0.0003298017,0.15886489,0.000046220004],"about_ca_topic_score_codex":0.9937509,"about_ca_topic_score_gemma":0.997044,"teacher_disagreement_score":0.03052335,"about_ca_system_score_codex":0.03052335,"about_ca_system_score_gemma":0.04848927,"threshold_uncertainty_score":0.22146344},"labels":[],"label_agreement":null},{"id":"W2149860707","doi":"10.71781/12877","title":"Changements dans la répartition des décès selon l'âge : une approche non paramétrique pour l'étude de la mortalité adulte","year":2011,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Social Sciences and Humanities Research Council of Canada","keywords":"Political science; Philosophy; Art","score_opus":0.00936384780963754,"score_gpt":0.21869859781351336,"score_spread":0.20933475000387583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149860707","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":[0.019619206,0.0015239035,0.9746363,0.0007654137,0.00019751041,0.00019150997,0.00086166343,0.0010154488,0.001189061],"genre_scores_gemma":[0.3850277,0.0037043132,0.5933406,0.00068988785,0.00043074755,0.0020755043,0.0031413208,0.00092354266,0.010666428],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9900177,0.0057076397,0.0007854313,0.0021532662,0.0010859179,0.0002500824],"domain_scores_gemma":[0.9772454,0.015416966,0.0018250337,0.0031133222,0.002168381,0.00023087018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015933517,0.0012576125,0.0023568626,0.0024794375,0.0007294296,0.0038607386,0.0028868935,0.002419973,0.0045441077],"category_scores_gemma":[0.052372504,0.0012895217,0.00434667,0.004192882,0.0014084036,0.0033867916,0.0020723438,0.003854818,0.0020874646],"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":[0.0012486075,0.00028652127,0.06505742,0.0017381053,0.001957223,0.00044587985,0.0029155805,0.36181405,0.007073661,0.034106527,0.0060770996,0.5172794],"study_design_scores_gemma":[0.00016165974,0.0007004551,0.03270798,0.00077145325,0.0006401641,0.00055294216,0.0008133283,0.88258696,0.0047422047,0.043561514,0.032457743,0.00030358316],"about_ca_topic_score_codex":0.014758509,"about_ca_topic_score_gemma":0.010266621,"teacher_disagreement_score":0.015933517,"about_ca_system_score_codex":0.0013925226,"about_ca_system_score_gemma":0.0025727896,"threshold_uncertainty_score":0.08426547},"labels":[],"label_agreement":null},{"id":"W214991793","doi":"","title":"Un modelo de balance actuarial para sistemas de pensiones DB PAYG con dos contingencias.","year":2013,"lang":"es","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Social security; Welfare economics; Solvency; Balance (ability); Economics; Actuarial science; Pension plan; Pension; Political science; Psychology; Finance; Market liquidity","score_opus":0.012792911385013078,"score_gpt":0.26602340216830017,"score_spread":0.2532304907832871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W214991793","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":[0.08545949,0.0016892405,0.83017796,0.0037553771,0.0003679994,0.0001733397,0.0013672537,0.0004367991,0.07657248],"genre_scores_gemma":[0.8647328,0.0015541137,0.0555676,0.0003033386,0.00028549312,0.00046453736,0.0009495035,0.00010501483,0.0760375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993988,0.00024642443,0.000025619162,0.00011305947,0.000118262236,0.00009773319],"domain_scores_gemma":[0.99880755,0.0007244216,0.00015973762,0.000054026408,0.00013991204,0.00011428166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021422377,0.0010046264,0.00078331225,0.0010290247,0.00081182295,0.0027407594,0.0016359291,0.0018701836,0.010873262],"category_scores_gemma":[0.0052782535,0.00056606985,0.0009561121,0.00090707466,0.0013889974,0.0028992363,0.001550497,0.0017916663,0.0012922524],"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":[0.00006280733,0.000041934534,0.00153951,0.000056293364,0.000041476942,0.00016010724,0.00024075477,0.60824597,0.0002703587,0.37981418,0.0018650257,0.0076615284],"study_design_scores_gemma":[0.000029645902,0.000034427423,0.00033543826,0.000024673696,0.000026537717,0.00006388784,0.00007331157,0.8830997,0.00006948185,0.11062083,0.005607552,0.000014493731],"about_ca_topic_score_codex":0.01577888,"about_ca_topic_score_gemma":0.007966241,"teacher_disagreement_score":0.01577888,"about_ca_system_score_codex":0.0022153838,"about_ca_system_score_gemma":0.0022342254,"threshold_uncertainty_score":0.03637469},"labels":[],"label_agreement":null},{"id":"W2152504615","doi":"10.5555/1516744.1516831","title":"Fast simulation of equity-linked life insurance contracts with a surrender option","year":2008,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Surrender; Life insurance; Monte Carlo methods for option pricing; Monte Carlo method; Equity (law); Actuarial science; Estimator; Control variates; Put option; Computer science; Importance sampling; Order (exchange); Econometrics; Economics; Valuation of options; Markov chain Monte Carlo; Mathematics; Finance; Hybrid Monte Carlo; Statistics","score_opus":0.06578289061576555,"score_gpt":0.34367951631977345,"score_spread":0.2778966257040079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152504615","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":[0.63578,0.0004435351,0.35647735,0.0009752799,0.000093050105,0.00008049536,0.00022963602,0.0004415319,0.005479111],"genre_scores_gemma":[0.9684058,0.000109612665,0.029548768,0.000072693096,0.000015692973,0.000067304536,0.00018917565,0.000038445054,0.0015524456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993474,0.00034845032,0.000026542464,0.000065836975,0.00011499213,0.000096845644],"domain_scores_gemma":[0.99147475,0.0067356867,0.00058851624,0.00027854685,0.00051795575,0.00040457267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025806846,0.0004960407,0.00093802344,0.0005842664,0.00054174016,0.0011428115,0.0011637172,0.0018619966,0.0029611741],"category_scores_gemma":[0.012583858,0.0005851121,0.00072603696,0.00059697794,0.0015098649,0.0015339683,0.0013882873,0.0014640542,0.00020057628],"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":[0.000054172222,0.000021074507,0.0013050453,0.00000709601,0.000009865777,0.00004608679,0.000029580839,0.9893183,0.00017211334,0.0080383755,0.00008233671,0.000915958],"study_design_scores_gemma":[0.0000069150597,0.0000042178294,0.00006156787,0.0000012398008,0.0000010421318,0.0000022218132,0.0000039263987,0.9986204,0.00004537648,0.0012200912,0.000031308024,0.0000017190715],"about_ca_topic_score_codex":0.014888617,"about_ca_topic_score_gemma":0.007279733,"teacher_disagreement_score":0.014888617,"about_ca_system_score_codex":0.0011312163,"about_ca_system_score_gemma":0.0011921307,"threshold_uncertainty_score":0.029603899},"labels":[],"label_agreement":null},{"id":"W2153998549","doi":"10.71781/12935","title":"Mortalité adulte et longévité exceptionnelle au Québec ancien","year":2009,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Longevity; Genealogy; Demography; Historical demography; Ancien regime; Geography; Inheritance (genetic algorithm); Population; Humanities; History; Gerontology; Research methodology; Sociology; Medicine; Art; Political science; Biology","score_opus":0.042121134988893666,"score_gpt":0.36113034525604826,"score_spread":0.31900921026715456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153998549","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":[0.94162893,0.013736911,0.002230462,0.0015148787,0.00017767918,0.000084621606,0.011116053,0.00009046224,0.029419973],"genre_scores_gemma":[0.9730152,0.0051583136,0.0012750934,0.00037524896,0.00004275507,0.00006538747,0.003709398,0.000019819834,0.016338708],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995402,0.000061852385,0.000029827535,0.000116704374,0.00014915831,0.00010223284],"domain_scores_gemma":[0.99846053,0.00022248119,0.00026462582,0.0000832109,0.0007990361,0.00017016353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006816598,0.0003036162,0.00038088375,0.0020731664,0.0017669491,0.0010232033,0.000529004,0.00031395123,0.005126603],"category_scores_gemma":[0.0020800815,0.00014910156,0.00044447824,0.0033288258,0.00064118416,0.00035906045,0.00048824673,0.00055694097,0.00021501623],"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":[0.00017034703,0.000029532395,0.9142175,0.00028693708,0.00039911573,0.00040255132,0.0042792433,0.001252498,0.0008656556,0.0016380356,0.0074158395,0.06904283],"study_design_scores_gemma":[0.0000030813108,0.00002898404,0.98607844,0.000109485125,0.00007309136,0.00006600264,0.00093518855,0.00029716472,0.00013393397,0.00007895543,0.012174282,0.000021330685],"about_ca_topic_score_codex":0.9872694,"about_ca_topic_score_gemma":0.99397534,"teacher_disagreement_score":0.012730598,"about_ca_system_score_codex":0.012662254,"about_ca_system_score_gemma":0.009777859,"threshold_uncertainty_score":0.09187144},"labels":[],"label_agreement":null},{"id":"W2154436850","doi":"10.1038/sj.embor.7400431","title":"Medicine, ageing and human longevity","year":2005,"lang":"en","type":"article","venue":"EMBO Reports","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Longevity; Ageing; Biology; Genetics","score_opus":0.022309661978611294,"score_gpt":0.3338439793868333,"score_spread":0.311534317408222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154436850","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":[0.2428636,0.51468027,0.0016097919,0.18824296,0.0030885183,0.000021937833,0.0010137989,0.00007815625,0.04840094],"genre_scores_gemma":[0.85365427,0.1285285,0.0008884589,0.0047344747,0.005481271,0.000020298621,0.00017530585,0.000006986308,0.006510437],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968445,0.00016373984,0.000025837011,0.000024551362,0.00006908707,0.00003234111],"domain_scores_gemma":[0.9978629,0.0010179025,0.00045559157,0.00008922861,0.00024721492,0.00032723587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016105713,0.00016389093,0.00029958607,0.000980117,0.00043299457,0.0020248303,0.00014678566,0.0012796139,0.005405705],"category_scores_gemma":[0.004487675,0.00008605069,0.00013971319,0.0010459153,0.00086222857,0.0010875693,0.0005270038,0.0007044563,0.0003942765],"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":[0.0008418781,0.00047791642,0.32657358,0.0014856261,0.00049398234,0.0010083661,0.004549683,0.0010632545,0.002082376,0.12538445,0.05303199,0.4830069],"study_design_scores_gemma":[0.00009888757,0.00041809247,0.7514897,0.00078437355,0.00024343454,0.0012115145,0.004895348,0.00070200476,0.0003349347,0.11134257,0.12843034,0.000048760045],"about_ca_topic_score_codex":0.0034988536,"about_ca_topic_score_gemma":0.007872813,"teacher_disagreement_score":0.005405705,"about_ca_system_score_codex":0.00063874107,"about_ca_system_score_gemma":0.0010083478,"threshold_uncertainty_score":0.01808387},"labels":[],"label_agreement":null},{"id":"W2156763096","doi":"10.1111/j.1539-6975.2009.01313.x","title":"<scp>Modeling Mortality With Jumps: Applications to Mortality Securitization</scp>","year":2009,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":122,"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 Manitoba","funders":"","keywords":"Securitization; Jump; Econometrics; Mortality rate; Bond; Economics; Multivariate statistics; Index (typography); Actuarial science; Statistics; Mathematics; Computer science; Finance; Medicine; Internal medicine","score_opus":0.023068191559039476,"score_gpt":0.3205074622028792,"score_spread":0.2974392706438397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156763096","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":[0.44990382,0.0006088559,0.539156,0.0032791558,0.00017891472,0.000054733202,0.0003435875,0.00037912998,0.0060957894],"genre_scores_gemma":[0.9876294,0.00018164604,0.010164366,0.000078692916,0.00005087449,0.000018720624,0.00006949528,0.000024990282,0.0017817569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996754,0.00017101978,0.000014516273,0.00005469948,0.000044251134,0.000040198236],"domain_scores_gemma":[0.9968352,0.0021674978,0.00044416962,0.0001660062,0.00019673158,0.00019031072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017681398,0.00043438387,0.00048987183,0.0005509266,0.0003776208,0.0008078496,0.0011653212,0.0013851151,0.0027796624],"category_scores_gemma":[0.007301731,0.00028950957,0.0007194435,0.0006024486,0.0011290332,0.0011700881,0.00092451816,0.0016129875,0.00014820119],"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":[0.00003237057,0.000040367824,0.008646015,0.000017489347,0.000032270338,0.00013083992,0.000056923574,0.93366295,0.00049503456,0.047965433,0.0005655363,0.008354837],"study_design_scores_gemma":[0.0000035566177,0.000014382298,0.0005295808,0.000003308976,0.0000037226994,0.0000131684565,0.000008468312,0.9909763,0.00012329558,0.008140842,0.00017788899,0.000005500985],"about_ca_topic_score_codex":0.016204597,"about_ca_topic_score_gemma":0.0074542025,"teacher_disagreement_score":0.016204597,"about_ca_system_score_codex":0.0010141119,"about_ca_system_score_gemma":0.00061732624,"threshold_uncertainty_score":0.032220542},"labels":[],"label_agreement":null},{"id":"W2158607454","doi":"10.1111/j.1539-6975.2012.01509.x","title":"Valuation and Hedging of the Ruin‐Contingent Life Annuity (RCLA)","year":2013,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Life annuity; Actuarial science; Life insurance; Economics; Annuity; Valuation (finance); Liberian dollar; Stochastic game; Arbitrage; Treasury; Financial economics; Finance; Microeconomics; Pension","score_opus":0.01484532389429075,"score_gpt":0.26695815802495276,"score_spread":0.252112834130662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158607454","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":[0.5796573,0.0015831984,0.39051256,0.0013123692,0.00014301902,0.00007646106,0.00010369797,0.000081066086,0.026530297],"genre_scores_gemma":[0.9922321,0.00012590385,0.005086699,0.000016975571,0.000018114366,0.000013061157,0.000013731722,0.00000602609,0.0024874415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995652,0.00022796981,0.000015203865,0.000060014263,0.00007216108,0.000059321184],"domain_scores_gemma":[0.99816126,0.0010198073,0.00028257462,0.0001502997,0.00018667801,0.00019930815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023706283,0.00042102992,0.0004307416,0.00041725053,0.00042374802,0.0019037151,0.00073702284,0.0013321985,0.0021002225],"category_scores_gemma":[0.0057091434,0.00030781876,0.0004575968,0.00026486715,0.0016543382,0.0020987622,0.00087877817,0.0012175465,0.00011084389],"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":[0.0001942885,0.00012555436,0.003594261,0.0000658536,0.00005101556,0.00050488405,0.00027933303,0.35408545,0.0065173837,0.61866045,0.0009733914,0.014948162],"study_design_scores_gemma":[0.000014175812,0.0001355829,0.0012426389,0.00003440712,0.000023302968,0.00013392934,0.0000834903,0.9086518,0.00097329833,0.086918496,0.0017613403,0.000027600268],"about_ca_topic_score_codex":0.0010673698,"about_ca_topic_score_gemma":0.00053003797,"teacher_disagreement_score":0.0023706283,"about_ca_system_score_codex":0.0011039538,"about_ca_system_score_gemma":0.00056026067,"threshold_uncertainty_score":0.012537241},"labels":[],"label_agreement":null},{"id":"W2159830737","doi":"10.1029/2007wr006427","title":"Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo","year":2009,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Reversible-jump Markov chain Monte Carlo; Bayesian probability; Covariate; Model selection; Bayesian inference; Bayesian average; Bayesian hierarchical modeling; Computer science; Mathematics; Gibbs sampling; Statistics; Algorithm","score_opus":0.0862613373586438,"score_gpt":0.3370777354482927,"score_spread":0.25081639808964895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159830737","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":[0.008756902,0.00013155912,0.9902244,0.00014006626,0.000012136733,0.000049691553,0.00009656653,0.000117657655,0.000471026],"genre_scores_gemma":[0.2910811,0.00070762023,0.7034111,0.00018524,0.00009433527,0.0008193666,0.0012652327,0.00020059747,0.0022352992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9940528,0.0044592577,0.00015978493,0.0005265634,0.0005679484,0.00023365085],"domain_scores_gemma":[0.9907748,0.0076034437,0.0004789892,0.0005635358,0.00044589367,0.0001334593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010116951,0.0008438608,0.0021831745,0.001954851,0.0008621484,0.0019903148,0.0023560354,0.0014657379,0.0029052733],"category_scores_gemma":[0.03314664,0.0010676093,0.0015817884,0.0018974339,0.0012454333,0.0019305308,0.002205154,0.002519952,0.0005082171],"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":[0.00010840334,0.000090120484,0.0054802997,0.00012120863,0.0002387173,0.0002441498,0.00024347595,0.75206566,0.00054746994,0.17620546,0.0018331914,0.06282188],"study_design_scores_gemma":[0.000029244933,0.000019854879,0.000940159,0.000028671791,0.000028554701,0.000053133244,0.000019513047,0.9068897,0.00020542198,0.09065514,0.0011001651,0.000030496816],"about_ca_topic_score_codex":0.010666672,"about_ca_topic_score_gemma":0.009483003,"teacher_disagreement_score":0.010666672,"about_ca_system_score_codex":0.0012153074,"about_ca_system_score_gemma":0.0030978112,"threshold_uncertainty_score":0.05350417},"labels":[],"label_agreement":null},{"id":"W2159848039","doi":"10.1002/sim.5808","title":"State‐space size considerations for disease‐progression models","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Insurance, Mortality, Demography, Risk Management","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":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov chain; State space; Stochastic matrix; Computer science; Set (abstract data type); Disease; Markov model; Basis (linear algebra); State (computer science); Space (punctuation); Econometrics; Markov process; Statistics; Mathematics; Algorithm; Medicine","score_opus":0.03652017716464257,"score_gpt":0.3702021972862839,"score_spread":0.33368202012164133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159848039","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":[0.018264793,0.0013500169,0.9642807,0.007641244,0.00017558147,0.00021825623,0.00043351864,0.0002762108,0.0073596565],"genre_scores_gemma":[0.69590825,0.0027042269,0.2859457,0.0020665142,0.0009372209,0.0018864634,0.0013972131,0.0003228091,0.008831669],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903606,0.006718574,0.00048264308,0.0009119348,0.0012000167,0.0003262937],"domain_scores_gemma":[0.7732154,0.21181624,0.0031396658,0.0072734803,0.0034812212,0.0010739444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030587537,0.0010794994,0.0025748904,0.0015168114,0.0016457598,0.0032643527,0.0044964477,0.0026739365,0.0061375885],"category_scores_gemma":[0.13820758,0.001242008,0.0017575409,0.0015002397,0.0032535659,0.00797672,0.004188562,0.0062914127,0.0005620176],"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":[0.00014928257,0.00008343797,0.0036476247,0.00013153552,0.000100774385,0.0001841064,0.00042561215,0.3633917,0.0004174216,0.6048682,0.0034711703,0.023129199],"study_design_scores_gemma":[0.00003285481,0.00003547834,0.00038858567,0.00004310828,0.000026215905,0.000047917987,0.000056867455,0.7158742,0.0001686168,0.28097415,0.0023330457,0.000019004372],"about_ca_topic_score_codex":0.01275813,"about_ca_topic_score_gemma":0.01042818,"teacher_disagreement_score":0.030587537,"about_ca_system_score_codex":0.0024634474,"about_ca_system_score_gemma":0.0025889603,"threshold_uncertainty_score":0.16176432},"labels":[],"label_agreement":null},{"id":"W2160439741","doi":"10.1002/for.2353","title":"A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates","year":2015,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Taiwan University; University of Waterloo; National Science Council","keywords":"Econometrics; Statistics; Linear regression; Regression; Mortality rate; Regression analysis; Lag; Logarithm; Linear model; Mathematics; Simple linear regression; Computer science; Demography","score_opus":0.21625717313170992,"score_gpt":0.375555006464455,"score_spread":0.15929783333274508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160439741","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":[0.025406161,0.00091225666,0.96747106,0.0006734133,0.00014816689,0.00014224142,0.00096124277,0.00091683253,0.0033685085],"genre_scores_gemma":[0.5642147,0.0039063394,0.40242362,0.00037318937,0.0006279546,0.00083327916,0.002007704,0.00021402768,0.025399249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986755,0.00063974096,0.000094888805,0.00031263105,0.00020186363,0.000075333795],"domain_scores_gemma":[0.9987632,0.0007190648,0.00021691594,0.000077100885,0.00019182934,0.000031941236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023422574,0.0012400475,0.0010029151,0.0018107315,0.00032041036,0.00096718356,0.002045984,0.0014806321,0.0041955705],"category_scores_gemma":[0.00583519,0.0005407095,0.0016857473,0.0025318516,0.00035739594,0.0012782734,0.00066226587,0.0015268801,0.001897004],"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":[0.00005930194,0.00011777021,0.0059307828,0.00022696365,0.00021644843,0.00022421955,0.00017901916,0.86898476,0.0020510666,0.03071479,0.0026977276,0.08859707],"study_design_scores_gemma":[0.000008307289,0.00005209122,0.0009774131,0.000015111691,0.000029095314,0.000042436466,0.000017023553,0.9865101,0.00028028482,0.009887455,0.0021539673,0.000026759239],"about_ca_topic_score_codex":0.013713714,"about_ca_topic_score_gemma":0.0100766495,"teacher_disagreement_score":0.013713714,"about_ca_system_score_codex":0.000796624,"about_ca_system_score_gemma":0.00091454526,"threshold_uncertainty_score":0.027267814},"labels":[],"label_agreement":null},{"id":"W2161424135","doi":"10.1111/j.1365-2966.2005.09974.x","title":"Annual variation of sporadic radar meteor rates","year":2006,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Liability; Bond; Fixed income; Econometrics; Cash flow; Actuarial science; Economics; Inflation (cosmology); Asset (computer security); Wage; Financial economics; Finance; Physics; Computer science","score_opus":0.006900265805577412,"score_gpt":0.22990180291136708,"score_spread":0.22300153710578968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161424135","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":[0.9910615,0.00023387931,0.00035247632,0.0000366405,0.000009048271,0.000008090114,0.004950484,0.000046026744,0.0033017835],"genre_scores_gemma":[0.9958276,0.00011968998,0.00014828109,0.0000057547036,0.000007185701,0.000004885389,0.0031659813,0.0000051850584,0.00071526546],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996296,0.000041903084,0.000029722069,0.00007655709,0.00014205174,0.0000802228],"domain_scores_gemma":[0.9980592,0.00023304767,0.0005124935,0.00012901379,0.00091982476,0.0001464301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005827531,0.00010681553,0.0001440008,0.0014797568,0.00017006851,0.00031485193,0.00024850716,0.00009039247,0.001122306],"category_scores_gemma":[0.0023327044,0.000054861383,0.00011852208,0.0011075742,0.000134577,0.0001196652,0.000175905,0.000138448,0.00019718183],"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":[0.00007607753,0.000010576687,0.9842978,0.00002376797,0.00008286309,0.000054440097,0.00025639485,0.0008007661,0.0011162333,0.00015709197,0.0011819572,0.0119419955],"study_design_scores_gemma":[5.929982e-7,0.000009544302,0.9987337,0.000002221451,0.000006548839,0.000037184975,0.00003951188,0.00023824716,0.000110914676,0.000009690287,0.00080928206,0.0000024896945],"about_ca_topic_score_codex":0.16950546,"about_ca_topic_score_gemma":0.21877338,"teacher_disagreement_score":0.16950546,"about_ca_system_score_codex":0.0006548757,"about_ca_system_score_gemma":0.0005284937,"threshold_uncertainty_score":0.33703768},"labels":[],"label_agreement":null},{"id":"W2162472800","doi":"","title":"Mortality Statistics for the Oldest-Old: An Evaluation of Canadian Data","year":2000,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistics; Geography; Mathematics","score_opus":0.22447607878193568,"score_gpt":0.4274178946737874,"score_spread":0.2029418158918517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162472800","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":[0.5392623,0.007291376,0.0046084253,0.0027237919,0.0002624765,0.00070341,0.42910236,0.00074394373,0.015301829],"genre_scores_gemma":[0.7637647,0.0065739704,0.010059008,0.0003246326,0.000087649074,0.00035983548,0.2139516,0.00018983385,0.004688648],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9894332,0.0009791643,0.0007661559,0.00055485626,0.0076763667,0.0005901769],"domain_scores_gemma":[0.9200268,0.0077742715,0.0034428262,0.0022118131,0.06420209,0.0023422057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011738349,0.001061585,0.0010846505,0.016893882,0.0028337287,0.0020795986,0.0032998996,0.0005187602,0.0023745424],"category_scores_gemma":[0.042713773,0.0003892486,0.0017913629,0.0280458,0.0005608153,0.0009810345,0.0011944073,0.0007195601,0.00040321008],"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":[0.0011325617,0.0002304815,0.8256235,0.001369023,0.00092114025,0.0002536218,0.001240981,0.010931711,0.00050449494,0.004163024,0.05573255,0.097896904],"study_design_scores_gemma":[0.00009197816,0.00009824256,0.9695682,0.00022093828,0.00035272064,0.00011084171,0.0007966612,0.00777889,0.00041285405,0.00021721383,0.02026909,0.00008237418],"about_ca_topic_score_codex":0.99602616,"about_ca_topic_score_gemma":0.9951448,"teacher_disagreement_score":0.046826564,"about_ca_system_score_codex":0.046826564,"about_ca_system_score_gemma":0.07269212,"threshold_uncertainty_score":0.33975208},"labels":[],"label_agreement":null},{"id":"W2162894200","doi":"10.7202/1011541ar","title":"Immigration et structure par âge de la population du Canada : quelles relations ?","year":2012,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Humanities; Political science; Geography; Art","score_opus":0.004079338187596099,"score_gpt":0.23752277012350329,"score_spread":0.2334434319359072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162894200","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":[0.9352075,0.0052239513,0.009797404,0.010153923,0.000097908734,0.00007109283,0.004207394,0.000079885554,0.035161067],"genre_scores_gemma":[0.98425907,0.0036233077,0.0024778706,0.000343357,0.000019552821,0.00002006503,0.0009578549,0.00001771226,0.0082811685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948263,0.00010837635,0.000013700231,0.00009915777,0.00012546664,0.00017059362],"domain_scores_gemma":[0.9990287,0.00030768604,0.0001295543,0.00006392587,0.00034352302,0.00012665182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009646684,0.00038060537,0.00046265387,0.00080791535,0.0014871795,0.0019566864,0.0007689599,0.00046185256,0.0036024954],"category_scores_gemma":[0.0036061672,0.00017991732,0.00058836257,0.0023263886,0.001070999,0.00064835534,0.0009928771,0.000967988,0.00018028516],"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":[0.00018952249,0.000051755578,0.795963,0.00025289448,0.00033564027,0.00036780772,0.0070663714,0.04945084,0.0009744155,0.07483452,0.0049998905,0.06551325],"study_design_scores_gemma":[0.000033200013,0.00009676357,0.8947914,0.0004816465,0.0002914965,0.00013610563,0.010189876,0.03810604,0.00072428654,0.014234186,0.040805213,0.00010976497],"about_ca_topic_score_codex":0.98160547,"about_ca_topic_score_gemma":0.98429847,"teacher_disagreement_score":0.01839453,"about_ca_system_score_codex":0.014565116,"about_ca_system_score_gemma":0.02531314,"threshold_uncertainty_score":0.10567784},"labels":[],"label_agreement":null},{"id":"W2163023007","doi":"10.5539/mas.v3n7p99","title":"The Actuarial Model for Implicit Pension Debt of China","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Pension; China; Debt; Actuarial science; Economics; Pension system; Finance; Political science; Law","score_opus":0.02001929075201048,"score_gpt":0.3082332308488237,"score_spread":0.2882139400968132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163023007","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":[0.34923407,0.0028480017,0.5438362,0.007942306,0.0005012031,0.00030366113,0.0059875227,0.0005542242,0.088792756],"genre_scores_gemma":[0.9355723,0.0014317331,0.010800556,0.00017434971,0.00019211089,0.00032642673,0.001125608,0.000061815605,0.050315082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952316,0.00014002096,0.000030954918,0.00009330645,0.00012187201,0.00009064381],"domain_scores_gemma":[0.9995277,0.00014245696,0.00009283822,0.000033549426,0.00013938382,0.0000640364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001499026,0.0006684491,0.00070369005,0.0010822142,0.0006293398,0.0015602005,0.0025438417,0.0014732098,0.0071540386],"category_scores_gemma":[0.0024948914,0.00043623665,0.00076488033,0.0010250937,0.0007118956,0.0018053459,0.0009524444,0.0011917123,0.00070114236],"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":[0.000037908623,0.00002190366,0.0022908684,0.000043309563,0.000029310391,0.00021875299,0.0001419921,0.819299,0.00028336374,0.16919702,0.0026198567,0.005816762],"study_design_scores_gemma":[0.00002045229,0.000011469773,0.0008436361,0.000010062012,0.00001479779,0.000046069817,0.000015284028,0.9752034,0.000040893716,0.02208732,0.0016913498,0.000015247282],"about_ca_topic_score_codex":0.044932116,"about_ca_topic_score_gemma":0.016827747,"teacher_disagreement_score":0.044932116,"about_ca_system_score_codex":0.0022377966,"about_ca_system_score_gemma":0.002667382,"threshold_uncertainty_score":0.08934116},"labels":[],"label_agreement":null},{"id":"W2164041936","doi":"10.4054/demres.2008.18.19","title":"Does the recent evolution of Canadian mortality agree with the epidemiologic transition theory?","year":2008,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transition (genetics); Epidemiological transition; Demographic transition; Demography; Genealogy; Geography; History; Sociology; Population; Biology; Fertility","score_opus":0.11888341133327937,"score_gpt":0.36558091527514364,"score_spread":0.24669750394186427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164041936","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":[0.51221204,0.04763202,0.019996293,0.24053441,0.001976391,0.00021020044,0.012086623,0.00032532614,0.16502671],"genre_scores_gemma":[0.96495605,0.014731026,0.0041800146,0.006135388,0.0005366221,0.000034598917,0.0021293082,0.00004969669,0.007247215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99810886,0.00016413805,0.000098663084,0.00036699863,0.0006556068,0.0006057026],"domain_scores_gemma":[0.9874643,0.0018653371,0.00173318,0.000986425,0.007005199,0.00094553403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064380323,0.00038617404,0.0008536515,0.0054138587,0.0049609835,0.0048071104,0.0032889768,0.0015767929,0.0073334863],"category_scores_gemma":[0.025579317,0.00028382085,0.0013784104,0.011720964,0.0035467192,0.0031896178,0.0018273193,0.002596396,0.0003433342],"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":[0.0004029124,0.00008073683,0.633695,0.0005061607,0.00035409542,0.00046449693,0.0074334107,0.0034748826,0.00044353475,0.22648603,0.015313551,0.1113452],"study_design_scores_gemma":[0.000027415479,0.000048281323,0.91086596,0.00030575413,0.00018306772,0.00021634556,0.0049853697,0.0034438716,0.0002160227,0.029175181,0.050424814,0.00010783983],"about_ca_topic_score_codex":0.98284435,"about_ca_topic_score_gemma":0.9782216,"teacher_disagreement_score":0.037274458,"about_ca_system_score_codex":0.037274458,"about_ca_system_score_gemma":0.03259811,"threshold_uncertainty_score":0.27044636},"labels":[],"label_agreement":null},{"id":"W2165002631","doi":"10.1093/pubmed/fdv111","title":"Impact of homicide and traffic crashes on life expectancy in the largest Latin American country","year":2015,"lang":"en","type":"article","venue":"Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","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":"Université de Montréal; Institut National de Santé Publique du Québec","funders":"","keywords":"Life expectancy; Homicide; Latin Americans; Injury prevention; Poison control; Occupational safety and health; Demography; Suicide prevention; Expectancy theory; Human factors and ergonomics; Public health; Geography; Medicine; Environmental health; Gerontology; Psychology; Political science; Sociology; Social psychology; Population","score_opus":0.09767722034292897,"score_gpt":0.3971714028846075,"score_spread":0.29949418254167853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165002631","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":[0.9906399,0.002137422,0.00010669407,0.0008097265,0.00001060462,0.000009911817,0.0015586639,0.000008216526,0.004718829],"genre_scores_gemma":[0.99907243,0.00048524453,0.00004109877,0.000028367958,0.0000028060824,0.0000031464779,0.00027541895,0.0000016292328,0.00008973836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971837,0.00007440449,0.000018331488,0.000040335293,0.00004160488,0.00010691373],"domain_scores_gemma":[0.9987085,0.00024421813,0.00040101472,0.00006777496,0.00030242218,0.00027612588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005196322,0.00019507427,0.00020330073,0.0008844298,0.00046989517,0.0006389143,0.0002637292,0.0001635138,0.0019525012],"category_scores_gemma":[0.0033119095,0.00007283746,0.00048814112,0.00093400455,0.00035184206,0.0003004582,0.0008092433,0.0003298727,0.000087018714],"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":[0.000035067136,0.000013914015,0.9933602,0.000034361372,0.00007164046,0.00010285528,0.000363325,0.00017740167,0.00008806575,0.00043596065,0.00022583394,0.0050913855],"study_design_scores_gemma":[0.00000222891,0.000016161714,0.9972536,0.00007578778,0.000054644926,0.00011264961,0.0010164706,0.00025366573,0.000049401482,0.00015465824,0.0010066438,0.000004054166],"about_ca_topic_score_codex":0.37018383,"about_ca_topic_score_gemma":0.4519762,"teacher_disagreement_score":0.37018383,"about_ca_system_score_codex":0.0021707355,"about_ca_system_score_gemma":0.0020242531,"threshold_uncertainty_score":0.73605824},"labels":[],"label_agreement":null},{"id":"W2165757716","doi":"10.1017/s0515036100014161","title":"Fair Valuation of Various Participation Schemes in Life Insurance","year":2005,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Valuation (finance); Actuarial science; Fair value; Liability; Life insurance; Cash flow; Economics; Profit (economics); Black–Scholes model; Microeconomics; Financial economics; Finance","score_opus":0.03403985124480752,"score_gpt":0.3137217379884516,"score_spread":0.2796818867436441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165757716","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":[0.38707504,0.00069405214,0.595091,0.00073563395,0.00008708174,0.00011841761,0.00007105825,0.00009488608,0.016032847],"genre_scores_gemma":[0.9556597,0.0002578972,0.04056687,0.000033739823,0.00003200399,0.000058154536,0.000040990133,0.000024481416,0.0033261706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976151,0.0012306793,0.000075260126,0.00016804026,0.00061907736,0.00029184556],"domain_scores_gemma":[0.99598914,0.0026092038,0.0003752766,0.00045133993,0.0003103303,0.0002645916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074806483,0.00042067986,0.00055701163,0.0011238487,0.0009674891,0.00330082,0.0010651383,0.0011988699,0.0033454658],"category_scores_gemma":[0.018385595,0.0002197689,0.0007807059,0.0008918155,0.004185226,0.0042922082,0.001894566,0.0012836575,0.0002632579],"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":[0.00009628905,0.00004914255,0.0011174446,0.000027884262,0.000012063786,0.00009631127,0.00029615106,0.09557865,0.0014269392,0.8892604,0.00024401111,0.011794623],"study_design_scores_gemma":[0.000028485572,0.0000993719,0.00069823617,0.000033001328,0.000014363346,0.0000912194,0.00018801393,0.446817,0.0015854981,0.5489969,0.0014167924,0.000031080683],"about_ca_topic_score_codex":0.0015112048,"about_ca_topic_score_gemma":0.00089643145,"teacher_disagreement_score":0.0074806483,"about_ca_system_score_codex":0.002061114,"about_ca_system_score_gemma":0.00126817,"threshold_uncertainty_score":0.039561927},"labels":[],"label_agreement":null},{"id":"W2166900700","doi":"10.1002/sim.5970","title":"Age‐period‐cohort models using smoothing splines: a generalized additive model approach","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Smoothing; Smoothing spline; Identifiability; Box spline; Computer science; Mathematics; Applied mathematics; Dependency (UML); Econometrics; Cohort effect; Mathematical optimization; Cohort; Statistics; Artificial intelligence","score_opus":0.05897676816116972,"score_gpt":0.35134934774278764,"score_spread":0.29237257958161794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166900700","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":[0.015539243,0.00034444948,0.9821578,0.00041308423,0.00008034291,0.00010064407,0.00041674534,0.00031357756,0.00063409476],"genre_scores_gemma":[0.4521749,0.0024855556,0.5344811,0.00026769043,0.0002712137,0.0011091472,0.0017331517,0.00025325335,0.007223959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931497,0.005479135,0.00017478873,0.00048633432,0.0004578537,0.0002522403],"domain_scores_gemma":[0.9840821,0.01249425,0.0008875127,0.0012679952,0.0010235102,0.00024460498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016605437,0.0010225923,0.0018374309,0.0022825839,0.0006816069,0.0016751448,0.002858999,0.0014795933,0.0029926454],"category_scores_gemma":[0.02969734,0.00066506345,0.0025760136,0.0038256338,0.00078947016,0.0014038454,0.0018303746,0.0025898495,0.0006222433],"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":[0.00033597377,0.00015486078,0.013336754,0.00028630925,0.0009732657,0.0003645389,0.0006651856,0.70271456,0.0005720243,0.17138068,0.0054490445,0.10376682],"study_design_scores_gemma":[0.000045897024,0.00012266795,0.0016465334,0.00003753716,0.00013878442,0.0000735547,0.000079099234,0.917998,0.00011142022,0.07643248,0.0032665052,0.000047488404],"about_ca_topic_score_codex":0.018388895,"about_ca_topic_score_gemma":0.013203821,"teacher_disagreement_score":0.018388895,"about_ca_system_score_codex":0.0009054487,"about_ca_system_score_gemma":0.0028507796,"threshold_uncertainty_score":0.08781898},"labels":[],"label_agreement":null},{"id":"W2167905880","doi":"10.1017/s1474747210000326","title":"Fundamentals of Private Pensions, 9th edition. Dan McGill, Kyle Brown, John Haley, Sylvester Schieber, and Mark Warshawsky. Oxford University Press, 2010, ISBN 978-0-19-954451-6, 818 pages.","year":2010,"lang":"en","type":"article","venue":"Journal of Pensions Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","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":"Law and economics; Art history; Art; Classics; Sociology","score_opus":0.018459824821823498,"score_gpt":0.231598949382227,"score_spread":0.21313912456040351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167905880","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":[0.0009752699,0.868127,0.0032713762,0.019995539,0.0067629293,0.000067480054,0.0053368113,0.000424351,0.0950392],"genre_scores_gemma":[0.013707495,0.5916348,0.0059584808,0.003914127,0.005708553,0.0001467258,0.004456678,0.00033653167,0.37413666],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927944,0.00009577769,0.00007974717,0.00008026693,0.00040402747,0.000060714814],"domain_scores_gemma":[0.9989679,0.00034038848,0.00017748098,0.00006944597,0.00030456058,0.00014035417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015098936,0.001404677,0.0014024315,0.0027650518,0.0005484928,0.0026205073,0.0010934903,0.0016029098,0.08192386],"category_scores_gemma":[0.0033587646,0.00074416614,0.0003662186,0.0028479306,0.00080990634,0.003542463,0.0013643886,0.0020720107,0.03729991],"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":[0.00002687886,0.000023437005,0.00027530515,0.0005043985,0.00000966146,0.000027591219,0.00014171671,0.00025100211,0.00016654033,0.008551944,0.83148336,0.1585382],"study_design_scores_gemma":[0.000005052512,0.000011531999,0.0011999905,0.0004897619,0.000005330784,0.00013515688,0.00007752049,0.0000793795,0.00004928545,0.0065231035,0.9914153,0.000008566193],"about_ca_topic_score_codex":0.015643429,"about_ca_topic_score_gemma":0.028239595,"teacher_disagreement_score":0.08192386,"about_ca_system_score_codex":0.0031994358,"about_ca_system_score_gemma":0.0033912577,"threshold_uncertainty_score":0.27406263},"labels":[],"label_agreement":null},{"id":"W2168682990","doi":"10.1016/j.jtcvs.2012.11.094","title":"Successful linking of the Society of Thoracic Surgeons Database to Social Security data to examine the accuracy of Society of Thoracic Surgeons mortality data","year":2013,"lang":"en","type":"article","venue":"Journal of Thoracic and Cardiovascular Surgery","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Medicine; Database; Cardiothoracic surgery; Social security; Cardiac surgery; General surgery; Mortality rate; Surgery; Computer science; Law","score_opus":0.09136099841435066,"score_gpt":0.37103167844237805,"score_spread":0.2796706800280274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168682990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79056203,0.0013172243,0.17109822,0.0033362152,0.00043656994,0.0028220778,0.011745259,0.0011541027,0.017528405],"genre_scores_gemma":[0.9211169,0.00025752233,0.07247033,0.00046974103,0.00010669419,0.00065591955,0.003979205,0.00011299263,0.0008306194],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.79372877,0.149609,0.01947072,0.012458806,0.022540353,0.0021924507],"domain_scores_gemma":[0.627254,0.22985524,0.04448698,0.0597018,0.03736585,0.0013361573],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14475091,0.00060709176,0.0006407858,0.0061441073,0.0010671183,0.0034442178,0.0015967032,0.0012666779,0.0020244631],"category_scores_gemma":[0.4009504,0.0007262923,0.0010732486,0.0077232136,0.0008916394,0.0035285486,0.004122619,0.0010017267,0.0012875163],"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":[0.00020663578,0.00010698889,0.9214071,0.00020268082,0.00044849186,0.00010992988,0.0009774024,0.0048723347,0.0007055868,0.002398445,0.0036305108,0.064933896],"study_design_scores_gemma":[0.00021537168,0.0005863466,0.7521883,0.0011590336,0.0008122911,0.0010427668,0.0018373616,0.19077085,0.013492238,0.008341792,0.029369755,0.00018389469],"about_ca_topic_score_codex":0.0049168705,"about_ca_topic_score_gemma":0.0037029106,"teacher_disagreement_score":0.8552491,"about_ca_system_score_codex":0.001527405,"about_ca_system_score_gemma":0.003605927,"threshold_uncertainty_score":0.7655251},"labels":[],"label_agreement":null},{"id":"W2169848085","doi":"10.1080/10920277.2008.10597499","title":"Multiperiod Optimal Investment-Consumption Strategies with Mortality Risk and Environment Uncertainty","year":2008,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Consumption (sociology); Investment (military); Economics; Expected utility hypothesis; Investment strategy; Terminal (telecommunication); Microeconomics; Econometrics; Computer science; Mathematical economics","score_opus":0.021993020502086662,"score_gpt":0.26901214486296626,"score_spread":0.2470191243608796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169848085","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":[0.3711618,0.001864347,0.6000504,0.0035941438,0.00008350886,0.00034534067,0.000564746,0.00017682266,0.022158962],"genre_scores_gemma":[0.96061766,0.0007165375,0.028814169,0.000120444776,0.000033872137,0.00020511357,0.00014211527,0.000026859436,0.009323219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912566,0.0004600621,0.000033494034,0.00012629539,0.00008505255,0.00016945429],"domain_scores_gemma":[0.9980704,0.001297353,0.00029347645,0.00006812001,0.0000851968,0.00018545869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020243216,0.0012739956,0.0011796255,0.0005333165,0.00035989578,0.0015546394,0.001531784,0.0019052268,0.0038878545],"category_scores_gemma":[0.0058602467,0.0007859572,0.0006638283,0.0005331524,0.0008095047,0.0023313952,0.0013153589,0.0016086579,0.00027719987],"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":[0.000236574,0.00014703984,0.0011179863,0.0001008171,0.00006919735,0.00019626536,0.00011048922,0.88258225,0.0007037991,0.099425994,0.00095116405,0.014358445],"study_design_scores_gemma":[0.00008251335,0.00012979042,0.00057156716,0.000040813233,0.00003531353,0.000059927348,0.00009361462,0.9398533,0.0004264712,0.057629745,0.0010540104,0.000022908584],"about_ca_topic_score_codex":0.0027345016,"about_ca_topic_score_gemma":0.001985988,"teacher_disagreement_score":0.0038878545,"about_ca_system_score_codex":0.0022685467,"about_ca_system_score_gemma":0.001368774,"threshold_uncertainty_score":0.016459584},"labels":[],"label_agreement":null},{"id":"W2175161454","doi":"10.1139/f2011-085","title":"Maximum likelihood estimation in nonlinear structured fisheries models using survey and catch-at-age data","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"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":"Likelihood function; Gadus; Maximum likelihood; Stock assessment; Statistics; Estimation theory; Marginal likelihood; Laplace's method; Mathematics; Bayesian probability; Monte Carlo method; Restricted maximum likelihood; Quasi-maximum likelihood; Maximum likelihood sequence estimation; Econometrics; Fishery; Fish <Actinopterygii>; Biology","score_opus":0.1157642957485246,"score_gpt":0.29617755706190996,"score_spread":0.18041326131338536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175161454","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":[0.06127103,0.00014241235,0.93786,0.00018462686,0.0000070966926,0.00003300355,0.00007372299,0.000120822755,0.00030735496],"genre_scores_gemma":[0.709521,0.00043494563,0.28728372,0.00011262103,0.00005412431,0.0002492072,0.00072989665,0.00008188403,0.001532639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736303,0.0020553942,0.000090448346,0.00026644577,0.00015228154,0.00007231445],"domain_scores_gemma":[0.9558373,0.040895447,0.0013173387,0.0012532094,0.00047011752,0.00022653778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00927255,0.00066152157,0.0010274108,0.0014723052,0.00048407304,0.0011639529,0.0016216171,0.0012900095,0.0010555983],"category_scores_gemma":[0.06534285,0.0011020157,0.0011146283,0.0013209626,0.0020421932,0.0026396788,0.0020092595,0.0015276347,0.00032147916],"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":[0.000084744315,0.000046862246,0.009176049,0.00008187376,0.00010225372,0.00011444428,0.00018962729,0.9451876,0.00040837412,0.018408274,0.00027958196,0.025920339],"study_design_scores_gemma":[0.000014323853,0.000011615086,0.0008067821,0.000010703626,0.000006067473,0.000017010936,0.000016319274,0.974707,0.0001327433,0.02416592,0.00010303668,0.00000841563],"about_ca_topic_score_codex":0.009114684,"about_ca_topic_score_gemma":0.008764801,"teacher_disagreement_score":0.00927255,"about_ca_system_score_codex":0.0010170611,"about_ca_system_score_gemma":0.0012065799,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2175617625","doi":"","title":"Modelling the Age Dynamics of Chronic Health Conditions: Life-Table-Consistent Transition Probabilities and Their Application","year":2011,"lang":"en","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Sequence (biology); Path (computing); Cohort; Econometrics; Mathematics; Table (database); Statistics; Population; Dynamics (music); Statistical physics; Demography; Medicine; Computer science; Psychology; Data mining; Physics; Environmental health","score_opus":0.03465390365841629,"score_gpt":0.24028115060009725,"score_spread":0.20562724694168097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175617625","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":[0.17006402,0.00096563227,0.8222001,0.0018962394,0.00014136091,0.00015129597,0.0017654323,0.0004847826,0.0023311535],"genre_scores_gemma":[0.9200998,0.0007700381,0.072080255,0.00017047496,0.00012083897,0.00029987644,0.001101537,0.000096384974,0.0052608154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842155,0.0008886764,0.00008172131,0.00035740557,0.000102823804,0.00014774001],"domain_scores_gemma":[0.97273165,0.024455901,0.0011713229,0.0005143853,0.0006961431,0.0004307047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006642198,0.0008059987,0.0020967317,0.0014726144,0.00068558444,0.0024632872,0.0029946137,0.0033883709,0.004674194],"category_scores_gemma":[0.03414146,0.0015780162,0.0017667894,0.0016965816,0.0015023326,0.003140905,0.0016942314,0.0026989742,0.0005866977],"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":[0.00005839694,0.000040423038,0.0034623374,0.000029610252,0.000057074758,0.000058901165,0.00012996356,0.969508,0.000050912135,0.020245472,0.00047728515,0.005881627],"study_design_scores_gemma":[0.00000863948,0.000006062048,0.00030619034,0.000008663961,0.000009554128,0.000011770004,0.0000127974,0.9901849,0.00001411435,0.009307338,0.00012253391,0.0000073863644],"about_ca_topic_score_codex":0.04968677,"about_ca_topic_score_gemma":0.030070323,"teacher_disagreement_score":0.04968677,"about_ca_system_score_codex":0.0021134014,"about_ca_system_score_gemma":0.002200448,"threshold_uncertainty_score":0.098795116},"labels":[],"label_agreement":null},{"id":"W2176989252","doi":"10.19030/iber.v10i1.924","title":"Population Distribution Model For Oman","year":2011,"lang":"en","type":"article","venue":"International Business & Economics Research Journal (IBER)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Sultan Qaboos University; Dalhousie University; Monash University","keywords":"Population; Distribution (mathematics); Census; Geography; Demography; Developing country; Exponential distribution; Population projection; Projections of population growth; Goodness of fit; Developed country; Statistics; Population growth; Mathematics; Economic growth; Sociology; Economics","score_opus":0.18849370313989927,"score_gpt":0.4107320219022988,"score_spread":0.22223831876239952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176989252","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":[0.26503515,0.007142244,0.55385315,0.011892648,0.0018003832,0.0018396467,0.04704147,0.0030137175,0.108381614],"genre_scores_gemma":[0.8020138,0.0048335777,0.048488252,0.00096172554,0.0005728326,0.0033403104,0.01870488,0.00035843495,0.12072613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987908,0.0003672091,0.000070332426,0.00036069023,0.00019634678,0.00021466152],"domain_scores_gemma":[0.99877447,0.00046932817,0.00023039042,0.00008587659,0.00039151876,0.000048452024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019223208,0.0011934601,0.0012630384,0.003181425,0.0013660977,0.0028284208,0.0026882233,0.0023528156,0.022575276],"category_scores_gemma":[0.0054195924,0.00045029412,0.0013744059,0.0042491304,0.0008098759,0.002729279,0.0014122865,0.002131287,0.008739194],"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":[0.00023381434,0.00023438477,0.04059725,0.0005413451,0.00025822426,0.0027493623,0.0033460755,0.46195406,0.0009785196,0.35110605,0.058899157,0.07910184],"study_design_scores_gemma":[0.00005320753,0.00013733315,0.017944708,0.00024977032,0.00008506838,0.0016464983,0.001386646,0.830474,0.000206029,0.087278366,0.060441587,0.000096857875],"about_ca_topic_score_codex":0.037765045,"about_ca_topic_score_gemma":0.019394299,"teacher_disagreement_score":0.037765045,"about_ca_system_score_codex":0.0032766326,"about_ca_system_score_gemma":0.0015128151,"threshold_uncertainty_score":0.07552189},"labels":[],"label_agreement":null},{"id":"W2179551177","doi":"10.5539/jsd.v8n9p121","title":"Trailing and Projecting the Real Population of Bangkok to 2030","year":2015,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fertility; Total fertility rate; Population; Demography; Population projection; Capital city; Geography; Socioeconomics; Projections of population growth; Birth rate; Age structure; Population growth; Economics; Family planning; Sociology; Research methodology","score_opus":0.03355443782696023,"score_gpt":0.31591951564719584,"score_spread":0.2823650778202356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179551177","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":[0.90357006,0.0029835573,0.01375328,0.0016606179,0.0002293405,0.0002695563,0.03224098,0.00034510036,0.044947557],"genre_scores_gemma":[0.95323247,0.0046754377,0.017086612,0.0002186499,0.00001340566,0.00026528694,0.015535268,0.000058324906,0.008914435],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975663,0.0000686846,0.000031359225,0.000040420167,0.00004259672,0.000060408336],"domain_scores_gemma":[0.9997998,0.000020660247,0.000031637963,0.00002053729,0.00009069778,0.000036799127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005730674,0.00067606446,0.00019376277,0.0010330653,0.00028556123,0.0011628767,0.00035746067,0.00025287195,0.003089736],"category_scores_gemma":[0.0008484376,0.00036264764,0.0009234287,0.0016882931,0.00020093442,0.000981458,0.001234595,0.0005054038,0.0008237545],"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":[0.0005755303,0.00016151252,0.5444537,0.0019335249,0.0011416394,0.0019696981,0.00157991,0.19176131,0.0061108405,0.015859356,0.016172394,0.21828069],"study_design_scores_gemma":[0.000072999705,0.00046413942,0.7659956,0.0012540013,0.0006267086,0.0013191294,0.008457602,0.11243213,0.005673118,0.007239457,0.09620198,0.0002631778],"about_ca_topic_score_codex":0.12798798,"about_ca_topic_score_gemma":0.10539239,"teacher_disagreement_score":0.12798798,"about_ca_system_score_codex":0.0024778848,"about_ca_system_score_gemma":0.004185103,"threshold_uncertainty_score":0.25448602},"labels":[],"label_agreement":null},{"id":"W2179769200","doi":"10.2469/faj.v56.n2.2350","title":"Investing by the Numbers (a review)","year":2000,"lang":"en","type":"article","venue":"Financial Analysts Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Business; Economics","score_opus":0.011220954104968303,"score_gpt":0.2886367568013221,"score_spread":0.2774158026963538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179769200","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":[0.00013257649,0.98850775,0.00035998214,0.0038236524,0.003585105,0.0000107242495,0.00006445396,0.000017395958,0.003498351],"genre_scores_gemma":[0.0011530813,0.98868865,0.0006484011,0.0033909392,0.0019807005,0.000015436144,0.00007986236,0.00001020917,0.0040327483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991792,0.00014180003,0.00011683535,0.0000865506,0.00043150928,0.000044096963],"domain_scores_gemma":[0.9957016,0.0018674968,0.00056503015,0.0001303415,0.0015348964,0.00020065517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022568002,0.0006016478,0.0010413278,0.0044894884,0.00036776534,0.0019044054,0.0007999913,0.0011854306,0.0076207486],"category_scores_gemma":[0.005962753,0.000406359,0.00039840542,0.0071537574,0.00067334354,0.0028953047,0.00069175375,0.001164337,0.0048073158],"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":[0.000040336436,0.000022165657,0.00022332724,0.0059167137,0.000038598104,0.00008295488,0.000056938963,0.000097507975,0.00025091117,0.0030652152,0.27760756,0.7125977],"study_design_scores_gemma":[0.000005281344,0.00004151493,0.0007143015,0.0029595385,0.000032100423,0.00029502192,0.00004344156,0.000020787427,0.00011180281,0.00083467027,0.9949319,0.000009585586],"about_ca_topic_score_codex":0.0029716047,"about_ca_topic_score_gemma":0.011630659,"teacher_disagreement_score":0.0076207486,"about_ca_system_score_codex":0.0007637736,"about_ca_system_score_gemma":0.0025315653,"threshold_uncertainty_score":0.02549398},"labels":[],"label_agreement":null},{"id":"W2183901607","doi":"","title":"An iterative method of estimating excess death rates and mortality ratios.","year":2000,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Alberta","funders":"","keywords":"Statistics; Mortality rate; Excess mortality; Mathematics; Population; Econometrics; Demography; Medicine; Internal medicine","score_opus":0.045439262806262454,"score_gpt":0.363815909410572,"score_spread":0.31837664660430953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183901607","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":[0.0012170809,0.00013644951,0.9967489,0.00005239924,0.000053925698,0.0003681224,0.00021277826,0.00038425613,0.0008260962],"genre_scores_gemma":[0.013771215,0.00011001396,0.9826835,0.000045087676,0.000036399008,0.0017927578,0.0003777821,0.0001621664,0.0010210765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9712497,0.021572536,0.0017389234,0.00201679,0.0031575218,0.0002645849],"domain_scores_gemma":[0.9592485,0.0306759,0.0022795275,0.0037604275,0.00384192,0.00019367706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024769751,0.0015734903,0.0017208781,0.0060820864,0.00079148705,0.0019775312,0.0026375316,0.0011891434,0.009793595],"category_scores_gemma":[0.13162011,0.0009135733,0.0026412522,0.0039046954,0.0011316782,0.0015833292,0.00243016,0.0021804785,0.0034736735],"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":[0.00065045635,0.00027309498,0.016665699,0.0015310002,0.0027408462,0.00021330632,0.00090308784,0.033739347,0.0037670147,0.06742151,0.01234576,0.8597489],"study_design_scores_gemma":[0.0006539855,0.0012987647,0.029350154,0.0011432183,0.002026224,0.004008128,0.0008129865,0.60098284,0.016400075,0.2433712,0.09950668,0.00044571827],"about_ca_topic_score_codex":0.002868473,"about_ca_topic_score_gemma":0.0031188473,"teacher_disagreement_score":0.024769751,"about_ca_system_score_codex":0.0010503418,"about_ca_system_score_gemma":0.0031735408,"threshold_uncertainty_score":0.13099653},"labels":[],"label_agreement":null},{"id":"W2185234758","doi":"","title":"Estimating relative survival for cancer: An analysis of bias introduced by outdated life tables.","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Relative survival; Demography; Cancer registry; Population; Survival analysis; Cancer; Medicine; Statistics; Internal medicine; Mathematics","score_opus":0.051818509955083206,"score_gpt":0.31572282668214197,"score_spread":0.2639043167270588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185234758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17715308,0.05329088,0.7545069,0.0024269894,0.0016506991,0.001337311,0.005188318,0.0008103461,0.0036354484],"genre_scores_gemma":[0.75595623,0.0061811274,0.22847518,0.0015121904,0.00069589255,0.0018749934,0.0037597502,0.00043286578,0.0011117797],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7475584,0.2202732,0.0077324524,0.007489062,0.016204854,0.00074208993],"domain_scores_gemma":[0.21129097,0.7255842,0.030123133,0.021981085,0.010534297,0.0004863944],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27852866,0.0012796066,0.0017322156,0.0049487064,0.00068987056,0.002425732,0.0027100618,0.0012945357,0.0022274547],"category_scores_gemma":[0.6072245,0.00077561045,0.0034891611,0.0072523374,0.0016199874,0.0032048724,0.0026777925,0.0025871228,0.00035972774],"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":[0.0024797437,0.00011392329,0.58663225,0.005266027,0.019653434,0.00052015594,0.0038162475,0.03570327,0.00040178973,0.030480878,0.0102623515,0.30466995],"study_design_scores_gemma":[0.00073531334,0.0033370843,0.39575535,0.009046341,0.01756757,0.005159469,0.0026958764,0.28837746,0.005063639,0.16448507,0.1071212,0.00065562234],"about_ca_topic_score_codex":0.0055772434,"about_ca_topic_score_gemma":0.0035695268,"teacher_disagreement_score":0.7214713,"about_ca_system_score_codex":0.003461051,"about_ca_system_score_gemma":0.0021612614,"threshold_uncertainty_score":0.8897026},"labels":[],"label_agreement":null},{"id":"W2187581550","doi":"10.2139/ssrn.2617350","title":"Lifetime Ruin Under Uncertain Hazard Rate","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Economics; Actuarial science","score_opus":0.027068398162831648,"score_gpt":0.30949151461905255,"score_spread":0.2824231164562209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187581550","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":[0.88967204,0.0016654087,0.096547395,0.0028078805,0.00014081641,0.00008197362,0.0015011353,0.00032269032,0.0072606257],"genre_scores_gemma":[0.99587566,0.00022557561,0.00066976453,0.000038364626,0.000053331405,0.000016856666,0.00014569619,0.000015672571,0.0029591285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983814,0.00046500136,0.00006855068,0.00029322793,0.00014303632,0.0006487276],"domain_scores_gemma":[0.96943194,0.023525067,0.00390897,0.0009612543,0.0007697901,0.0014029852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005211197,0.0007003386,0.0020349137,0.001317636,0.0006627265,0.0021784138,0.0015340027,0.0018592239,0.0061610015],"category_scores_gemma":[0.02365681,0.0005559519,0.00084153743,0.0008562665,0.0015656799,0.002999307,0.0014956696,0.0017857645,0.00040430052],"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":[0.0018433462,0.0003794221,0.08729484,0.0005346116,0.00045148825,0.004723626,0.0015408271,0.67608196,0.0034129773,0.1881126,0.0062954566,0.029328816],"study_design_scores_gemma":[0.0000564465,0.0002768581,0.017735293,0.00004493822,0.0001574457,0.0010011389,0.00048523265,0.9208828,0.0004249607,0.058254153,0.00060622476,0.00007455074],"about_ca_topic_score_codex":0.0043413797,"about_ca_topic_score_gemma":0.0017757532,"teacher_disagreement_score":0.0061610015,"about_ca_system_score_codex":0.001054895,"about_ca_system_score_gemma":0.0007268694,"threshold_uncertainty_score":0.027559757},"labels":[],"label_agreement":null},{"id":"W2188680519","doi":"","title":"Survival from cancer--up-to-date predictions using period analysis.","year":2006,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Relative survival; Cancer registry; Medicine; Cohort; Cancer; Survival analysis; Prostate cancer; Cervix; Demography; Rectum; Relative risk; Life table; Oncology; Internal medicine; Population; Confidence interval; Environmental health","score_opus":0.040678535049599965,"score_gpt":0.29952246886213485,"score_spread":0.2588439338125349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188680519","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":[0.6524099,0.01705445,0.19062778,0.0013983339,0.00036687605,0.00078369817,0.10544696,0.0012797945,0.03063226],"genre_scores_gemma":[0.95393157,0.0021688996,0.017054308,0.0000610558,0.000047439327,0.00017517027,0.023852527,0.000083745814,0.0026253425],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925023,0.00028759646,0.000043737884,0.00012382826,0.00021234367,0.000082186976],"domain_scores_gemma":[0.9969399,0.0015475673,0.0004570436,0.00026888432,0.0006514846,0.0001351223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041702366,0.00043032796,0.00028351118,0.002461372,0.00032219006,0.0009913377,0.0006247525,0.0002750637,0.002843514],"category_scores_gemma":[0.0120872585,0.00016168773,0.0013772623,0.0019295895,0.00015668043,0.0006590987,0.000624977,0.00063041854,0.00043150256],"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":[0.0013816203,0.000075236145,0.5052456,0.0009568214,0.0019115316,0.000279976,0.00048042723,0.28177238,0.000593064,0.013264249,0.015997877,0.17804122],"study_design_scores_gemma":[0.00009039936,0.0004124092,0.48697558,0.000386918,0.0013521106,0.00072371785,0.00040011143,0.45812696,0.0011756918,0.0126840705,0.03756161,0.000110409375],"about_ca_topic_score_codex":0.21913782,"about_ca_topic_score_gemma":0.15813251,"teacher_disagreement_score":0.21913782,"about_ca_system_score_codex":0.0033251103,"about_ca_system_score_gemma":0.0036788443,"threshold_uncertainty_score":0.43572462},"labels":[],"label_agreement":null},{"id":"W2195116863","doi":"10.25336/p6m30m","title":"Demographic and Epidemiological Transitions in Nepal: Developmental Implications","year":2015,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Alberta","funders":"","keywords":"Epidemiology; Geography; Demography; Sociology; Medicine","score_opus":0.15940149395996017,"score_gpt":0.38975640810077644,"score_spread":0.23035491414081627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195116863","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":[0.49959692,0.056276213,0.0011353,0.29282087,0.0014602322,0.00021255261,0.007066446,0.000111119276,0.14132048],"genre_scores_gemma":[0.91287565,0.0669028,0.0011207291,0.0072445963,0.0002936575,0.0001913189,0.0015194269,0.000022925897,0.009828958],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99966645,0.00009385766,0.000019546242,0.000031901807,0.000034938246,0.00015338327],"domain_scores_gemma":[0.99893135,0.00033898503,0.00010950478,0.000024843557,0.0002499462,0.00034529433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088082784,0.0001970326,0.00018178936,0.0012479936,0.002360925,0.0031469378,0.0008134069,0.00091695215,0.00641413],"category_scores_gemma":[0.0038882424,0.00023675419,0.00023379452,0.0022282847,0.0015464482,0.004133438,0.003122357,0.0022766741,0.0004128225],"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":[0.00019388925,0.00042930106,0.19322863,0.0012397243,0.000075875425,0.008987486,0.1899945,0.0011505001,0.0007101027,0.19643298,0.09454991,0.31300712],"study_design_scores_gemma":[0.000012451351,0.00013036859,0.4650511,0.0023399626,0.000029188954,0.0027244422,0.25003862,0.0005553045,0.0003443433,0.038417425,0.24025358,0.00010316536],"about_ca_topic_score_codex":0.117702655,"about_ca_topic_score_gemma":0.15399258,"teacher_disagreement_score":0.117702655,"about_ca_system_score_codex":0.005009723,"about_ca_system_score_gemma":0.0061921887,"threshold_uncertainty_score":0.23403513},"labels":[],"label_agreement":null},{"id":"W2198732287","doi":"10.1016/j.jtbi.2007.08.021","title":"Addendum to “Modeling human mortality using mixtures of bathtub shaped failure distributions”","year":2007,"lang":"en","type":"letter","venue":"Journal of Theoretical Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mixture model; Outlier; Mixture distribution; Expectation–maximization algorithm; Cluster analysis; Multivariate statistics; Computer science; Applied mathematics; Mathematics; Algorithm; Statistics; Probability density function; Maximum likelihood","score_opus":0.05140405249510186,"score_gpt":0.38262588455614444,"score_spread":0.33122183206104255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2198732287","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":[0.0005309213,0.0014752765,0.0020365098,0.8123198,0.1740498,0.00006481054,0.00047037538,0.00021793664,0.0088345315],"genre_scores_gemma":[0.004832611,0.00092849194,0.0012435227,0.77662766,0.17097229,0.00015223867,0.00023346591,0.00008333022,0.04492637],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954249,0.0013891683,0.0005522008,0.00052991917,0.001530645,0.00057315716],"domain_scores_gemma":[0.9756555,0.016088646,0.0009867904,0.000876677,0.0053628637,0.0010296177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004197087,0.0016303011,0.0024626323,0.0010542896,0.0028800701,0.003342826,0.0038098688,0.03926936,0.022299886],"category_scores_gemma":[0.049394887,0.00079633103,0.0022525555,0.00069918425,0.0024857041,0.0018799119,0.0026489873,0.030072618,0.021723669],"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":[0.000036383444,0.000020147014,0.00017872166,0.000026668678,0.000014637317,0.00037154352,0.0000351747,0.00010105626,0.00010952148,0.0012499465,0.9944904,0.0033656985],"study_design_scores_gemma":[0.000170229,0.0000788274,0.0026750648,0.00022690654,0.000099021454,0.00097494177,0.00018073771,0.003358707,0.00060380297,0.017766213,0.97376925,0.00009630109],"about_ca_topic_score_codex":0.0066196374,"about_ca_topic_score_gemma":0.013482571,"teacher_disagreement_score":0.03926936,"about_ca_system_score_codex":0.0034756418,"about_ca_system_score_gemma":0.002183162,"threshold_uncertainty_score":0.07460058},"labels":[],"label_agreement":null},{"id":"W2201907921","doi":"10.1017/cbo9780511753855","title":"The Calculus of Retirement Income","year":2006,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":138,"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":"Life insurance; Life annuity; Actuarial science; Mathematical proof; Terminology; Longevity risk; Equity (law); Economics; Retirement planning; Ideal (ethics); Pension; Perspective (graphical); Financial economics; Finance; Computer science; Mathematics; Political science","score_opus":0.016248032806367732,"score_gpt":0.23494856933220268,"score_spread":0.21870053652583493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201907921","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":[0.007081057,0.03375924,0.12608582,0.010274349,0.0023113452,0.00006364117,0.000731436,0.00025950882,0.8194337],"genre_scores_gemma":[0.39353064,0.044141524,0.06142881,0.0035766512,0.004449992,0.0003565605,0.0007986389,0.00026653468,0.49145067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994381,0.00013151378,0.000029626333,0.000091730195,0.0002503641,0.00005870452],"domain_scores_gemma":[0.9997018,0.00011754417,0.000031120304,0.00005571521,0.00006611381,0.000027674063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089798047,0.0005925298,0.00043378698,0.001164459,0.0011803146,0.0033762995,0.0007090718,0.0010155342,0.010011424],"category_scores_gemma":[0.0020102933,0.00028004005,0.0005936196,0.0010941644,0.0034200978,0.004435207,0.0014895862,0.0021406077,0.003028785],"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":[7.192212e-7,0.0000011820308,0.000012710454,0.000007832446,7.0899773e-7,0.000009133204,0.000065933265,0.00017724767,0.000021123034,0.9924194,0.003729771,0.003554256],"study_design_scores_gemma":[0.0000018757462,0.000004298673,0.00009720984,0.000033841265,0.0000018003107,0.000047694077,0.000044630444,0.00090519927,0.000038072933,0.84715044,0.15167095,0.000003984752],"about_ca_topic_score_codex":0.002564908,"about_ca_topic_score_gemma":0.0015481808,"teacher_disagreement_score":0.010011424,"about_ca_system_score_codex":0.0024150466,"about_ca_system_score_gemma":0.0013999449,"threshold_uncertainty_score":0.03349161},"labels":[],"label_agreement":null},{"id":"W2202911286","doi":"10.1017/cbo9780511800146.001","title":"Preface","year":2009,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Philosophy","score_opus":0.025612131334057934,"score_gpt":0.23602991084026526,"score_spread":0.21041777950620733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2202911286","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":[0.0008331597,0.009014474,0.003648534,0.012282845,0.041736756,0.0001774803,0.0036962796,0.0006445765,0.92796594],"genre_scores_gemma":[0.0034774095,0.0035774913,0.0012279152,0.0018811458,0.005522081,0.00007327036,0.0022182749,0.00036807623,0.9816543],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995509,0.000052006268,0.000024920108,0.00009114475,0.00024361635,0.000037315087],"domain_scores_gemma":[0.9988751,0.00019579339,0.000048456626,0.00010560771,0.0005829779,0.00019204724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060622155,0.00075485546,0.00052286685,0.0016513381,0.0018088002,0.0027419054,0.0009949123,0.001173017,0.40412244],"category_scores_gemma":[0.0031938655,0.00024208023,0.00034976474,0.0013562036,0.0006404592,0.0027453732,0.0015449587,0.0026468693,0.25986075],"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":[0.000014388497,0.000016707178,0.00006610975,0.00008836799,0.0000011600896,0.000047400674,0.00012267652,0.00006649578,0.0001504785,0.017160088,0.9345831,0.04768304],"study_design_scores_gemma":[9.768758e-7,0.0000044657654,0.00009344235,0.000055468932,4.3914324e-7,0.000041369443,0.000028033428,0.000013536934,0.000036711426,0.0020394619,0.99768436,0.0000015762014],"about_ca_topic_score_codex":0.0027254908,"about_ca_topic_score_gemma":0.0034122744,"teacher_disagreement_score":0.40412244,"about_ca_system_score_codex":0.0017235948,"about_ca_system_score_gemma":0.0012034007,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2203127082","doi":"10.1134/s1995080215020109","title":"Pricing guaranteed minimum death benefit contracts under the phase-type law of mortality","year":2015,"lang":"en","type":"article","venue":"Lobachevskii Journal of Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Regina","funders":"","keywords":"Mathematics; Type (biology); Monte Carlo method; Random variable; Variable (mathematics); Black–Scholes model; Actuarial science; Distribution (mathematics); Mathematical economics; Econometrics; Applied mathematics; Statistics; Economics; Mathematical analysis","score_opus":0.10470994763975837,"score_gpt":0.3777022210930024,"score_spread":0.272992273453244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203127082","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":[0.15196349,0.0003675071,0.8385926,0.0013141833,0.00007703228,0.00010103342,0.0002123247,0.00016351728,0.007208225],"genre_scores_gemma":[0.96004814,0.00029001196,0.03415703,0.00010642604,0.00009091502,0.00012707051,0.00012923245,0.0000369565,0.0050142007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978157,0.0010180785,0.00008713532,0.00020653679,0.0006328886,0.00023951192],"domain_scores_gemma":[0.99056447,0.006239718,0.0012710822,0.0007116789,0.00066611404,0.0005469965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006627506,0.00055832055,0.00097277836,0.0006200103,0.00042365532,0.0016468571,0.0020005857,0.0021701027,0.0038034548],"category_scores_gemma":[0.030073158,0.0004716419,0.0008493691,0.00067932834,0.0018380485,0.0034878382,0.0013605335,0.0022272395,0.0002793651],"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":[0.00017464033,0.00006923221,0.0016880687,0.00006458359,0.000035481484,0.00024586977,0.00017891245,0.18679,0.0014238192,0.79570365,0.0012043691,0.012421396],"study_design_scores_gemma":[0.0000693549,0.00007969875,0.00068209856,0.000015600917,0.000011359666,0.00012020354,0.000032893047,0.81376165,0.00032729693,0.18408348,0.0007953878,0.000021010834],"about_ca_topic_score_codex":0.0011235494,"about_ca_topic_score_gemma":0.0006973735,"teacher_disagreement_score":0.006627506,"about_ca_system_score_codex":0.0013194953,"about_ca_system_score_gemma":0.0012582588,"threshold_uncertainty_score":0.035049975},"labels":[],"label_agreement":null},{"id":"W2208060256","doi":"10.25336/p6w02t","title":"Diversity and convergence of population aging: evidence from China and Canada","year":2004,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal; Statistics Canada","funders":"Social Sciences and Humanities Research Council of Canada; Université de Montréal","keywords":"Convergence (economics); Diversity (politics); Context (archaeology); China; Population; Economic geography; Population ageing; Geography; Demographic economics; Development economics; Demography; Economic growth; Political science; Economics; Sociology","score_opus":0.049597134147845355,"score_gpt":0.3108937607634878,"score_spread":0.26129662661564246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208060256","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":[0.9900893,0.0025387767,0.00010369556,0.0006818662,0.000014239339,0.000025864003,0.000928739,0.0000036598744,0.005613826],"genre_scores_gemma":[0.99700326,0.001834286,0.00013214772,0.00010799354,0.000007702548,0.000009419098,0.00053715386,0.000002404094,0.000365663],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982058,0.00026061575,0.00012169119,0.00022989478,0.00063731533,0.00054457446],"domain_scores_gemma":[0.99097544,0.0011893506,0.0013176511,0.0003850695,0.005060975,0.0010715738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025809398,0.00027520643,0.00055321876,0.0051865727,0.0045378287,0.0013308256,0.00095786236,0.00039663436,0.0017816188],"category_scores_gemma":[0.009704782,0.00019238224,0.00045461245,0.010952684,0.0021715052,0.0006491814,0.00208471,0.0006506664,0.00007688216],"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":[0.00015385073,0.000037524504,0.9588564,0.00014511209,0.0001762248,0.0002555663,0.012620063,0.00024751134,0.00011518549,0.0016484855,0.001311552,0.024432663],"study_design_scores_gemma":[0.000008834857,0.000023635976,0.98923886,0.00010448004,0.00006227619,0.000079380436,0.007789964,0.0001750117,0.00008034864,0.00014926448,0.0022694108,0.000018600438],"about_ca_topic_score_codex":0.99063444,"about_ca_topic_score_gemma":0.9942726,"teacher_disagreement_score":0.019939581,"about_ca_system_score_codex":0.019939581,"about_ca_system_score_gemma":0.030069448,"threshold_uncertainty_score":0.14467245},"labels":[],"label_agreement":null},{"id":"W2209452456","doi":"10.25336/p6x01r","title":"Fertility decline and social change: new trends and challenges","year":2003,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University; Statistics Canada","funders":"Université de Montréal","keywords":"Fertility; Loneliness; Developed country; Immigration; Social change; Population growth; Population; Demographic change; Development economics; Demographic economics; Developing country; Age structure; Economic growth; Economics; Political science; Demography; Sociology; Psychology","score_opus":0.2008179979870279,"score_gpt":0.384146620194165,"score_spread":0.1833286222071371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209452456","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":[0.009648387,0.6653161,0.0013317707,0.31245732,0.0031326641,0.00002364264,0.00033795135,0.00007058544,0.007681596],"genre_scores_gemma":[0.09164564,0.8455815,0.0034398052,0.038209207,0.017800776,0.00009169774,0.00034214932,0.00005420852,0.0028349452],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975828,0.0009354032,0.00025003593,0.00037121857,0.00057310145,0.00028743246],"domain_scores_gemma":[0.9878671,0.0071001886,0.0009798331,0.00033847126,0.0023122868,0.0014021666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008831801,0.00056444714,0.0019118957,0.0034917088,0.002912156,0.0065835537,0.001523626,0.004712146,0.0059432606],"category_scores_gemma":[0.008032215,0.00036037594,0.000667317,0.0058376472,0.0075117717,0.013858311,0.0036825566,0.005884236,0.00084990176],"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":[0.00019577357,0.00018569705,0.031649698,0.004345115,0.0001657168,0.0010020252,0.012858315,0.00066438614,0.00045082768,0.08441795,0.09227036,0.77179414],"study_design_scores_gemma":[0.000035791323,0.00033758476,0.05448199,0.005337726,0.00012861594,0.002908111,0.07419489,0.0013397386,0.00020011327,0.1073249,0.7535036,0.00020700834],"about_ca_topic_score_codex":0.021264335,"about_ca_topic_score_gemma":0.04688115,"teacher_disagreement_score":0.9787357,"about_ca_system_score_codex":0.0055992203,"about_ca_system_score_gemma":0.007445056,"threshold_uncertainty_score":0.04670757},"labels":[],"label_agreement":null},{"id":"W2209849327","doi":"","title":"Stochastic Conditional Duration Model with a Mixture-of-Normal Error Distribution: Theoretical Properties and Monte-Carlo Results","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Western University","funders":"","keywords":"Monte Carlo method; Duration (music); Statistical physics; Monte Carlo method in statistical physics; Econometrics; Mathematics; Hybrid Monte Carlo; Applied mathematics; Computer science; Statistics; Markov chain Monte Carlo; Physics","score_opus":0.012516862662080836,"score_gpt":0.2389528712942999,"score_spread":0.22643600863221908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209849327","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":[0.026040316,0.00054930395,0.97045803,0.0005558468,0.000044324483,0.000050270883,0.00020973626,0.00016423139,0.0019279044],"genre_scores_gemma":[0.7609815,0.001959565,0.2202133,0.00031860324,0.00031783668,0.00052468915,0.00095943775,0.00016639853,0.014558637],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963296,0.0016858458,0.00017674117,0.00074373605,0.0007302326,0.00033389713],"domain_scores_gemma":[0.96255606,0.029744003,0.0028839756,0.0021311431,0.0020201607,0.0006645592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012831907,0.0013110464,0.0024039196,0.0019556372,0.000783429,0.0025623974,0.0043602637,0.003347258,0.005128133],"category_scores_gemma":[0.04362846,0.0010907297,0.0017943376,0.002289578,0.0037175505,0.005937269,0.0026831017,0.0041288575,0.0008711424],"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":[0.00011243453,0.000040446328,0.0035179164,0.000085817315,0.00007904256,0.00012226705,0.00024099967,0.36886528,0.00037027203,0.61451495,0.00097564474,0.011074979],"study_design_scores_gemma":[0.000020087402,0.000025640433,0.00068055134,0.000028116145,0.00003682896,0.00010565726,0.00002689653,0.9022353,0.00017794457,0.0958236,0.0008024033,0.00003687721],"about_ca_topic_score_codex":0.008694403,"about_ca_topic_score_gemma":0.0038837239,"teacher_disagreement_score":0.012831907,"about_ca_system_score_codex":0.00213924,"about_ca_system_score_gemma":0.0014110013,"threshold_uncertainty_score":0.06786245},"labels":[],"label_agreement":null},{"id":"W2230106531","doi":"10.25336/p6ps38","title":"The Probability of Divorce in Canada, 1981-1995","year":2000,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Demographic economics; Psychology; Demography; Sociology; Economics","score_opus":0.03927568858527341,"score_gpt":0.3142190965156596,"score_spread":0.2749434079303862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2230106531","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":[0.94482785,0.006769947,0.00022764712,0.0032735185,0.00011367521,0.00006559946,0.036561564,0.00007936248,0.008080938],"genre_scores_gemma":[0.9826413,0.0024962048,0.00016374048,0.00020468654,0.000028406868,0.000019374203,0.008297672,0.000014644778,0.0061340146],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99832004,0.000107155574,0.00014440744,0.0001722762,0.00062617456,0.0006298907],"domain_scores_gemma":[0.9930801,0.00034568997,0.0011688999,0.00016203987,0.0035397606,0.0017035006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010100603,0.00038744803,0.00070246024,0.0032309897,0.003862362,0.0022104545,0.0025893003,0.0011914666,0.0038085987],"category_scores_gemma":[0.005575802,0.00051722536,0.0010960714,0.0069313413,0.00090101745,0.00068003434,0.0013962587,0.0018422874,0.0005534015],"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":[0.00030127834,0.00006398499,0.9742276,0.000105378684,0.00014433698,0.00019915486,0.0013632633,0.0006338796,0.00007962993,0.0009109947,0.010348708,0.011621758],"study_design_scores_gemma":[0.000012983468,0.000014339408,0.99414957,0.0000424026,0.000034021086,0.00009178372,0.0012415146,0.00051940937,0.00004954039,0.000053340966,0.003771766,0.00001927081],"about_ca_topic_score_codex":0.99874073,"about_ca_topic_score_gemma":0.9992176,"teacher_disagreement_score":0.062283777,"about_ca_system_score_codex":0.062283777,"about_ca_system_score_gemma":0.053681023,"threshold_uncertainty_score":0.45190257},"labels":[],"label_agreement":null},{"id":"W2235192596","doi":"10.2202/2153-3792.1077","title":"IFRS Convergence: The Role of Stochastic Mortality Models in the Disclosure of Longevity Risk for Defined Benefit Plans","year":2011,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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 Waterloo","funders":"","keywords":"Longevity; Longevity risk; Accounting; Actuarial science; Pension; International Financial Reporting Standards; Convergence (economics); Business; Economics; Finance; Medicine; Economic growth; Gerontology","score_opus":0.024354538444536004,"score_gpt":0.26176656906034185,"score_spread":0.23741203061580585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2235192596","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":[0.143806,0.0021768257,0.80558354,0.014096705,0.00034808455,0.000247729,0.000613636,0.00042711667,0.03270033],"genre_scores_gemma":[0.92899907,0.0015472805,0.06298453,0.00064479955,0.0003387669,0.00018490014,0.0003701914,0.000084446714,0.0048459573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.983721,0.010322628,0.0010149693,0.0015892711,0.0025582819,0.0007937684],"domain_scores_gemma":[0.91920835,0.055826955,0.0143785,0.0049384395,0.0042618806,0.0013858209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037235413,0.0009719882,0.0017391425,0.0028379518,0.0012492816,0.006042583,0.002857716,0.0030207972,0.004551678],"category_scores_gemma":[0.12816636,0.0007047511,0.0016961843,0.0018861054,0.003722545,0.012819161,0.005729451,0.0055715707,0.0004440708],"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":[0.00006598271,0.000047673188,0.00805639,0.00007813374,0.00006499184,0.00025594304,0.00065560074,0.12033392,0.000121401645,0.8462869,0.0017128435,0.022320207],"study_design_scores_gemma":[0.000019395487,0.000069149515,0.0016986796,0.00015388089,0.000027858741,0.00013864232,0.00026838583,0.36068454,0.00019439145,0.63361573,0.003058755,0.000070635266],"about_ca_topic_score_codex":0.0050204066,"about_ca_topic_score_gemma":0.0028046959,"teacher_disagreement_score":0.037235413,"about_ca_system_score_codex":0.0035111664,"about_ca_system_score_gemma":0.003019518,"threshold_uncertainty_score":0.196922},"labels":[],"label_agreement":null},{"id":"W2236031128","doi":"","title":"Cohort Working Life Tables for Older Canadians","year":2009,"lang":"en","type":"article","venue":"Social and Economic Dimensions of an Aging Population Research Papers","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Life expectancy; Cohort; Demography; Life table; Gerontology; Cohort study; Statistics; Psychology; Geography; Medicine; Mathematics; Sociology; Population","score_opus":0.05688240707547983,"score_gpt":0.3730694484613006,"score_spread":0.31618704138582077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2236031128","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":[0.032401487,0.00092132366,0.0010227787,0.00023806794,0.00010895586,0.00019750703,0.94848114,0.00043222192,0.01619651],"genre_scores_gemma":[0.15295558,0.002459356,0.005011919,0.0002773043,0.000064927124,0.0004352726,0.80035126,0.00021433627,0.038230054],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99941945,0.00003509188,0.000059229897,0.000086360815,0.0002310657,0.0001688705],"domain_scores_gemma":[0.995771,0.00038896318,0.0003869641,0.00030345103,0.0027947878,0.00035482267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436946,0.0004280542,0.00047475472,0.0077295196,0.0011113844,0.0010578305,0.0013274349,0.00034678314,0.039364006],"category_scores_gemma":[0.0059545394,0.0003120072,0.0011699169,0.008840897,0.00011414339,0.00043323362,0.00056154106,0.00058385526,0.0038933335],"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":[0.0005277351,0.00011024153,0.27097487,0.00080860336,0.0005308653,0.0001866739,0.0008861143,0.0032169619,0.00024133368,0.006169908,0.6187306,0.09761612],"study_design_scores_gemma":[0.00010658093,0.000053045285,0.753999,0.00032935213,0.00016989553,0.00017725896,0.0010249368,0.0024103469,0.0001339171,0.00091823813,0.24060656,0.00007087954],"about_ca_topic_score_codex":0.9681616,"about_ca_topic_score_gemma":0.9704432,"teacher_disagreement_score":0.039364006,"about_ca_system_score_codex":0.0058126473,"about_ca_system_score_gemma":0.012965998,"threshold_uncertainty_score":0.13168573},"labels":[],"label_agreement":null},{"id":"W22392122","doi":"10.1007/978-94-007-2297-2_20","title":"Demosim, Statistics Canada’s Microsimulation Model for Projecting Population Diversity","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Immigration; Pace; Geography; Metropolitan area; Equity (law); Population; Microsimulation; Diversity (politics); Demographic economics; Demography; Political science; Sociology; Economics","score_opus":0.0686829947608711,"score_gpt":0.2880973115043956,"score_spread":0.21941431674352452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W22392122","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":[0.008680696,0.004968016,0.8339372,0.0047541186,0.0007862271,0.00019109591,0.050129853,0.007870152,0.08868262],"genre_scores_gemma":[0.21503155,0.009358022,0.60713714,0.0012611217,0.0003472589,0.0008856166,0.040677954,0.0054790424,0.119822286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997099,0.000069576665,0.000013483138,0.000043449454,0.00011838877,0.000045273235],"domain_scores_gemma":[0.9992291,0.00030159802,0.00003222075,0.00007906934,0.00031591888,0.00004224075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084149215,0.0010267862,0.0007995874,0.001155072,0.0011299595,0.001664776,0.0022831366,0.0009488432,0.013711527],"category_scores_gemma":[0.004191847,0.0008332065,0.0012538084,0.0022987758,0.0005604348,0.0012778692,0.0008178576,0.0014485802,0.0027645165],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033293858,0.000021062293,0.0030832589,0.00013365613,0.00010355366,0.00009821136,0.00016873721,0.492296,0.00016410364,0.16139305,0.24862732,0.093877755],"study_design_scores_gemma":[0.000019940433,0.0000063713883,0.0014212836,0.000111312074,0.000051322913,0.000060953218,0.000077938945,0.74137044,0.0003660417,0.09947969,0.15698393,0.000050805298],"about_ca_topic_score_codex":0.82617325,"about_ca_topic_score_gemma":0.8492374,"teacher_disagreement_score":0.99217933,"about_ca_system_score_codex":0.007820686,"about_ca_system_score_gemma":0.012355821,"threshold_uncertainty_score":0.34970087},"labels":[],"label_agreement":null},{"id":"W2242673412","doi":"10.1093/ije/dyv096.172","title":"Cause-Specific Gender Differences in Potential Gains in Life Expectancy at Birth in Japan, 1965–2010.","year":2015,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Calgary","funders":"","keywords":"Life expectancy; Demography; Psychology; Population; Sociology","score_opus":0.2025621662446371,"score_gpt":0.39060991635200537,"score_spread":0.18804775010736827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242673412","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":[0.99660563,0.000684474,0.000037969385,0.000075868804,0.000015168422,0.0000023295115,0.0021170434,0.0000035818805,0.0004579281],"genre_scores_gemma":[0.9983033,0.00019483609,0.000022406351,0.00001079089,0.0000070802134,0.0000020906966,0.001178095,0.0000013569776,0.0002800296],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998381,0.000019314415,0.000025643236,0.0000397487,0.000014513113,0.00006258777],"domain_scores_gemma":[0.9992786,0.0000772458,0.00026424247,0.000054394834,0.000113259004,0.00021227213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004006189,0.00020787913,0.00015552019,0.0007533255,0.00027885102,0.00028475892,0.0002482088,0.00027059935,0.0011237646],"category_scores_gemma":[0.0013289017,0.00021433647,0.00044641257,0.0010535864,0.00019632299,0.00033328703,0.00050066196,0.00043941295,0.00017992452],"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":[0.000221425,0.00001432106,0.9967083,0.000017511256,0.00007505037,0.000101818056,0.00030700155,0.000090287904,0.00020596645,0.000044706496,0.0003418958,0.0018715538],"study_design_scores_gemma":[0.0000012969139,0.000009809157,0.9995957,0.0000025026006,0.000017616234,0.000029934119,0.00015661129,0.000039003455,0.000016071092,0.000008848036,0.00012119457,0.0000014490117],"about_ca_topic_score_codex":0.06818547,"about_ca_topic_score_gemma":0.12169575,"teacher_disagreement_score":0.06818547,"about_ca_system_score_codex":0.0005515868,"about_ca_system_score_gemma":0.00046542328,"threshold_uncertainty_score":0.1355772},"labels":[],"label_agreement":null},{"id":"W2243567669","doi":"10.71781/12980","title":"Causes multiples de décès chez les personnes âgées au Québec, 2000-2004","year":2010,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Humanities; Medicine; Gynecology; Art","score_opus":0.0405447499351978,"score_gpt":0.33499990426998216,"score_spread":0.29445515433478436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243567669","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":[0.9267256,0.0054409155,0.0015449005,0.0015601073,0.00013268564,0.00024644873,0.05654349,0.00009395148,0.007711864],"genre_scores_gemma":[0.97388667,0.0026824593,0.0011531212,0.00037128938,0.00004875694,0.00020538326,0.01370453,0.000016024263,0.007931725],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993987,0.00008231199,0.00006778876,0.0001417652,0.00017798491,0.00013145241],"domain_scores_gemma":[0.9961029,0.00028521367,0.0009987453,0.00015283913,0.0020567449,0.0004035394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001035764,0.00042101508,0.00044094914,0.0017968093,0.0012246071,0.0009955387,0.0008653542,0.0003850167,0.0032179628],"category_scores_gemma":[0.0041274535,0.0002584232,0.00072575995,0.0026398706,0.00039591943,0.0004721275,0.0007085648,0.000897583,0.0002608857],"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":[0.00012083976,0.000018810571,0.9840493,0.000186687,0.00015692163,0.00007524358,0.00069789315,0.00022721874,0.00014418489,0.00015707807,0.0041648094,0.010001035],"study_design_scores_gemma":[0.000005447775,0.00002149764,0.99713445,0.000079156074,0.000055977598,0.000044585842,0.0004674952,0.00022310404,0.00006347486,0.000021557013,0.0018765202,0.000006690865],"about_ca_topic_score_codex":0.98063046,"about_ca_topic_score_gemma":0.989885,"teacher_disagreement_score":0.019369543,"about_ca_system_score_codex":0.01570603,"about_ca_system_score_gemma":0.012484748,"threshold_uncertainty_score":0.113955796},"labels":[],"label_agreement":null},{"id":"W2249335086","doi":"10.2139/ssrn.2379040","title":"How Long Does the Market Think You Will Live? Implying Longevity from Annuity Prices","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Longevity; Annuity; Longevity risk; Economics; Life annuity; Actuarial science; Financial economics; Business; Monetary economics; Pension; Finance; Gerontology; Medicine","score_opus":0.007269282971035577,"score_gpt":0.2493781937503961,"score_spread":0.24210891077936053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249335086","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":[0.9036803,0.0015994598,0.026331006,0.031242395,0.00020687061,0.000035400168,0.00059971336,0.000055626828,0.036249217],"genre_scores_gemma":[0.99627835,0.0004686506,0.00074908504,0.00040207218,0.00024233156,0.000008309174,0.00012451778,0.000006824303,0.001719848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992778,0.00032027543,0.00003745962,0.000150476,0.00010203499,0.000111993424],"domain_scores_gemma":[0.9667174,0.024235489,0.0064038374,0.0008131613,0.0008875551,0.0009425175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028777446,0.00029159596,0.00068201195,0.00058192573,0.00047807256,0.00217112,0.00065062736,0.0021906057,0.011444835],"category_scores_gemma":[0.03692055,0.00024686096,0.00052862574,0.00070743024,0.0019994618,0.004730845,0.00096002716,0.003239541,0.00076970324],"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":[0.0010713143,0.0005217715,0.28555226,0.0003900471,0.00051845657,0.0016514775,0.005394623,0.023484062,0.0017496143,0.56831497,0.012278394,0.09907299],"study_design_scores_gemma":[0.00008408936,0.00026125813,0.123929754,0.00009544515,0.0001804808,0.0005075132,0.0018939486,0.04562844,0.00049418764,0.82278967,0.004042829,0.00009243005],"about_ca_topic_score_codex":0.001597771,"about_ca_topic_score_gemma":0.0012634781,"teacher_disagreement_score":0.011444835,"about_ca_system_score_codex":0.0006512397,"about_ca_system_score_gemma":0.00037720238,"threshold_uncertainty_score":0.038286805},"labels":[],"label_agreement":null},{"id":"W2249527931","doi":"10.1353/mos.2015.0045","title":"The Mortality Project","year":2015,"lang":"en","type":"article","venue":"Mosaic","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","score_opus":0.1014564943523839,"score_gpt":0.38162207308805657,"score_spread":0.28016557873567266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249527931","genre_codex":"dataset","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":[0.05392515,0.0018149975,0.0077275117,0.027525973,0.002298886,0.0028539014,0.6556773,0.0018443323,0.24633195],"genre_scores_gemma":[0.107617654,0.0032019804,0.024740918,0.00822101,0.0014370899,0.015337137,0.5492489,0.0011103562,0.2890849],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99633104,0.0015045812,0.00028841392,0.0003136908,0.0010924713,0.00046983626],"domain_scores_gemma":[0.98572284,0.00232781,0.0011413725,0.0012119123,0.004701134,0.0048949877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008270651,0.00062976434,0.00038554484,0.0031251975,0.0017389222,0.002470273,0.0012074237,0.0006891776,0.05839329],"category_scores_gemma":[0.020757187,0.00040329696,0.0003799231,0.0034468689,0.00038813215,0.0015816295,0.0061932923,0.0017293781,0.014542335],"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":[0.00020821445,0.00018894536,0.024637623,0.00017776692,0.000033687793,0.000036490623,0.00075810147,0.00009457765,0.00007981634,0.0074641053,0.85869074,0.10762987],"study_design_scores_gemma":[0.0002891227,0.00027217658,0.12936598,0.00062242814,0.000039408395,0.00014779367,0.0025656496,0.0003693093,0.00019588502,0.0034230053,0.86267674,0.000032552212],"about_ca_topic_score_codex":0.016771786,"about_ca_topic_score_gemma":0.018889932,"teacher_disagreement_score":0.05839329,"about_ca_system_score_codex":0.0015111832,"about_ca_system_score_gemma":0.007974053,"threshold_uncertainty_score":0.19534498},"labels":[],"label_agreement":null},{"id":"W2256725308","doi":"10.2139/ssrn.1366773","title":"The Role of Consumption and Listed Alternative Investments on the Lifetime-Ruin Probability of U.S. Households","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Consumption (sociology); Economics; Actuarial science; Ruin theory; Business; Econometrics; Risk model; Sociology","score_opus":0.016435752825912857,"score_gpt":0.2739762788101035,"score_spread":0.25754052598419064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256725308","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":[0.99731135,0.00046293237,0.00012277272,0.0009081704,0.000007913399,0.0000026734747,0.00036287843,0.0000048089837,0.00081637414],"genre_scores_gemma":[0.99928635,0.000165026,0.000023253815,0.000028455164,0.000014291791,0.0000011612335,0.00019148117,0.0000014786572,0.00028850738],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995763,0.00019414634,0.00002934217,0.00006278795,0.000030009396,0.00010745609],"domain_scores_gemma":[0.9850553,0.008882075,0.0037128753,0.00041200206,0.00048780255,0.0014500024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018036154,0.00021212794,0.00030743517,0.0006628825,0.00035714966,0.0013092683,0.0005862039,0.0011691265,0.0062089516],"category_scores_gemma":[0.009154485,0.00023855557,0.0005204292,0.00086064026,0.0006528346,0.0010224739,0.0007220942,0.0012262751,0.00042066985],"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":[0.00029926756,0.00008911736,0.98988485,0.000010759816,0.00011561297,0.00014022483,0.00020858478,0.0022338904,0.000088501714,0.001826871,0.0006334083,0.004468844],"study_design_scores_gemma":[0.000022630371,0.0001354612,0.97550833,0.00003821861,0.00016119433,0.0001567447,0.0012518637,0.019467695,0.00014332328,0.0025180876,0.00057427236,0.0000221729],"about_ca_topic_score_codex":0.024766933,"about_ca_topic_score_gemma":0.030769512,"teacher_disagreement_score":0.024766933,"about_ca_system_score_codex":0.0006189744,"about_ca_system_score_gemma":0.00034500938,"threshold_uncertainty_score":0.049245536},"labels":[],"label_agreement":null},{"id":"W2263292572","doi":"10.2469/faj.v72.n2.4","title":"It’s Time to Retire Ruin (Probabilities)","year":2016,"lang":"en","type":"article","venue":"Financial Analysts Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Longevity; Longevity risk; Portfolio; Actuarial science; Metric (unit); Pension; Economics; Path (computing); Operations management; Computer science; Finance; Gerontology; Medicine","score_opus":0.013138803083787252,"score_gpt":0.2772264808653976,"score_spread":0.26408767778161035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2263292572","genre_codex":"other","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":[0.15153877,0.015177891,0.20594387,0.30751204,0.0078701535,0.00018604932,0.0022036287,0.0013741022,0.30819336],"genre_scores_gemma":[0.92464286,0.0044673337,0.018491633,0.009037103,0.0021869517,0.000110981135,0.00032046487,0.00020019918,0.040542506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982168,0.00076788117,0.00012338703,0.00027371917,0.00037379633,0.00024442654],"domain_scores_gemma":[0.9879425,0.0063689826,0.0022149677,0.0008636439,0.001487547,0.0011223139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037439438,0.00044004357,0.00037943193,0.0010054456,0.0018210758,0.0045923595,0.0007693769,0.002146361,0.015080175],"category_scores_gemma":[0.031537108,0.00031624085,0.00071612146,0.00092977827,0.003075567,0.006395704,0.0015585152,0.0035102488,0.0030007018],"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":[0.0004184277,0.00018402754,0.041393533,0.00029091642,0.00016028804,0.0011453755,0.0069411243,0.00714587,0.001091203,0.4702938,0.14612003,0.32481542],"study_design_scores_gemma":[0.000045014353,0.00049260695,0.033742856,0.0006524796,0.000115956944,0.0029774215,0.0063155484,0.009958828,0.001418455,0.5390906,0.4049809,0.00020936443],"about_ca_topic_score_codex":0.002706412,"about_ca_topic_score_gemma":0.0029227855,"teacher_disagreement_score":0.015080175,"about_ca_system_score_codex":0.0012738846,"about_ca_system_score_gemma":0.0008784964,"threshold_uncertainty_score":0.05044824},"labels":[],"label_agreement":null},{"id":"W2264945873","doi":"10.1142/s0219024915500478","title":"EFFICIENT HEDGING FOR DEFAULTABLE SECURITIES AND ITS APPLICATION TO EQUITY-LINKED LIFE INSURANCE CONTRACTS","year":2015,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Equity (law); Life insurance; Actuarial science; Simple (philosophy); Duality (order theory); Economics; Econometrics; Mathematical economics; Mathematical optimization; Mathematics; Pure mathematics","score_opus":0.01955513177513884,"score_gpt":0.32262316207733377,"score_spread":0.3030680303021949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2264945873","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":[0.062205274,0.0010894069,0.9320343,0.0007614993,0.00005816978,0.00006564262,0.00004232034,0.00004953727,0.003693864],"genre_scores_gemma":[0.8868146,0.0012629292,0.10531684,0.00013387243,0.00012684242,0.0001105485,0.000114917166,0.00004560656,0.006073848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987702,0.0006991672,0.000062534695,0.00015279015,0.0002221079,0.00009326532],"domain_scores_gemma":[0.9967771,0.002297515,0.0003029228,0.00017375196,0.00022875867,0.00021995122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045518875,0.00090834295,0.0013728708,0.000820417,0.00045945783,0.0016692685,0.0011574207,0.0021978428,0.00243466],"category_scores_gemma":[0.011321406,0.00062952726,0.0011742522,0.00077736855,0.001743523,0.002173888,0.0021696452,0.002266207,0.00011385358],"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":[0.000065987915,0.00015364846,0.0010497555,0.00013607957,0.00007049267,0.00040717373,0.00017013338,0.61243296,0.0024317326,0.35942727,0.0007809915,0.02287385],"study_design_scores_gemma":[0.000019064357,0.000045855955,0.00019237654,0.000013139517,0.000011862982,0.00006114381,0.000024839675,0.92220753,0.00037406202,0.076547354,0.0004901505,0.000012682703],"about_ca_topic_score_codex":0.0009868569,"about_ca_topic_score_gemma":0.00060824724,"teacher_disagreement_score":0.0045518875,"about_ca_system_score_codex":0.001013002,"about_ca_system_score_gemma":0.0012115482,"threshold_uncertainty_score":0.024072945},"labels":[],"label_agreement":null},{"id":"W2269062277","doi":"10.1017/9781108784184.015","title":"Universal life insurance","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life insurance; Actuarial science; Cash flow; Computer science; Finance; Economics","score_opus":0.023626671384006977,"score_gpt":0.22528057798464032,"score_spread":0.20165390660063334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269062277","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":[0.0014827566,0.016721962,0.00295809,0.0020030208,0.0013330696,0.00003419206,0.0007698681,0.00035843908,0.9743386],"genre_scores_gemma":[0.020374374,0.009664881,0.0027729708,0.0012798418,0.0007523572,0.00005330086,0.001219888,0.00018421964,0.96369815],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961025,0.000039347666,0.000015844784,0.00007371519,0.00021545193,0.00004538665],"domain_scores_gemma":[0.9997136,0.000073604584,0.000018079429,0.000070760405,0.0000705559,0.000053380216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004338916,0.00054341473,0.00037107526,0.0017158982,0.0007854204,0.0019669095,0.00060287764,0.00089246087,0.13144419],"category_scores_gemma":[0.0015821069,0.00021352974,0.0002936731,0.0013798178,0.0006326947,0.0023442071,0.0015418425,0.001563528,0.037084423],"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":[0.000013216787,0.000026362617,0.00024567367,0.00016765196,0.0000031705833,0.00005839957,0.00029352115,0.00035888687,0.0003276649,0.26346022,0.40398005,0.33106524],"study_design_scores_gemma":[7.8280584e-7,0.000007700793,0.00050881295,0.00013094899,0.0000011535891,0.00011001989,0.000033613807,0.00014499018,0.00007209318,0.014036241,0.9849508,0.0000029977693],"about_ca_topic_score_codex":0.0017303901,"about_ca_topic_score_gemma":0.002645305,"teacher_disagreement_score":0.13144419,"about_ca_system_score_codex":0.0014031329,"about_ca_system_score_gemma":0.00083253626,"threshold_uncertainty_score":0.43972462},"labels":[],"label_agreement":null},{"id":"W2270040251","doi":"10.1016/j.insmatheco.2016.06.006","title":"Generalized linear models for dependent frequency and severity of insurance claims","year":2016,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Generalized linear model; Econometrics; Poisson distribution; Conditional expectation; Mathematics; Poisson regression; Statistics; Linear model; Term (time); Product (mathematics); Economics; Population; Medicine","score_opus":0.031801282868079844,"score_gpt":0.2737689823037591,"score_spread":0.24196769943567928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270040251","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":[0.20870537,0.0019371695,0.7621981,0.0035976223,0.00033073514,0.00062921765,0.016285762,0.0026865175,0.0036295373],"genre_scores_gemma":[0.82075465,0.0013035395,0.1329679,0.0007279402,0.00043391107,0.0018145277,0.019716052,0.0005126716,0.021768907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891879,0.006171646,0.000461298,0.0027414474,0.0006439054,0.0007938086],"domain_scores_gemma":[0.9707318,0.020716159,0.0028696004,0.00387017,0.0014554998,0.00035670717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019047255,0.0017266423,0.002153989,0.0027284848,0.00097152375,0.0031700404,0.00670689,0.0033869059,0.0109651815],"category_scores_gemma":[0.03394231,0.001353432,0.004636284,0.003897965,0.0020637342,0.0030048199,0.002083879,0.0051731663,0.0039596646],"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":[0.0009649842,0.0005483785,0.103913985,0.0005356738,0.0019421116,0.0010139374,0.0027063468,0.5390423,0.0016862829,0.21517342,0.019006914,0.1134657],"study_design_scores_gemma":[0.00014214887,0.0002255455,0.017399032,0.00011193864,0.00022537995,0.00026902874,0.00037425497,0.8524214,0.00030620937,0.12054249,0.007862738,0.00011989856],"about_ca_topic_score_codex":0.03162378,"about_ca_topic_score_gemma":0.034543358,"teacher_disagreement_score":0.03162378,"about_ca_system_score_codex":0.0027063799,"about_ca_system_score_gemma":0.0018327946,"threshold_uncertainty_score":0.100732744},"labels":[],"label_agreement":null},{"id":"W2272377578","doi":"10.1002/wilm.10509","title":"A Note on Jointly Backtesting Models for Multiple Assets and Horizons","year":2016,"lang":"en","type":"article","venue":"Wilmott","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; HEC Montréal","funders":"","keywords":"Econometrics; Economics; Computer science","score_opus":0.04234564887444071,"score_gpt":0.31378324701060883,"score_spread":0.27143759813616813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272377578","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":[0.021042876,0.00056235096,0.972137,0.0032471148,0.0003429249,0.00008477769,0.00019577828,0.00050739385,0.0018798076],"genre_scores_gemma":[0.45277876,0.00074501167,0.5390263,0.0023786728,0.0006487979,0.00047567466,0.0005825204,0.00037104558,0.002993284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97577435,0.019630684,0.00081036636,0.0017456918,0.0015347525,0.00050408626],"domain_scores_gemma":[0.69741756,0.27704433,0.0036731623,0.017278861,0.0034027535,0.0011832906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057816807,0.0015489637,0.0033290994,0.0009970593,0.0007407259,0.0023525567,0.003133869,0.0037212577,0.0036592204],"category_scores_gemma":[0.20541343,0.0008990309,0.0032174215,0.0013575959,0.0028006858,0.005822824,0.0033061346,0.0066508907,0.0003760712],"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":[0.00041492085,0.000106535445,0.010276885,0.00011201763,0.0008303207,0.000358161,0.00015122545,0.88734525,0.0014371837,0.05489712,0.0033804595,0.040690053],"study_design_scores_gemma":[0.000074146796,0.00026559044,0.0012258568,0.000040585022,0.000099890814,0.00009984015,0.0000284052,0.9177485,0.00086298096,0.077817485,0.0016611818,0.00007546291],"about_ca_topic_score_codex":0.01069798,"about_ca_topic_score_gemma":0.007803027,"teacher_disagreement_score":0.057816807,"about_ca_system_score_codex":0.00095017615,"about_ca_system_score_gemma":0.0020602674,"threshold_uncertainty_score":0.30576813},"labels":[],"label_agreement":null},{"id":"W2274873549","doi":"","title":"A Population Aging Analysis for Canada Using the National Transfer Accounts Approach","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"National accounts; Consumption (sociology); Projection (relational algebra); Population; Economics; Projections of population growth; Estimation; Transfer (computing); Private consumption; National Income and Product Accounts; Econometrics; Accounting; Computer science; Macroeconomics; Population growth; Fiscal policy; Demography","score_opus":0.10012675507407864,"score_gpt":0.36878271126412193,"score_spread":0.2686559561900433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274873549","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":[0.8476153,0.0017776554,0.017679764,0.0013534547,0.00008232732,0.0003803295,0.091807604,0.00044998107,0.03885357],"genre_scores_gemma":[0.9220665,0.0017929296,0.018537952,0.00013212652,0.00002976743,0.0001541006,0.042409956,0.000091847585,0.014784703],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994879,0.000042731965,0.00002199691,0.00004990211,0.00028034236,0.00011704802],"domain_scores_gemma":[0.9987702,0.00007503205,0.00007645167,0.000055573062,0.0009250821,0.00009767441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007200677,0.000292342,0.00034951855,0.0041840156,0.0014812752,0.0012720979,0.0005113096,0.00017983482,0.002756225],"category_scores_gemma":[0.0025025262,0.00011827585,0.00069601904,0.0066356827,0.0002152429,0.00049535435,0.0007275361,0.00047075195,0.00026138936],"study_design_candidate":"simulation_or_modeling","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":[0.00028322556,0.00015140107,0.59809434,0.00032464927,0.0004959044,0.0006549442,0.0018214518,0.07177662,0.0011188518,0.06300766,0.055243116,0.20702785],"study_design_scores_gemma":[0.000030994524,0.000052863634,0.7233725,0.00011875434,0.00025122942,0.00023492314,0.002176532,0.15335263,0.0014262832,0.0051954845,0.11369253,0.00009533156],"about_ca_topic_score_codex":0.99405426,"about_ca_topic_score_gemma":0.9910625,"teacher_disagreement_score":0.02232786,"about_ca_system_score_codex":0.02232786,"about_ca_system_score_gemma":0.031742044,"threshold_uncertainty_score":0.16200072},"labels":[],"label_agreement":null},{"id":"W2278189445","doi":"10.1080/07350015.2016.1213635","title":"Eliciting Subjective Survival Curves: Lessons from Partial Identification","year":2016,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Network for Studies on Pensions, Aging and Retirement","keywords":"Rounding; Construct (python library); Inference; Econometrics; Statistics; Interpolation (computer graphics); Respondent; Mathematics; Identification (biology); Computer science; Artificial intelligence","score_opus":0.03523701649380812,"score_gpt":0.31650276664587,"score_spread":0.2812657501520619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278189445","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":[0.048301857,0.00037612036,0.9453191,0.0014951905,0.00003727848,0.00019832951,0.0008366253,0.00034215624,0.0030932606],"genre_scores_gemma":[0.6569791,0.0007269102,0.33746287,0.00073247525,0.00009142769,0.0006918078,0.0020549633,0.0001948771,0.0010655612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95144325,0.040551297,0.0022717798,0.0022736154,0.0029533168,0.0005066362],"domain_scores_gemma":[0.42359623,0.50922865,0.012200675,0.04728572,0.0067504654,0.0009381938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.067382865,0.0012289679,0.0020467103,0.0018825524,0.0007469837,0.0039317044,0.002485008,0.002545673,0.00360254],"category_scores_gemma":[0.42927215,0.0014727358,0.0020940606,0.0027715296,0.002581151,0.0064602974,0.005330749,0.0035844164,0.0007949182],"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":[0.0009327313,0.00032390593,0.056700844,0.0015369913,0.00072702096,0.00085207,0.010906616,0.29027417,0.0015268073,0.32166946,0.004922399,0.30962697],"study_design_scores_gemma":[0.00008074013,0.00020785067,0.007461279,0.00043207305,0.000072257935,0.00030534694,0.00095171225,0.35306868,0.0019428295,0.6302373,0.0051265415,0.00011343955],"about_ca_topic_score_codex":0.0029308496,"about_ca_topic_score_gemma":0.001794523,"teacher_disagreement_score":0.067382865,"about_ca_system_score_codex":0.0013884982,"about_ca_system_score_gemma":0.001830136,"threshold_uncertainty_score":0.3563589},"labels":[],"label_agreement":null},{"id":"W2279704908","doi":"10.48550/arxiv.1601.04351","title":"On bivariate lifetime modelling in life insurance applications","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Copula (linguistics); Bivariate analysis; Novelty; Econometrics; Actuarial science; Joint probability distribution; Life insurance; Mathematics; Statistics; Economics; Psychology; Social psychology","score_opus":0.0704824003224548,"score_gpt":0.22602328807964253,"score_spread":0.15554088775718772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279704908","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":[0.008496641,0.0038861784,0.98159504,0.0011984458,0.00008729644,0.00003220741,0.00020281397,0.00012150428,0.0043799733],"genre_scores_gemma":[0.6219696,0.026605086,0.3299613,0.0006215482,0.0012756819,0.0004909757,0.001241838,0.00035628365,0.017477693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99757344,0.0018405392,0.000108820146,0.00018379107,0.0001838975,0.00010957285],"domain_scores_gemma":[0.9910712,0.0074292244,0.00053384766,0.00039291635,0.00042811144,0.00014461037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051240963,0.0011649333,0.0010829272,0.0019231507,0.00071607984,0.0022922386,0.0013859477,0.0019584163,0.0038871323],"category_scores_gemma":[0.017758135,0.0004617515,0.0015850043,0.0046744933,0.0011234012,0.0020685105,0.0022879425,0.0023962543,0.0011294117],"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":[0.000039462146,0.00007371423,0.004648039,0.00019507999,0.00010244277,0.00034240348,0.0004922658,0.48919138,0.0004694228,0.4373844,0.0034720995,0.06358926],"study_design_scores_gemma":[0.0000035738722,0.000023412349,0.00070415146,0.000054845328,0.000020828178,0.00007429027,0.00007559126,0.8435777,0.0000783345,0.15004738,0.0053212144,0.000018762003],"about_ca_topic_score_codex":0.011785676,"about_ca_topic_score_gemma":0.00694946,"teacher_disagreement_score":0.011785676,"about_ca_system_score_codex":0.0009615248,"about_ca_system_score_gemma":0.0009806416,"threshold_uncertainty_score":0.027099133},"labels":[],"label_agreement":null},{"id":"W2285251285","doi":"","title":"Indigenous fertility transitions in developed countries","year":2012,"lang":"en","type":"article","venue":"ANU Open Research (Australian National University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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":"Fertility; Indigenous; Developing country; Geography; Population; Socioeconomics; Demography; Economic growth; Economics; Sociology; Biology","score_opus":0.22345643768337262,"score_gpt":0.4397078484725883,"score_spread":0.2162514107892157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2285251285","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":[0.993972,0.0007514906,0.000045308316,0.00049317215,0.000011773841,0.000009319668,0.00012529561,0.0000027992646,0.0045888107],"genre_scores_gemma":[0.9983335,0.0006801903,0.0000465835,0.00008455954,0.000007377029,0.000006975411,0.00005671404,0.0000011962566,0.00078290317],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995122,0.00013958292,0.000019127117,0.000051790637,0.000031778713,0.00024547568],"domain_scores_gemma":[0.9994605,0.000080028374,0.00016191651,0.000027092789,0.00008496939,0.00018551378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000872203,0.000086418026,0.00019511374,0.0008995728,0.0020045673,0.00096163375,0.00033776023,0.00031960523,0.0024946965],"category_scores_gemma":[0.0018383774,0.0001414371,0.00020889424,0.0011434135,0.0012297092,0.0007768065,0.0017485006,0.0007527023,0.00014454819],"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":[0.00031944318,0.00022651936,0.6272533,0.00030987075,0.00008925111,0.0021049099,0.29227912,0.00094023976,0.0012976559,0.020143576,0.0031367417,0.051899444],"study_design_scores_gemma":[0.000010665357,0.00012240148,0.8993471,0.000117621916,0.000015138636,0.0004242099,0.0878559,0.0001406922,0.00011363673,0.0007657824,0.011062637,0.00002411946],"about_ca_topic_score_codex":0.09870304,"about_ca_topic_score_gemma":0.12021294,"teacher_disagreement_score":0.09870304,"about_ca_system_score_codex":0.002158034,"about_ca_system_score_gemma":0.0015880164,"threshold_uncertainty_score":0.196257},"labels":[],"label_agreement":null},{"id":"W2293006230","doi":"10.71781/12981","title":"Estimation de la mortalité évitable au Québec de 1981-1985 à 2005-2009","year":2014,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Estimation; Geography; Statistics; Economics; Mathematics","score_opus":0.0206533149745683,"score_gpt":0.3519361814268735,"score_spread":0.3312828664523052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293006230","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":[0.8746326,0.0032342304,0.0077637453,0.0010771976,0.00010065806,0.00027943778,0.093035206,0.0001777565,0.019699233],"genre_scores_gemma":[0.93693346,0.0015969868,0.004274145,0.00024517553,0.000031064803,0.00026128333,0.040652715,0.000026555757,0.015978634],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994868,0.00007786325,0.000040920502,0.000117355936,0.00019226495,0.000084744635],"domain_scores_gemma":[0.997591,0.00018804686,0.0003151791,0.00010821581,0.0016708685,0.0001266958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010563647,0.00042604093,0.0003227416,0.0022724983,0.00086503016,0.0006860256,0.0007263037,0.0002957763,0.0028995257],"category_scores_gemma":[0.002437528,0.00020055773,0.00073399063,0.003389305,0.00024270536,0.000300799,0.0004081248,0.00047585953,0.0004783267],"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":[0.000135628,0.00003945731,0.95498604,0.00019146832,0.0003401506,0.00007494976,0.00057509367,0.0043469137,0.00043209572,0.0006502455,0.009581857,0.028645983],"study_design_scores_gemma":[0.0000069752264,0.000020072976,0.9906213,0.00006878263,0.000053558677,0.00001508869,0.00032332158,0.0026860188,0.00018561575,0.00003412123,0.005974367,0.000010839076],"about_ca_topic_score_codex":0.9889593,"about_ca_topic_score_gemma":0.99206793,"teacher_disagreement_score":0.01514851,"about_ca_system_score_codex":0.01514851,"about_ca_system_score_gemma":0.010593329,"threshold_uncertainty_score":0.10991067},"labels":[],"label_agreement":null},{"id":"W2294384412","doi":"10.1080/10920277.2011.10597607","title":"Structural Changes in the Lee-Carter Mortality Indexes","year":2011,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":78,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Econometrics; Index (typography); Annuity; Pension; Economics; Statistics; Actuarial science; Mathematics; Life annuity; Computer science","score_opus":0.05811950152912955,"score_gpt":0.3100275424862922,"score_spread":0.2519080409571627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294384412","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":[0.9785905,0.0002297045,0.009999757,0.00046185523,0.000023919896,0.000040275256,0.00080135,0.00004391184,0.009808589],"genre_scores_gemma":[0.99806744,0.000044876204,0.0009738073,0.000022707434,0.000017804396,0.000009357461,0.0003546004,0.0000034929014,0.0005058627],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918145,0.00021679187,0.000064228254,0.00016565481,0.00023336736,0.00013852863],"domain_scores_gemma":[0.992262,0.0031049347,0.0023017102,0.0007972109,0.0012566012,0.00027750357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029775754,0.00017947903,0.0002302493,0.0018179027,0.00032899113,0.0010778024,0.0004159727,0.00030869307,0.002890051],"category_scores_gemma":[0.01596677,0.00013014731,0.00032291177,0.001468691,0.0008412011,0.00094458257,0.00080463354,0.0007021332,0.00024724158],"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":[0.00024045391,0.00005453023,0.86319906,0.00006301857,0.00015991507,0.00018321471,0.0016388152,0.028848514,0.0020584962,0.06025048,0.0023561586,0.040947486],"study_design_scores_gemma":[0.00001219695,0.00015151905,0.91359186,0.000037719314,0.000047894373,0.00015372793,0.0012709877,0.044158652,0.0029243075,0.03163732,0.0059535895,0.000060136226],"about_ca_topic_score_codex":0.0054384856,"about_ca_topic_score_gemma":0.0037472325,"teacher_disagreement_score":0.0054384856,"about_ca_system_score_codex":0.0008735683,"about_ca_system_score_gemma":0.00045494843,"threshold_uncertainty_score":0.01574713},"labels":[],"label_agreement":null},{"id":"W2294515031","doi":"10.7202/010133ar","title":"Nouvelles perspectives de l’analyse biographique","year":2004,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Humanities; Political science; Art; Philosophy","score_opus":0.011036962679109322,"score_gpt":0.26636255694631994,"score_spread":0.25532559426721063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294515031","genre_codex":"commentary","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":[0.0038402376,0.25556135,0.26632205,0.32587948,0.0102948,0.000084446925,0.0008330889,0.0004037053,0.13678081],"genre_scores_gemma":[0.30172583,0.31884378,0.20643803,0.046621423,0.035571165,0.001004483,0.0012100189,0.00065654906,0.087928705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98493224,0.009628634,0.0004725767,0.0015960039,0.0029821545,0.00038829006],"domain_scores_gemma":[0.9627959,0.028958535,0.0010737377,0.0025721658,0.003972893,0.00062674517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020628354,0.0015212485,0.0013505168,0.009725564,0.0041597937,0.019335872,0.0026424206,0.0056563327,0.008730511],"category_scores_gemma":[0.029629776,0.00069610524,0.0016200931,0.009425772,0.043822784,0.016798368,0.0039365063,0.009287956,0.0028972367],"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":[0.0000049724104,0.0000045692454,0.00015753394,0.00011433229,0.00001418418,0.000049606617,0.0010911885,0.0004117855,0.000060803995,0.97262156,0.013181258,0.012288346],"study_design_scores_gemma":[0.000004500544,0.000006446608,0.0004895242,0.00044767497,0.000007658736,0.0001419813,0.0008852751,0.0016120843,0.00008003321,0.6900092,0.30629015,0.00002549824],"about_ca_topic_score_codex":0.040269636,"about_ca_topic_score_gemma":0.02200076,"teacher_disagreement_score":0.040269636,"about_ca_system_score_codex":0.012153787,"about_ca_system_score_gemma":0.0061339857,"threshold_uncertainty_score":0.10909444},"labels":[],"label_agreement":null},{"id":"W2297405967","doi":"10.1017/cbo9780511800146","title":"Actuarial Mathematics for Life Contingent Risks","year":2009,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":167,"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":"Rigour; Intuition; Computer science; Perspective (graphical); Management science; Scale (ratio); Engineering ethics; Engineering; Psychology; Artificial intelligence; Mathematics; Cognitive science","score_opus":0.0589768037649738,"score_gpt":0.2834134196989687,"score_spread":0.22443661593399492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297405967","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":[0.003414122,0.029947935,0.13249236,0.014694442,0.0028486976,0.000043573684,0.00033487237,0.00038357696,0.8158404],"genre_scores_gemma":[0.16142114,0.05031019,0.0601243,0.004286621,0.004881401,0.00022174582,0.00066873437,0.0004540027,0.7176318],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996617,0.00006792756,0.000013783432,0.000041164098,0.00019415922,0.000021237185],"domain_scores_gemma":[0.999468,0.0002982978,0.000044124565,0.00006876616,0.000084466286,0.000036409136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057590345,0.0006797545,0.0003584756,0.00096936675,0.000782467,0.00235825,0.00058939896,0.0008904011,0.022340737],"category_scores_gemma":[0.0025910903,0.0003366043,0.0004930254,0.0010186582,0.0020466635,0.0032782992,0.0010920335,0.0032526413,0.008319589],"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":[0.000002364233,0.000007464529,0.000057550562,0.00003838999,0.0000031785878,0.0000291279,0.0001136664,0.0017073571,0.00009664901,0.92179275,0.052568052,0.023583487],"study_design_scores_gemma":[0.0000020045288,0.0000063884545,0.00018183445,0.00008184181,0.0000023461414,0.000089223664,0.000042290012,0.0035581347,0.00005908335,0.6787327,0.31723836,0.0000058402443],"about_ca_topic_score_codex":0.0011094665,"about_ca_topic_score_gemma":0.0010699028,"teacher_disagreement_score":0.022340737,"about_ca_system_score_codex":0.0014845444,"about_ca_system_score_gemma":0.0009480778,"threshold_uncertainty_score":0.07473725},"labels":[],"label_agreement":null},{"id":"W2323016303","doi":"10.1080/10920277.2003.10596079","title":"“Comparison of Future Lifetime Distribution and Its Approximations,” Esther Frostig, April 2002","year":2003,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Distribution (mathematics); Computer graphics (images); Computer science; Mathematics; Mathematical analysis","score_opus":0.01623566268174082,"score_gpt":0.3085816913410451,"score_spread":0.29234602865930426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323016303","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":[0.06468278,0.027679274,0.8088646,0.02932445,0.00347842,0.0001524306,0.0027517763,0.000852754,0.062213503],"genre_scores_gemma":[0.74158174,0.027833296,0.17328553,0.004751615,0.0028393983,0.00045361446,0.0053675924,0.0013366615,0.04255058],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99628204,0.0021245733,0.0001277442,0.00034786787,0.0009195372,0.00019827133],"domain_scores_gemma":[0.9755289,0.017795501,0.0010993994,0.0021093406,0.0031239328,0.00034288233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014790723,0.00066071755,0.0009100674,0.0021903806,0.00077918044,0.0023488987,0.0020847218,0.0015711181,0.008072264],"category_scores_gemma":[0.09124196,0.00044826668,0.0017341436,0.0030856086,0.0013217888,0.0053060334,0.0021440275,0.003652374,0.0014465338],"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":[0.00032058006,0.000036627054,0.004171858,0.00014998672,0.00011256102,0.00013838035,0.00040993662,0.13519354,0.00012734185,0.7144322,0.07000734,0.074899696],"study_design_scores_gemma":[0.000040919113,0.00010316941,0.0059624636,0.00036538314,0.000098957564,0.00056223106,0.00038915646,0.45999715,0.000545978,0.4721698,0.059665434,0.00009936117],"about_ca_topic_score_codex":0.0158964,"about_ca_topic_score_gemma":0.00521271,"teacher_disagreement_score":0.0158964,"about_ca_system_score_codex":0.0032393595,"about_ca_system_score_gemma":0.0014789138,"threshold_uncertainty_score":0.0782218},"labels":[],"label_agreement":null},{"id":"W2326441531","doi":"10.1080/10920277.2001.10596017","title":"“Principal Applications of Bayesian Methods in Actuarial Science: A Perspective”, Udi E. Makov, October 2001","year":2001,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Calgary","funders":"","keywords":"Bayesian probability; Principal (computer security); Perspective (graphical); Computer science; Econometrics; Actuarial science; Statistics; Mathematics; Artificial intelligence; Economics; Computer security","score_opus":0.02417253216176848,"score_gpt":0.40401109865362467,"score_spread":0.3798385664918562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326441531","genre_codex":"methods","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":[0.0019281227,0.14208692,0.64919657,0.16278219,0.008386922,0.00006365093,0.00021729757,0.00028694936,0.03505126],"genre_scores_gemma":[0.13471568,0.25978625,0.46182156,0.020697147,0.05866263,0.0004702984,0.00037226023,0.000716125,0.06275806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99276215,0.0048420494,0.00030770924,0.00044168535,0.0015085276,0.0001378608],"domain_scores_gemma":[0.9692285,0.025840199,0.0007836249,0.00078914006,0.0029053735,0.00045315723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024976196,0.0015042208,0.0017360044,0.0046143574,0.0015275242,0.006436684,0.001872781,0.004241458,0.008892952],"category_scores_gemma":[0.061741725,0.0013105945,0.0011687307,0.004871662,0.0066553624,0.008218158,0.0026954815,0.0085853515,0.0027859188],"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":[0.000049454284,0.00003419475,0.00082342024,0.00022543818,0.00007390674,0.000056867706,0.00022443403,0.006732748,0.00010932515,0.7749639,0.113290004,0.10341622],"study_design_scores_gemma":[0.000017441105,0.00001978875,0.000538713,0.00028106378,0.00002020668,0.000102219,0.0000895623,0.018605813,0.00018516919,0.9043485,0.07575306,0.00003847602],"about_ca_topic_score_codex":0.0031831341,"about_ca_topic_score_gemma":0.006128721,"teacher_disagreement_score":0.024976196,"about_ca_system_score_codex":0.002253577,"about_ca_system_score_gemma":0.0019555264,"threshold_uncertainty_score":0.1320883},"labels":[],"label_agreement":null},{"id":"W2328214815","doi":"10.7202/010305ar","title":"Stone, Leroy O., éd. Succession de cohortes et conséquences du vieillissement de la population. Une analyse et une revue internationales [CD-ROM]. Ottawa, Statistique Canada. 1 disque au laser d’ordinateur, 4 3/4 po, 1999, 370 pages. No 89-569-XCB au catalogue.","year":2001,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humanities; Art","score_opus":0.008054299664559285,"score_gpt":0.2686110023034922,"score_spread":0.26055670263893294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328214815","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":[0.00044238073,0.97334176,0.0012475596,0.011762522,0.0032283235,0.000043774977,0.0028126095,0.000117795455,0.0070032827],"genre_scores_gemma":[0.005664931,0.9552625,0.002457604,0.0013983743,0.0014442495,0.00008527483,0.0032097464,0.0001351226,0.030342225],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9974861,0.00050802133,0.00024189295,0.00024090278,0.001290591,0.00023240753],"domain_scores_gemma":[0.994787,0.001285894,0.00042031094,0.0002546666,0.0027244566,0.00052767823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004129999,0.0028276034,0.00260673,0.009344614,0.0023338154,0.0028202154,0.0029565291,0.0019641875,0.02768954],"category_scores_gemma":[0.0066606607,0.0019583297,0.0015703039,0.017628122,0.003359341,0.003955426,0.0013077628,0.003346948,0.011014646],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055966193,0.000016876465,0.004202107,0.0014584849,0.00007352851,0.00008489003,0.000598224,0.00035878178,0.00011726941,0.0023654015,0.82649773,0.16417062],"study_design_scores_gemma":[0.00002578406,0.00003524515,0.03357576,0.0043839756,0.00011573678,0.00043353412,0.0011396724,0.00028739942,0.00024646378,0.004829093,0.9548465,0.000080804544],"about_ca_topic_score_codex":0.80476266,"about_ca_topic_score_gemma":0.8612435,"teacher_disagreement_score":0.9827205,"about_ca_system_score_codex":0.017279508,"about_ca_system_score_gemma":0.028415674,"threshold_uncertainty_score":0.39277422},"labels":[],"label_agreement":null},{"id":"W2329723617","doi":"10.1017/s0714980800003688","title":"Le Rapport de la Commission Romanow et les Soins à Domicile","year":2003,"lang":"fr","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Political science","score_opus":0.016676607698311714,"score_gpt":0.2545306852473022,"score_spread":0.2378540775489905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329723617","genre_codex":"commentary","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":[0.14028533,0.05045847,0.002849463,0.45585692,0.008660372,0.000060578972,0.0004555259,0.00010419869,0.34126917],"genre_scores_gemma":[0.69677716,0.015537116,0.0022357057,0.029989373,0.0010738994,0.00007382545,0.00018477258,0.00014249323,0.25398555],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969368,0.0012532914,0.000091478105,0.00023986393,0.0006687785,0.00080977863],"domain_scores_gemma":[0.9966536,0.0012958529,0.00034076362,0.00018990696,0.0004471737,0.0010726802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003324764,0.0001847851,0.00040171802,0.00055692217,0.0045489348,0.003585176,0.00038723278,0.00260135,0.011998919],"category_scores_gemma":[0.010102401,0.00022462936,0.0001899357,0.0009527634,0.0022299285,0.0022559704,0.0023459007,0.0046594203,0.0016135329],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018331913,0.00004776965,0.011966724,0.00016640092,0.000025005826,0.0011615289,0.019092439,0.00016736251,0.0010609693,0.54492366,0.3081644,0.11304038],"study_design_scores_gemma":[0.000010897293,0.00001860122,0.008767923,0.0002008497,0.0000053954664,0.00024227466,0.0045951703,0.00007681237,0.00024507442,0.004860765,0.9809549,0.0000214023],"about_ca_topic_score_codex":0.07448699,"about_ca_topic_score_gemma":0.124060094,"teacher_disagreement_score":0.9964926,"about_ca_system_score_codex":0.003507388,"about_ca_system_score_gemma":0.013802898,"threshold_uncertainty_score":0.14810687},"labels":[],"label_agreement":null},{"id":"W2332054451","doi":"10.6000/1927-5129.2012.08.02.01","title":"Poisson Regression Models for Count Data: Use in the Number of Deaths in the Santo Angelo (Brazil)","year":2012,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Poisson regression; Poisson distribution; Count data; Statistics; Generalized linear model; Mathematics; Linear regression; Variance (accounting); Regression analysis; Econometrics; Range (aeronautics); Regression; Demography","score_opus":0.1115325046768296,"score_gpt":0.3935136124565257,"score_spread":0.2819811077796961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332054451","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":[0.029736547,0.037625533,0.90386426,0.008346464,0.0010974726,0.0005836729,0.0043974807,0.0010725455,0.013276013],"genre_scores_gemma":[0.54262716,0.04124168,0.38918945,0.0016297871,0.0019351488,0.0020299961,0.005619555,0.000716853,0.015010417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9883161,0.008156006,0.0005123036,0.0012468542,0.0015008617,0.0002678542],"domain_scores_gemma":[0.97824895,0.017496284,0.0017827216,0.0009366166,0.0013191341,0.00021631157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013951459,0.0010358182,0.0013804705,0.0029379379,0.00074265397,0.0028212026,0.0031027647,0.0022500695,0.0044196392],"category_scores_gemma":[0.03954789,0.0006698731,0.001857898,0.005700394,0.0012423978,0.0023751594,0.002053988,0.0039300034,0.001289523],"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":[0.00022469429,0.0002643116,0.05943476,0.0026498055,0.000657805,0.001744445,0.0033740986,0.10607535,0.0007936864,0.50957435,0.040923018,0.2742837],"study_design_scores_gemma":[0.000051334893,0.0003997167,0.02008362,0.0019928606,0.00043541423,0.0015992968,0.0017833433,0.55178934,0.000624087,0.31833145,0.10267734,0.00023213908],"about_ca_topic_score_codex":0.015264467,"about_ca_topic_score_gemma":0.011969328,"teacher_disagreement_score":0.015264467,"about_ca_system_score_codex":0.0019974161,"about_ca_system_score_gemma":0.0024581815,"threshold_uncertainty_score":0.07378328},"labels":[],"label_agreement":null},{"id":"W2333811855","doi":"10.1017/s0714980800001677","title":"Some Demographic Consequences of Revising the Definition of “Old Age” to Reflect Future Changes in Life Table Probabilities","year":2002,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":36,"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":"Table (database); Life table; Demography; History; Computer science; Sociology; Population; Data mining","score_opus":0.030048124271512922,"score_gpt":0.24576738138544596,"score_spread":0.21571925711393303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333811855","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":[0.51101005,0.007853395,0.08380164,0.2778283,0.003213764,0.00027050375,0.0014227112,0.00014808605,0.11445154],"genre_scores_gemma":[0.9810998,0.0010695349,0.010692198,0.004163701,0.00072254223,0.000086836924,0.00015916528,0.000021342943,0.001985028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9944384,0.0039033636,0.00022575265,0.0002960018,0.00085011974,0.00028646772],"domain_scores_gemma":[0.96922326,0.018749228,0.0030678643,0.0015449548,0.0065422123,0.0008725005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01591739,0.00037116636,0.00042392398,0.0013939701,0.001598636,0.0026180283,0.0019335875,0.0018337467,0.0029035844],"category_scores_gemma":[0.09419812,0.0001968448,0.0005421927,0.0017925423,0.0050413944,0.0044863354,0.0017502076,0.002775766,0.0002950612],"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":[0.00023753251,0.000089680216,0.050313797,0.00012182501,0.00004468496,0.00045880332,0.004899389,0.014612068,0.00041118552,0.85551125,0.016581839,0.056717914],"study_design_scores_gemma":[0.00007967303,0.0002600911,0.13970213,0.00049331016,0.00009002327,0.0012706436,0.010665854,0.07058216,0.00070168625,0.7104836,0.06547387,0.000197037],"about_ca_topic_score_codex":0.05506025,"about_ca_topic_score_gemma":0.046668746,"teacher_disagreement_score":0.05506025,"about_ca_system_score_codex":0.0046375585,"about_ca_system_score_gemma":0.0026040124,"threshold_uncertainty_score":0.10947949},"labels":[],"label_agreement":null},{"id":"W2336585578","doi":"","title":"Markovian Approaches to Joint-life Mortality with Applications in Risk Management","year":2011,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Copula (linguistics); Bivariate analysis; Spouse; Econometrics; Life insurance; Joint probability distribution; Actuarial science; Pension; Shock (circulatory); Markov process; Economics; Mathematics; Statistics; Sociology; Finance","score_opus":0.046589029625259806,"score_gpt":0.23279606151476345,"score_spread":0.18620703188950366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336585578","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":[0.0055421037,0.0026421268,0.9810952,0.0018896724,0.0002152221,0.000046009663,0.00009970774,0.000078856654,0.008391082],"genre_scores_gemma":[0.5781969,0.016116943,0.37561542,0.0009881187,0.0019299133,0.00079556706,0.00035400034,0.00019050957,0.025812592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979685,0.0011278429,0.00010151932,0.00026399016,0.00038881128,0.00014943237],"domain_scores_gemma":[0.9891105,0.008637687,0.00088829716,0.0004282164,0.000601033,0.0003341799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005161905,0.0015647813,0.0012001198,0.0016371695,0.0010403532,0.0024620758,0.00254217,0.002173101,0.0059593394],"category_scores_gemma":[0.012979632,0.0010174981,0.0023476242,0.0019001188,0.0031624609,0.0035153683,0.0025688233,0.005753415,0.00068669283],"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":[0.00000980389,0.00004448136,0.0007448047,0.00006429214,0.000038919028,0.00008640083,0.00024353641,0.123251,0.00016476959,0.86583966,0.0008975466,0.008614759],"study_design_scores_gemma":[0.000007705481,0.000030538347,0.00022390614,0.000044328663,0.000018035624,0.00003564205,0.00006844895,0.4813828,0.00006770151,0.51493365,0.003165087,0.000022256205],"about_ca_topic_score_codex":0.011107307,"about_ca_topic_score_gemma":0.0079098325,"teacher_disagreement_score":0.011107307,"about_ca_system_score_codex":0.0032720754,"about_ca_system_score_gemma":0.0027019612,"threshold_uncertainty_score":0.027299106},"labels":[],"label_agreement":null},{"id":"W2336596092","doi":"10.7202/1091628ar","title":"Quantification du risque systématiquede mortalité pour un régime de rentesen cours de service","year":2006,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Political science; Humanities; Philosophy","score_opus":0.02897866810256904,"score_gpt":0.30189917149077183,"score_spread":0.2729205033882028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336596092","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":[0.93384683,0.0035728917,0.04223096,0.0010498904,0.000068038054,0.00025501443,0.00872727,0.00016726885,0.010081842],"genre_scores_gemma":[0.9742691,0.00088922057,0.01929856,0.00004954429,0.0000327218,0.00017732107,0.0028456552,0.000025312376,0.0024124726],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99524224,0.002316396,0.0002666697,0.00059341255,0.0012334798,0.00034780716],"domain_scores_gemma":[0.9912839,0.0052872775,0.0017203281,0.00046542953,0.0010215563,0.00022147245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055537582,0.0004093842,0.0007586368,0.0027688346,0.00037293442,0.0012505227,0.00066798454,0.00073874986,0.0036188369],"category_scores_gemma":[0.017013669,0.00022710962,0.0013144723,0.0025691704,0.00060515624,0.0008007292,0.0014192936,0.00088302355,0.00036234024],"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":[0.0008347771,0.00011250249,0.83058715,0.00047066502,0.00073168514,0.00015213646,0.0010134585,0.057700247,0.0039066733,0.011723156,0.0023159897,0.09045146],"study_design_scores_gemma":[0.00002747063,0.00056595076,0.91985583,0.00017194821,0.00014633597,0.00034512373,0.0008569522,0.06425625,0.0026762602,0.005205558,0.0058066715,0.000085635045],"about_ca_topic_score_codex":0.031921457,"about_ca_topic_score_gemma":0.023929508,"teacher_disagreement_score":0.031921457,"about_ca_system_score_codex":0.00298289,"about_ca_system_score_gemma":0.0016937718,"threshold_uncertainty_score":0.06347126},"labels":[],"label_agreement":null},{"id":"W2338844166","doi":"","title":"Merging Asset Allocation and Longevity Insurance: An Optimal Perspective on Payout Annuities","year":2003,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":34,"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":"Longevity risk; Actuarial science; Economics; Annuity; Asset allocation; Bequest; Asset (computer security); Life insurance; Basis risk; Life annuity; Pension; Finance; Capital asset pricing model; Computer science","score_opus":0.02269184133007583,"score_gpt":0.3313320571018206,"score_spread":0.3086402157717448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338844166","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":[0.07051787,0.008215651,0.8700657,0.0064467895,0.00025552412,0.00012728917,0.00015698458,0.000080561884,0.044133585],"genre_scores_gemma":[0.9150522,0.006224155,0.06480559,0.00039273204,0.0005005741,0.00013888131,0.00006957352,0.000062403145,0.012753859],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984737,0.00089370937,0.000050888695,0.00019417444,0.00022684022,0.00016062708],"domain_scores_gemma":[0.99883884,0.0006704642,0.000154245,0.00009165395,0.00011699845,0.00012780898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029936626,0.0012782972,0.0017389695,0.00092500495,0.00075515045,0.003349403,0.0016029319,0.002693913,0.0049267914],"category_scores_gemma":[0.0059759864,0.00087822694,0.0010609031,0.0010412635,0.0022132662,0.0049149627,0.0021195232,0.0023908166,0.0002918218],"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":[0.00007146526,0.00007162191,0.00053771125,0.00009455284,0.000032320415,0.00011890612,0.00021310653,0.2118086,0.0007624709,0.76738346,0.0009135722,0.017992083],"study_design_scores_gemma":[0.00003630564,0.00017077195,0.0006011093,0.000110226916,0.000052484087,0.00007196501,0.00014865834,0.30714548,0.0005138014,0.68537545,0.0057355682,0.000038232614],"about_ca_topic_score_codex":0.0024313084,"about_ca_topic_score_gemma":0.0017029381,"teacher_disagreement_score":0.0049267914,"about_ca_system_score_codex":0.0030031975,"about_ca_system_score_gemma":0.0017767829,"threshold_uncertainty_score":0.02178979},"labels":[],"label_agreement":null},{"id":"W2343221479","doi":"10.5351/kjas.2016.29.1.041","title":"A modified Lee-Carter model based on the projection of the skewness of the mortality","year":2016,"lang":"en","type":"article","venue":"Korean Journal of Applied Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Skewness; Life expectancy; Projection (relational algebra); Index (typography); Statistics; Population projection; Population; Econometrics; Mortality rate; Mathematics; Projection pursuit; Demography; Actuarial science; Computer science; Economics; Developed country","score_opus":0.032823239868948584,"score_gpt":0.2848194975874614,"score_spread":0.2519962577185128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343221479","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":[0.07980917,0.0016709833,0.8797893,0.0029959707,0.00073714583,0.00027922387,0.002529213,0.0005630894,0.031625822],"genre_scores_gemma":[0.89875096,0.0025392848,0.059313692,0.00073720154,0.00070449366,0.0006959432,0.0020600636,0.00018771489,0.035010755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907553,0.00037908508,0.0000389193,0.00018208045,0.0001675204,0.0001568077],"domain_scores_gemma":[0.9987765,0.00046002984,0.00016179998,0.0000886382,0.00038515386,0.00012786049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017337293,0.000767526,0.0006656535,0.00077449303,0.0005176266,0.0015341867,0.0021227652,0.0010688998,0.0063571124],"category_scores_gemma":[0.00445466,0.00025356704,0.0012461453,0.0011363253,0.0008557739,0.0017251513,0.0010741072,0.001148366,0.0009080829],"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":[0.00023581756,0.00010632709,0.009611566,0.00022578308,0.00013177184,0.00044637866,0.00033267975,0.664863,0.0016738172,0.25794873,0.012460029,0.05196417],"study_design_scores_gemma":[0.00003826602,0.00007563678,0.002493059,0.000054613905,0.000052634252,0.00020199487,0.00011083406,0.9246256,0.0004264397,0.06395645,0.007896493,0.00006792074],"about_ca_topic_score_codex":0.010890092,"about_ca_topic_score_gemma":0.008253764,"teacher_disagreement_score":0.010890092,"about_ca_system_score_codex":0.0011913311,"about_ca_system_score_gemma":0.0021362435,"threshold_uncertainty_score":0.021653414},"labels":[],"label_agreement":null},{"id":"W2343935361","doi":"10.1017/cbo9781139208499.001","title":"Preface","year":2012,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"History; Mathematics; Philosophy","score_opus":0.03184818800382408,"score_gpt":0.2376368179488731,"score_spread":0.205788629945049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343935361","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007862924,0.013143343,0.008244154,0.016349217,0.07402905,0.0003129717,0.009029641,0.0016787281,0.8764265],"genre_scores_gemma":[0.003537975,0.006104954,0.002567244,0.0023484554,0.012411906,0.0001549308,0.00629935,0.00089897023,0.9656762],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993975,0.000069247886,0.000040858355,0.00010674077,0.0003422203,0.000043411732],"domain_scores_gemma":[0.99767345,0.00041205177,0.00009468924,0.00022727594,0.0012898587,0.00030267978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091312686,0.000903257,0.0007214054,0.0026909346,0.0016679944,0.0033647188,0.0012252214,0.0011587164,0.5165491],"category_scores_gemma":[0.005721096,0.00032777735,0.0005655745,0.002233237,0.0005800898,0.0031060746,0.0017143559,0.0025235214,0.36227852],"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":[0.000013168443,0.000012243369,0.000044336983,0.000087388704,0.0000013175005,0.000026537182,0.000052301006,0.000068899666,0.000096356314,0.007290885,0.9443389,0.04796759],"study_design_scores_gemma":[0.0000018822767,0.0000072121748,0.00011979081,0.0000889687,7.925418e-7,0.00003939943,0.000026178535,0.00002530455,0.0000390136,0.0029403104,0.9967084,0.0000027292815],"about_ca_topic_score_codex":0.002589699,"about_ca_topic_score_gemma":0.0028810042,"teacher_disagreement_score":0.5165491,"about_ca_system_score_codex":0.0015746563,"about_ca_system_score_gemma":0.0013930113,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2344625650","doi":"10.1016/j.jval.2015.03.086","title":"Comparing the predictive performance of two variants of the elixhauser comorbidity measures for all-cause in-hospital mortality in a large multi-payer u.s. Administrative database","year":2015,"lang":"en","type":"article","venue":"Value in Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Ottawa","funders":"","keywords":"Comorbidity; Medicine; Confounding; Logistic regression; Emergency medicine; Statistic; Demography; Health care; Medical diagnosis; Internal medicine; Statistics","score_opus":0.23330155053984405,"score_gpt":0.4206595807583505,"score_spread":0.18735803021850647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344625650","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":[0.9972324,0.00023489028,0.00020792193,0.00014810122,0.000016083815,0.000010425096,0.001917711,0.000010634241,0.00022187676],"genre_scores_gemma":[0.99667823,0.0001001504,0.00034749086,0.000045378372,0.000024081364,0.000015182132,0.0027264517,0.000004067918,0.00005895494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99616647,0.002004705,0.0005245736,0.00061604794,0.0004375416,0.00025084274],"domain_scores_gemma":[0.97389966,0.017323721,0.0044950424,0.0022997786,0.0011592734,0.0008225055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054315086,0.00052347395,0.00070487533,0.0017089173,0.00036984886,0.0015700302,0.0011926203,0.00086096587,0.0011062729],"category_scores_gemma":[0.026048064,0.00032985696,0.0012574752,0.0021124883,0.0003862602,0.001193647,0.0011826177,0.0010327927,0.00019306951],"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":[0.0015390328,0.00014025907,0.9923463,0.000024051127,0.00071157474,0.000027203485,0.00007414304,0.0006660206,0.00007488257,0.00013060973,0.0005148599,0.0037510998],"study_design_scores_gemma":[0.00024579413,0.00027572512,0.98527783,0.00003055786,0.0006242314,0.00014711174,0.00028675332,0.012359064,0.0001772639,0.00026701877,0.0002821884,0.00002643171],"about_ca_topic_score_codex":0.011757414,"about_ca_topic_score_gemma":0.0141302105,"teacher_disagreement_score":0.011757414,"about_ca_system_score_codex":0.0007529035,"about_ca_system_score_gemma":0.0006970865,"threshold_uncertainty_score":0.02872485},"labels":[],"label_agreement":null},{"id":"W2356398580","doi":"","title":"A Study on Optimal Financial Choices Considering Life Insurance","year":2003,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Hamilton–Jacobi–Bellman equation; Dynamic programming; Portfolio; Optimal control; Stochastic control; Consumption (sociology); Mathematical optimization; Life insurance; Stochastic programming; Bellman equation; Constant (computer programming); Event (particle physics); Economics; Selection (genetic algorithm); Mathematical economics; Mathematics; Actuarial science; Computer science; Finance","score_opus":0.04294006462690736,"score_gpt":0.32212345166913203,"score_spread":0.2791833870422247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356398580","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":[0.33298403,0.01674812,0.5414152,0.014894357,0.00043793206,0.00017618,0.0002796819,0.00007551767,0.09298894],"genre_scores_gemma":[0.96247494,0.0051459624,0.02288978,0.0003944058,0.00033136684,0.0000985161,0.00006137169,0.00003060604,0.008573095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898344,0.0006314834,0.000030092733,0.00012861988,0.00012831994,0.000097890275],"domain_scores_gemma":[0.99809617,0.0013426291,0.00021361478,0.000059617676,0.0001270783,0.00016089561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017055959,0.00067144766,0.00082846824,0.00051933556,0.0006742474,0.0017904225,0.0007770235,0.0021798334,0.0041034915],"category_scores_gemma":[0.005477903,0.00042236975,0.0009110725,0.00068861694,0.0017981277,0.0024554748,0.0009953916,0.0016764412,0.0001657705],"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":[0.00006075799,0.0001365636,0.0011054929,0.00020214093,0.00007772385,0.00032041056,0.00028394436,0.13586417,0.00095401646,0.8501112,0.0013752762,0.0095082745],"study_design_scores_gemma":[0.00005046611,0.00018360923,0.0012215176,0.00011961133,0.000059561284,0.00012527834,0.00030286165,0.33293763,0.0003661639,0.6584095,0.006192659,0.00003122162],"about_ca_topic_score_codex":0.0019253413,"about_ca_topic_score_gemma":0.0012313061,"teacher_disagreement_score":0.0041034915,"about_ca_system_score_codex":0.0018629531,"about_ca_system_score_gemma":0.001099055,"threshold_uncertainty_score":0.013727486},"labels":[],"label_agreement":null},{"id":"W2408889751","doi":"10.1079/20010411600038","title":"Foreword","year":2000,"lang":"fr","type":"article","venue":"British Journal Of Nutrition","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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 and Kapodistrian University of Athens; University of Toronto","keywords":"Computer science","score_opus":0.013407486716660859,"score_gpt":0.26758268664179324,"score_spread":0.2541751999251324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408889751","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003863588,0.015379631,0.0018015299,0.20062289,0.52768743,0.00027750645,0.003016132,0.00082027743,0.25000829],"genre_scores_gemma":[0.00346692,0.012359491,0.0009745745,0.07936465,0.121283,0.00022594634,0.0028131215,0.0005925067,0.7789198],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871075,0.00015104361,0.00011251326,0.00017869464,0.0007275332,0.00011943315],"domain_scores_gemma":[0.99077004,0.0015413892,0.00031998244,0.00037587885,0.005894264,0.001098504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015395808,0.000986176,0.0009264254,0.0019884375,0.0018972396,0.004284679,0.0014572102,0.0040639127,0.4126863],"category_scores_gemma":[0.01521895,0.00030357015,0.00074634864,0.0013102301,0.0007796606,0.0030142,0.0017033134,0.0051925,0.41113168],"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":[0.000008505193,0.0000046856226,0.000019903027,0.00005609029,8.4594114e-7,0.000022554095,0.000013898462,0.0000073290403,0.000029463226,0.0006997002,0.9855239,0.013613203],"study_design_scores_gemma":[0.0000033852218,0.000007878146,0.0001255553,0.00020322831,0.0000017317282,0.00008754848,0.000043080316,0.000009404731,0.000044080833,0.0010859816,0.9983841,0.0000040700643],"about_ca_topic_score_codex":0.004150911,"about_ca_topic_score_gemma":0.0040175486,"teacher_disagreement_score":0.4126863,"about_ca_system_score_codex":0.001835051,"about_ca_system_score_gemma":0.0024530932,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2412889226","doi":"10.7202/1106037ar","title":"Perturbations extrêmes sur la dérivede mortalité anticipée -Application à un régime de rentes","year":2023,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Physics; Humanities; Political science; Philosophy","score_opus":0.03920927481612664,"score_gpt":0.32909686603522476,"score_spread":0.28988759121909813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412889226","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":[0.6273918,0.00076871656,0.35192087,0.002545143,0.00025079446,0.000087656954,0.0009641461,0.0003776252,0.015693277],"genre_scores_gemma":[0.988987,0.00034296978,0.0066366415,0.000058577203,0.00002766759,0.000056173598,0.00012717846,0.00003489036,0.003728804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953175,0.00020274024,0.000018674198,0.00009963652,0.00006554,0.000081611906],"domain_scores_gemma":[0.9956708,0.0033327595,0.0003395923,0.00015913745,0.00033812626,0.00015953253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016270494,0.00070232566,0.0006900536,0.000549364,0.00043046393,0.0019656387,0.00090405997,0.002066052,0.0034023458],"category_scores_gemma":[0.009412713,0.00041754235,0.0013114674,0.0004395648,0.0011818458,0.0012934807,0.0013621295,0.0020296809,0.00022729916],"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":[0.00003934835,0.00001437579,0.0015686181,0.00002293939,0.000018626875,0.000054639462,0.000045380722,0.9884952,0.0006329098,0.0074570593,0.00014937817,0.0015016499],"study_design_scores_gemma":[0.000005188754,0.000035405585,0.0012438269,0.000009610409,0.000010219481,0.000013000427,0.000044001736,0.9947724,0.00026988515,0.0033336778,0.00024827677,0.000014617609],"about_ca_topic_score_codex":0.028790891,"about_ca_topic_score_gemma":0.008509272,"teacher_disagreement_score":0.028790891,"about_ca_system_score_codex":0.0012794196,"about_ca_system_score_gemma":0.0011838846,"threshold_uncertainty_score":0.057246625},"labels":[],"label_agreement":null},{"id":"W2416956333","doi":"10.25336/p6vp5j","title":"Epidemiologic Transition in Australia – the last hundred years","year":2016,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Life expectancy; Demography; Epidemiological transition; Demographic transition; Mortality rate; Pandemic; Cause of death; Gerontology; Medicine; Coronavirus disease 2019 (COVID-19); Population; Disease; Fertility; Sociology; Pathology","score_opus":0.12008129110008263,"score_gpt":0.3900138367683154,"score_spread":0.26993254566823277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2416956333","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":[0.94533694,0.036297318,0.0013422734,0.0049257996,0.00018756068,0.00008984699,0.0017790342,0.000039777908,0.010001549],"genre_scores_gemma":[0.982621,0.013138403,0.0008186555,0.00043955643,0.0000928693,0.000054305852,0.0010622602,0.000006076074,0.0017668707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993863,0.00017584408,0.000099137054,0.00008560178,0.00015255452,0.00010054144],"domain_scores_gemma":[0.998793,0.00015139827,0.0004890833,0.00005725911,0.00030655623,0.000202539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00126211,0.00010238925,0.00019561611,0.0018782038,0.0006282263,0.0010104771,0.00028608745,0.00041607386,0.001003109],"category_scores_gemma":[0.0030811455,0.0001454824,0.0003635161,0.0035965322,0.0005134103,0.0012183355,0.0011479853,0.0006847529,0.00014227671],"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":[0.00015415117,0.0000873323,0.84568566,0.00092410116,0.0001925064,0.000931182,0.027050273,0.00095016666,0.0007247843,0.00728357,0.0047800126,0.11123625],"study_design_scores_gemma":[0.0000010865906,0.00004540102,0.9843492,0.0001303971,0.000012364524,0.00025604246,0.0019584259,0.00027751617,0.000027670469,0.000338075,0.0125967655,0.00000710631],"about_ca_topic_score_codex":0.10397508,"about_ca_topic_score_gemma":0.12873499,"teacher_disagreement_score":0.10397508,"about_ca_system_score_codex":0.0023477585,"about_ca_system_score_gemma":0.0021989788,"threshold_uncertainty_score":0.20673978},"labels":[],"label_agreement":null},{"id":"W2437026174","doi":"10.1111/jori.12158","title":"Dynamic Longevity Hedging in the Presence of Population Basis Risk: A Feasibility Analysis From Technical and Economic Perspectives","year":2016,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Swap (finance); Bespoke; Basis risk; Hedge; Population; Actuarial science; Futures contract; Economics; Longevity risk; Business; Financial economics; Finance","score_opus":0.011819413831482765,"score_gpt":0.302167407118377,"score_spread":0.2903479932868942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2437026174","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":[0.7169405,0.0009944628,0.2659545,0.002013357,0.00008331785,0.00031785757,0.00015202022,0.00007890738,0.013465036],"genre_scores_gemma":[0.9854054,0.00016729026,0.01341829,0.0000280751,0.000034637458,0.00004674926,0.000040165894,0.0000050208355,0.00085432664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981499,0.0010941604,0.00006894241,0.00019713405,0.0003169035,0.00017310877],"domain_scores_gemma":[0.9810555,0.015337831,0.0012615315,0.0010949578,0.000899979,0.00035024987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085090855,0.0007293403,0.00088443066,0.0010196635,0.0004900769,0.0022174981,0.0012390204,0.0016637271,0.003521241],"category_scores_gemma":[0.021691862,0.0003999235,0.0010083483,0.0005184006,0.0013426681,0.0028077436,0.002486454,0.0017789757,0.00011297037],"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":[0.0011267954,0.00056849763,0.020572295,0.00027822918,0.00019849812,0.001955125,0.00023195388,0.7005801,0.0068306695,0.2133553,0.0012660973,0.053036395],"study_design_scores_gemma":[0.000095607495,0.0006976434,0.0033307932,0.000042721513,0.00012046681,0.00033556845,0.00022857639,0.953572,0.0017299746,0.038838204,0.00095996575,0.000048551803],"about_ca_topic_score_codex":0.0011833857,"about_ca_topic_score_gemma":0.00049404934,"teacher_disagreement_score":0.0085090855,"about_ca_system_score_codex":0.0009574855,"about_ca_system_score_gemma":0.001235771,"threshold_uncertainty_score":0.04500085},"labels":[],"label_agreement":null},{"id":"W2460395938","doi":"10.69645/ponl7569","title":"A dynamic stochastic pension fund model 1","year":2013,"lang":"en","type":"article","venue":"The business & management collection.","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Pension fund; Pension; Business; Actuarial science; Econometrics; Economics; Finance","score_opus":0.02068889912981996,"score_gpt":0.27422659422644674,"score_spread":0.25353769509662677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460395938","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":[0.404397,0.0057942504,0.42924082,0.022400372,0.0007850764,0.00027339172,0.0080223,0.00073531,0.12835135],"genre_scores_gemma":[0.9216707,0.0016296589,0.008491442,0.00036384806,0.00028773583,0.0002052059,0.0011201283,0.000055412333,0.0661758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993938,0.00024965464,0.000027646389,0.00011351374,0.000090689835,0.00012470601],"domain_scores_gemma":[0.99792707,0.0010906962,0.00037563246,0.00007947991,0.00021689667,0.00031019305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014791123,0.00085422874,0.0016269409,0.00089493825,0.0006956195,0.0029387176,0.0022951541,0.003915699,0.008939501],"category_scores_gemma":[0.0055685174,0.0006020531,0.0007688953,0.00092610327,0.0013439166,0.0018996259,0.00123356,0.0021121467,0.0009255012],"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":[0.000116978736,0.00008139653,0.0021209107,0.00008099079,0.00006918768,0.00037406624,0.00013292936,0.63988155,0.00031415132,0.34645283,0.005754384,0.0046205977],"study_design_scores_gemma":[0.00009875993,0.000045031968,0.0005763949,0.000029006946,0.000038322847,0.00007859199,0.000066129236,0.9236605,0.00004667808,0.072340496,0.002990222,0.000029856394],"about_ca_topic_score_codex":0.02498055,"about_ca_topic_score_gemma":0.011859864,"teacher_disagreement_score":0.02498055,"about_ca_system_score_codex":0.002149075,"about_ca_system_score_gemma":0.0020918995,"threshold_uncertainty_score":0.04967028},"labels":[],"label_agreement":null},{"id":"W2460748582","doi":"10.1007/s13385-016-0134-y","title":"Rank-based methods for modeling dependence between loss triangles","year":2016,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Excellence Research Chairs, Government of Canada; Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Copula (linguistics); Econometrics; Portfolio; Inference; Multivariate statistics; Model selection; Computer science; Mathematics; Economics; Statistics; Finance; Artificial intelligence","score_opus":0.07999562808320786,"score_gpt":0.39591600048852155,"score_spread":0.3159203724053137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460748582","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":[0.004161189,0.000049343,0.99515486,0.000033613273,0.000004811526,0.000035249228,0.000065629836,0.00015122433,0.0003441822],"genre_scores_gemma":[0.29933247,0.00035566196,0.694173,0.000093672905,0.000093326846,0.0005313824,0.00077149516,0.000247371,0.004401573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99481565,0.003499497,0.00020363757,0.00046578262,0.00079887616,0.00021655658],"domain_scores_gemma":[0.9659114,0.026245201,0.0029038198,0.0025725767,0.0020623512,0.00030461256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011011676,0.0011638524,0.0009900404,0.0023357049,0.00058812066,0.0013668109,0.0023567954,0.0011556746,0.0043508825],"category_scores_gemma":[0.038852263,0.00074661255,0.0013361088,0.0019163087,0.0012284588,0.0020226624,0.0018586825,0.002278745,0.0010581209],"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":[0.00008537767,0.00011846497,0.0056285807,0.00011963893,0.0001497989,0.0001916827,0.0003359066,0.6833772,0.0010339642,0.19143325,0.0017184186,0.11580777],"study_design_scores_gemma":[0.00000527476,0.000025634208,0.00037524267,0.00000875703,0.0000075786716,0.000023010462,0.000017068363,0.97368026,0.00017152245,0.025016686,0.0006562842,0.0000127645],"about_ca_topic_score_codex":0.006828882,"about_ca_topic_score_gemma":0.0064689578,"teacher_disagreement_score":0.011011676,"about_ca_system_score_codex":0.00092497724,"about_ca_system_score_gemma":0.0016104087,"threshold_uncertainty_score":0.058236003},"labels":[],"label_agreement":null},{"id":"W2465157410","doi":"10.1016/j.insmatheco.2016.06.004","title":"Valuing guaranteed equity-linked contracts under piecewise constant forces of mortality","year":2016,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Actua","funders":"China Scholarship Council; Simon Fraser University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Piecewise; Mathematics; Constant (computer programming); Random variable; Equity (law); Econometrics; Exponential function; Economics; Annuity; Exponential distribution; Life annuity; Actuarial science; Applied mathematics; Statistics; Computer science; Finance; Mathematical analysis; Pension","score_opus":0.050870439944766456,"score_gpt":0.31975211359864214,"score_spread":0.26888167365387566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465157410","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":[0.693569,0.00096629513,0.29463407,0.0025671597,0.00007916289,0.000065299144,0.00026073836,0.00009544315,0.0077629155],"genre_scores_gemma":[0.98457575,0.00046467557,0.00982647,0.00006822089,0.000081895676,0.000037396727,0.0001227893,0.0000308068,0.0047920407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979159,0.0010717836,0.00008440479,0.0002809174,0.00030836504,0.0003385891],"domain_scores_gemma":[0.97888213,0.01630116,0.0018497523,0.00070248055,0.0007308405,0.0015335935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010229282,0.0011183731,0.0015267978,0.001985304,0.00077914627,0.004520361,0.0020294508,0.003576173,0.003923217],"category_scores_gemma":[0.04124708,0.0009096447,0.0011296835,0.0013946526,0.0036010288,0.009980328,0.0030776702,0.0031029417,0.00018600766],"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":[0.00024280402,0.000117134434,0.0045464104,0.00008797311,0.000079451456,0.0003108591,0.00041738473,0.35570973,0.0009880929,0.6271825,0.00066711725,0.009650574],"study_design_scores_gemma":[0.000029072758,0.000084194646,0.0012499484,0.000039925024,0.000024214341,0.000089520516,0.00015332612,0.6933773,0.00022053118,0.30419987,0.00050472823,0.000027262486],"about_ca_topic_score_codex":0.002878255,"about_ca_topic_score_gemma":0.0014231713,"teacher_disagreement_score":0.010229282,"about_ca_system_score_codex":0.0034805685,"about_ca_system_score_gemma":0.0017920579,"threshold_uncertainty_score":0.05409825},"labels":[],"label_agreement":null},{"id":"W2468369477","doi":"","title":"Strategies for handling normality assumptions in multi-level modeling: a case study estimating trajectories of Health Utilities Index Mark 3 scores.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Statistics; Normality; Health Utilities Index; Index (typography); Econometrics; Mathematics; Goodness of fit; Normal distribution; Variance (accounting); Population; Demography; Medicine; Computer science; Economics; Environmental health; Health related quality of life","score_opus":0.3450288216177719,"score_gpt":0.37843054094369993,"score_spread":0.03340171932592806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468369477","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":[0.029426139,0.00023011773,0.9673996,0.00077809795,0.00003687098,0.00051703514,0.00016375582,0.00029451484,0.0011538523],"genre_scores_gemma":[0.20647898,0.00025093803,0.7898957,0.00021589776,0.00003852273,0.0017397603,0.00025920238,0.00018125327,0.00093967345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94318455,0.051972535,0.0010212469,0.0014739336,0.0018664913,0.00048127436],"domain_scores_gemma":[0.6759901,0.30487344,0.006326721,0.007915354,0.004184324,0.0007100222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.104224324,0.0014504187,0.0015384803,0.0017433141,0.0013550537,0.0026910806,0.0037124483,0.0026881236,0.0038789583],"category_scores_gemma":[0.2713062,0.0014379731,0.0029559124,0.0027578596,0.0018732552,0.0026392858,0.0046020974,0.0042526885,0.0005107589],"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":[0.0008171169,0.0007592521,0.115632646,0.0013558117,0.0027008085,0.0055660447,0.013528078,0.3874571,0.0017068835,0.17405595,0.009839349,0.286581],"study_design_scores_gemma":[0.00013068118,0.00037201925,0.0071501196,0.00034759706,0.00036090592,0.00091773673,0.0012844405,0.89880055,0.001259823,0.082567066,0.006675375,0.00013374983],"about_ca_topic_score_codex":0.019680355,"about_ca_topic_score_gemma":0.022445865,"teacher_disagreement_score":0.104224324,"about_ca_system_score_codex":0.0026037002,"about_ca_system_score_gemma":0.0054870183,"threshold_uncertainty_score":0.5511975},"labels":[],"label_agreement":null},{"id":"W2471804988","doi":"10.25336/p6r016","title":"A Comparison of Fertility in Canada and Australia, 1926-2011","year":2016,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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":"Fertility; Geography; Demography; Socioeconomics; Political science; Population; Sociology","score_opus":0.11546497144671208,"score_gpt":0.394427193049652,"score_spread":0.27896222160293993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471804988","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":[0.96014935,0.005579135,0.00018897075,0.002263981,0.000070302034,0.000049927545,0.014995023,0.00002766332,0.016675737],"genre_scores_gemma":[0.98819935,0.002858086,0.00023397688,0.00018581786,0.000026132979,0.000015751903,0.0046733785,0.0000092567725,0.0037982964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99922645,0.00004590393,0.000036224315,0.00008204672,0.0002924112,0.0003169326],"domain_scores_gemma":[0.9979235,0.00012621527,0.00027849106,0.000048910904,0.0012762441,0.00034658393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065871567,0.0001912939,0.00026851177,0.004142939,0.0026974576,0.0011272018,0.0008059861,0.0003324816,0.0020764275],"category_scores_gemma":[0.003083559,0.00017700842,0.00044345364,0.009624908,0.0008145622,0.0004695021,0.0010488658,0.00079851376,0.00015409102],"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":[0.00017853503,0.00004920443,0.91171145,0.0002838292,0.00019716847,0.0005236553,0.013842629,0.00089632795,0.00036157225,0.0058790394,0.015330629,0.050746],"study_design_scores_gemma":[0.0000012505072,0.0000069012463,0.99257916,0.000027614175,0.000010452739,0.000052035968,0.001974703,0.0001023015,0.00003022954,0.000036493493,0.0051712426,0.000007647074],"about_ca_topic_score_codex":0.99582505,"about_ca_topic_score_gemma":0.998271,"teacher_disagreement_score":0.035393625,"about_ca_system_score_codex":0.035393625,"about_ca_system_score_gemma":0.02979248,"threshold_uncertainty_score":0.25679994},"labels":[],"label_agreement":null},{"id":"W2481372441","doi":"10.1080/03461238.2016.1167115","title":"Incorporating the Bühlmann credibility into mortality models to improve forecasting performances","year":2016,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan","keywords":"Credibility; Mean absolute percentage error; Credibility theory; Econometrics; Statistics; Mathematics; Mean squared error","score_opus":0.06515037095156873,"score_gpt":0.3179527386203707,"score_spread":0.252802367668802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481372441","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":[0.28766766,0.0025683912,0.7004738,0.0022425984,0.00040995478,0.00009072136,0.00036751686,0.0006987039,0.0054807295],"genre_scores_gemma":[0.9600974,0.00053590175,0.037855845,0.00011152095,0.00015355308,0.000026036134,0.00023440858,0.000053274827,0.000932012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966479,0.0017805366,0.00022027911,0.00053547125,0.0006242708,0.00019156533],"domain_scores_gemma":[0.96662104,0.026206953,0.0028812636,0.0020340232,0.001746415,0.0005102897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011207642,0.001260601,0.0013092352,0.0016298005,0.00058686966,0.002014347,0.0014824831,0.001788702,0.0015757566],"category_scores_gemma":[0.06461107,0.00056234235,0.0010888501,0.0010577051,0.0008022678,0.0043539507,0.0017361788,0.0033563264,0.00037655677],"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":[0.0002441373,0.00006457677,0.016537804,0.0000822189,0.00022833986,0.00018796381,0.00019909228,0.92595357,0.0010846237,0.014917839,0.0011218492,0.03937802],"study_design_scores_gemma":[0.000013695864,0.000052895626,0.001586173,0.000026796488,0.000036050435,0.000031709107,0.000023953922,0.99099344,0.0003885568,0.0063852645,0.00043329285,0.0000282747],"about_ca_topic_score_codex":0.010889315,"about_ca_topic_score_gemma":0.0057111527,"teacher_disagreement_score":0.011207642,"about_ca_system_score_codex":0.0011031003,"about_ca_system_score_gemma":0.0011273678,"threshold_uncertainty_score":0.05927235},"labels":[],"label_agreement":null},{"id":"W2492969377","doi":"10.1017/ccol0521825512.004","title":"Fixed-Population Principles","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.034973801306528794,"score_gpt":0.24351058289526084,"score_spread":0.20853678158873204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492969377","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":[0.007004834,0.0012422458,0.47219545,0.0043178583,0.0005971093,0.00015022783,0.0003850423,0.00014760975,0.5139596],"genre_scores_gemma":[0.5041458,0.0031280664,0.2859022,0.0041919732,0.0013133084,0.0012299205,0.0006804838,0.00050101423,0.19890727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99685127,0.0011894351,0.00010747264,0.00049292087,0.0010959972,0.00026301024],"domain_scores_gemma":[0.99812585,0.0009517594,0.00009279275,0.0003540999,0.0003822505,0.00009321707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036025383,0.00066595076,0.0007162393,0.00091522175,0.001837689,0.00273877,0.001537816,0.0014950465,0.017293448],"category_scores_gemma":[0.0064365747,0.00032990982,0.0009689485,0.0008265683,0.004704106,0.0041131102,0.0027155327,0.0033776239,0.002880699],"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":[8.216112e-7,0.0000021457113,0.00001751518,0.0000066288235,0.000001425733,0.0000040681684,0.000027874634,0.00029079907,0.0000152490065,0.996292,0.0013576886,0.0019838768],"study_design_scores_gemma":[0.0000032697208,0.000004597482,0.00005758596,0.000010924398,0.0000016881976,0.000028455264,0.000022492713,0.0014743897,0.00003678833,0.9810559,0.017300544,0.0000033821364],"about_ca_topic_score_codex":0.0018531203,"about_ca_topic_score_gemma":0.0022707896,"teacher_disagreement_score":0.017293448,"about_ca_system_score_codex":0.0024152512,"about_ca_system_score_gemma":0.0013445049,"threshold_uncertainty_score":0.057852387},"labels":[],"label_agreement":null},{"id":"W2502936412","doi":"10.25336/p6801v","title":"Trends, patterns, and differentials in Canadian mortality over nearly a century, 1921-2011","year":2016,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Longevity; Demography; Socioeconomic status; Life span; Geography; Life expectancy; Mortality rate; Gerontology; Population; Medicine; Sociology","score_opus":0.04637082539744004,"score_gpt":0.3453520519771964,"score_spread":0.29898122657975634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502936412","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":[0.79628175,0.034336377,0.0011409064,0.010565449,0.00024428594,0.00013824129,0.12514433,0.00021070939,0.031937957],"genre_scores_gemma":[0.9612778,0.012960495,0.0011095398,0.000484233,0.000060826183,0.000044841217,0.019473527,0.000031599902,0.004557098],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988796,0.000048227164,0.00009697549,0.00017171957,0.0004954425,0.00030805086],"domain_scores_gemma":[0.9959138,0.00015940658,0.00053719385,0.00009589288,0.0028455902,0.00044817757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012884401,0.0004138109,0.0003666729,0.006306232,0.0028862897,0.0015382415,0.0013302946,0.0004754537,0.0037143468],"category_scores_gemma":[0.004475757,0.00022668797,0.0007187846,0.013711638,0.00072496565,0.00074906874,0.0011835091,0.0011650177,0.00030525491],"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":[0.00015607692,0.000036947877,0.902548,0.000475909,0.00020774135,0.00014702367,0.004948996,0.00056432106,0.00030585463,0.0032994014,0.024417548,0.06289209],"study_design_scores_gemma":[0.000004007466,0.000010102218,0.98514324,0.00013850807,0.000056595858,0.00007024591,0.002026736,0.0003580485,0.00007421393,0.00011255334,0.011978956,0.000026748476],"about_ca_topic_score_codex":0.99826247,"about_ca_topic_score_gemma":0.9990717,"teacher_disagreement_score":0.052591007,"about_ca_system_score_codex":0.052591007,"about_ca_system_score_gemma":0.06041206,"threshold_uncertainty_score":0.3815763},"labels":[],"label_agreement":null},{"id":"W2504085284","doi":"10.1017/cbo9780511542428.009","title":"A solution to the problem of obtaining a mortality schedule for paleodemographic data","year":2002,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Manitoba","funders":"","keywords":"Schedule; Estimation; Computer science; Point (geometry); Operations research; Management science; Econometrics; Data science; Mathematics; Economics","score_opus":0.10044402711604775,"score_gpt":0.28537796224236156,"score_spread":0.18493393512631381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504085284","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":[0.0018650439,0.00021571813,0.992393,0.0022337926,0.00017161391,0.00007495069,0.0006816315,0.00041906722,0.0019451568],"genre_scores_gemma":[0.019219697,0.00045604163,0.97462,0.00037960673,0.00027485815,0.00034397363,0.0013788007,0.00033068992,0.0029964212],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9934018,0.0036882414,0.00052632095,0.0011803423,0.0010271181,0.0001761751],"domain_scores_gemma":[0.97337294,0.015273211,0.0014717778,0.0058437046,0.003469741,0.0005685953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01621878,0.0009775986,0.0012830219,0.0022383481,0.0014678835,0.0029714718,0.0027335705,0.002048585,0.010179724],"category_scores_gemma":[0.07825216,0.0014768547,0.0012040737,0.0037371987,0.0012332804,0.004722462,0.0036084843,0.0052539944,0.003748856],"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":[0.00010684361,0.000107391774,0.0036928884,0.00038194633,0.00013362039,0.00015204363,0.00092269224,0.055561736,0.0011881592,0.30172512,0.047900032,0.5881275],"study_design_scores_gemma":[0.00010064968,0.00011547777,0.0036514292,0.00034852567,0.000061424485,0.00057013275,0.0006364991,0.18096158,0.0018882087,0.6938874,0.11765292,0.00012578211],"about_ca_topic_score_codex":0.0057787313,"about_ca_topic_score_gemma":0.00429055,"teacher_disagreement_score":0.01621878,"about_ca_system_score_codex":0.0015772787,"about_ca_system_score_gemma":0.0045824368,"threshold_uncertainty_score":0.08577412},"labels":[],"label_agreement":null},{"id":"W2506596529","doi":"10.1057/9781403905406_6","title":"Population Ageing and Care of the Elderly: What Are the Lessons of Asia for Sub-Saharan Africa?","year":2001,"lang":"en","type":"book-chapter","venue":"Palgrave Macmillan UK eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Tanzania; Sierra leone; Geography; Latin Americans; Population; Quarter (Canadian coin); Socioeconomics; Fertility; Demographic transition; Developing country; Total fertility rate; Demography; Economic growth; Family planning; Political science; Research methodology; Economics","score_opus":0.034294606186691924,"score_gpt":0.2770239037835179,"score_spread":0.24272929759682596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506596529","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":[0.0016709798,0.7820393,0.00056017266,0.15766443,0.009251837,0.000022339951,0.000071466035,0.000034090943,0.04868536],"genre_scores_gemma":[0.014971273,0.9036867,0.0014209895,0.03333871,0.0042585377,0.000064847154,0.00006638534,0.000028068845,0.042164553],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997328,0.00009463509,0.000017297725,0.000018893401,0.00006993549,0.000066485496],"domain_scores_gemma":[0.9994204,0.00021049549,0.000046836834,0.000026347881,0.000121992394,0.00017390895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160195,0.0008562918,0.0007078669,0.0010461565,0.0012654383,0.0036706654,0.000955142,0.0022577334,0.0065582385],"category_scores_gemma":[0.0021275897,0.00020738777,0.00038101015,0.0017473458,0.0016714075,0.006470463,0.0017851364,0.004028391,0.0022211126],"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":[0.000023030067,0.000069362904,0.0008469494,0.0016040831,0.00001743591,0.00040123137,0.008230358,0.00018674567,0.00010460788,0.04531074,0.42930442,0.51390105],"study_design_scores_gemma":[0.000008715457,0.0000381558,0.0014935145,0.0035544266,0.00001520381,0.00032828693,0.0046401517,0.000051318395,0.000041960975,0.018278474,0.97153866,0.000010938692],"about_ca_topic_score_codex":0.007752345,"about_ca_topic_score_gemma":0.017609412,"teacher_disagreement_score":0.007752345,"about_ca_system_score_codex":0.0015811303,"about_ca_system_score_gemma":0.0046251738,"threshold_uncertainty_score":0.021939516},"labels":[],"label_agreement":null},{"id":"W2507995014","doi":"10.1111/jori.12135","title":"Semicoherent Multipopulation Mortality Modeling: The Impact on Longevity Risk Securitization","year":2016,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":35,"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":"Longevity risk; Longevity; Econometrics; Life expectancy; Economics; Population; Divergence (linguistics); Coherence (philosophical gambling strategy); Securitization; Mathematics; Demography; Biology; Statistics","score_opus":0.027637428641705188,"score_gpt":0.33558331735162356,"score_spread":0.30794588870991835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507995014","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":[0.5875739,0.00037704513,0.40748957,0.0010015835,0.00006729388,0.000033681405,0.00016635028,0.00020788678,0.00308269],"genre_scores_gemma":[0.98374003,0.000107721884,0.015202903,0.0000674617,0.000018703002,0.000018334486,0.00004679692,0.000012957971,0.0007851668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955887,0.00026696897,0.000022624208,0.00007287203,0.000038086804,0.000040528656],"domain_scores_gemma":[0.99600863,0.0028765118,0.00046688633,0.00022956077,0.00025667623,0.00016173921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027422064,0.00030495587,0.00041008572,0.00055501127,0.00037982315,0.0009336535,0.0009550665,0.0010890419,0.0012595808],"category_scores_gemma":[0.0070850877,0.0002548465,0.000492463,0.00051148335,0.0007367139,0.001087837,0.00091999356,0.0008207216,0.00010224426],"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":[0.000029484545,0.00003134127,0.005013125,0.000013181325,0.000023882778,0.000049451166,0.00006145757,0.9762913,0.00026746307,0.011464959,0.00016144376,0.0065928916],"study_design_scores_gemma":[0.0000018352866,0.000008172351,0.00022331932,0.0000022644188,0.0000028593201,0.0000045399047,0.0000067364413,0.9978502,0.00003832202,0.0018208056,0.00003840588,0.0000025808633],"about_ca_topic_score_codex":0.011602107,"about_ca_topic_score_gemma":0.006780025,"teacher_disagreement_score":0.011602107,"about_ca_system_score_codex":0.0007669722,"about_ca_system_score_gemma":0.0007750449,"threshold_uncertainty_score":0.023069143},"labels":[],"label_agreement":null},{"id":"W2509314489","doi":"10.7202/1037212ar","title":"Les modèles factoriels et la gestion du risque de longévité","year":2016,"lang":"fr","type":"article","venue":"L Actualité économique","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.05166320521238573,"score_gpt":0.30332404106993843,"score_spread":0.2516608358575527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509314489","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":[0.24361917,0.0042862548,0.69064033,0.010560892,0.00048354323,0.00022767871,0.0034972418,0.00087676186,0.04580811],"genre_scores_gemma":[0.8981149,0.0036505025,0.038509797,0.00031338615,0.00019567358,0.00047890906,0.0011649762,0.00014350985,0.05742824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884653,0.00052091357,0.000048979775,0.00022603979,0.00017297947,0.00018453071],"domain_scores_gemma":[0.9954472,0.003326853,0.0004764295,0.00020730936,0.00037686806,0.00016523336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028752745,0.0011562884,0.00095046556,0.0010809031,0.0006640146,0.003175575,0.0016492138,0.0019612769,0.015269758],"category_scores_gemma":[0.013527718,0.00067157863,0.0017783144,0.001346452,0.0011777837,0.0026299448,0.0012075843,0.0024242108,0.0016248124],"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":[0.00019538516,0.0001159288,0.020591639,0.00023986006,0.0003021801,0.00026990962,0.0007339151,0.6356757,0.000659949,0.3067848,0.0042921384,0.03013862],"study_design_scores_gemma":[0.00007485442,0.00015085345,0.0071783867,0.00014640373,0.00023450181,0.0001870068,0.00035547675,0.8011554,0.00032504144,0.17382051,0.016294062,0.000077594224],"about_ca_topic_score_codex":0.034876604,"about_ca_topic_score_gemma":0.019449417,"teacher_disagreement_score":0.034876604,"about_ca_system_score_codex":0.0025517861,"about_ca_system_score_gemma":0.0032616402,"threshold_uncertainty_score":0.0693472},"labels":[],"label_agreement":null},{"id":"W2515268201","doi":"","title":"H l OW many people will there be in the world in the next generation? How will","year":2016,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Falling (accident); Period (music); Fertility; Demography; Western europe; Quarter (Canadian coin); Total fertility rate; Geography; Life expectancy; Economic history; Demographic economics; History; Population; Economics; Sociology; Medicine; Archaeology; Family planning; Research methodology; Economic policy","score_opus":0.054868400875308115,"score_gpt":0.3020330953776433,"score_spread":0.24716469450233516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515268201","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":[0.029886704,0.016555296,0.0014070133,0.77166057,0.008453883,0.000040934374,0.0007514848,0.000121080324,0.17112303],"genre_scores_gemma":[0.48394847,0.047612127,0.0022313562,0.219552,0.0068218317,0.000148624,0.0008468191,0.00008980846,0.23874901],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932337,0.00030571912,0.000014664023,0.00006985335,0.0001221893,0.00016422263],"domain_scores_gemma":[0.99894446,0.00013976516,0.00013222708,0.00004780091,0.00018457098,0.0005512542],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014471511,0.00027481784,0.00031400897,0.0005516694,0.00190195,0.004344376,0.00038985614,0.002937572,0.04133887],"category_scores_gemma":[0.0033813657,0.00012696358,0.0002356271,0.00042138548,0.002693334,0.0071309293,0.0012585566,0.0018076134,0.011163741],"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":[0.00011683467,0.00013802575,0.0367925,0.00024983843,0.000058212765,0.0006566225,0.009795574,0.00017027235,0.0004884416,0.0841003,0.6641416,0.2032918],"study_design_scores_gemma":[0.00001446195,0.00011798217,0.01996898,0.00065373135,0.000017715047,0.00077051815,0.04018482,0.00016045435,0.00019075755,0.035145827,0.90271866,0.000055994427],"about_ca_topic_score_codex":0.011708377,"about_ca_topic_score_gemma":0.016595256,"teacher_disagreement_score":0.95866114,"about_ca_system_score_codex":0.0015261213,"about_ca_system_score_gemma":0.0011701983,"threshold_uncertainty_score":0.13829225},"labels":[],"label_agreement":null},{"id":"W2520319580","doi":"10.1016/j.insmatheco.2016.09.002","title":"A pair of optimal reinsurance–investment strategies in the two-sided exit framework","year":2016,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinsurance; Hamilton–Jacobi–Bellman equation; Investment strategy; Asset (computer security); Investment (military); Time horizon; Economics; First-hitting-time model; Mathematical economics; Stopping time; Mathematics; Actuarial science; Microeconomics; Bellman equation; Computer science; Finance; Statistics","score_opus":0.025184861412450733,"score_gpt":0.28677857483151026,"score_spread":0.2615937134190595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520319580","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":[0.3915173,0.0018000603,0.53064555,0.011084668,0.000270018,0.0002646023,0.00078997016,0.0005059455,0.06312184],"genre_scores_gemma":[0.9440816,0.0007493251,0.026915606,0.00044367218,0.00011488368,0.00018080567,0.00018068709,0.000091105925,0.027242325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823725,0.00086508854,0.00007163599,0.00025470878,0.00014866484,0.00042264006],"domain_scores_gemma":[0.9954803,0.0026309125,0.00047124585,0.00017779689,0.0002892889,0.00095041795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041176477,0.0014823575,0.0030110104,0.001106855,0.0008509743,0.0041143503,0.002047976,0.0053801923,0.01207834],"category_scores_gemma":[0.011862859,0.0011689984,0.0012610682,0.0005652905,0.0022502847,0.0052185785,0.0024913421,0.0029729146,0.0008500293],"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":[0.0003962959,0.0002319865,0.0016418389,0.0001526554,0.00010541367,0.0004184681,0.00019369634,0.13334656,0.000985053,0.84482604,0.0047494047,0.012952644],"study_design_scores_gemma":[0.00020005964,0.00025958917,0.0013143619,0.00011067019,0.000064012514,0.00025207797,0.00021321121,0.55474466,0.00044067577,0.44014913,0.0021725208,0.00007911957],"about_ca_topic_score_codex":0.0017517587,"about_ca_topic_score_gemma":0.0010703508,"teacher_disagreement_score":0.01207834,"about_ca_system_score_codex":0.0021582413,"about_ca_system_score_gemma":0.0021092843,"threshold_uncertainty_score":0.040406108},"labels":[],"label_agreement":null},{"id":"W2527543320","doi":"","title":"소수연령 독립 가정에서 탈퇴율의 성질","year":2008,"lang":"ko","type":"article","venue":"응용통계연구 = The Korean journal of applied statistics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Mathematics; Independence (probability theory); Statistics; Quarter (Canadian coin); Series (stratigraphy); Generalization; Demography; Econometrics; Geography; Mathematical analysis; Geology; Sociology","score_opus":0.02395781384764207,"score_gpt":0.2732985838235372,"score_spread":0.24934076997589516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527543320","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":[0.047266413,0.009465907,0.21021779,0.00930378,0.0030403787,0.0012894919,0.121922195,0.0081691155,0.5893249],"genre_scores_gemma":[0.45428318,0.011521424,0.20986985,0.003480599,0.0013284938,0.0011888304,0.0749703,0.0013238358,0.24203342],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987097,0.0003182415,0.00018570901,0.00024898342,0.0004574473,0.00007994889],"domain_scores_gemma":[0.997074,0.00094099855,0.00038211886,0.0004076296,0.001074468,0.00012076819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012852809,0.0005209506,0.00037305447,0.0020792542,0.00072451984,0.0033994382,0.0007379892,0.0005624394,0.07928126],"category_scores_gemma":[0.0061475094,0.00030245908,0.0006742657,0.003257448,0.00044121424,0.0032248348,0.00075059145,0.00087910437,0.027390333],"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":[0.00030739192,0.00009012647,0.02659516,0.0011602328,0.00013465057,0.00048691765,0.0007324313,0.007303444,0.0015326506,0.1778177,0.25291526,0.5309239],"study_design_scores_gemma":[0.000045270812,0.00012325404,0.0137687195,0.00033096064,0.00007335939,0.0007183118,0.0009951328,0.0066043762,0.0016940515,0.07384998,0.9016926,0.00010388764],"about_ca_topic_score_codex":0.006728162,"about_ca_topic_score_gemma":0.0053797155,"teacher_disagreement_score":0.07928126,"about_ca_system_score_codex":0.0016573714,"about_ca_system_score_gemma":0.0014918105,"threshold_uncertainty_score":0.26522225},"labels":[],"label_agreement":null},{"id":"W2527761280","doi":"10.1016/j.cam.2016.10.005","title":"Efficient valuation of SCR via a neural network approach","year":2016,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":28,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solvency; Artificial neural network; Valuation (finance); Computer science; Performance metric; Portfolio; Project portfolio management; Metric (unit); Mathematical optimization; Capital requirement; Finance; Mathematics; Machine learning; Economics; Project management","score_opus":0.02990267698561858,"score_gpt":0.27921778999824765,"score_spread":0.24931511301262907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527761280","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":[0.045941677,0.0004270905,0.9460552,0.0007117808,0.00007380479,0.000053543994,0.00013320346,0.0001942927,0.0064094444],"genre_scores_gemma":[0.9090712,0.00038096885,0.082046464,0.00010886113,0.0001524267,0.000084092804,0.0001506983,0.00007292215,0.007932319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929166,0.00031969522,0.00003056696,0.0001133694,0.00014129706,0.000103372375],"domain_scores_gemma":[0.9970132,0.0022406569,0.00018658693,0.00018794564,0.000248315,0.0001233436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020871984,0.0007508944,0.0012816436,0.0010508527,0.00039541684,0.001919052,0.0015275882,0.0018290326,0.0050155544],"category_scores_gemma":[0.0081340745,0.0006413571,0.0005033013,0.00095150206,0.0011164412,0.0034981456,0.0015112257,0.0017611982,0.0003137941],"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":[0.00012290975,0.00007619176,0.0007463735,0.000060724517,0.0000413177,0.00011543716,0.00003729445,0.87538356,0.00094528426,0.084765755,0.0014784738,0.036226686],"study_design_scores_gemma":[0.000003507234,0.000005334568,0.000056571353,0.0000034951468,0.0000029659957,0.000007863368,0.0000025471982,0.9815167,0.000068656685,0.018247329,0.000082080915,0.000002935209],"about_ca_topic_score_codex":0.0038653293,"about_ca_topic_score_gemma":0.003744266,"teacher_disagreement_score":0.0050155544,"about_ca_system_score_codex":0.0014417156,"about_ca_system_score_gemma":0.0011812239,"threshold_uncertainty_score":0.016778708},"labels":[],"label_agreement":null},{"id":"W2543797495","doi":"10.1073/pnas.1612191113","title":"The emergence of longevous populations","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":186,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Syddansk Universitet; National Institute on Aging; Max-Planck-Institut für demografische Forschung; Max-Planck-Gesellschaft","keywords":"Geography; Biology; Ecology; Zoology","score_opus":0.07823063529477045,"score_gpt":0.3675310178444545,"score_spread":0.28930038254968404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543797495","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":[0.98461074,0.0005213753,0.0027125783,0.0009106259,0.00003567499,0.000010290342,0.00010243506,0.000025192438,0.011071019],"genre_scores_gemma":[0.9976714,0.00015147928,0.0006733154,0.00013293984,0.000034604895,0.000006088109,0.000055334967,0.0000074532845,0.0012672824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999529,0.0001037188,0.000024047786,0.00018447303,0.000074988435,0.00008378531],"domain_scores_gemma":[0.9985827,0.00028679322,0.00033269328,0.00021089651,0.0003072819,0.00027964707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011488342,0.00011900882,0.0002887157,0.0007178649,0.0010857023,0.0010375363,0.00033594645,0.00046521882,0.0036909762],"category_scores_gemma":[0.0035086013,0.00014712631,0.000117406445,0.00041712905,0.0018079954,0.0010553724,0.0017386557,0.0010191305,0.0003122664],"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":[0.00039903805,0.0001326433,0.5255677,0.00014964805,0.00010348384,0.00093217124,0.017101312,0.0007140456,0.03186541,0.17377944,0.003854668,0.24540037],"study_design_scores_gemma":[0.000018434797,0.00022948747,0.92719215,0.000068924484,0.000032461878,0.001402283,0.0071961167,0.001204299,0.002836638,0.035990234,0.023783045,0.0000459348],"about_ca_topic_score_codex":0.0011972191,"about_ca_topic_score_gemma":0.0019897153,"teacher_disagreement_score":0.0036909762,"about_ca_system_score_codex":0.0005608118,"about_ca_system_score_gemma":0.00034913156,"threshold_uncertainty_score":0.012347579},"labels":[],"label_agreement":null},{"id":"W2553158549","doi":"","title":"DEMOGRAPHIC WINDOW IN THE CZECH REPUBLIC (WITH INCREASING RETIREMENT AGE)","year":2015,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Czech; Population projection; Population; Demographic dividend; Age structure; Demography; Fertility; Population ageing; Demographic change; Period (music); Retirement age; Quarter (Canadian coin); Demographic economics; Geography; Economics; Pension; Sociology","score_opus":0.058903913755657206,"score_gpt":0.3144877636026876,"score_spread":0.2555838498470304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553158549","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":[0.97826844,0.002528327,0.00081707176,0.00048584468,0.00007282015,0.000049436003,0.0027542792,0.00003159116,0.014992269],"genre_scores_gemma":[0.9972656,0.0007077915,0.00023577968,0.00003281239,0.000013686968,0.0000125772,0.00054924167,0.0000052628598,0.0011773305],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975675,0.00003046048,0.000028429218,0.000055170007,0.000040356386,0.00008875532],"domain_scores_gemma":[0.9996774,0.000024270388,0.00013009146,0.00001740779,0.0000624437,0.00008831043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003363749,0.00009015565,0.000099821074,0.0012671279,0.0003929701,0.0008950231,0.00018187397,0.00015529183,0.0025472946],"category_scores_gemma":[0.0011318603,0.000097606906,0.00028411,0.0010843539,0.00020432661,0.00054373575,0.00096061546,0.0003681782,0.00029126895],"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":[0.00069816865,0.00012420323,0.8473772,0.0004694568,0.00011057846,0.0013371621,0.0053046467,0.0033037579,0.0050970693,0.020725152,0.00975955,0.10569306],"study_design_scores_gemma":[0.0000067648484,0.000047135298,0.98085827,0.00006318502,0.0000165194,0.00055840914,0.0013094825,0.00036021348,0.00026692607,0.0007020022,0.01579081,0.000020310685],"about_ca_topic_score_codex":0.018540615,"about_ca_topic_score_gemma":0.018820018,"teacher_disagreement_score":0.018540615,"about_ca_system_score_codex":0.00062657625,"about_ca_system_score_gemma":0.0011172183,"threshold_uncertainty_score":0.036865413},"labels":[],"label_agreement":null},{"id":"W2553693313","doi":"10.1017/asb.2016.33","title":"THE LOCALLY LINEAR CAIRNS–BLAKE–DOWD MODEL: A NOTE ON DELTA–NUGA HEDGING OF LONGEVITY RISK","year":2016,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"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":"Longevity risk; Hedge; Longevity; Econometrics; Economics; Actuarial science; Computer science; Biology; Ecology","score_opus":0.01501830703043798,"score_gpt":0.28372996430021497,"score_spread":0.26871165726977697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553693313","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":[0.10699245,0.0019051195,0.87564087,0.0024982358,0.00024288373,0.000080683414,0.00040295167,0.00032037759,0.01191649],"genre_scores_gemma":[0.95967144,0.00062586303,0.022955997,0.00031300657,0.00010572816,0.00009032368,0.00015282608,0.000040581283,0.016044248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993267,0.00027979029,0.000031778847,0.00017589076,0.00010796633,0.00007787158],"domain_scores_gemma":[0.9978531,0.001266889,0.0003162086,0.00019346706,0.00022804906,0.00014236094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028319976,0.00075137726,0.00134797,0.0006701103,0.0004315634,0.0017261698,0.0024907237,0.0022073416,0.0037321618],"category_scores_gemma":[0.0063657085,0.00043131685,0.001115934,0.0007002074,0.0017832731,0.0014438397,0.0013365198,0.0026222714,0.00043650367],"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":[0.00010726644,0.00005498477,0.004210768,0.00012849369,0.00009711578,0.00042540513,0.00027566528,0.7314554,0.0014435594,0.23648585,0.0031643272,0.02215118],"study_design_scores_gemma":[0.000012547435,0.000032910997,0.00043636534,0.000016593918,0.00001796011,0.000048301954,0.000025291607,0.9619872,0.00011323784,0.03645164,0.0008381383,0.000019799314],"about_ca_topic_score_codex":0.009285205,"about_ca_topic_score_gemma":0.0044125905,"teacher_disagreement_score":0.009285205,"about_ca_system_score_codex":0.00096844597,"about_ca_system_score_gemma":0.00077218306,"threshold_uncertainty_score":0.01846236},"labels":[],"label_agreement":null},{"id":"W2555420981","doi":"10.3390/risks4040041","title":"Incorporation of Stochastic Policyholder Behavior in Analytical Pricing of GMABs and GMDBs","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"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 Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surrender; Life insurance; Maturity (psychological); Economics; Actuarial science; Stochastic modelling; Variable (mathematics); Cox–Ingersoll–Ross model; Financial market; Econometrics; Interest rate; Finance; Mathematics","score_opus":0.06479999742872179,"score_gpt":0.3729896586299695,"score_spread":0.30818966120124774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555420981","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":[0.051084235,0.00031839937,0.94239855,0.00033679808,0.00006766492,0.00007507266,0.000062179875,0.00011591741,0.0055412157],"genre_scores_gemma":[0.92293787,0.000551274,0.069249846,0.000107844935,0.00009874479,0.00009029983,0.00007460362,0.00008849468,0.0068010045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989465,0.00045824793,0.00005141713,0.00010508819,0.0002701696,0.00016854703],"domain_scores_gemma":[0.99733317,0.0015527462,0.00039766563,0.00027829662,0.00028596623,0.00015223764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034863225,0.0007470104,0.0009950048,0.000823607,0.00047994373,0.002149756,0.0018850467,0.0019303721,0.002929834],"category_scores_gemma":[0.011763001,0.00067715585,0.001236833,0.0006082029,0.0013854852,0.0023441412,0.0014908442,0.0015122318,0.0004234615],"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":[0.0000413037,0.0000739047,0.001941474,0.00005335272,0.00003400363,0.0003877771,0.00017158057,0.77565235,0.002355053,0.20908834,0.00037020587,0.009830719],"study_design_scores_gemma":[0.000002807099,0.0000068222953,0.00007194137,0.0000046232226,0.0000035713845,0.000031192816,0.000007415262,0.993331,0.00014309258,0.006212094,0.00018028716,0.0000051071634],"about_ca_topic_score_codex":0.004607617,"about_ca_topic_score_gemma":0.0032347683,"teacher_disagreement_score":0.004607617,"about_ca_system_score_codex":0.0015357644,"about_ca_system_score_gemma":0.0016829041,"threshold_uncertainty_score":0.018437624},"labels":[],"label_agreement":null},{"id":"W2558195001","doi":"","title":"The Alberta GPI Accounts: Auto Crashes and Injuries","year":2001,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business","score_opus":0.012196130467624406,"score_gpt":0.29052153117963314,"score_spread":0.27832540071200873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558195001","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":[0.3651481,0.013361808,0.0005617172,0.010189049,0.00060881814,0.00014621581,0.5269355,0.00041309983,0.08263568],"genre_scores_gemma":[0.77399856,0.008674673,0.0007429042,0.0008797697,0.00037952958,0.000080104975,0.16602653,0.00008609547,0.049131896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992955,0.000061852574,0.000030492889,0.000040401446,0.0004366545,0.00013499799],"domain_scores_gemma":[0.99667823,0.00035218228,0.0006642946,0.0001238438,0.0015451574,0.0006362539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006060601,0.00049418263,0.00018300954,0.006849994,0.0008978268,0.0019975938,0.0010821316,0.0007974386,0.008647934],"category_scores_gemma":[0.004639663,0.00028127385,0.0003188824,0.012427243,0.00033083867,0.0007889197,0.0009375933,0.00073820446,0.00093446247],"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":[0.00013607164,0.00004661818,0.6340213,0.00011516926,0.00011254086,0.00031072815,0.0005083152,0.00176895,0.000056957106,0.0026104262,0.3299677,0.030345326],"study_design_scores_gemma":[0.000011144737,0.000011344082,0.93840116,0.00009288492,0.000053783097,0.00016160222,0.0011952973,0.0018329378,0.000056442466,0.0006849107,0.0574739,0.000024591638],"about_ca_topic_score_codex":0.97199404,"about_ca_topic_score_gemma":0.9832187,"teacher_disagreement_score":0.028005958,"about_ca_system_score_codex":0.010392394,"about_ca_system_score_gemma":0.015767066,"threshold_uncertainty_score":0.0754025},"labels":[],"label_agreement":null},{"id":"W2560512335","doi":"10.3390/risks4040046","title":"Deflation Risk and Implications for Life Insurers","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal; Society of Actuaries","keywords":"Deflation; Economics; Inflation (cosmology); Life insurance; Interest rate; Econometrics; Investment (military); Variance (accounting); Real interest rate; Monetary economics; Actuarial science; Financial economics; Monetary policy","score_opus":0.06686069432047792,"score_gpt":0.36740675908466447,"score_spread":0.30054606476418655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560512335","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":[0.9018112,0.0048539755,0.048883583,0.022134757,0.00012069693,0.0000368252,0.00019504753,0.000059654285,0.021904305],"genre_scores_gemma":[0.99781066,0.00045500367,0.0011378706,0.000143956,0.00006183787,0.0000050238295,0.000021085174,0.0000044206454,0.00036017294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978027,0.001205056,0.000135484,0.00022530559,0.00044504995,0.00018635608],"domain_scores_gemma":[0.97123563,0.019483866,0.0063012154,0.0009969276,0.0011645842,0.00081767625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005856934,0.00023171601,0.0004147253,0.0011015838,0.00071303896,0.0024332022,0.000554189,0.0014944518,0.0029729202],"category_scores_gemma":[0.04062075,0.00017591087,0.0004987992,0.00072694913,0.0022986599,0.002798697,0.0020003335,0.002356533,0.00012233593],"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":[0.00038950963,0.00034583543,0.21157546,0.00022538436,0.0002030567,0.0012390732,0.0043099597,0.037381124,0.001986485,0.6489823,0.0032218473,0.09014],"study_design_scores_gemma":[0.00002586264,0.0002895958,0.1200833,0.00029332176,0.00008882254,0.0014082977,0.003727106,0.13699944,0.0013605534,0.7302548,0.005379669,0.00008921884],"about_ca_topic_score_codex":0.0016139592,"about_ca_topic_score_gemma":0.0011811947,"teacher_disagreement_score":0.005856934,"about_ca_system_score_codex":0.0011108023,"about_ca_system_score_gemma":0.00069793034,"threshold_uncertainty_score":0.030974805},"labels":[],"label_agreement":null},{"id":"W2561763806","doi":"10.1017/s0269964816000504","title":"THE ANALYTIC APPROACH FOR THE STOCHASTIC PROJECTION OF THE PUBLIC PENSION FUND","year":2016,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Pension fund; Valuation (finance); Moment (physics); Stochastic differential equation; Pension; Projection (relational algebra); Matching (statistics); Order (exchange); Actuarial science; Revenue; Econometrics; Economics; Mathematics; Computer science; Mathematical optimization; Applied mathematics; Finance; Statistics; Algorithm","score_opus":0.05019828892213434,"score_gpt":0.2825738307035427,"score_spread":0.23237554178140835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561763806","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":[0.002645187,0.00018592544,0.99455833,0.0002570559,0.00003486642,0.000015583535,0.000023915603,0.000031533153,0.0022475095],"genre_scores_gemma":[0.4609027,0.0037670513,0.5177689,0.00041361226,0.00075547234,0.00049505767,0.00026269493,0.00021401557,0.015420434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923,0.00032832014,0.00003534247,0.00011948886,0.0002347757,0.000052036445],"domain_scores_gemma":[0.9991242,0.00042883345,0.000119125674,0.00009396218,0.00017844465,0.000055470373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019450744,0.0008299802,0.0005125397,0.0013624271,0.00052807503,0.0012931407,0.0011657748,0.0007915278,0.0044083195],"category_scores_gemma":[0.0055761877,0.00039056607,0.0013844399,0.00076298905,0.0014416422,0.0028982442,0.0016044964,0.0019934163,0.0004610258],"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":[0.000009010775,0.000024097808,0.0005179,0.00009342006,0.000031055453,0.00009183902,0.00013475679,0.1293123,0.001317402,0.8492265,0.0010027654,0.01823904],"study_design_scores_gemma":[0.0000064161472,0.000033671597,0.00037140193,0.00004181815,0.000021853892,0.00014842855,0.00006168899,0.64631206,0.0008219144,0.34601632,0.006136899,0.000027567534],"about_ca_topic_score_codex":0.0017346563,"about_ca_topic_score_gemma":0.00083496096,"teacher_disagreement_score":0.0044083195,"about_ca_system_score_codex":0.0011610432,"about_ca_system_score_gemma":0.0015277759,"threshold_uncertainty_score":0.014747322},"labels":[],"label_agreement":null},{"id":"W2566429317","doi":"10.71781/31185","title":"L’évaluation des déterminants des paramètres hémodynamiques centraux à l’aide de la cohorte populationnelle CARTaGENE","year":2016,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Université Laval","keywords":"Medicine","score_opus":0.08144177116020992,"score_gpt":0.3939485353254668,"score_spread":0.3125067641652569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566429317","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":[0.9438757,0.016021945,0.019854438,0.002385467,0.0005551979,0.00038547686,0.009493783,0.00019477108,0.007233271],"genre_scores_gemma":[0.97893286,0.003539787,0.0067905337,0.000603048,0.00025993376,0.00053227606,0.0035513712,0.00008568682,0.005704599],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99624383,0.0018493038,0.0002866552,0.00064378144,0.00066706305,0.00030933187],"domain_scores_gemma":[0.9863495,0.0054012653,0.0024259794,0.0020041578,0.0032775018,0.00054159685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009721376,0.0006508143,0.0009767611,0.0015772742,0.000792379,0.0021976493,0.0009756962,0.00071834633,0.003895237],"category_scores_gemma":[0.021431942,0.00037794944,0.0020947054,0.0030165408,0.00039406665,0.0013646543,0.0009864592,0.0010479509,0.0006422116],"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":[0.00062344794,0.000052399515,0.9681561,0.0003297777,0.0020577668,0.00012724081,0.0010768814,0.0003723319,0.0007580794,0.000480134,0.0017524704,0.024213342],"study_design_scores_gemma":[0.000031792602,0.0004945225,0.982578,0.00020944134,0.000993709,0.00028194702,0.0007510647,0.0012134408,0.00045443772,0.00034242484,0.012617175,0.00003204104],"about_ca_topic_score_codex":0.06063929,"about_ca_topic_score_gemma":0.056678988,"teacher_disagreement_score":0.06063929,"about_ca_system_score_codex":0.0009468749,"about_ca_system_score_gemma":0.003008186,"threshold_uncertainty_score":0.120572686},"labels":[],"label_agreement":null},{"id":"W2572188385","doi":"10.25336/p6fp4f","title":"Using the probabilistic fertility table to test the statistical significance of fertility trends","year":2016,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"Fertility; Demography; Statistics; Humanities; Mathematics; Population; Sociology; Philosophy","score_opus":0.10409127091232465,"score_gpt":0.38415000366433627,"score_spread":0.28005873275201165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572188385","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":[0.24846749,0.0004921605,0.73135215,0.00086134765,0.00009502763,0.0003115658,0.0051649376,0.00076704886,0.012488248],"genre_scores_gemma":[0.9198036,0.00036640032,0.074524656,0.00009632529,0.00004533551,0.0002228538,0.0020378113,0.00007467772,0.0028282579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965072,0.0015453873,0.00017119953,0.00062611414,0.000929654,0.00022042704],"domain_scores_gemma":[0.9746705,0.020239249,0.0025124424,0.001352461,0.001079292,0.00014601398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009598762,0.00033127706,0.00062213064,0.0019074869,0.00047271288,0.0017407426,0.0012213815,0.0006151637,0.0049711484],"category_scores_gemma":[0.03387996,0.00032009557,0.001378786,0.0020858839,0.0010513393,0.0020067443,0.0009758157,0.0011833772,0.00048401926],"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":[0.00031938867,0.000075269374,0.27558255,0.00023919647,0.00070239947,0.00040062188,0.0010550335,0.3770387,0.0016845311,0.20524487,0.004176489,0.133481],"study_design_scores_gemma":[0.00004297827,0.00043797973,0.1217783,0.000087788794,0.00020380245,0.0004844758,0.0005900573,0.7623777,0.0020303377,0.094772235,0.017047549,0.00014666258],"about_ca_topic_score_codex":0.02081928,"about_ca_topic_score_gemma":0.013544543,"teacher_disagreement_score":0.02081928,"about_ca_system_score_codex":0.001589169,"about_ca_system_score_gemma":0.001956154,"threshold_uncertainty_score":0.050763726},"labels":[],"label_agreement":null},{"id":"W2573751300","doi":"10.3934/qfe.2017.2.125","title":"A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuities","year":2017,"lang":"en","type":"article","venue":"Quantitative Finance and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":22,"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 Toronto","funders":"","keywords":"Computer science; Valuation (finance); Portfolio; Key (lock); Monte Carlo method; Interpolation (computer graphics); Forcing (mathematics); Econometrics; Actuarial science; Mathematical optimization; Finance; Economics; Mathematics; Artificial intelligence; Statistics","score_opus":0.051695398181589816,"score_gpt":0.35331572642132764,"score_spread":0.30162032823973783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573751300","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":[0.01463425,0.0001418018,0.98222786,0.00018694968,0.000023868495,0.000027088498,0.00006552688,0.00008692831,0.0026056818],"genre_scores_gemma":[0.4919937,0.00047059846,0.5031949,0.000117594536,0.00008305431,0.00015679533,0.0001860819,0.00010954077,0.003687702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943274,0.00028846378,0.00002574983,0.00006158235,0.00013163568,0.000059820155],"domain_scores_gemma":[0.9980286,0.0012877111,0.00016081383,0.00017504253,0.0002561789,0.00009170204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025431942,0.0004907418,0.00070859253,0.00092090387,0.00066261564,0.0011223841,0.0015297221,0.00110179,0.0036207202],"category_scores_gemma":[0.00707919,0.0003785262,0.0009236086,0.0012285601,0.0010848235,0.001678737,0.0013993637,0.0015400125,0.00030239625],"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":[0.00003240152,0.00002594043,0.0007566499,0.000030466697,0.000013547652,0.00006995678,0.00006153703,0.851304,0.000701067,0.13225862,0.00053275493,0.01421312],"study_design_scores_gemma":[0.0000042167385,0.000009168908,0.000072577466,0.0000048703782,0.0000020023404,0.000011861556,0.0000067245774,0.98276764,0.000114078386,0.016439363,0.00056313386,0.0000043274767],"about_ca_topic_score_codex":0.011470218,"about_ca_topic_score_gemma":0.0074407407,"teacher_disagreement_score":0.011470218,"about_ca_system_score_codex":0.0014375494,"about_ca_system_score_gemma":0.001673111,"threshold_uncertainty_score":0.022806942},"labels":[],"label_agreement":null},{"id":"W2575679751","doi":"10.1007/s00186-017-0574-4","title":"A maximum principle for Markov regime-switching forward–backward stochastic differential games and applications","year":2017,"lang":"en","type":"article","venue":"Mathematical Methods of Operations Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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 Imperial Bank of Commerce (Canada)","funders":"Seventh Framework Programme; Alexander von Humboldt-Stiftung","keywords":"Maximum principle; Mathematics; Differential game; Stochastic differential equation; Markov chain; Corollary; Applied mathematics; Differential (mechanical device); Mathematical optimization; Principle of maximum entropy; Mathematical economics; Optimal control; Discrete mathematics","score_opus":0.13523819339582657,"score_gpt":0.5324272925989078,"score_spread":0.3971890992030812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575679751","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":[0.014436446,0.0008469825,0.96657133,0.0012065904,0.00010170514,0.00006911515,0.00009476445,0.0000703351,0.016602695],"genre_scores_gemma":[0.8118675,0.0024955769,0.16982844,0.000649667,0.00036601946,0.0006098466,0.00018097881,0.00013504288,0.013866835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985915,0.0006343024,0.000068370144,0.00026802684,0.00032022642,0.00011765473],"domain_scores_gemma":[0.9977769,0.0016091281,0.00017986874,0.00007543488,0.00022319223,0.00013549937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032376663,0.0012994232,0.0011982213,0.0008653863,0.000858155,0.0015749,0.0012330743,0.0019608065,0.0043513426],"category_scores_gemma":[0.0055925073,0.0006098714,0.0021419097,0.00069941685,0.0029645436,0.0027346967,0.0027272722,0.0034297898,0.00039740515],"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":[0.000017085906,0.00002924137,0.00010721254,0.00012231557,0.000036428628,0.00007074857,0.00009854367,0.044321444,0.0021521626,0.94710803,0.00075382134,0.0051828725],"study_design_scores_gemma":[0.00002339105,0.000060386323,0.0001427627,0.000039785613,0.000018625495,0.000059256363,0.000030986215,0.32321995,0.00062095845,0.67349446,0.0022655965,0.000023903793],"about_ca_topic_score_codex":0.00091193826,"about_ca_topic_score_gemma":0.00063845504,"teacher_disagreement_score":0.0043513426,"about_ca_system_score_codex":0.001542443,"about_ca_system_score_gemma":0.0015606299,"threshold_uncertainty_score":0.017122626},"labels":[],"label_agreement":null},{"id":"W2577366808","doi":"10.1515/apjri-2016-0023","title":"Longevity Risk-Sharing Annuities: Partial Indexation in Mortality Experience","year":2017,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Annuity; Longevity risk; Indexation; Longevity; Life annuity; Actuarial science; Economics; Business; Medicine; Finance; Gerontology; Monetary economics; Pension","score_opus":0.027682715199529355,"score_gpt":0.3242257028519673,"score_spread":0.2965429876524379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577366808","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":[0.7028993,0.00065271155,0.2638793,0.0013482716,0.000109008855,0.00016318286,0.00040758404,0.00021918048,0.030321402],"genre_scores_gemma":[0.9962226,0.00007082481,0.002475917,0.000024403284,0.000016088567,0.00001636301,0.000027667367,0.0000050380063,0.0011411223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988432,0.00045334155,0.00006704585,0.00022105844,0.00019298922,0.00022236617],"domain_scores_gemma":[0.9969213,0.0009849243,0.0007347103,0.00070378085,0.00028758612,0.0003677781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023679244,0.0004268623,0.0006416267,0.00047093484,0.00044990669,0.0017279695,0.0013508641,0.00071271224,0.005306655],"category_scores_gemma":[0.0080128955,0.00022284151,0.0007082033,0.00068272965,0.001444253,0.0019630706,0.0025296465,0.0012205946,0.00036042044],"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":[0.00078597054,0.00036764276,0.026757475,0.0001735052,0.000236007,0.0005674471,0.0008253787,0.48810872,0.004897518,0.35887542,0.002631853,0.115773074],"study_design_scores_gemma":[0.00015016888,0.0013529074,0.020345272,0.00011372688,0.00023322903,0.00073747995,0.0008739761,0.68626106,0.0032760652,0.2790548,0.007499027,0.000102370635],"about_ca_topic_score_codex":0.0011597399,"about_ca_topic_score_gemma":0.0007736875,"teacher_disagreement_score":0.005306655,"about_ca_system_score_codex":0.00092142436,"about_ca_system_score_gemma":0.0008209516,"threshold_uncertainty_score":0.017752528},"labels":[],"label_agreement":null},{"id":"W2578708464","doi":"10.25336/p69w3w","title":"Changes in cause-specific mortality among the elderly in Canada, 1979–2011","year":2017,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Demography; Demographic economics; Socioeconomics; Geography; Gerontology; Environmental health; Economics; Medicine; Sociology","score_opus":0.10268722361617387,"score_gpt":0.3460490428762501,"score_spread":0.24336181926007622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578708464","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":[0.88813317,0.01198551,0.0005587457,0.004539526,0.00021006205,0.000094113246,0.081001356,0.00008505716,0.013392363],"genre_scores_gemma":[0.9740936,0.005161217,0.00043818972,0.00044411793,0.000047745125,0.00002770379,0.015308405,0.000014406983,0.004464608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992962,0.000032059957,0.000055772598,0.00008092838,0.00028519655,0.00024985985],"domain_scores_gemma":[0.9973558,0.000051726536,0.00029212053,0.00004745825,0.0019209202,0.00033191397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005428652,0.00033900706,0.00036837131,0.0020832175,0.0017813577,0.0010637367,0.00092605874,0.00037630394,0.0018147089],"category_scores_gemma":[0.0027578217,0.00020076054,0.00076489855,0.005134584,0.00043555934,0.00045658136,0.00073640747,0.0008537788,0.00028546702],"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":[0.00014152225,0.000029038967,0.9656055,0.00017327224,0.00018026106,0.00012585355,0.000848843,0.0006173347,0.00021210451,0.0006209575,0.010175256,0.021270035],"study_design_scores_gemma":[0.000006270279,0.000008530304,0.9940222,0.000057095815,0.000043499716,0.000043405023,0.0005835991,0.00030148003,0.00008086068,0.0000441241,0.0047981935,0.000010862107],"about_ca_topic_score_codex":0.9979826,"about_ca_topic_score_gemma":0.9988765,"teacher_disagreement_score":0.050918303,"about_ca_system_score_codex":0.050918303,"about_ca_system_score_gemma":0.06144129,"threshold_uncertainty_score":0.3694399},"labels":[],"label_agreement":null},{"id":"W2586559039","doi":"10.1016/j.insmatheco.2017.02.001","title":"Optimal investment strategies for participating contracts","year":2017,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio insurance; Martingale (probability theory); Portfolio; Actuarial science; Stochastic game; Selection (genetic algorithm); Investment portfolio; Application portfolio management; Investment strategy; Replicating portfolio; Portfolio optimization; Microeconomics; Economics; Mathematical optimization; Business; Project portfolio management; Computer science; Mathematics; Financial economics; Applied mathematics; Profit (economics); Artificial intelligence","score_opus":0.059059006006476504,"score_gpt":0.3309946863556354,"score_spread":0.2719356803491589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586559039","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":[0.6134357,0.00080147217,0.32037848,0.005258098,0.000099089106,0.0006949641,0.00086585456,0.00037306466,0.05809332],"genre_scores_gemma":[0.9556155,0.0004964293,0.023959221,0.00020384729,0.00005531057,0.00032630994,0.00024346726,0.00008112036,0.019018615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978052,0.0010516627,0.00011454082,0.00023561249,0.00021128647,0.0005817614],"domain_scores_gemma":[0.9884554,0.007981037,0.0007609845,0.0005182784,0.00062350545,0.0016608309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005917099,0.0013487566,0.001958694,0.0011690034,0.00096159533,0.0050729034,0.0031228482,0.004734063,0.012842392],"category_scores_gemma":[0.023337038,0.0012988466,0.0009730824,0.0010663109,0.0015881192,0.0052634478,0.0022348547,0.0032042724,0.0009860966],"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":[0.0011228112,0.00062939513,0.0043353755,0.00024879555,0.00013129033,0.00055206625,0.0009759643,0.31450474,0.0036322076,0.615955,0.0059894035,0.05192287],"study_design_scores_gemma":[0.00022159074,0.00026897763,0.001510885,0.00010617783,0.00006977903,0.0001290468,0.0006113026,0.64891046,0.0010542421,0.3445954,0.0024818098,0.000040354284],"about_ca_topic_score_codex":0.0026404469,"about_ca_topic_score_gemma":0.0020112502,"teacher_disagreement_score":0.012842392,"about_ca_system_score_codex":0.0029510944,"about_ca_system_score_gemma":0.0030409447,"threshold_uncertainty_score":0.042962074},"labels":[],"label_agreement":null},{"id":"W2587904010","doi":"10.1002/asmb.2233","title":"Application of the phase‐type mortality law to life contingencies and risk management","year":2017,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Regina","funders":"","keywords":"Diversification (marketing strategy); Life insurance; Interest rate; Actuarial science; Econometrics; Index (typography); Popularity; Interest rate risk; Computer science; Economics; Business; Finance","score_opus":0.04031354556190001,"score_gpt":0.31822346759890596,"score_spread":0.27790992203700593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587904010","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":[0.03823964,0.0004676139,0.95236987,0.0006393307,0.000062868654,0.000027544895,0.00007188041,0.00006605181,0.008055154],"genre_scores_gemma":[0.9133466,0.0012233861,0.07616403,0.00024722825,0.0002640004,0.000083710656,0.000117735006,0.000041668434,0.008511612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999561,0.00017411016,0.000019760591,0.000068203924,0.00013263097,0.000044287233],"domain_scores_gemma":[0.998089,0.0010953924,0.00034142303,0.00018520039,0.0001920726,0.00009690928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014021891,0.0003815117,0.0003344941,0.0007331361,0.00028699823,0.0008536812,0.0005424897,0.0007597763,0.0031182913],"category_scores_gemma":[0.0064909067,0.00019715609,0.00060865545,0.00050817325,0.0011266678,0.0014577091,0.0010133566,0.0013014637,0.00026591774],"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":[0.00001716407,0.0000321419,0.0025580672,0.00003852701,0.000022786979,0.00020023102,0.00009400279,0.14844754,0.0016905769,0.8212609,0.0015082619,0.024129855],"study_design_scores_gemma":[0.000007059331,0.00003468593,0.0010127989,0.000012204366,0.00000699866,0.00018216467,0.000029256908,0.7012434,0.00043634526,0.29531768,0.0017047104,0.0000126998775],"about_ca_topic_score_codex":0.0010281203,"about_ca_topic_score_gemma":0.0007759331,"teacher_disagreement_score":0.0031182913,"about_ca_system_score_codex":0.000627455,"about_ca_system_score_gemma":0.0004775351,"threshold_uncertainty_score":0.010431647},"labels":[],"label_agreement":null},{"id":"W2595786059","doi":"","title":"Frailty Models for Modeling Heterogeneity: Simulation Study and Application to Quebec Pension Plan","year":2016,"lang":"en","type":"article","venue":"World Academy of Science, Engineering and Technology, International Journal of Mathematical and Computational Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Pension plan; Pension; Computer science; Business; Geography; Archaeology; Finance","score_opus":0.04019108966281873,"score_gpt":0.34608779512389737,"score_spread":0.3058967054610786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595786059","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":[0.9778016,0.0006666084,0.015062858,0.0012115316,0.000068041605,0.00016851156,0.0015787291,0.00015457976,0.0032875575],"genre_scores_gemma":[0.98703253,0.00038511946,0.006475392,0.0001456772,0.000025147045,0.0001499167,0.0008762704,0.000045803183,0.0048641455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926823,0.00038917956,0.00002444854,0.00007972613,0.0000616783,0.00017667869],"domain_scores_gemma":[0.98815304,0.009262436,0.000542098,0.00043736922,0.0009372785,0.00066771376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056303614,0.0009932895,0.0019629577,0.001603289,0.0023203238,0.00218947,0.0031251425,0.0025861817,0.005701175],"category_scores_gemma":[0.01206692,0.00064389454,0.0017438468,0.0024211684,0.0014416861,0.0015237704,0.0012356079,0.002550323,0.0003256916],"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":[0.00029344833,0.00033087656,0.01819596,0.00005454818,0.00020009567,0.00028509827,0.00035481728,0.96121216,0.00016246144,0.010228961,0.0031152507,0.005566271],"study_design_scores_gemma":[0.00009810895,0.000051737414,0.0037178486,0.000017672439,0.00004692688,0.000022028153,0.00016680745,0.9936593,0.00004374229,0.0017628312,0.0003843801,0.000028596374],"about_ca_topic_score_codex":0.85874355,"about_ca_topic_score_gemma":0.75965947,"teacher_disagreement_score":0.14125645,"about_ca_system_score_codex":0.011232978,"about_ca_system_score_gemma":0.006116589,"threshold_uncertainty_score":0.28417665},"labels":[],"label_agreement":null},{"id":"W2600129581","doi":"10.1111/rmir.12073","title":"Yes We Can (Price Derivatives on Survivor Indices)","year":2017,"lang":"en","type":"article","venue":"Risk Management and Insurance Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Center for Interuniversity Research and Analysis on Organizations; Western University; HEC Montréal","funders":"","keywords":"Monetary economics; Economics; Financial economics; Business; Actuarial science; Financial system","score_opus":0.028654472670810803,"score_gpt":0.3239849462776935,"score_spread":0.2953304736068827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600129581","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":[0.08687075,0.0057845577,0.83071196,0.019158337,0.001070524,0.000085082844,0.00053942564,0.00048623397,0.055293147],"genre_scores_gemma":[0.83570176,0.0052968995,0.13521104,0.0016605684,0.0005247037,0.00012333695,0.00022104697,0.00013983055,0.021120822],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952066,0.00023039439,0.000029191135,0.00007696269,0.00011712209,0.000025663958],"domain_scores_gemma":[0.99777335,0.0014011516,0.00027860908,0.00023917787,0.00025006913,0.000057731093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020898678,0.00042349577,0.00051897776,0.00058460224,0.0002673102,0.0017795796,0.0007977579,0.0013751377,0.01217419],"category_scores_gemma":[0.01195596,0.00020049271,0.0005883216,0.0007125207,0.0008277908,0.0037270042,0.0007658113,0.0014205762,0.0017451936],"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":[0.0000647111,0.00007475513,0.0058371667,0.00024125523,0.00013099944,0.0001825855,0.00017553683,0.089838594,0.00087145343,0.7570988,0.0116106225,0.1338736],"study_design_scores_gemma":[0.000030659725,0.000100326324,0.0018723666,0.00019055321,0.00004218081,0.00019288911,0.0001580593,0.25084555,0.0008575741,0.71364915,0.031995267,0.000065576976],"about_ca_topic_score_codex":0.0020916138,"about_ca_topic_score_gemma":0.0019069503,"teacher_disagreement_score":0.01217419,"about_ca_system_score_codex":0.00045521633,"about_ca_system_score_gemma":0.00052326394,"threshold_uncertainty_score":0.04072672},"labels":[],"label_agreement":null},{"id":"W2604581892","doi":"10.1017/s174849951700001x","title":"Demographic risk in deep-deferred annuity valuation","year":2017,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Annuity; Actuarial science; Valuation (finance); Life annuity; Economics; Pension; Finance","score_opus":0.10056478797231042,"score_gpt":0.40923440290411284,"score_spread":0.30866961493180245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604581892","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":[0.423432,0.0027221474,0.5414851,0.0027287388,0.00017648491,0.00018417432,0.00053188775,0.00012292371,0.028616577],"genre_scores_gemma":[0.9875345,0.00034492405,0.010023992,0.000045240395,0.000040941195,0.000024492001,0.000083529674,0.0000106132375,0.0018917671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815696,0.0008701965,0.00007903544,0.00020678264,0.0005053357,0.00018169371],"domain_scores_gemma":[0.99236155,0.004402754,0.0012149271,0.0005432635,0.0010425673,0.00043488093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005852898,0.00065324374,0.0004957778,0.0012037118,0.0003880635,0.0024067082,0.0011462561,0.0010488059,0.0023853919],"category_scores_gemma":[0.02009495,0.00034758082,0.0006014222,0.00076055323,0.0013938341,0.003544844,0.0018396921,0.001912532,0.00021211358],"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":[0.00011919922,0.00010318007,0.018424809,0.00008524546,0.0000479396,0.0004132118,0.00040930312,0.49349287,0.0012285876,0.44113022,0.0018597963,0.042685572],"study_design_scores_gemma":[0.000009021602,0.00014424365,0.0055882744,0.000117339296,0.000031638418,0.0002686902,0.00027381122,0.7548857,0.0007001055,0.23463173,0.0032936374,0.000055931923],"about_ca_topic_score_codex":0.0015853341,"about_ca_topic_score_gemma":0.00096541544,"teacher_disagreement_score":0.005852898,"about_ca_system_score_codex":0.0015718933,"about_ca_system_score_gemma":0.0007348134,"threshold_uncertainty_score":0.030953407},"labels":[],"label_agreement":null},{"id":"W2604847315","doi":"10.71781/13018","title":"Projections de la mortalité pour le Canada, les provinces et les territoires 2003-2056 : comparaison de deux méthodes","year":2006,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.010225302705094577,"score_gpt":0.23080515066676066,"score_spread":0.22057984796166608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604847315","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":[0.7597698,0.005412143,0.060772967,0.0019867977,0.00023567391,0.0013158194,0.15584815,0.0010720822,0.013586527],"genre_scores_gemma":[0.8674567,0.0038267716,0.06152721,0.00017633157,0.000034577344,0.0013890977,0.057338197,0.00019086183,0.008060297],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783,0.0010399647,0.00013542622,0.00024151361,0.00048483108,0.0002681529],"domain_scores_gemma":[0.9962735,0.0012737518,0.00026108,0.00026572484,0.0017621599,0.00016385561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006498036,0.0012794549,0.0010492691,0.004321749,0.00073794177,0.0019516384,0.0016849274,0.0007047071,0.00400284],"category_scores_gemma":[0.009424606,0.00070755236,0.0027727133,0.0073326007,0.00039813126,0.0009574075,0.0015729241,0.001053499,0.0003782315],"study_design_candidate":"simulation_or_modeling","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":[0.0039191744,0.0003449474,0.4321532,0.0011749711,0.0060498053,0.00026263518,0.0012163833,0.42284876,0.00069695915,0.011903443,0.012623031,0.10680668],"study_design_scores_gemma":[0.0009586275,0.0005493444,0.5949068,0.0005635136,0.0018832469,0.00026361403,0.0036478215,0.3661872,0.002224758,0.0056572217,0.022777101,0.0003807791],"about_ca_topic_score_codex":0.9435079,"about_ca_topic_score_gemma":0.9089983,"teacher_disagreement_score":0.05649209,"about_ca_system_score_codex":0.014035423,"about_ca_system_score_gemma":0.016702149,"threshold_uncertainty_score":0.11364949},"labels":[],"label_agreement":null},{"id":"W2605760791","doi":"10.2307/j.ctt1w0ddf1.6","title":"FUTURE CANADIAN POPULATION TRENDS","year":2017,"lang":"en","type":"book-chapter","venue":"MQUP eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Geography","score_opus":0.02425000009254273,"score_gpt":0.28550605137823404,"score_spread":0.2612560512856913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605760791","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":[0.0096751,0.033421192,0.0020690814,0.028055722,0.00289569,0.00015333049,0.080536425,0.0015263058,0.8416672],"genre_scores_gemma":[0.09466999,0.116006285,0.014220897,0.005023266,0.0006455641,0.00028192013,0.06295064,0.0006739862,0.7055274],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882644,0.000033195745,0.000038722956,0.00010107369,0.0007577831,0.00024287698],"domain_scores_gemma":[0.9981823,0.000048425696,0.00003741159,0.00004039618,0.0014853387,0.00020616507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011936409,0.000819431,0.00029312764,0.0062836697,0.0045080846,0.0042663543,0.0013819167,0.0008096934,0.061016366],"category_scores_gemma":[0.002220205,0.0002846756,0.0008597817,0.010940413,0.00054468546,0.0020509327,0.00090688025,0.0012199346,0.013099037],"study_design_candidate":"simulation_or_modeling","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":[0.000024332297,0.000010624217,0.0035638008,0.0004968466,0.000010252524,0.00010783732,0.001422365,0.0004808551,0.00019283261,0.0441701,0.7926442,0.15687598],"study_design_scores_gemma":[0.0000023183331,0.0000034384514,0.0047214166,0.00014167863,0.000007916101,0.00006511622,0.00047278756,0.00016129843,0.00006036434,0.0010281524,0.9933227,0.000012856184],"about_ca_topic_score_codex":0.98218405,"about_ca_topic_score_gemma":0.9913748,"teacher_disagreement_score":0.06398215,"about_ca_system_score_codex":0.06398215,"about_ca_system_score_gemma":0.09671428,"threshold_uncertainty_score":0.46422517},"labels":[],"label_agreement":null},{"id":"W2605895862","doi":"10.71781/12911","title":"Évolution de la mortalité différentielle selon la province au Canada, 1921-2000","year":2007,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.01518583464969614,"score_gpt":0.3270917084304762,"score_spread":0.31190587378078005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605895862","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":[0.9300533,0.013869948,0.0010142014,0.003706297,0.00007687738,0.000024253093,0.030296247,0.00017048413,0.020788489],"genre_scores_gemma":[0.97768754,0.003379972,0.0010304195,0.00009368395,0.000026102622,0.0000140314805,0.0046649533,0.00003008364,0.0130731305],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996234,0.000028383549,0.000018164154,0.000086452696,0.00009039685,0.00015316822],"domain_scores_gemma":[0.9976949,0.00025812123,0.0003207571,0.00007485626,0.0013544464,0.0002969408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067728944,0.000353827,0.00038091972,0.0048570856,0.0017063016,0.002004827,0.0007721165,0.00046277733,0.0075685186],"category_scores_gemma":[0.0025980128,0.0002823055,0.0006675011,0.008616334,0.00085798,0.00064876047,0.0006981176,0.00080485863,0.00040229288],"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":[0.00023895058,0.000034284854,0.88395476,0.00020229213,0.00040347208,0.0002512925,0.0050722654,0.0038517476,0.00090882357,0.011262169,0.0121786725,0.08164122],"study_design_scores_gemma":[0.0000074268637,0.000008303585,0.9857054,0.0000799635,0.000051457362,0.000048319114,0.0009922523,0.0009806588,0.00012836135,0.0002331537,0.011746233,0.000018521841],"about_ca_topic_score_codex":0.9973736,"about_ca_topic_score_gemma":0.99814963,"teacher_disagreement_score":0.03476063,"about_ca_system_score_codex":0.03476063,"about_ca_system_score_gemma":0.034798272,"threshold_uncertainty_score":0.25220722},"labels":[],"label_agreement":null},{"id":"W2612176674","doi":"10.4024/n05th17a.ntp.13.01","title":"Corroboration of the J-value model for life-expectancy growth in industrialized countries","year":2017,"lang":"en","type":"article","venue":"Nanotechnology Perceptions","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Life expectancy; Value (mathematics); Expectancy theory; Demography; Psychology; Sociology; Statistics; Mathematics; Social psychology; Population","score_opus":0.041836502465850356,"score_gpt":0.34226821417369196,"score_spread":0.3004317117078416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612176674","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":[0.79090375,0.0010034817,0.1779076,0.004674465,0.00018971959,0.00010787703,0.0013218637,0.00018969552,0.02370166],"genre_scores_gemma":[0.99085605,0.00022063666,0.0070665535,0.00016880024,0.0000330587,0.000034347268,0.00037299216,0.000029017898,0.001218609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977932,0.0011945032,0.00011502975,0.00039157807,0.00023664712,0.0002689477],"domain_scores_gemma":[0.98231,0.012806123,0.0014753286,0.0012493135,0.0017366742,0.00042247592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008981536,0.0004510876,0.0010094936,0.00086289627,0.0006813412,0.0018510768,0.0014105352,0.0012216211,0.0025016265],"category_scores_gemma":[0.036260683,0.00036063595,0.0014625682,0.0009980871,0.0009771623,0.0020776156,0.0011526318,0.0017819767,0.00039483808],"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":[0.0002693058,0.00007634508,0.054427084,0.000115612995,0.00021256514,0.0005173019,0.0010417983,0.799103,0.00033386238,0.11627251,0.0030632394,0.024567315],"study_design_scores_gemma":[0.000059453967,0.00013634037,0.033449296,0.000096418036,0.00006134299,0.00019810523,0.00048383095,0.8657435,0.00031703807,0.09694445,0.0024185274,0.00009174487],"about_ca_topic_score_codex":0.02577857,"about_ca_topic_score_gemma":0.011602403,"teacher_disagreement_score":0.02577857,"about_ca_system_score_codex":0.0012313401,"about_ca_system_score_gemma":0.0012660772,"threshold_uncertainty_score":0.051257014},"labels":[],"label_agreement":null},{"id":"W2612349808","doi":"10.4324/9780203517390-9","title":"What drives the purchase decision in pensions and long-term investment products in the UK?","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Term (time); Investment (military); Business; Actuarial science; Economics; Political science; Physics","score_opus":0.03606224123321651,"score_gpt":0.301690444454132,"score_spread":0.2656282032209155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612349808","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":[0.5331261,0.023166869,0.002001013,0.021315992,0.00061619165,0.000059979393,0.00060344243,0.000049772392,0.4190606],"genre_scores_gemma":[0.88991374,0.008073895,0.0008625762,0.0018840682,0.000085154235,0.000017214568,0.00033109012,0.000034997356,0.09879723],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99937576,0.00020641826,0.000025391611,0.000074192394,0.00019171284,0.00012658532],"domain_scores_gemma":[0.99856734,0.00072389137,0.0002508715,0.00004707253,0.00022026723,0.00019060978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000901579,0.0002256759,0.00026820577,0.000522482,0.00088275434,0.004788738,0.000381341,0.0011406015,0.02315449],"category_scores_gemma":[0.0039913985,0.00020569493,0.00029602568,0.0009060195,0.0012885832,0.0019938727,0.0008032741,0.0010098212,0.0039131967],"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":[0.00094953337,0.0005283055,0.15529713,0.0012561657,0.00010799081,0.0035856995,0.07690024,0.0016048432,0.0027717678,0.10124933,0.15529004,0.50045896],"study_design_scores_gemma":[0.000073523144,0.0003136598,0.43211523,0.002062862,0.00014931955,0.0018006696,0.098940656,0.0037069032,0.0015184561,0.050597224,0.40849516,0.00022637662],"about_ca_topic_score_codex":0.054711353,"about_ca_topic_score_gemma":0.09119008,"teacher_disagreement_score":0.054711353,"about_ca_system_score_codex":0.0045571732,"about_ca_system_score_gemma":0.001996805,"threshold_uncertainty_score":0.10878575},"labels":[],"label_agreement":null},{"id":"W2614175443","doi":"10.1007/s13524-017-0579-x","title":"Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements","year":2017,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Demography; Gerontology; Econometrics; Economics; Sociology; Medicine","score_opus":0.03047403284089751,"score_gpt":0.3033085498135389,"score_spread":0.2728345169726414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614175443","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":[0.464452,0.0004893097,0.530092,0.00083427655,0.00003826989,0.00012484817,0.00026424136,0.00025625026,0.0034488707],"genre_scores_gemma":[0.9343604,0.00016691946,0.063485764,0.00011017573,0.000032806634,0.00011080659,0.00024953746,0.00006272399,0.0014208539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986406,0.0008774649,0.000035160505,0.00019553474,0.00007382547,0.0001774362],"domain_scores_gemma":[0.99256414,0.006135595,0.0005186715,0.0002629151,0.00031814064,0.00020047741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006135931,0.0005181432,0.001049757,0.0009930128,0.0003077815,0.0008181865,0.0010872275,0.00088843534,0.002613043],"category_scores_gemma":[0.020098574,0.00043613766,0.000800838,0.0008087275,0.00063593476,0.0012996013,0.0010213188,0.0010473481,0.00013598823],"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":[0.00023094098,0.00010655169,0.014779108,0.0000653274,0.00014279978,0.00015197712,0.00019175903,0.91076475,0.00074508006,0.028786937,0.00063384004,0.04340087],"study_design_scores_gemma":[0.000024140589,0.0001159343,0.0031013528,0.000010843723,0.00003068279,0.000019450823,0.00007618305,0.9857129,0.00037766827,0.010078427,0.00043479635,0.000017599054],"about_ca_topic_score_codex":0.010388378,"about_ca_topic_score_gemma":0.010017376,"teacher_disagreement_score":0.010388378,"about_ca_system_score_codex":0.0009034309,"about_ca_system_score_gemma":0.0017271931,"threshold_uncertainty_score":0.03245026},"labels":[],"label_agreement":null},{"id":"W2614986146","doi":"10.1016/s0140-6736(16)31012-1","title":"Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015","year":2016,"lang":"en","type":"letter","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6689,"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":"Colorado School of Public Health; University of Colorado School of Medicine, Anschutz Medical Campus; Mailman School of Public Health, Columbia University; National Institute on Aging; Rijksinstituut voor Volksgezondheid en Milieu; Economic and Social Research Council; School of Public Health and Family Medicine, University of Cape Town; Johns Hopkins Bloomberg School of Public Health; Perelman School of Medicine, University of Pennsylvania; U.S. Food and Drug Administration; Centers for Disease Control and Prevention; Russian Academy of Sciences; Ministry of Health of the Russian Federation; Universidade Federal de Sergipe; National Health Laboratory Service; Fakultet Medicinskih Nauka, Univerziteta U Kragujevcu; University of Peradeniya; University of Gondar; Arak University of Medical Sciences; Centro de Investigación Biomédica en Red de Salud Mental; Children's Hospital of Michigan; Hacettepe Üniversitesi; Universitair Medisch Centrum Groningen; National Institutes of Health; Tehran University of Medical Sciences and Health Services; Simon Fraser University; Jordan University of Science and Technology; Hospital de Clínicas de Porto Alegre; National Cancer Institute; Universitat de València; Universidade do Estado de Santa Catarina; Virginia Commonwealth University; Central South University; University of Tasmania; Seoul National University; University College Cork; Universität Bielefeld; Public Health Foundation of India; Wageningen University and Research; University of Massachusetts Boston; Arabian Gulf University; National University of Singapore; Arkansas State University; South African Medical Research Council; Heart and Stroke Foundation of Canada; Rijksuniversiteit Groningen; University of Louisville; Imperial College London; Academy of Medical Sciences; Universitat Pompeu Fabra; National Cerebral and Cardiovascular Center; Birzeit University; James Cook University; Wellcome Trust; Heller School for Social Policy and Management; National Institute for Health and Care Research; University of North Carolina at Chapel Hill; Universidade Federal do Rio Grande do Sul; Wayne State University; Queen Mary University of London; Brown University; Auckland University of Technology, New Zealand; University of Delhi; Brandeis University; Secretaría de Salud; University of Cincinnati; Aswan University; Sree Chitra Tirunal Institute for Medical Sciences and Technology; Children's National Hospital; Ministry of Health and Medical Education; Johns Hopkins University; Medical Research Council; School of Medicine, Wayne State University; Aarhus Universitet; Universitat de Barcelona; King's College London; Children's Hospital of Philadelphia; University of Pennsylvania; George Washington University; National Institute on Minority Health and Health Disparities; University of Aberdeen; University of Cape Town; Massachusetts General Hospital; Oklahoma State University; Emory University; United States Agency for International Development; George Mason University; Swansea University; Shahid Beheshti University of Medical Sciences; Universiteit Gent; U.S. Department of Health and Human Services; Fudan University; Sun Yat-sen University; Bill and Melinda Gates Foundation","keywords":"Life expectancy; Cause of death; Disease; Demography; Burden of disease; Mortality rate; Medicine; Environmental health; Internal medicine; Population","score_opus":0.17998098305014876,"score_gpt":0.4054945618357161,"score_spread":0.22551357878556733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614986146","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":[0.12222395,0.7678149,0.007001195,0.0014030794,0.00030505477,0.0006752231,0.09837009,0.0001712394,0.0020352402],"genre_scores_gemma":[0.6710662,0.27668476,0.010400111,0.0011972468,0.00024133937,0.0015996551,0.03826753,0.000077149416,0.00046593658],"study_design_codex":"observational","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99532074,0.0014327376,0.0014917477,0.00086326426,0.00072213984,0.00016932377],"domain_scores_gemma":[0.99083215,0.0036142631,0.0026952594,0.0007291566,0.001925922,0.00020318844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008717489,0.0009116538,0.002296277,0.010192007,0.00036410862,0.0008089033,0.000801325,0.0004949796,0.0013440107],"category_scores_gemma":[0.018177442,0.00073689886,0.01001889,0.011978134,0.000514645,0.0008524586,0.0016660009,0.00073982985,0.00025533218],"study_design_candidate":"meta_analysis","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":[0.00062928797,0.000034078992,0.7248123,0.07563182,0.07620266,0.00022479355,0.0008337327,0.0024889095,0.00044554018,0.0014685085,0.020181501,0.097046964],"study_design_scores_gemma":[0.00033436454,0.00023228842,0.7979093,0.047250763,0.11246007,0.00090850017,0.00071554445,0.0029100021,0.00058833323,0.002080272,0.03442195,0.00018853496],"about_ca_topic_score_codex":0.03118423,"about_ca_topic_score_gemma":0.07388026,"teacher_disagreement_score":0.03118423,"about_ca_system_score_codex":0.0014543066,"about_ca_system_score_gemma":0.0042260797,"threshold_uncertainty_score":0.0620054},"labels":[],"label_agreement":null},{"id":"W2620187578","doi":"10.71781/12932","title":"Évolution et projection du fardeau de la mortalité au Canada, de 1921 à 2047","year":2016,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Geography; Political science; Humanities; Philosophy","score_opus":0.019542679706869005,"score_gpt":0.33891351196523445,"score_spread":0.31937083225836543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620187578","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":[0.9125102,0.0028059657,0.0020793187,0.004803853,0.00008549705,0.00004109292,0.055937443,0.00022355313,0.021513045],"genre_scores_gemma":[0.9750651,0.0011551514,0.0012341108,0.00014420616,0.000012390201,0.000024035275,0.010468864,0.000035126883,0.011860894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995332,0.00003739401,0.00001525603,0.00009238352,0.00017585566,0.00014598202],"domain_scores_gemma":[0.9978027,0.00015310242,0.00019618303,0.000076741366,0.0015226349,0.00024869773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006611692,0.0002886571,0.00028571155,0.0025723602,0.0016709944,0.0015897441,0.00066384475,0.00036369148,0.0041497652],"category_scores_gemma":[0.0024222294,0.00021828723,0.0005847159,0.0041862195,0.0006880981,0.00041092496,0.0009011307,0.00075824646,0.00039364744],"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":[0.00019284319,0.000020927291,0.9090229,0.00012124412,0.00025758997,0.00017886078,0.0043507046,0.0054035955,0.0007656648,0.0074375547,0.01684121,0.055406943],"study_design_scores_gemma":[0.000005416148,0.000018581515,0.9693476,0.000073995,0.000037905167,0.000059872305,0.002750151,0.0028292804,0.00023148954,0.00040375267,0.02420522,0.000036816426],"about_ca_topic_score_codex":0.9957534,"about_ca_topic_score_gemma":0.9970139,"teacher_disagreement_score":0.025086416,"about_ca_system_score_codex":0.025086416,"about_ca_system_score_gemma":0.036780268,"threshold_uncertainty_score":0.18201554},"labels":[],"label_agreement":null},{"id":"W2654033153","doi":"10.4050/f-0072-2016-11476","title":"Employing Legacy HUMS Data to Support Aging Aircraft ASIP","year":2016,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Computer science","score_opus":0.11355351085391131,"score_gpt":0.3842094559045092,"score_spread":0.2706559450505979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2654033153","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":[0.90249264,0.00020990583,0.011656677,0.00029013064,0.000074283744,0.0005457524,0.06453777,0.0016727708,0.018520098],"genre_scores_gemma":[0.93035406,0.00019233656,0.023502933,0.00011078928,0.000045726218,0.00015709673,0.042489488,0.00009469734,0.003052924],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951684,0.000061749095,0.00005349619,0.00009465074,0.00020421235,0.000069007794],"domain_scores_gemma":[0.9976241,0.00020942518,0.00020055524,0.0003829831,0.0014517024,0.00013120373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010816107,0.00042580403,0.0002864652,0.0030689163,0.00043673845,0.0009275728,0.00060967903,0.0003397961,0.0013647167],"category_scores_gemma":[0.0022627455,0.00009849383,0.00017978047,0.0024045259,0.00021161522,0.00042935682,0.000846813,0.00026539143,0.0006372134],"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":[0.0005326638,0.00023868702,0.7605448,0.0002635446,0.0002343764,0.0010514315,0.0018065999,0.020799637,0.016228588,0.0013003675,0.0137096625,0.18328975],"study_design_scores_gemma":[0.000035020897,0.00020438744,0.9070842,0.00012281699,0.000094306524,0.00022930838,0.0019115158,0.041567344,0.012928056,0.0006364965,0.035122003,0.000064569336],"about_ca_topic_score_codex":0.17424726,"about_ca_topic_score_gemma":0.22387306,"teacher_disagreement_score":0.17424726,"about_ca_system_score_codex":0.0011689634,"about_ca_system_score_gemma":0.0016463381,"threshold_uncertainty_score":0.34646606},"labels":[],"label_agreement":null},{"id":"W2728427291","doi":"10.1177/0962280217708671","title":"Bayesian cure rate models induced by frailty in survival analysis","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Markov chain Monte Carlo; Bayesian probability; Computer science; Bayesian inference; Poisson distribution; Inference; Markov chain; Econometrics; Statistics; Survival analysis; Mathematics; Artificial intelligence; Machine learning","score_opus":0.28525290172911694,"score_gpt":0.6164067651596781,"score_spread":0.33115386343056114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728427291","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":[0.038223594,0.00036165366,0.95914,0.000522665,0.00003387594,0.00006597738,0.00021326398,0.00015987805,0.0012790061],"genre_scores_gemma":[0.805688,0.0013623512,0.18436524,0.00039718833,0.00018613029,0.0006001478,0.0009887771,0.00015747412,0.0062546115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957408,0.0023403745,0.00016481681,0.00074003334,0.0006624123,0.00035167995],"domain_scores_gemma":[0.98388517,0.012252719,0.0014320719,0.0011597039,0.00089876883,0.0003715456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014804743,0.0008861767,0.0016448778,0.0020710651,0.0007313661,0.0021335778,0.0028740226,0.002558106,0.0026824777],"category_scores_gemma":[0.044526074,0.0010120749,0.0018680042,0.0015320071,0.0029755628,0.003505367,0.0026193198,0.004185673,0.00055610004],"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":[0.00009514613,0.000053353517,0.004972872,0.000097190336,0.00009221105,0.00021601489,0.00065765285,0.44042116,0.00080964033,0.5269484,0.0012786862,0.02435764],"study_design_scores_gemma":[0.000027951888,0.00003245167,0.0011023325,0.000043346976,0.000033037515,0.00011473341,0.00004434319,0.7594903,0.00022034244,0.23783545,0.001015458,0.000040245613],"about_ca_topic_score_codex":0.0061130105,"about_ca_topic_score_gemma":0.0033055034,"teacher_disagreement_score":0.014804743,"about_ca_system_score_codex":0.0017440983,"about_ca_system_score_gemma":0.0014239075,"threshold_uncertainty_score":0.07829589},"labels":[],"label_agreement":null},{"id":"W2730089776","doi":"10.1038/nature22786","title":"Many possible maximum lifespan trajectories","year":2017,"lang":"en","type":"article","venue":"Nature","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Biology; Evolutionary biology","score_opus":0.016258846286377897,"score_gpt":0.3244510069031908,"score_spread":0.30819216061681287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730089776","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":[0.738063,0.0060878904,0.13887486,0.0077020032,0.00020357745,0.00009933625,0.00475406,0.0007375502,0.10347776],"genre_scores_gemma":[0.96262324,0.0016788596,0.02520613,0.0001902173,0.000055001346,0.0001271443,0.0011928468,0.000057088164,0.008869491],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99945337,0.00015829467,0.000039540242,0.00015277909,0.00007690102,0.00011910016],"domain_scores_gemma":[0.9965861,0.0023233371,0.00026872277,0.00034452832,0.00020633262,0.000270973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014032678,0.00054824166,0.00080608553,0.0011800167,0.0019407397,0.0022783764,0.0014509271,0.0018544339,0.014134721],"category_scores_gemma":[0.009900896,0.0006238828,0.0011908414,0.0014615608,0.000958684,0.004150404,0.0021051161,0.0016879259,0.00088294747],"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":[0.0006211851,0.00018464636,0.024605954,0.00058739755,0.00021794286,0.001306437,0.0014700892,0.060223874,0.0021201894,0.8076811,0.009566797,0.0914143],"study_design_scores_gemma":[0.000061932304,0.00009937744,0.012521221,0.00023653662,0.0000901911,0.0010236901,0.0007350986,0.060467884,0.0007795982,0.9137984,0.010122694,0.00006348691],"about_ca_topic_score_codex":0.00090218574,"about_ca_topic_score_gemma":0.0020858224,"teacher_disagreement_score":0.014134721,"about_ca_system_score_codex":0.0010418772,"about_ca_system_score_gemma":0.0005494077,"threshold_uncertainty_score":0.047285378},"labels":[],"label_agreement":null},{"id":"W2734972487","doi":"10.1007/s13524-017-0623-x","title":"The Methuselah Effect: The Pernicious Impact of Unreported Deaths on Old-Age Mortality Estimates","year":2017,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; York University; Purdue University","keywords":"Demography; Medicine; Mortality rate; Internal medicine; Sociology","score_opus":0.03345877569187987,"score_gpt":0.3992662161757526,"score_spread":0.3658074404838727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734972487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.531724,0.031969856,0.3074496,0.055236466,0.0022026305,0.0011525669,0.003461013,0.00065883907,0.066145025],"genre_scores_gemma":[0.9597652,0.0028695373,0.021625035,0.008567196,0.0014879081,0.0003209702,0.00035481266,0.00012301492,0.0048862156],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.93643224,0.049558695,0.002008035,0.0049996395,0.006042221,0.0009590477],"domain_scores_gemma":[0.42425996,0.5082435,0.036134712,0.026663475,0.003572643,0.0011257934],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.073680356,0.0006298994,0.0015943138,0.0032076247,0.0019400489,0.003271477,0.0025886071,0.002617274,0.006752757],"category_scores_gemma":[0.3649455,0.0005551703,0.0013728603,0.0024409078,0.005483415,0.0062905517,0.0065474636,0.0043429965,0.0006661437],"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":[0.0006108412,0.000194919,0.691808,0.0006397258,0.0029259818,0.0015434156,0.0034722367,0.006139076,0.0003720385,0.13722192,0.008503675,0.14656824],"study_design_scores_gemma":[0.00044882606,0.0012400827,0.5205663,0.0025628507,0.0033581813,0.004034526,0.0056387205,0.05732325,0.0071456656,0.32859612,0.068739444,0.00034605942],"about_ca_topic_score_codex":0.006499874,"about_ca_topic_score_gemma":0.007878276,"teacher_disagreement_score":0.92631966,"about_ca_system_score_codex":0.0012923318,"about_ca_system_score_gemma":0.0011456911,"threshold_uncertainty_score":0.38966364},"labels":[],"label_agreement":null},{"id":"W2735404575","doi":"10.25336/p6ns4b","title":"Two centuries of demographic change in Canada","year":2014,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Population momentum; Fertility; Vital rates; Demographic transition; Demographic change; Population; Demography; Age structure; Population growth; Momentum (technical analysis); Total fertility rate; Population size; Mortality rate; Geography; Birth rate; Economics; Research methodology; Sociology; Family planning","score_opus":0.05194958158475774,"score_gpt":0.3381806383701832,"score_spread":0.2862310567854255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735404575","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":[0.9582939,0.0058327713,0.00054521253,0.002691147,0.00010121463,0.000066200155,0.0069814594,0.000034200115,0.025453802],"genre_scores_gemma":[0.9853139,0.0027089075,0.00075072824,0.0002895501,0.00001823718,0.000027055148,0.0020548995,0.000014285521,0.008822475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999181,0.00005352107,0.00003366594,0.000099382276,0.00032416152,0.00030824138],"domain_scores_gemma":[0.997079,0.00032084595,0.00027062447,0.00009692032,0.001746839,0.00048586584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009559331,0.00019150507,0.00041547653,0.0039298767,0.006619402,0.002487767,0.0008000634,0.00050522725,0.0023904182],"category_scores_gemma":[0.0041661533,0.00023968548,0.0003564772,0.0076740156,0.0012610826,0.0009543133,0.0016560467,0.0010722098,0.00011309646],"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":[0.0002546797,0.000079368285,0.7395731,0.00041534286,0.0002268874,0.000872886,0.07766563,0.0019299521,0.00082214904,0.028411875,0.02242696,0.12732115],"study_design_scores_gemma":[0.0000051824413,0.000014999483,0.94124657,0.00014608732,0.000033822074,0.000103637576,0.012071399,0.0008287982,0.00015675902,0.0004360027,0.044915244,0.000041488467],"about_ca_topic_score_codex":0.9977513,"about_ca_topic_score_gemma":0.9991773,"teacher_disagreement_score":0.08243524,"about_ca_system_score_codex":0.08243524,"about_ca_system_score_gemma":0.07598188,"threshold_uncertainty_score":0.59811234},"labels":[],"label_agreement":null},{"id":"W2736545652","doi":"10.1093/oso/9780199251919.003.0010","title":"The Demographic Impact of a Mild Famine in an African City: The Case of Antananarivo, 1985-7","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"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":"Famine; China; Development economics; Isolation (microbiology); Geography; Quarter (Canadian coin); Spanish Civil War; Economics; Political science","score_opus":0.042290221669148045,"score_gpt":0.3184749942615056,"score_spread":0.2761847725923576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736545652","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":[0.9810403,0.0022712909,0.000025605728,0.0040965844,0.00004006233,0.000011045999,0.00057439995,0.0000028040374,0.011937926],"genre_scores_gemma":[0.9918745,0.002846008,0.000029366593,0.00015763023,0.000026142165,0.000008647475,0.00024327649,0.0000017859969,0.004812675],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999385,0.0000151599015,0.0000019433005,0.0000039859456,0.000005303589,0.000035195797],"domain_scores_gemma":[0.9999087,0.000010624898,0.000034712935,0.0000025563547,0.000009812865,0.00003365584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010019481,0.0001110861,0.00006858331,0.00048667347,0.0014718391,0.00061992847,0.0003014561,0.0003171618,0.0028248148],"category_scores_gemma":[0.00031930982,0.00007778346,0.00007051503,0.0010794484,0.00045612716,0.00041447647,0.0009178511,0.0005076494,0.0002482088],"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":[0.00037375462,0.00023744265,0.67997146,0.00043233912,0.000057506793,0.025139757,0.12690546,0.0012340109,0.0016325819,0.019279685,0.05568583,0.08905022],"study_design_scores_gemma":[0.0000070214965,0.0000432096,0.8535064,0.00020252907,0.000017103968,0.002341946,0.07356698,0.00029458618,0.00018507644,0.00070539623,0.069115795,0.000014066216],"about_ca_topic_score_codex":0.1476569,"about_ca_topic_score_gemma":0.29531193,"teacher_disagreement_score":0.1476569,"about_ca_system_score_codex":0.0018616449,"about_ca_system_score_gemma":0.00076139584,"threshold_uncertainty_score":0.2935949},"labels":[],"label_agreement":null},{"id":"W2737251932","doi":"10.20381/ruor-20713","title":"Three Essays on Modeling Aging Population","year":2017,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population ageing; Population; Sociology; Demography","score_opus":0.10100059886457706,"score_gpt":0.3920386557700835,"score_spread":0.29103805690550644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737251932","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":[0.04610123,0.08623682,0.49375802,0.09503163,0.0071808477,0.00032190382,0.0042670514,0.0013133073,0.26578927],"genre_scores_gemma":[0.45937127,0.1001812,0.14716226,0.008129129,0.0034572417,0.00037425675,0.0018419534,0.0005905704,0.27889222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996942,0.00006765959,0.000012213624,0.00005200083,0.00013085268,0.000043121956],"domain_scores_gemma":[0.99933857,0.00024011274,0.00003897145,0.00007589192,0.00024868536,0.00005773829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080102764,0.0008648489,0.0005208821,0.0008205352,0.0012044711,0.0017776578,0.0015783048,0.0014849968,0.007446649],"category_scores_gemma":[0.003269503,0.00031464428,0.0008267936,0.0020720263,0.0018799773,0.0017642834,0.0010723405,0.001577504,0.0009669159],"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":[0.000035101177,0.00004118689,0.00516427,0.00031591902,0.00005548627,0.00032286014,0.0013602535,0.1576788,0.000604403,0.60437506,0.12936865,0.100677975],"study_design_scores_gemma":[0.000016486021,0.000030148214,0.0043630814,0.00057260576,0.000055270542,0.00017940681,0.00087730866,0.13055809,0.00053906266,0.35784274,0.50489587,0.00006988198],"about_ca_topic_score_codex":0.4402387,"about_ca_topic_score_gemma":0.4216653,"teacher_disagreement_score":0.4402387,"about_ca_system_score_codex":0.011046778,"about_ca_system_score_gemma":0.005714048,"threshold_uncertainty_score":0.8753525},"labels":[],"label_agreement":null},{"id":"W2737767256","doi":"","title":"Pricing and Hedging GMWB Riders in a Binomial Framework","year":2012,"lang":"en","type":"preprint","venue":"Spectrum Research Repository (Concordia University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Binomial options pricing model; Diversification (marketing strategy); Toolbox; Binomial (polynomial); Asset (computer security); Binomial distribution; Valuation of options; Econometrics; Trinomial tree; Black–Scholes model; Computer science; Actuarial science; Economics; Mathematics; Business; Statistics","score_opus":0.04374396169061167,"score_gpt":0.3234768796507879,"score_spread":0.27973291796017624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737767256","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":[0.05630414,0.0017370338,0.92644423,0.00085462583,0.00015284942,0.000058299294,0.0001036929,0.000099122204,0.014245999],"genre_scores_gemma":[0.84196544,0.0032078421,0.13186325,0.00032804583,0.00024298705,0.00012878462,0.00018226629,0.000079095844,0.022002187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989937,0.0004274945,0.00004979954,0.000118450494,0.00028033688,0.00013029827],"domain_scores_gemma":[0.99835,0.001030539,0.00019419502,0.00010354973,0.00016383518,0.00015783202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033490146,0.0007756892,0.0010724815,0.0007112107,0.0005140055,0.0027885085,0.001674118,0.0017419683,0.0049895253],"category_scores_gemma":[0.0075749317,0.00066606054,0.0012897261,0.00082216505,0.0018605085,0.0032711052,0.001446584,0.0019218178,0.00046050758],"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":[0.0000630879,0.00004977896,0.0012229156,0.00009460956,0.0000330536,0.00029013297,0.00012470635,0.4300712,0.001114102,0.55202335,0.0011827543,0.0137303285],"study_design_scores_gemma":[0.000013070737,0.000021551255,0.00015160088,0.000014947435,0.000008196403,0.00004431859,0.000020967887,0.9465298,0.00012623434,0.052433327,0.00062502956,0.00001097096],"about_ca_topic_score_codex":0.004916927,"about_ca_topic_score_gemma":0.0033509636,"teacher_disagreement_score":0.0049895253,"about_ca_system_score_codex":0.0013041464,"about_ca_system_score_gemma":0.0011049696,"threshold_uncertainty_score":0.01771152},"labels":[],"label_agreement":null},{"id":"W2749235679","doi":"10.2307/j.ctt1w1vmjq.7","title":"Dimensions of Fertility","year":2017,"lang":"en","type":"book-chapter","venue":"MQUP eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fertility; Sociology; Demography","score_opus":0.04494872972798027,"score_gpt":0.30933807959420395,"score_spread":0.2643893498662237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749235679","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":[0.47447217,0.025083914,0.0014254415,0.0058662575,0.00019005295,0.00012770813,0.010974008,0.0001662822,0.4816942],"genre_scores_gemma":[0.9409499,0.009361201,0.0014526619,0.00016533186,0.00003060129,0.00002218187,0.0021967595,0.000017692706,0.04580365],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954623,0.000039355917,0.000012636404,0.000033027103,0.0002520452,0.00011669773],"domain_scores_gemma":[0.99951386,0.00009509398,0.000053541506,0.000016207183,0.00016972005,0.00015161539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037886016,0.00013778478,0.00013181982,0.0027243735,0.0027392544,0.0013052257,0.00048840616,0.00017079491,0.007243351],"category_scores_gemma":[0.001191289,0.00009221583,0.00014944373,0.0050410978,0.0018878208,0.0003547083,0.000657991,0.0004033108,0.00031543663],"study_design_candidate":"not_applicable","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":[0.00012562545,0.000037154296,0.20005438,0.00035214925,0.000023476066,0.00062868337,0.031600628,0.00170639,0.0010098458,0.2322142,0.0545135,0.47773403],"study_design_scores_gemma":[0.0000029394807,0.000026920108,0.7431139,0.00021933441,0.000007101428,0.00080340443,0.014200531,0.00062391517,0.00021281643,0.011170598,0.2295819,0.000036704725],"about_ca_topic_score_codex":0.9524145,"about_ca_topic_score_gemma":0.9801414,"teacher_disagreement_score":0.9524145,"about_ca_system_score_codex":0.025851846,"about_ca_system_score_gemma":0.020034568,"threshold_uncertainty_score":0.18756914},"labels":[],"label_agreement":null},{"id":"W2749700975","doi":"10.2105/ajph.2017.303932","title":"Standard Period Life Table Used to Compute the Life Expectancy of Diseased Subpopulations: More Confusing Than Helpful","year":2017,"lang":"en","type":"article","venue":"American Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","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":false,"ca_institutions":"Centre hospitalier universitaire de Québec","funders":"","keywords":"Life expectancy; Cohort; Life table; Metric (unit); Medicine; Demography; Population; Gerontology; Statistics; Mathematics; Internal medicine; Operations management; Environmental health","score_opus":0.08948390002647778,"score_gpt":0.3956246599318844,"score_spread":0.3061407599054066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749700975","genre_codex":"methods","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":[0.020290222,0.0074238335,0.8930071,0.006065068,0.003404845,0.000975576,0.04542228,0.0053086216,0.018102363],"genre_scores_gemma":[0.23502128,0.0069032456,0.6988481,0.0038532268,0.0017932238,0.002847778,0.03746009,0.002915971,0.010357101],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99348193,0.0039298567,0.0010268161,0.0004979931,0.0009629041,0.00010052271],"domain_scores_gemma":[0.95763177,0.029906731,0.0026289716,0.0046556457,0.0048349714,0.00034193814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008190059,0.0007163341,0.0009934126,0.003036032,0.00046813238,0.0026820377,0.0009536153,0.000755157,0.018219978],"category_scores_gemma":[0.079621285,0.0003829189,0.0011275847,0.00567072,0.00052059366,0.0024147297,0.0012553534,0.0029023327,0.0045227474],"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":[0.0006247138,0.00010157439,0.031240987,0.002398704,0.00054148136,0.0005200487,0.0014697227,0.020560583,0.0012897102,0.14006594,0.26638323,0.5348034],"study_design_scores_gemma":[0.00021836189,0.00072269567,0.030560423,0.0032536867,0.00032310074,0.0032326395,0.0012230485,0.06785963,0.0037374976,0.19023882,0.6982622,0.0003678602],"about_ca_topic_score_codex":0.0029049404,"about_ca_topic_score_gemma":0.0024650323,"teacher_disagreement_score":0.018219978,"about_ca_system_score_codex":0.0008703073,"about_ca_system_score_gemma":0.0012246432,"threshold_uncertainty_score":0.06095189},"labels":[],"label_agreement":null},{"id":"W2751792491","doi":"10.1016/s0140-6736(18)32335-3","title":"Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017","year":2018,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4191,"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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Deafness and Other Communication Disorders; National Institute of Mental Health; Dipartimento di Medicina e Chirurgia, Università degli Studi di Milano-Bicocca; Erasmus Universitair Medisch Centrum Rotterdam; Center for International Health; Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences; Queensland Brain Institute; Sydney Medical School; National Health and Medical Research Council; Medical Research Council; Department of Global Health and Population, Harvard T.H. Chan School of Public Health; University of California, Los Angeles; Seqirus; College of Engineering, Michigan State University; Friedrich Schiedel Stiftung für Energietechnik; Wolkite University; Uniwersytet Opolski; Universidad de Ciencias Aplicadas y Ambientales; Debre Tabor University; Abbott Diagnostics; Directorate for Biological Sciences; Debre Markos University; Universitair Ziekenhuis Antwerpen; Alfaisal University; Laboratório Associado para a Química Verde; Western Sydney University; Ministry of Health of the Russian Federation; Bispebjerg Hospital; Universidade Federal de Sergipe; Universidad de Extremadura; Duke Kunshan University; I.M. Sechenov First Moscow State Medical University; Kurdistan University Of Medical Sciences; Wageningen University and Research; Samara University; H. Lundbeck A/S; University of Namibia; Lorestan University of Medical Sciences; Alborz University of Medical Sciences; Savitribai Phule Pune University; University of Thessaly; Fakultet Medicinskih Nauka, Univerziteta U Kragujevcu; Charotar University of Science and Technology; Alexandria University; Hawassa University; National Center of Neurology and Psychiatry; Mekelle University; Mansoura University; George Institute for Global Health; University of Peradeniya; Xiamen University; NSW Ministry of Health; Tartu Ülikool; University of Tabriz; Chungnam National University Hospital; University of the Philippines; Urmia University; National Research University Higher School of Economics; University of Cape Town; Università degli Studi di Salerno; Servier; Wuhan University; Universitas Muhammadiyah Surakarta; Universidade do Porto; Centro de Investigación Biomédica en Red de Salud Mental; University of Tsukuba; University of Dhaka; University of Social Welfare and Rehabilitation Sciences; Fundação Oswaldo Cruz; National Taiwan University; Qazvin University of Medical Sciences; Université de Bordeaux; Hospital for Sick Children; Bahir Dar University; Golestan University of Medical Sciences; Universitatea de Medicină şi Farmacie \"Carol Davila\" Bucureşti; Shahid Beheshti University of Medical Sciences; NIH Clinical Center; Baqiyatallah University of Medical Sciences; Högskolan Dalarna; University of Hail; Universitat de Barcelona; University of Health and Allied Sciences; Universidade de Lisboa; Göteborgs Universitet; University of Haifa; Menzies Institute for Medical Research; Babol University of Medical Sciences; Technische Universität München; Universidade de São Paulo; Guilan University of Medical Sciences; Hacettepe Üniversitesi; Universitetet i Oslo; National and Kapodistrian University of Athens; Versus Arthritis; Universitair Medisch Centrum Groningen; Michigan State University; Astellas Pharma; Umeå Universitet; National Institute on Aging; Chinese University of Hong Kong; Public Health Agency of Canada; Kerman University of Medical Sciences; Euratom Research and Training Programme; Kuwait University; Universidad Autónoma Metropolitana; Seoul National University Hospital; American University of Beirut; Vetenskapsrådet; Hebrew University of Jerusalem; Novo Nordisk; Tsinghua University; Karolinska Institutet; Universitat de València; Georg-August-Universität Göttingen; University of Canberra; Lunds Universitet; Bundesministerium für Gesundheit; National Institutes of Health; Banaras Hindu University; Kyung Hee University; U.S. Department of Veterans Affairs; Kosin University; The Wellcome Trust DBT India Alliance; Nanjing University; Chungnam National University; Ain Shams University; Cochrane South Africa; University of Toronto; Imperial College London; Public Health England; Universiti Sains Malaysia; King Khalid University; Curtin University of Technology; Helsingin Yliopisto; RMIT University; Deakin University; York University; University of Technology Sydney; Universidad del Valle; Kermanshah University of Medical Sciences; University of New South Wales; Universitat Pompeu Fabra; Sun Yat-sen University; Institut National de la Santé et de la Recherche Médicale; Public Health Agency; Muhimbili University of Health and Allied Sciences; Applied Molecular Biosciences Unit; Public Health Foundation of India; Universidad de Chile; Department of Biotechnology, Ministry of Science and Technology, India; University of Tasmania; Student Research Committee, Tabriz University of Medical Sciences; Frankfurt University of Applied Sciences; La Trobe University; National Heart Foundation of Australia; Australian Catholic University; University of Glasgow; Menzies Health Institute Queensland; Sanofi; University of Oxford; Jackson State University; University of Southampton; Lomonosov Moscow State University; University of Queensland; National Institute for Health and Care Research; University College London; Universiti Kebangsaan Malaysia; Korea Health Industry Development Institute; Novavax; University of Chichester; Universitas Negeri Semarang; King's College London; Sree Chitra Tirunal Institute for Medical Sciences and Technology; Brien Holden Vision Institute; Krishna Institute Of Medical Sciences Deemed To Be University; University of Embu; European Commission; EUROfusion; Ahmadu Bello University; Inyuvesi Yakwazulu-Natali; Anglia Ruskin University; Queen Elizabeth Hospital Birmingham Charity; Universität Bielefeld; Flinders University; Oklahoma State University; Invasive Fungi Research Center, Mazandaran University of Medical Sciences; Indivior; Murdoch Children's Research Institute; Norges Teknisk-Naturvitenskapelige Universitet; Lawson Health Research Institute; Scottish Government; King Abdulaziz University; Case Western Reserve University; Fudan University; Valeant Pharmaceuticals International; An-Najah National University; Aksum University; University of Warwick; Ohio State University; Johns Hopkins University; Korea University; Wellcome Trust; University of Ottawa; NIHR Oxford Biomedical Research Centre; Universität Ulm; Horizon Pharmaceuticals; Tampereen Yliopisto; Tabriz University of Medical Sciences; Duke Global Health Institute, Duke University; Universitetet i Bergen; Nova Southeastern University; Chest Research Foundation; Jordan University of Science and Technology; National Cerebral and Cardiovascular Center; National Health Research Institutes; University of West Florida; University of Leicester; McMaster University; Seoul National University; Burnet Institute; University of Massachusetts Boston; University of Colombo; Universidade Federal do Rio Grande do Sul; Seattle Children's Research Institute; Arabian Gulf University; London School of Economics and Political Science; Faculty of Medicine and Health, University of Sydney; Universiti Brunei Darussalam; Norwegian Institute of Public Health; United Nations Population Fund; Rafsanjan University of Medical Sciences; Uniwersytet Jagielloński Collegium Medicum; University of California, Irvine; Universität Basel; Sveučilište u Zagrebu; Université de Lorraine; University of South Florida; Harvard University; Deutsches Krebsforschungszentrum; Taipei Medical University; Tel Aviv University; International Medical University; Aristotle University of Thessaloniki; Islamic Azad University; West Virginia University; Bournemouth University; Uniwersytet Łódzki; Maragheh University of Medical Sciences; University of Rochester; Nanyang Technological University; Auckland University of Technology, New Zealand; Pomorski Uniwersytet Medyczny W Szczecinie; Bayer; Università degli Studi di Milano; University of Otago; Queensland University of Technology; Universidad de Costa Rica; Università di Bologna; Drexel University; Universitetet i Tromsø; Economic and Social Research Council; Sanjay Gandhi Postgraduate Institute of Medical Sciences; University of Louisville; Iran University of Medical Sciences; Jazan University; Ahvaz Jundishapur University of Medical Sciences; Griffith University; Rede de Química e Tecnologia; Tulane University; University of Oklahoma; Brandeis University; National Center for Global Health and Medicine; Universiteit Gent; Bill and Melinda Gates Foundation; Injury Prevention Research Center; Háskólinn í Reykjavík; Pfizer; University of Bristol; University of Washington; Univerzita Komenského v Bratislave; King Fahd University of Petroleum and Minerals; Durban University of Technology; Saint Paul's Hospital Millennium Medical College; Aarhus Universitet; Washington University in St. Louis; Southern University of Science and Technology; Bristol-Myers Squibb; Delhi Technological University; Università degli Studi di Firenze; Danone; Lee Kong Chian School of Medicine, Nanyang Technological University; Medical University - Varna; School of Medicine, Boston University; Universiti Malaya; Isfahan University of Medical Sciences; Instituto de Salud Carlos III; San Diego State University; National Drug and Alcohol Research Centre; Friedrich-Schiller-Universität Jena; Amgen; National Heart, Lung, and Blood Institute; AstraZeneca; Moscow Institute of Physics and Technology; Emory University; Children’s Hospital of Wisconsin Research Institute; Yale University; Birmingham City University; North-West University; King Saud University; Simmons College; National Center for Child Health and Development; Mazandaran University of Medical Sciences; Ministry of Health and Medical Education; University of Alberta; Kaiser Permanente; Universidade Federal de Santa Catarina; University of the Punjab; Mashhad University of Medical Sciences; Biomedical Research Council; Gilead Sciences; Chinese Center for Disease Control and Prevention; George Mason University; Shanghai Jiao Tong University; University College Cork; Cleveland Clinic; Universidad Nacional de Colombia; Northwestern University; University of California, San Diego; Hamadan University of Medical Sciences; South Australian Health and Medical Research Institute; Hamad Medical Corporation; Trường Đại học Nguyễn Tất Thành; Universidade Federal de Minas Gerais; Alzheimer's Association; Damon Runyon Cancer Research Foundation","keywords":"Life expectancy; Global health; Quality-adjusted life year; Medicine; Environmental health; Gerontology; Demography; Geography; Socioeconomics; Economic growth; Public health; Economics; Sociology; Cost effectiveness; Population","score_opus":0.05452043896401063,"score_gpt":0.3641609304960273,"score_spread":0.30964049153201667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751792491","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":[0.19555503,0.44567534,0.0036549272,0.0011808915,0.00036997968,0.00085189636,0.34955505,0.00021959844,0.0029373318],"genre_scores_gemma":[0.6859207,0.18271123,0.006263284,0.0008740022,0.00023204106,0.0026733156,0.12053263,0.000092048234,0.00070076215],"study_design_codex":"observational","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99547464,0.0011019708,0.0014757906,0.00076991017,0.00087490503,0.00030279713],"domain_scores_gemma":[0.9945425,0.00149997,0.0022046375,0.00029741283,0.0012582225,0.00019724756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053470563,0.000929939,0.0030385558,0.008161018,0.0003637225,0.00087888405,0.00081585563,0.00043516923,0.0016967473],"category_scores_gemma":[0.012508634,0.00062623475,0.009729105,0.013293007,0.0005558888,0.0009778105,0.001795902,0.0008487805,0.00032694283],"study_design_candidate":"meta_analysis","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":[0.00085620413,0.000030336172,0.798257,0.056865737,0.068288095,0.00018356186,0.0006594239,0.0019341587,0.00020734551,0.00094700325,0.023507573,0.048263557],"study_design_scores_gemma":[0.0005874588,0.00018623477,0.86260533,0.02854994,0.07176922,0.000766179,0.0010767645,0.001602145,0.00039786426,0.0013241062,0.030967386,0.00016734273],"about_ca_topic_score_codex":0.054556083,"about_ca_topic_score_gemma":0.09231747,"teacher_disagreement_score":0.054556083,"about_ca_system_score_codex":0.001648144,"about_ca_system_score_gemma":0.00440387,"threshold_uncertainty_score":0.108477056},"labels":[],"label_agreement":null},{"id":"W2752116363","doi":"10.1016/j.insmatheco.2017.08.008","title":"IME’s Editorial Board","year":2017,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo; University of Toronto","funders":"","keywords":"Editorial board; State (computer science); Political science; Current (fluid); Library science; Computer science; Engineering; Algorithm; Electrical engineering","score_opus":0.022215100108219563,"score_gpt":0.28787132755543865,"score_spread":0.26565622744721906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752116363","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000116488765,0.004469693,0.0002587721,0.085713215,0.90527797,0.000023330616,0.00012427679,0.00006975951,0.003946607],"genre_scores_gemma":[0.0015655694,0.0037627346,0.00032296922,0.03148314,0.93235314,0.000056826102,0.0001063447,0.00013504601,0.030214217],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938877,0.0010662094,0.00076901796,0.0008162354,0.0028898825,0.0005709384],"domain_scores_gemma":[0.95632505,0.01071645,0.0036805777,0.0018034806,0.021695124,0.0057793334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00923755,0.0017096153,0.0020042174,0.004163578,0.0025333865,0.011496877,0.0020860715,0.009643466,0.041011196],"category_scores_gemma":[0.056939185,0.00064714317,0.0015660365,0.0018224492,0.0017912416,0.0043367636,0.0024085566,0.010046168,0.021190073],"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":[0.000026710584,0.000009539732,0.00006396912,0.00008471356,0.0000082455645,0.00006134356,0.000010141868,0.000015659154,0.000031585816,0.00037492276,0.9929603,0.006352915],"study_design_scores_gemma":[0.000024572211,0.000010878872,0.00024102606,0.00029304755,0.000017028286,0.00011199232,0.00004270806,0.00009740395,0.00008961822,0.0009946417,0.998066,0.000011050041],"about_ca_topic_score_codex":0.0010054887,"about_ca_topic_score_gemma":0.0021316216,"teacher_disagreement_score":0.041011196,"about_ca_system_score_codex":0.0024138568,"about_ca_system_score_gemma":0.0034451673,"threshold_uncertainty_score":0.13719606},"labels":[],"label_agreement":null},{"id":"W2760870430","doi":"10.1016/j.insmatheco.2017.09.007","title":"A strategy for hedging risks associated with period and cohort effects using q-forwards","year":2017,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cohort; Hedge; Actuarial science; Liability; Longevity risk; Economics; Pension; Life annuity; Cohort effect; Basis risk; Population; Econometrics; Medicine; Finance; Internal medicine; Biology; Environmental health","score_opus":0.06499114315169921,"score_gpt":0.33313692733340383,"score_spread":0.26814578418170465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760870430","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":[0.0081401905,0.00007474935,0.99034566,0.0002739302,0.0000704723,0.0000592896,0.00006278118,0.00020719676,0.00076566765],"genre_scores_gemma":[0.27102768,0.0002535145,0.71827376,0.00024155334,0.00015909539,0.00018837632,0.00028347326,0.00015628686,0.009416162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987245,0.0005694146,0.00012144689,0.00017451498,0.00029657446,0.00011352355],"domain_scores_gemma":[0.9932989,0.0040412075,0.00029146046,0.0012196758,0.00095574674,0.00019314044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011133442,0.0009966191,0.0013676669,0.0017452335,0.00082198647,0.0018941585,0.0024440587,0.0019050954,0.0066173295],"category_scores_gemma":[0.0181531,0.00089309417,0.0018870195,0.0013542778,0.0008204889,0.0031066579,0.0021127267,0.0021029767,0.0009366634],"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":[0.00054071384,0.0004258056,0.009427227,0.00012192509,0.00054476765,0.0005125634,0.00038902633,0.421198,0.0063483007,0.21552752,0.0037783207,0.34118584],"study_design_scores_gemma":[0.000048325113,0.00009985371,0.0006682889,0.000021793867,0.000074421274,0.00009128604,0.000043175405,0.9063589,0.001705695,0.08883556,0.0020156784,0.00003712459],"about_ca_topic_score_codex":0.005308352,"about_ca_topic_score_gemma":0.0048611043,"teacher_disagreement_score":0.011133442,"about_ca_system_score_codex":0.00070505746,"about_ca_system_score_gemma":0.0018125562,"threshold_uncertainty_score":0.05887997},"labels":[],"label_agreement":null},{"id":"W2766646457","doi":"10.5539/mas.v11n11p60","title":"A Physics Model for Analysis on Demographic Structures","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Population; Census; Matching (statistics); Statistical physics; Demographic analysis; Set (abstract data type); Order (exchange); Statistics; Econometrics; Mathematics; Demography; Physics; Computer science; Sociology; Economics","score_opus":0.059419708584849705,"score_gpt":0.3581049439636261,"score_spread":0.2986852353787764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766646457","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":[0.0074399603,0.0006927914,0.97361493,0.0014553037,0.00024878324,0.00007775685,0.0006593159,0.00022687708,0.015584211],"genre_scores_gemma":[0.62459785,0.0062355413,0.2909762,0.0019928778,0.0019934655,0.0017596481,0.0024039764,0.0003756634,0.0696648],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930584,0.0002071759,0.000041132374,0.00015708455,0.00021080583,0.00007791467],"domain_scores_gemma":[0.99884593,0.00058865146,0.00016429076,0.00012997027,0.00019795826,0.000073093426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011751296,0.00074890064,0.00087172363,0.001805712,0.00096342975,0.0016086395,0.0019312543,0.0016286535,0.0073082973],"category_scores_gemma":[0.0037152716,0.00036525977,0.0017811133,0.0016056639,0.0014073229,0.0034601386,0.0012311536,0.0021874115,0.0016155428],"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":[0.000005184663,0.00002550049,0.0009070711,0.00005172793,0.000023145129,0.00013786448,0.00014821028,0.054115057,0.0005768084,0.9335792,0.0030813268,0.0073487656],"study_design_scores_gemma":[0.000013584693,0.00003926519,0.00080225634,0.000027439375,0.000026955884,0.00028278286,0.000055505552,0.39380944,0.00016970483,0.5868575,0.017883828,0.00003175286],"about_ca_topic_score_codex":0.007072282,"about_ca_topic_score_gemma":0.0029505333,"teacher_disagreement_score":0.0073082973,"about_ca_system_score_codex":0.0016342532,"about_ca_system_score_gemma":0.0017778887,"threshold_uncertainty_score":0.024448693},"labels":[],"label_agreement":null},{"id":"W2767346769","doi":"10.1016/j.insmatheco.2017.09.001","title":"An efficient algorithm for the valuation of a guaranteed annuity option with correlated financial and mortality risks","year":2017,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"Division of Mathematical Sciences; Philippine Council for Industry, Energy, and Emerging Technology Research and Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Annuity; Life annuity; Valuation (finance); Quantile; Econometrics; Interest rate swap; Actuarial science; Economics; Interest rate; Mathematics; Computer science; Finance","score_opus":0.06076403081696313,"score_gpt":0.33203991065607724,"score_spread":0.2712758798391141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767346769","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":[0.012768187,0.00013434507,0.9845221,0.00023701116,0.000049770384,0.00009785601,0.000096788106,0.00058451865,0.0015093865],"genre_scores_gemma":[0.16820735,0.0001370668,0.82763755,0.00012306572,0.000085878004,0.0003205393,0.0003725077,0.00020661781,0.0029093912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991322,0.0002781575,0.00006695775,0.0001744807,0.00020748093,0.00014072495],"domain_scores_gemma":[0.9969298,0.0021935275,0.00014767652,0.00020085633,0.000354877,0.00017319224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024988817,0.0010202699,0.0017648318,0.0013527322,0.0008083674,0.00308894,0.0024817123,0.0029030198,0.010712621],"category_scores_gemma":[0.008416527,0.0007292868,0.001140111,0.0012510259,0.0010140921,0.002192154,0.0026711014,0.0022127577,0.0016582499],"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":[0.00055000244,0.000308743,0.002891288,0.00021843001,0.0001446041,0.00036425385,0.00025958917,0.5541168,0.0045627537,0.086270705,0.007737214,0.34257564],"study_design_scores_gemma":[0.00010061885,0.000034505203,0.00014038308,0.000014975952,0.000012506959,0.00006093659,0.000023355113,0.9675896,0.00040279372,0.030900287,0.0007093613,0.000010724501],"about_ca_topic_score_codex":0.0034172905,"about_ca_topic_score_gemma":0.003576224,"teacher_disagreement_score":0.010712621,"about_ca_system_score_codex":0.0014986472,"about_ca_system_score_gemma":0.0032698682,"threshold_uncertainty_score":0.035837293},"labels":[],"label_agreement":null},{"id":"W2767505677","doi":"10.1007/s13385-017-0161-3","title":"Quantile hedging pension payoffs: an analysis of investment incentives","year":2017,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hedge; Economics; Context (archaeology); Quantile; Asset (computer security); Bond; Portfolio; Asset allocation; Investment strategy; Hedge fund; Stochastic game; Market neutral; Sharpe ratio; Econometrics; Actuarial science; Financial economics; Microeconomics; Finance; Computer science","score_opus":0.04702255601729736,"score_gpt":0.35238535721760184,"score_spread":0.30536280120030446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767505677","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":[0.68098056,0.0024313615,0.29614773,0.0032808906,0.000152625,0.00018548679,0.00055975205,0.00030356768,0.015958022],"genre_scores_gemma":[0.9849726,0.0005462671,0.0070922715,0.00009848447,0.00007104348,0.000040964012,0.00012307553,0.00003859496,0.007016653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984896,0.0007485936,0.00005452446,0.00018452987,0.00020148123,0.00032115038],"domain_scores_gemma":[0.9822635,0.0143285375,0.0013619732,0.00068738847,0.00058814744,0.00077050994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009936302,0.00074138294,0.0019726183,0.001063135,0.0004455101,0.0028018395,0.0020330553,0.0026578053,0.008056864],"category_scores_gemma":[0.032123826,0.000842056,0.000948179,0.0010410537,0.0015586858,0.0025904742,0.0016623235,0.0027490454,0.00030003427],"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":[0.00038635204,0.0002760862,0.013740082,0.00015145865,0.0001652273,0.00041047687,0.00043521012,0.56375223,0.0016645232,0.3761273,0.003407562,0.03948346],"study_design_scores_gemma":[0.0000452324,0.00013061245,0.0072233006,0.000035418332,0.00006083615,0.00008305813,0.000096819706,0.9042519,0.0002352767,0.08692968,0.00087066094,0.00003726305],"about_ca_topic_score_codex":0.0037722816,"about_ca_topic_score_gemma":0.0027686635,"teacher_disagreement_score":0.009936302,"about_ca_system_score_codex":0.0018723309,"about_ca_system_score_gemma":0.0015508145,"threshold_uncertainty_score":0.052548826},"labels":[],"label_agreement":null},{"id":"W2768301016","doi":"10.1016/j.jedc.2017.11.002","title":"Moment matching machine learning methods for risk management of large variable annuity portfolios","year":2017,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":45,"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":"Portfolio; Computer science; Liberian dollar; Valuation (finance); Monte Carlo method; Econometrics; Variable (mathematics); Project portfolio management; Machine learning; Actuarial science; Economics; Artificial intelligence; Mathematical optimization; Mathematics; Finance; Project management; Statistics","score_opus":0.010350257699414469,"score_gpt":0.3418259321824908,"score_spread":0.33147567448307635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768301016","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":[0.011869542,0.0006906175,0.9864825,0.00026441662,0.000036199883,0.000027634002,0.00006414384,0.00017218344,0.00039288405],"genre_scores_gemma":[0.64205146,0.0020380556,0.3458473,0.00024348867,0.0004818511,0.00039030463,0.00056168553,0.00024883225,0.008136948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987802,0.0007076304,0.00007704457,0.00017631255,0.00016455968,0.00009423759],"domain_scores_gemma":[0.98583317,0.011912601,0.00079033256,0.00054900517,0.000677914,0.0002370332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069177235,0.0007108868,0.0019625926,0.0016984249,0.0005863922,0.0013942283,0.0020864825,0.0018599267,0.0034811904],"category_scores_gemma":[0.022486188,0.00093838107,0.0011697125,0.0017881996,0.00092010753,0.0021887938,0.0017359235,0.0022297725,0.0005166405],"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":[0.00011573331,0.00010644113,0.0017993086,0.00008857795,0.00014891337,0.000051324474,0.00006377494,0.8643518,0.0004620119,0.049039844,0.0014308367,0.082341425],"study_design_scores_gemma":[0.00000563399,0.000008246492,0.00011938259,0.0000049920577,0.000006484318,0.0000043961622,0.0000028714558,0.9860612,0.00005662877,0.01358146,0.00014357438,0.0000051101824],"about_ca_topic_score_codex":0.0047234325,"about_ca_topic_score_gemma":0.0035439783,"teacher_disagreement_score":0.0069177235,"about_ca_system_score_codex":0.0011466416,"about_ca_system_score_gemma":0.001459938,"threshold_uncertainty_score":0.036584914},"labels":[],"label_agreement":null},{"id":"W2776066572","doi":"10.25336/p6r02k","title":"The Biostatistics of Aging: From Gompertzian Mortality to an Index of Aging-Relatedness","year":2017,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Index (typography); Gerontology; Biostatistics; Demography; Sociology; Medicine; Computer science; Epidemiology","score_opus":0.08643329108396045,"score_gpt":0.4106312497734372,"score_spread":0.32419795868947676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776066572","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":[0.0002474278,0.93153566,0.013304691,0.032631204,0.016228452,0.00004557041,0.00066611567,0.00024946066,0.005091464],"genre_scores_gemma":[0.005708602,0.90823036,0.016703475,0.018487878,0.030837994,0.00019419733,0.00082229776,0.000448194,0.018566933],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951154,0.0022408199,0.00035324862,0.00043191263,0.0017721475,0.000086453976],"domain_scores_gemma":[0.9762477,0.018099463,0.0009071909,0.0005829999,0.0035074933,0.0006551057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009831095,0.0020510217,0.0024513411,0.008188088,0.0005036211,0.0031719545,0.0016362017,0.0016997285,0.012198607],"category_scores_gemma":[0.036867958,0.0012420471,0.0011397663,0.008067244,0.0046719094,0.0029747596,0.0015259491,0.0061824922,0.009436013],"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":[0.000052060306,0.000019400692,0.0017880624,0.0017886175,0.00017384144,0.00005452632,0.00013504406,0.000593336,0.00019858826,0.008705608,0.7391723,0.24731863],"study_design_scores_gemma":[0.000039591934,0.0001202601,0.010395787,0.0042493674,0.0001637757,0.0010705969,0.0003141048,0.0015910626,0.00020180056,0.07385087,0.90789926,0.0001034467],"about_ca_topic_score_codex":0.018692207,"about_ca_topic_score_gemma":0.025201738,"teacher_disagreement_score":0.018692207,"about_ca_system_score_codex":0.0028799414,"about_ca_system_score_gemma":0.004625984,"threshold_uncertainty_score":0.051992476},"labels":[],"label_agreement":null},{"id":"W2777594229","doi":"10.3917/gs1.151.0041","title":"La forme de la courbe de mortalité des centenaires canadiens-français","year":2016,"lang":"fr","type":"article","venue":"Gérontologie et société","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.033166947897114364,"score_gpt":0.3673294909089715,"score_spread":0.3341625430118571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777594229","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":[0.94670594,0.0024070765,0.011142586,0.0014593748,0.0000791415,0.000048187,0.017590862,0.00027239433,0.020294473],"genre_scores_gemma":[0.98654,0.00069940905,0.0015983684,0.000069096575,0.000011321737,0.000033867316,0.0033160695,0.000024774647,0.007707159],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99914575,0.00015809509,0.00005768345,0.000250806,0.00021789364,0.00016989409],"domain_scores_gemma":[0.9941987,0.0017715283,0.0009892666,0.000468666,0.0023020902,0.00026979332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023317137,0.0004679113,0.00039305983,0.002280195,0.00092948414,0.0019948103,0.00068601454,0.000538052,0.0073372778],"category_scores_gemma":[0.010025438,0.00031722742,0.0008622967,0.004369108,0.0007240045,0.0007710566,0.00069475395,0.0006703483,0.0009945699],"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":[0.00016679165,0.000014815719,0.9343896,0.00027702766,0.00041611926,0.000226634,0.00501358,0.009574277,0.001136664,0.0021945427,0.0037401125,0.04284989],"study_design_scores_gemma":[0.0000044518983,0.00003044007,0.9822635,0.00014353203,0.00007035864,0.00011618701,0.002997223,0.0031290688,0.0002262937,0.0004300365,0.010543827,0.000045045286],"about_ca_topic_score_codex":0.85037214,"about_ca_topic_score_gemma":0.86224425,"teacher_disagreement_score":0.14962786,"about_ca_system_score_codex":0.00464312,"about_ca_system_score_gemma":0.0034647323,"threshold_uncertainty_score":0.30101806},"labels":[],"label_agreement":null},{"id":"W2781103269","doi":"10.25336/p6vs5k","title":"Adjustment of Nigeria population censuses using mathematical methods","year":2017,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Population; Population projection; Geography; Estimation; Demography; Statistics; Research methodology; Mathematics; Sociology; Economics","score_opus":0.1824289674540398,"score_gpt":0.4970326262337753,"score_spread":0.3146036587797355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781103269","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":[0.037186008,0.00076070026,0.9523534,0.00041818628,0.00033360042,0.0004332515,0.0009074752,0.00065266254,0.0069546932],"genre_scores_gemma":[0.22188956,0.0027177539,0.76397854,0.00016437676,0.00024170552,0.0012907409,0.0025424925,0.000334509,0.006840337],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971214,0.0013415506,0.0002612287,0.0003952703,0.00077122386,0.00010930147],"domain_scores_gemma":[0.99644285,0.0014594248,0.00067263574,0.0005074637,0.0008812268,0.00003628824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041374858,0.0006992539,0.00041999586,0.0032325091,0.00067200465,0.001616399,0.000956216,0.0002583535,0.0025081139],"category_scores_gemma":[0.025662301,0.00044090813,0.0009855382,0.003203345,0.00054068654,0.001055393,0.0015239293,0.000970643,0.0009888319],"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":[0.00010432501,0.000089084,0.041617107,0.00043668007,0.00017227727,0.0002752858,0.001125621,0.32689023,0.0030091081,0.16109684,0.0068565635,0.45832697],"study_design_scores_gemma":[0.000055558612,0.00009441532,0.04563069,0.0002702663,0.00008341306,0.0003870681,0.0009838284,0.80890995,0.004313863,0.049526244,0.08963709,0.00010761508],"about_ca_topic_score_codex":0.01594888,"about_ca_topic_score_gemma":0.010343609,"teacher_disagreement_score":0.01594888,"about_ca_system_score_codex":0.001633194,"about_ca_system_score_gemma":0.0023469087,"threshold_uncertainty_score":0.031712115},"labels":[],"label_agreement":null},{"id":"W2784246487","doi":"10.1017/asb.2017.44","title":"SMOOTHING POISSON COMMON FACTOR MODEL FOR PROJECTING MORTALITY JOINTLY FOR BOTH SEXES","year":2018,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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":"Smoothing; Poisson distribution; Mathematics; Projection (relational algebra); Econometrics; Generalized linear model; Statistics; Sample size determination; Applied mathematics; Algorithm","score_opus":0.07918036894108249,"score_gpt":0.368883161346797,"score_spread":0.2897027924057145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784246487","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":[0.042569824,0.00027754513,0.95356494,0.00055284676,0.00015322265,0.00013222355,0.0009780461,0.00040655176,0.0013648064],"genre_scores_gemma":[0.74312633,0.00074732467,0.23520343,0.00033208093,0.0002752856,0.0008789837,0.0033812386,0.0002946682,0.015760679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940328,0.0031988118,0.00024571782,0.0013151047,0.0007356579,0.00047191785],"domain_scores_gemma":[0.98975986,0.0056762174,0.0010426285,0.0015660437,0.0016889852,0.00026617953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014101052,0.00096616545,0.0018306526,0.0019053057,0.00068002497,0.001798637,0.0044758017,0.0023097862,0.0068242783],"category_scores_gemma":[0.028815135,0.00080463284,0.0032835777,0.0032882018,0.0015175735,0.0025207165,0.0017762419,0.0028262013,0.0014565145],"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":[0.00033846434,0.00011944256,0.024006134,0.00019500463,0.0004414129,0.0005095951,0.00062998984,0.73998344,0.0009956524,0.16149695,0.005367985,0.06591592],"study_design_scores_gemma":[0.000029903555,0.000101692494,0.0038774142,0.000041040923,0.00007636956,0.00015808905,0.00009990664,0.94094706,0.0002633382,0.05127593,0.0030673484,0.00006188697],"about_ca_topic_score_codex":0.03481296,"about_ca_topic_score_gemma":0.019933809,"teacher_disagreement_score":0.03481296,"about_ca_system_score_codex":0.0016414602,"about_ca_system_score_gemma":0.0026579127,"threshold_uncertainty_score":0.07457441},"labels":[],"label_agreement":null},{"id":"W2785858829","doi":"10.2105/ajph.2017.304268","title":"Period Life Tables for Calculating Life Expectancy: Options to Assess and Minimize the Potential for Bias","year":2018,"lang":"en","type":"letter","venue":"American Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Public Health Agency of Canada","funders":"","keywords":"Life expectancy; Demography; Statistics; Gerontology; Medicine; Environmental health; Mathematics; Sociology; Population","score_opus":0.1455242321804005,"score_gpt":0.38796933562576447,"score_spread":0.24244510344536396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785858829","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058519426,0.017760703,0.61256063,0.23231354,0.01490936,0.0021635676,0.03193837,0.0050690873,0.077432774],"genre_scores_gemma":[0.07484765,0.013994911,0.7561601,0.09049269,0.014482972,0.0069490722,0.011350835,0.0029891494,0.028732555],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96183175,0.031002568,0.0025072887,0.0005277159,0.003835184,0.0002954569],"domain_scores_gemma":[0.81199086,0.15631977,0.006446572,0.0071860636,0.017089263,0.00096744986],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.040534176,0.00080537575,0.00092016056,0.0025959553,0.0007217729,0.0026618722,0.001693791,0.0038765508,0.032678332],"category_scores_gemma":[0.2540364,0.0008232378,0.0013661516,0.0036245603,0.0009214591,0.0042874445,0.0018852323,0.006741407,0.0071949596],"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":[0.00030518085,0.000033144428,0.0022664433,0.0006312359,0.00013899115,0.000119718374,0.0004540079,0.0026132928,0.00014313444,0.044718962,0.73928475,0.20929106],"study_design_scores_gemma":[0.00050874474,0.00022248592,0.0042532864,0.0033499703,0.00014791965,0.0012601597,0.00034953747,0.015844299,0.00069365354,0.16384926,0.8092771,0.00024349784],"about_ca_topic_score_codex":0.0021396524,"about_ca_topic_score_gemma":0.0035882606,"teacher_disagreement_score":0.9594658,"about_ca_system_score_codex":0.001477753,"about_ca_system_score_gemma":0.0023499776,"threshold_uncertainty_score":0.2143678},"labels":[],"label_agreement":null},{"id":"W2786505706","doi":"10.1016/0967-0653(96)80374-a","title":"10.1016/0967-0653(96)80374-a","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Selection (genetic algorithm); Econometrics; Statistics; Mathematics; Computer science","score_opus":0.009012440133619137,"score_gpt":0.21640199290011922,"score_spread":0.2073895527665001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786505706","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":[0.00080139464,0.000559632,0.000731256,0.00066022,0.00037218747,0.00017455209,0.0009479902,0.00076175446,0.9949911],"genre_scores_gemma":[0.0010396391,0.00029968287,0.0004726033,0.00040530987,0.00007791078,0.00007951429,0.000428399,0.00019110867,0.99700576],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898905,0.000103830665,0.00010404377,0.0003617527,0.00020572574,0.00023557349],"domain_scores_gemma":[0.997334,0.00073625904,0.00019154625,0.00034859736,0.00060100935,0.0007886291],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016657946,0.003365186,0.0022863725,0.0033843429,0.0029986738,0.004623984,0.0036572376,0.007873715,0.9895969],"category_scores_gemma":[0.0020100244,0.0013037409,0.001676968,0.0028292765,0.0029033676,0.007060652,0.004052184,0.003286419,0.991392],"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":[0.00046859967,0.0003851428,0.001482295,0.0007321256,0.000057723322,0.00048181278,0.0001751926,0.00061434816,0.0024788452,0.006682693,0.32383156,0.66260964],"study_design_scores_gemma":[0.0000768211,0.00015961786,0.001363006,0.00068312977,0.000022529839,0.00060646574,0.00023477683,0.00033713915,0.00041084865,0.0011597406,0.9949065,0.000039366514],"about_ca_topic_score_codex":0.0044597047,"about_ca_topic_score_gemma":0.0034848794,"teacher_disagreement_score":0.010403097,"about_ca_system_score_codex":0.0012903786,"about_ca_system_score_gemma":0.0009843671,"threshold_uncertainty_score":0.014838755},"labels":[],"label_agreement":null},{"id":"W2794983862","doi":"","title":"Assessing basis risk for longevity transactions – Phase 2","year":2017,"lang":"en","type":"article","venue":"British Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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":"Longevity; Longevity risk; Actuarial science; Economics; Business; Gerontology; Medicine","score_opus":0.05159778403899402,"score_gpt":0.3995388133334107,"score_spread":0.34794102929441667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794983862","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":[0.7990757,0.00088393566,0.14477158,0.001067566,0.00009231452,0.031344227,0.009084627,0.0007241732,0.012955985],"genre_scores_gemma":[0.7960564,0.00049174763,0.14656343,0.00046151777,0.0000742935,0.02029373,0.021716822,0.000325454,0.014016592],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937862,0.0032681965,0.000348639,0.0006355422,0.0014504035,0.0005110303],"domain_scores_gemma":[0.97666353,0.012843772,0.0013930711,0.002969556,0.005190424,0.00093960454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016839506,0.001287996,0.0015831038,0.0014248461,0.0006966704,0.0027075964,0.0025143248,0.0018307745,0.011188325],"category_scores_gemma":[0.0475809,0.0014709277,0.0027270308,0.0010722567,0.00064485596,0.0037682373,0.0042004962,0.001946621,0.0026548177],"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":[0.009426938,0.010649228,0.5048322,0.0010820037,0.0015482367,0.0002385973,0.0015176757,0.11169825,0.006873932,0.009848914,0.010820607,0.3314634],"study_design_scores_gemma":[0.0036373073,0.023319589,0.6435828,0.00068015076,0.0015613264,0.00076682173,0.0020482715,0.233527,0.029244693,0.025859943,0.03536916,0.00040289952],"about_ca_topic_score_codex":0.012629366,"about_ca_topic_score_gemma":0.011948966,"teacher_disagreement_score":0.016839506,"about_ca_system_score_codex":0.0016303344,"about_ca_system_score_gemma":0.0075075715,"threshold_uncertainty_score":0.08905685},"labels":[],"label_agreement":null},{"id":"W2795775339","doi":"10.5663/aps.v7i1.29326","title":"Fertility of Aboriginal People in Canada: An Overview of Trends at the Turn of the 21st Century","year":2018,"lang":"en","type":"article","venue":"aboriginal policy studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Aboriginal Affairs Northern Dev Canada; Statistics Canada","funders":"Indigenous and Northern Affairs Canada","keywords":"Fertility; Population; Geography; Multivariate analysis; Multivariate statistics; Demography; Total fertility rate; Socioeconomics; Research methodology; Sociology; Family planning; Medicine; Statistics","score_opus":0.046588667904714456,"score_gpt":0.40337969562204784,"score_spread":0.3567910277173334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795775339","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":[0.8022115,0.112460114,0.00066461123,0.006015321,0.00020930097,0.0001501578,0.055311378,0.00017953444,0.022797966],"genre_scores_gemma":[0.88166344,0.09603535,0.0010752622,0.00058934063,0.00012272966,0.000059243404,0.014508319,0.000033736338,0.0059125307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99924016,0.000039177285,0.00005422959,0.000073116564,0.00031789322,0.00027546732],"domain_scores_gemma":[0.997477,0.00015154135,0.00031160467,0.000037337806,0.0016409368,0.00038156472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079774146,0.00027575457,0.00038993158,0.0061644535,0.0022834789,0.0012366228,0.000684143,0.0003281044,0.0020091475],"category_scores_gemma":[0.0016649528,0.00014507554,0.00060499273,0.013400721,0.00064502284,0.00038914135,0.0007508472,0.00072029966,0.00020885779],"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":[0.00015107034,0.000047203495,0.8569537,0.0014111444,0.00019050023,0.00041489903,0.006625492,0.0009176136,0.00047327587,0.0017534915,0.013398567,0.117663085],"study_design_scores_gemma":[0.0000021521087,0.000026743493,0.9816804,0.00023589136,0.000041605184,0.00019385527,0.002313468,0.0001447892,0.00009364176,0.00005707907,0.015189669,0.000020745962],"about_ca_topic_score_codex":0.99273974,"about_ca_topic_score_gemma":0.9945674,"teacher_disagreement_score":0.024791185,"about_ca_system_score_codex":0.024791185,"about_ca_system_score_gemma":0.040298056,"threshold_uncertainty_score":0.17987347},"labels":[],"label_agreement":null},{"id":"W2796203969","doi":"10.1017/s1748499518000076","title":"On age difference in joint lifetime modelling with life insurance annuity applications","year":2018,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Copula (linguistics); Annuity; Actuarial science; Econometrics; Novelty; Life annuity; Joint probability distribution; Economics; Life insurance; Pension; Generalized Pareto distribution; Mathematics; Statistics; Psychology; Extreme value theory; Finance","score_opus":0.07405414140497217,"score_gpt":0.3449796649036265,"score_spread":0.27092552349865434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796203969","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":[0.13208649,0.0013409128,0.86001503,0.0009680659,0.00008751725,0.00008993697,0.00034111377,0.000155146,0.0049158926],"genre_scores_gemma":[0.9118858,0.001402472,0.077860855,0.0001294777,0.00012235192,0.00018954466,0.0004377625,0.00008469042,0.00788696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790204,0.0014815333,0.000079833284,0.00020985304,0.00018396087,0.0001428657],"domain_scores_gemma":[0.9849606,0.012893345,0.00079290546,0.00043095462,0.00070207147,0.00022017246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009025784,0.0007872277,0.0010398771,0.0012423599,0.00062932534,0.0016998394,0.0020400516,0.0018168982,0.003038235],"category_scores_gemma":[0.022124272,0.0004101078,0.00151209,0.0016324758,0.0011424543,0.0013495025,0.0021305983,0.0021012942,0.00037304452],"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":[0.000047583184,0.000053320586,0.0069515174,0.000049321097,0.00006235568,0.00019772911,0.00029016964,0.8916551,0.00018704198,0.08699912,0.0005844803,0.012922327],"study_design_scores_gemma":[0.0000015334489,0.000013312566,0.0005107849,0.0000083862315,0.000007829197,0.000021999425,0.00002426162,0.9901155,0.0000293337,0.008881425,0.0003787059,0.000006993713],"about_ca_topic_score_codex":0.03394408,"about_ca_topic_score_gemma":0.015808351,"teacher_disagreement_score":0.03394408,"about_ca_system_score_codex":0.001288572,"about_ca_system_score_gemma":0.0011811059,"threshold_uncertainty_score":0.06749296},"labels":[],"label_agreement":null},{"id":"W2800437156","doi":"10.1016/j.cam.2018.03.035","title":"Optimal excess-of-loss reinsurance and investment problem with delay and jump–diffusion risk process under the CEV model","year":2018,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Guangxi University of Finance and Economics; National Natural Science Foundation of China","keywords":"Reinsurance; Jump diffusion; Hamilton–Jacobi–Bellman equation; Exponential utility; Mathematics; Economics; Bellman equation; Econometrics; Jump; Mathematical optimization; Actuarial science","score_opus":0.015317120742169546,"score_gpt":0.2781340134950182,"score_spread":0.26281689275284864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800437156","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":[0.48258215,0.005617902,0.47136843,0.02029261,0.0006966085,0.00037206203,0.001795111,0.000461488,0.01681359],"genre_scores_gemma":[0.9718227,0.0010122895,0.0115770325,0.00031963992,0.00018060447,0.0001575427,0.000315302,0.00008072227,0.014534146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828416,0.00072259124,0.000095615884,0.00034247633,0.00016362824,0.0003915054],"domain_scores_gemma":[0.9886827,0.008350308,0.0010130588,0.00021611189,0.00063323457,0.0011045892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056274785,0.0020390567,0.0059573767,0.0017825867,0.0007468545,0.0038988336,0.003338874,0.0073274425,0.0064687496],"category_scores_gemma":[0.015893834,0.002086258,0.0016246438,0.0010631522,0.002504741,0.003974476,0.0029362275,0.0046111085,0.00031748728],"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":[0.0005190626,0.00019538982,0.002165603,0.0003178442,0.00019799414,0.0007991513,0.00012545478,0.88453275,0.0009242321,0.10301714,0.0024138999,0.0047914796],"study_design_scores_gemma":[0.00011534101,0.00009300609,0.00046511163,0.000031294905,0.00006796176,0.00008104372,0.0000628326,0.9755931,0.00015838849,0.022969691,0.0003234325,0.000038699818],"about_ca_topic_score_codex":0.013135926,"about_ca_topic_score_gemma":0.0055655898,"teacher_disagreement_score":0.013135926,"about_ca_system_score_codex":0.0030000012,"about_ca_system_score_gemma":0.0037974652,"threshold_uncertainty_score":0.029761314},"labels":[],"label_agreement":null},{"id":"W2801460810","doi":"10.4236/jmf.2018.82023","title":"Valuation and Risk Assessment of a Portfolio of Variable Annuities: A Vector Autoregression Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Mathematical Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Portfolio; Actuarial science; Valuation (finance); Economics; Econometrics; Rate of return; Variable (mathematics); Financial economics; Finance; Mathematics","score_opus":0.023695476160357123,"score_gpt":0.3341580926366335,"score_spread":0.3104626164762764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801460810","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":[0.05501275,0.0009908043,0.94171447,0.0003437238,0.000041054147,0.00003213287,0.00006446379,0.000090121095,0.0017104663],"genre_scores_gemma":[0.91863275,0.00201736,0.07465356,0.00007605976,0.00019203311,0.00007850979,0.00018626585,0.000043973105,0.004119449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889576,0.0005929749,0.00005512686,0.00016082345,0.00018662539,0.00010867018],"domain_scores_gemma":[0.99746656,0.0018564588,0.00032749356,0.00008058877,0.00019614183,0.00007269446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003141759,0.00088945264,0.0012768066,0.0013357335,0.00023750051,0.0022867585,0.0012038265,0.0013526105,0.0013468792],"category_scores_gemma":[0.006658433,0.00047567993,0.0010483135,0.0011264135,0.0006842261,0.0018139742,0.000828117,0.0012024661,0.00019869999],"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":[0.000050287297,0.0000647022,0.0034116923,0.00006109202,0.00013956816,0.000182293,0.0000757787,0.9019514,0.0013881928,0.062201433,0.00047960662,0.029994046],"study_design_scores_gemma":[0.0000023901348,0.000023656177,0.00042038687,0.000008708882,0.00001537585,0.000021101932,0.000011861588,0.98911136,0.00013767919,0.010065144,0.00017244401,0.000009794555],"about_ca_topic_score_codex":0.0037668736,"about_ca_topic_score_gemma":0.0015874872,"teacher_disagreement_score":0.0037668736,"about_ca_system_score_codex":0.0008075394,"about_ca_system_score_gemma":0.00084537186,"threshold_uncertainty_score":0.01661539},"labels":[],"label_agreement":null},{"id":"W2803544802","doi":"10.3390/jrfm11020025","title":"Mean-Variance Portfolio Selection in a Jump-Diffusion Financial Market with Common Shock Dependence","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Efficient frontier; Jump diffusion; Hamilton–Jacobi–Bellman equation; Portfolio; Bellman equation; Mathematical optimization; Mathematics; Econometrics; Portfolio optimization; Financial market; Economics; Jump; Finance","score_opus":0.006400916101298773,"score_gpt":0.24416145313683638,"score_spread":0.2377605370355376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803544802","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":[0.3133154,0.0016971604,0.6742343,0.002555345,0.00009314428,0.0001012915,0.00019583448,0.00014777824,0.007659779],"genre_scores_gemma":[0.978118,0.0005711901,0.014631748,0.00014637387,0.000060152837,0.00009530336,0.00009055052,0.000027663375,0.0062589687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987632,0.0006126777,0.000049024104,0.0002247714,0.00017310216,0.00017725416],"domain_scores_gemma":[0.9961743,0.0027656716,0.00044646763,0.00008356321,0.00021484408,0.00031523313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038220154,0.001040789,0.00262637,0.0008116931,0.00054624263,0.0020812636,0.0013984005,0.0027844207,0.002122342],"category_scores_gemma":[0.0073180282,0.0010038782,0.0010354602,0.00080625876,0.0017384074,0.002620424,0.0015124623,0.0015576006,0.00017868483],"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":[0.00016602948,0.00012706075,0.0022707686,0.00013853706,0.0001660708,0.00052310724,0.00008342652,0.849413,0.0014555547,0.13792615,0.000830008,0.0069002565],"study_design_scores_gemma":[0.000040843028,0.00004199922,0.00040340156,0.000008153382,0.000026578637,0.000035868725,0.000018549765,0.9703371,0.0001289484,0.028800026,0.00014135266,0.000017128901],"about_ca_topic_score_codex":0.0036020828,"about_ca_topic_score_gemma":0.001872077,"teacher_disagreement_score":0.0038220154,"about_ca_system_score_codex":0.0015990342,"about_ca_system_score_gemma":0.0013496425,"threshold_uncertainty_score":0.020213008},"labels":[],"label_agreement":null},{"id":"W2804292829","doi":"10.5539/ijsp.v7n2p91","title":"Assessing Impacts on Mortality of Lifestyle Factors: Allowing for Model Uncertainty","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cohort; Demography; Medicine; Attributable risk; Population; Regression analysis; Cohort study; Proportional hazards model; Environmental health; Statistics; Mathematics; Surgery","score_opus":0.06492897661188213,"score_gpt":0.39885333262508577,"score_spread":0.33392435601320364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804292829","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":[0.43037128,0.0031238883,0.5506067,0.008104786,0.00032018233,0.00030364716,0.0016553366,0.00033099184,0.0051831794],"genre_scores_gemma":[0.97395915,0.0010478317,0.022610912,0.00045680176,0.00019703088,0.00019034052,0.0004553714,0.00005354259,0.0010290245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900497,0.007875768,0.00028491963,0.00069906114,0.0006096334,0.00048096819],"domain_scores_gemma":[0.8740668,0.11769472,0.003397041,0.0029524392,0.0011711156,0.0007178832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034855373,0.0014555126,0.0018077219,0.001063674,0.00066581497,0.0027196128,0.0024854217,0.002563197,0.0011185867],"category_scores_gemma":[0.09783289,0.0007955563,0.002740109,0.0009297981,0.0017016828,0.0035161485,0.0031593419,0.0036297545,0.000113519796],"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":[0.00017236134,0.00005042149,0.019963935,0.0001528252,0.0006657561,0.00021546094,0.00016931185,0.9548922,0.00018029411,0.01501999,0.00037574724,0.008141603],"study_design_scores_gemma":[0.00004428591,0.0003395692,0.00809428,0.00014765616,0.00047065227,0.00017361254,0.00028437487,0.91211236,0.0002850741,0.0766206,0.00136561,0.000061888466],"about_ca_topic_score_codex":0.016246362,"about_ca_topic_score_gemma":0.007560682,"teacher_disagreement_score":0.034855373,"about_ca_system_score_codex":0.0015529231,"about_ca_system_score_gemma":0.0024824538,"threshold_uncertainty_score":0.18433505},"labels":[],"label_agreement":null},{"id":"W2805334223","doi":"10.3390/math9141629","title":"Mortality/Longevity Risk-Minimization with or without Securitization","year":2021,"lang":"en","type":"preprint","venue":"Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Agentschap voor Innovatie door Wetenschap en Technologie","keywords":"Securitization; Longevity risk; Martingale (probability theory); Longevity; Actuarial science; Econometrics; Economics; Mathematics; Finance; Statistics; Biology","score_opus":0.04363289064928536,"score_gpt":0.33788560373042015,"score_spread":0.29425271308113476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805334223","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":[0.333768,0.0012787185,0.6492853,0.0012243906,0.00006655173,0.000076872435,0.00011643057,0.000087096334,0.014096551],"genre_scores_gemma":[0.9440543,0.0007337,0.043217603,0.00012178881,0.00011146964,0.000083490806,0.00009834244,0.000060855367,0.011518468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999315,0.00026844212,0.00003323537,0.00015772608,0.00012415675,0.000101424666],"domain_scores_gemma":[0.9986744,0.0005788871,0.0003070488,0.00013427729,0.000107783344,0.00019766642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022495857,0.0010516014,0.00093052397,0.00047791243,0.0003271898,0.0012312235,0.0010113429,0.0012751392,0.0017810109],"category_scores_gemma":[0.0049171294,0.00039108656,0.0009817764,0.00026666824,0.0014556028,0.002305566,0.0016121714,0.0013622612,0.00018691225],"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":[0.00016620109,0.00013642831,0.0019639926,0.00020179707,0.00011091966,0.00028071468,0.00026864957,0.17927907,0.0076246955,0.776745,0.0010485566,0.03217399],"study_design_scores_gemma":[0.000039251096,0.00027930585,0.001590854,0.00007739514,0.00006428569,0.00020639038,0.00009119542,0.59179914,0.0036476532,0.3996778,0.0024885403,0.00003814708],"about_ca_topic_score_codex":0.0006382652,"about_ca_topic_score_gemma":0.00026969917,"teacher_disagreement_score":0.0022495857,"about_ca_system_score_codex":0.0009423399,"about_ca_system_score_gemma":0.0010620301,"threshold_uncertainty_score":0.011897087},"labels":[],"label_agreement":null},{"id":"W2805772982","doi":"10.71781/12835","title":"Nouveaux regards sur la longévité : analyse de l'âge modal au décès et de la dispersion des durées de vie selon les principales causes de décès au Canada (1974-2011)","year":2017,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Philosophy; Humanities; Art","score_opus":0.008277878750372034,"score_gpt":0.229783178578243,"score_spread":0.22150529982787098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805772982","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":[0.89372885,0.021946628,0.0043990803,0.0070611453,0.00024472136,0.00010787336,0.03183491,0.000086739376,0.04059005],"genre_scores_gemma":[0.9683331,0.010139249,0.0018511502,0.00056460337,0.000067056295,0.00004778114,0.005718212,0.00003157496,0.013247242],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990891,0.000092691516,0.00005011752,0.00014404755,0.00038765528,0.00023637307],"domain_scores_gemma":[0.99610585,0.0005806314,0.00057724526,0.00015027312,0.0022254798,0.00036044454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014498767,0.00033117316,0.00039286038,0.0022011108,0.0019432867,0.0022304973,0.00078593363,0.0003590829,0.004272957],"category_scores_gemma":[0.005123182,0.0001601877,0.00071806123,0.005707577,0.0012915621,0.0007579745,0.0011011175,0.00091255154,0.00033009765],"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":[0.00011581685,0.000017246617,0.9161065,0.0006095602,0.00031985654,0.00019575293,0.01617842,0.0013857861,0.0005815448,0.007244669,0.0064068874,0.050837874],"study_design_scores_gemma":[0.0000021976093,0.000022106684,0.961583,0.00037354993,0.000094133895,0.000061113875,0.0105587505,0.00037226343,0.00024670645,0.00054504763,0.02611244,0.000028683407],"about_ca_topic_score_codex":0.9872565,"about_ca_topic_score_gemma":0.9939482,"teacher_disagreement_score":0.015355079,"about_ca_system_score_codex":0.015355079,"about_ca_system_score_gemma":0.03834373,"threshold_uncertainty_score":0.111409426},"labels":[],"label_agreement":null},{"id":"W2807467829","doi":"10.1111/mafi.12244","title":"A martingale representation theorem and valuation of defaultable securities","year":2020,"lang":"en","type":"preprint","venue":"Mathematical Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Martingale representation theorem; Local martingale; Martingale (probability theory); Securitization; Life insurance; Econometrics; Credit risk; Actuarial science; Valuation (finance); Economics; Mathematics; Mathematical economics; Finance; Statistics; Economy","score_opus":0.07214252793540836,"score_gpt":0.34612435444690204,"score_spread":0.27398182651149366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807467829","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":[0.20210196,0.00200146,0.76238126,0.0035340958,0.00014750488,0.00007575925,0.00037312866,0.0001643414,0.029220477],"genre_scores_gemma":[0.9606851,0.000985734,0.03015138,0.00019232131,0.0002638342,0.000101658974,0.00020361181,0.000045577228,0.0073708086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908686,0.00038922695,0.000045530523,0.00012875898,0.00020106336,0.00014863041],"domain_scores_gemma":[0.9950034,0.0033004968,0.0005313482,0.0002889251,0.0005222381,0.00035357685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049274014,0.0006594314,0.0008308621,0.0016857176,0.0005563158,0.002789284,0.0013161459,0.0013636026,0.00432743],"category_scores_gemma":[0.012669297,0.00032714175,0.0013662415,0.001144988,0.0025417916,0.0054277233,0.0015641883,0.0021533207,0.00029381228],"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":[0.000009043582,0.000009468714,0.00023478319,0.000021609541,0.000009655632,0.00003663427,0.000078355944,0.007257987,0.00022854554,0.9895387,0.00038714742,0.0021880884],"study_design_scores_gemma":[0.00001005912,0.000026647824,0.000289318,0.000029096589,0.000007262994,0.00004556843,0.000034719273,0.121157266,0.00015544481,0.8774914,0.0007409742,0.000012148249],"about_ca_topic_score_codex":0.0015460706,"about_ca_topic_score_gemma":0.0006359908,"teacher_disagreement_score":0.0049274014,"about_ca_system_score_codex":0.0019284335,"about_ca_system_score_gemma":0.0010575524,"threshold_uncertainty_score":0.026058853},"labels":[],"label_agreement":null},{"id":"W2808398587","doi":"10.1017/asb.2018.9","title":"DYNAMIC HEDGING STRATEGIES FOR CASH BALANCE PENSION PLANS","year":2018,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hedge; Bond; Valuation (finance); Liability; Actuarial science; Economics; Cash flow; Pension; Coupon; Econometrics; Business; Finance","score_opus":0.017760248301621078,"score_gpt":0.3072038602809579,"score_spread":0.2894436119793368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808398587","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":[0.87233126,0.00049165287,0.11945669,0.00048388395,0.000037038695,0.000083699306,0.00014365776,0.000102790815,0.0068693375],"genre_scores_gemma":[0.9939573,0.000076159704,0.004316182,0.00001362435,0.0000056334593,0.000011355129,0.000043164473,0.0000038073097,0.0015727278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996076,0.00012771135,0.000027865117,0.00008049984,0.00008973455,0.00006654174],"domain_scores_gemma":[0.99874216,0.00066693476,0.00028062655,0.00010876244,0.00010331169,0.00009818373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019774118,0.00070288073,0.00065720937,0.00069371384,0.0002957343,0.0016973377,0.0009075038,0.00092184846,0.0020611119],"category_scores_gemma":[0.00499289,0.00030625678,0.0005511509,0.00037375162,0.000575617,0.0013910275,0.0008790088,0.0007519574,0.00011542211],"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":[0.00020045675,0.000088235676,0.004452297,0.00003061433,0.0000730132,0.00022179272,0.00013034295,0.94171447,0.0016620811,0.027272193,0.00043921705,0.023715274],"study_design_scores_gemma":[0.000023292268,0.0001439283,0.0011902185,0.000009422036,0.000023591947,0.000040605086,0.000053282176,0.98584366,0.00053666305,0.011733996,0.00038739716,0.0000138513715],"about_ca_topic_score_codex":0.0044466523,"about_ca_topic_score_gemma":0.0018987799,"teacher_disagreement_score":0.0044466523,"about_ca_system_score_codex":0.0011337828,"about_ca_system_score_gemma":0.0005492106,"threshold_uncertainty_score":0.0104576945},"labels":[],"label_agreement":null},{"id":"W2808600279","doi":"10.1177/0962280218779408","title":"Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":40,"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 Health Centre","funders":"Agence Nationale de la Recherche; Cancer Research UK","keywords":"Spline (mechanical); Survival analysis; Computer science; Elastic net regularization; Estimator; Econometrics; Statistics; Parametric statistics; Smoothing; Proportional hazards model; Poisson distribution; Mathematics; Mathematical optimization; Regression","score_opus":0.10006292289328535,"score_gpt":0.5157611724700032,"score_spread":0.41569824957671786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808600279","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":[0.018591566,0.00007254854,0.9806912,0.00014650653,0.000021223701,0.00004062357,0.000070162794,0.00010849598,0.0002576589],"genre_scores_gemma":[0.5389898,0.00036111506,0.45340636,0.00018215356,0.00009507388,0.0008552628,0.0007268424,0.00016111268,0.005222324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967031,0.0024990693,0.00009238201,0.00027425922,0.00028047967,0.00015066653],"domain_scores_gemma":[0.992272,0.0060894666,0.0005104148,0.0004408247,0.000533975,0.00015336747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010215846,0.0006422192,0.001033364,0.0008609647,0.00031804654,0.0008683227,0.0017169089,0.0010654309,0.0018777479],"category_scores_gemma":[0.015483115,0.0004411055,0.0016651644,0.0008409246,0.0007598831,0.00086765277,0.001224294,0.0018269741,0.0003704454],"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":[0.00018634222,0.00009192266,0.005104424,0.000072952906,0.00013606228,0.00018325042,0.00021476822,0.89271337,0.0014476124,0.05670248,0.0008816967,0.04226503],"study_design_scores_gemma":[0.000008620781,0.00003761146,0.00039532775,0.0000053854014,0.0000124491235,0.000017482946,0.0000075956837,0.9945344,0.00009086202,0.0045861737,0.0002955057,0.000008650899],"about_ca_topic_score_codex":0.0041090082,"about_ca_topic_score_gemma":0.0030391712,"teacher_disagreement_score":0.010215846,"about_ca_system_score_codex":0.000498659,"about_ca_system_score_gemma":0.0014960143,"threshold_uncertainty_score":0.0540272},"labels":[],"label_agreement":null},{"id":"W2811188583","doi":"10.1017/s1748499518000179","title":"Assessing basis risk in index-based longevity swap transactions","year":2018,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Swap (finance); Longevity risk; Hedge; Portfolio; Econometrics; Basis risk; Index (typography); Longevity; Futures contract; Interest rate swap; Actuarial science; Range (aeronautics); Computer science; Statistics; Economics; Mathematics; Pension; Finance; Engineering; Medicine","score_opus":0.08217005474706969,"score_gpt":0.4124759566329121,"score_spread":0.3303059018858424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811188583","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":[0.9308339,0.00022635907,0.06717665,0.00012284357,0.000016374472,0.000037547907,0.00014783788,0.00008203837,0.0013563747],"genre_scores_gemma":[0.9960598,0.000033460015,0.003596085,0.000006250417,0.00000411266,0.00000766645,0.00006278032,0.0000053184585,0.00022459119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984823,0.0009142856,0.000075400225,0.00014778043,0.00028881954,0.000091518275],"domain_scores_gemma":[0.9863538,0.009967303,0.001615111,0.0010723659,0.0007028111,0.00028861169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062770126,0.00048220213,0.00066541217,0.00086809014,0.0002089058,0.0012571944,0.00088478555,0.00093037507,0.0015148859],"category_scores_gemma":[0.018570583,0.0002530162,0.0004318031,0.0007614082,0.000639507,0.0016202381,0.00088909216,0.0008993783,0.00011552495],"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":[0.00041097833,0.00008373262,0.026241349,0.00003810777,0.00008858193,0.0001746388,0.00012010708,0.9481204,0.0017329132,0.010577934,0.0002047719,0.012206489],"study_design_scores_gemma":[0.000009730485,0.00017643753,0.005883224,0.000008419413,0.000018843048,0.000055772944,0.000037104284,0.9881087,0.00073396525,0.0048071584,0.00014603185,0.000014720098],"about_ca_topic_score_codex":0.0014663063,"about_ca_topic_score_gemma":0.0005900866,"teacher_disagreement_score":0.0062770126,"about_ca_system_score_codex":0.0007507628,"about_ca_system_score_gemma":0.00031330893,"threshold_uncertainty_score":0.03319639},"labels":[],"label_agreement":null},{"id":"W2828591083","doi":"","title":"Of infants and infections: Investigating 20th century mortality patterns in Newfoundland and Labrador","year":2018,"lang":"en","type":"article","venue":"The 87th Annual Meeting of the American Association of Physical Anthropologists, Austin, TX","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; History; Demography","score_opus":0.014796687454861701,"score_gpt":0.3270669456766787,"score_spread":0.31227025822181703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2828591083","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":[0.9917774,0.0008233637,0.00010277143,0.00042958456,0.000012584849,0.000010283344,0.0050485795,0.000007828439,0.001787671],"genre_scores_gemma":[0.9926133,0.00096820504,0.0002166297,0.00014736195,0.000011336824,0.00002287371,0.0039845137,0.000009378215,0.0020263505],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948967,0.00005319255,0.000055786903,0.00007305701,0.0000748406,0.00025347213],"domain_scores_gemma":[0.99832755,0.000116167044,0.0006061855,0.00010738994,0.00053983944,0.00030287908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075342174,0.00024731187,0.00022889525,0.0028224974,0.0014712575,0.0010156285,0.001120569,0.00053270004,0.0013241387],"category_scores_gemma":[0.0018246155,0.00028252037,0.00045646055,0.0042590476,0.00053578615,0.00093353837,0.0015648252,0.00070788315,0.0002691664],"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":[0.00001778903,0.000015564716,0.9950478,0.000011596371,0.000022876602,0.00007077105,0.001161078,0.000051773502,0.00007713913,0.000065941276,0.00070375676,0.0027539637],"study_design_scores_gemma":[4.3804974e-7,0.000003985684,0.9979267,0.000012526074,0.0000047322146,0.000032052376,0.0013888698,0.00004520192,0.000022624778,0.0000038775224,0.00055633177,0.0000024771123],"about_ca_topic_score_codex":0.9358559,"about_ca_topic_score_gemma":0.9770329,"teacher_disagreement_score":0.064144075,"about_ca_system_score_codex":0.007039086,"about_ca_system_score_gemma":0.005790161,"threshold_uncertainty_score":0.12904364},"labels":[],"label_agreement":null},{"id":"W2884020332","doi":"","title":"Recent Population Trends in the Midwest","year":2011,"lang":"en","type":"article","venue":"Economic Trends","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Fell; Population; Demography; Geography; Quarter (Canadian coin); Cartography; Archaeology; Sociology","score_opus":0.06328238638635642,"score_gpt":0.3150232720704122,"score_spread":0.2517408856840558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884020332","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":[0.73746014,0.035385877,0.0009712435,0.020445226,0.0010416439,0.00014390267,0.13441245,0.00025165183,0.069887884],"genre_scores_gemma":[0.88421005,0.038962193,0.0021434326,0.0037061167,0.00046604587,0.00035036806,0.054462675,0.000068165224,0.015630977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961925,0.000032143293,0.00006887764,0.000099260455,0.00008271452,0.00009769118],"domain_scores_gemma":[0.99899787,0.000057300025,0.00038715772,0.000033927132,0.0003844655,0.0001392936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005722858,0.00023326458,0.00017804927,0.0019592694,0.00080611825,0.0010283808,0.00058146066,0.0003769346,0.0063659316],"category_scores_gemma":[0.0027488035,0.00016364326,0.00037276506,0.0039153425,0.00018647773,0.0011404266,0.00082518166,0.0007360129,0.0014564999],"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":[0.00013515021,0.00010107332,0.8165116,0.0005863451,0.00012724163,0.00027672848,0.003472004,0.00033636915,0.00046515805,0.00217365,0.0590949,0.11671983],"study_design_scores_gemma":[0.000007843879,0.00006755005,0.88628924,0.00054766674,0.00004642806,0.00036208576,0.002618654,0.00015931051,0.00015128206,0.00033484487,0.10939446,0.000020556501],"about_ca_topic_score_codex":0.21881378,"about_ca_topic_score_gemma":0.28651845,"teacher_disagreement_score":0.21881378,"about_ca_system_score_codex":0.0019680734,"about_ca_system_score_gemma":0.0016564068,"threshold_uncertainty_score":0.4350803},"labels":[],"label_agreement":null},{"id":"W2884765514","doi":"10.1201/9781315119731","title":"Multistate Models for the Analysis of Life History Data","year":2018,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":116,"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":"Computer science; History","score_opus":0.16816697334472394,"score_gpt":0.3448852714330977,"score_spread":0.17671829808837378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884765514","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":[0.0006897944,0.0076988325,0.9711599,0.0022980762,0.0007765955,0.000119751465,0.0018413964,0.0010348826,0.014380816],"genre_scores_gemma":[0.05820735,0.032368354,0.7882415,0.004276769,0.0027785378,0.0033176546,0.010112709,0.0026003371,0.09809687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99541485,0.0024000134,0.00025998525,0.00059164094,0.0011563967,0.00017712267],"domain_scores_gemma":[0.9880336,0.009237932,0.0006283279,0.0011510906,0.0007515318,0.00019745983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007715704,0.0019245801,0.0015737293,0.0019328265,0.000719582,0.0032775607,0.0034980506,0.003016264,0.028838899],"category_scores_gemma":[0.018941384,0.0014172898,0.0031866794,0.0037777456,0.0014842348,0.0036217545,0.0027023274,0.00657087,0.011924389],"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":[0.000043071963,0.000068957175,0.0010850588,0.0005169069,0.00024071083,0.00015101675,0.0002825096,0.058275368,0.00046318248,0.7314906,0.104708105,0.10267442],"study_design_scores_gemma":[0.000022549179,0.000035581295,0.00083497004,0.00024450364,0.000057008765,0.00018165185,0.000043153497,0.16066043,0.00016924387,0.6866787,0.15101741,0.000054760418],"about_ca_topic_score_codex":0.0046371864,"about_ca_topic_score_gemma":0.0057945657,"teacher_disagreement_score":0.028838899,"about_ca_system_score_codex":0.002308413,"about_ca_system_score_gemma":0.0022112601,"threshold_uncertainty_score":0.09647572},"labels":[],"label_agreement":null},{"id":"W2889540676","doi":"10.25336/csp29404","title":"The Global Spread of Fertility Decline: Population, Fear and Uncertainty","year":2018,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"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 Lethbridge","funders":"","keywords":"Fertility; Population; Demographic economics; Demography; Economics; Sociology","score_opus":0.03649944077045078,"score_gpt":0.3674411820933659,"score_spread":0.3309417413229151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889540676","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":[0.0008385996,0.75650847,0.00038147508,0.21830282,0.0051953737,0.000014613757,0.00028420473,0.000024503683,0.018449938],"genre_scores_gemma":[0.040860984,0.85700476,0.0009945681,0.032373454,0.016235933,0.00003897614,0.00028218902,0.00009700934,0.052112088],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99940073,0.00020407884,0.00002469141,0.000099124496,0.0002145633,0.0000568714],"domain_scores_gemma":[0.9982389,0.00115703,0.00015221293,0.000049088452,0.00022424414,0.00017851281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019233668,0.001289149,0.0009791543,0.0017003143,0.0018048666,0.004664684,0.0008975703,0.0023271579,0.015117431],"category_scores_gemma":[0.0046370565,0.00072362553,0.000580327,0.0031589393,0.0061596846,0.0061080786,0.0020989408,0.005893985,0.003195741],"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":[0.000036311336,0.000014515084,0.0011051956,0.0005084445,0.000033833658,0.00007328498,0.0018628178,0.00054171856,0.00007964255,0.026365807,0.89314616,0.07623232],"study_design_scores_gemma":[0.000011165907,0.000019820252,0.0037509822,0.0025871585,0.00001808086,0.00023302874,0.0036445884,0.00036896268,0.000044923567,0.03951617,0.9497542,0.00005103101],"about_ca_topic_score_codex":0.13193898,"about_ca_topic_score_gemma":0.19436638,"teacher_disagreement_score":0.868061,"about_ca_system_score_codex":0.005581643,"about_ca_system_score_gemma":0.0043363767,"threshold_uncertainty_score":0.26234204},"labels":[],"label_agreement":null},{"id":"W2890638138","doi":"10.23889/ijpds.v3i4.708","title":"Methods for identifying health state transitions from administrative data: the case of metastasis in prostate cancer","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institute for Clinical Evaluative Sciences; University Health Network; University of Toronto","funders":"","keywords":"Medicine; False positive paradox; Bone metastasis; Malignancy; Population; Identification (biology); Prostate cancer; Cancer; Metastasis; Medical record; Medical prescription; Cancer registry; Data mining; Computer science; Internal medicine; Machine learning","score_opus":0.34104380065959883,"score_gpt":0.6057042374598454,"score_spread":0.26466043680024653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890638138","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":[0.25632966,0.0012512904,0.73306453,0.001011041,0.00006890346,0.0011306979,0.004656318,0.0004899455,0.0019975682],"genre_scores_gemma":[0.5815858,0.0003368476,0.41301507,0.00011707396,0.000079452715,0.0009277954,0.0032859815,0.000053677,0.0005982573],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9880976,0.00841102,0.00072747236,0.0011529131,0.0012175587,0.00039341094],"domain_scores_gemma":[0.9192429,0.07018809,0.005763385,0.0023197099,0.0020447448,0.0004412794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01784669,0.0007591318,0.00092045875,0.0054391823,0.0010808886,0.0016067964,0.0019749017,0.0013158482,0.0016615994],"category_scores_gemma":[0.077044375,0.00048898335,0.0021011552,0.0042368528,0.0008278145,0.0010478531,0.0018345803,0.0016037761,0.00023985351],"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":[0.0005534025,0.00037940897,0.49892905,0.0006961413,0.0009783845,0.00086487463,0.0013130738,0.27602962,0.00084966555,0.018272223,0.0038557185,0.19727841],"study_design_scores_gemma":[0.000069604386,0.00010861879,0.05361008,0.00017750286,0.00013260478,0.00042345483,0.00040580583,0.9139506,0.0007636888,0.027603239,0.0026873655,0.00006745961],"about_ca_topic_score_codex":0.031532776,"about_ca_topic_score_gemma":0.035490878,"teacher_disagreement_score":0.031532776,"about_ca_system_score_codex":0.0015467494,"about_ca_system_score_gemma":0.0024152293,"threshold_uncertainty_score":0.09438342},"labels":[],"label_agreement":null},{"id":"W2893237262","doi":"10.4236/ojs.2018.85055","title":"An Examination of Male and Female Monthly Employment Rates over Time in Canada and the United States Using Hidden Markov Probability Models","year":2018,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Saskatchewan","funders":"","keywords":"Hidden Markov model; Multivariate statistics; Markov chain; Markov model; Demography; Econometrics; Statistics; Multivariate analysis; Demographic economics; Geography; Economics; Mathematics; Computer science; Sociology; Artificial intelligence","score_opus":0.034342364853974176,"score_gpt":0.3106313745297916,"score_spread":0.27628900967581743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893237262","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":[0.99087256,0.00041180395,0.0022700585,0.00042299097,0.0000081721555,0.000018108152,0.0040846774,0.000029506009,0.0018822113],"genre_scores_gemma":[0.9954823,0.00028417315,0.0008467677,0.000026260635,0.0000058146775,0.000009745938,0.0026608175,0.0000070354517,0.0006771857],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995295,0.000101801634,0.000024997933,0.0000878795,0.00013716306,0.00011866286],"domain_scores_gemma":[0.99653804,0.0018295457,0.0005855381,0.00014254353,0.0006589735,0.00024534113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016501043,0.00017864327,0.00026159306,0.0018728871,0.00068418996,0.0009742898,0.0005536228,0.00030313953,0.0015602502],"category_scores_gemma":[0.00857918,0.00017839254,0.00045732313,0.0036717134,0.0003063222,0.00043712353,0.00042217693,0.00050805503,0.00016571644],"study_design_candidate":"simulation_or_modeling","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":[0.000055299,0.00002591147,0.9772046,0.000021874108,0.000075066324,0.00012117629,0.00068687514,0.007144473,0.00012545865,0.0015324393,0.0009957072,0.012011158],"study_design_scores_gemma":[0.000002651093,0.000014619664,0.96024764,0.000031936546,0.000028454895,0.00005910228,0.0011197746,0.03629933,0.00008661456,0.00061710813,0.0014770662,0.000015824731],"about_ca_topic_score_codex":0.9136642,"about_ca_topic_score_gemma":0.92870516,"teacher_disagreement_score":0.08633578,"about_ca_system_score_codex":0.0032801852,"about_ca_system_score_gemma":0.0045639337,"threshold_uncertainty_score":0.17368841},"labels":[],"label_agreement":null},{"id":"W2896186508","doi":"10.1111/issr.12176","title":"Measuring and reporting obligations of social security retirement systems: Actuarial perspectives","year":2018,"lang":"en","type":"article","venue":"International Social Security Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Social security; Pension; Accounting; Actuarial science; Sustainability; Business; Economics; Finance","score_opus":0.08863690484161917,"score_gpt":0.38764707820395,"score_spread":0.2990101733623308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896186508","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":[0.13587765,0.035229377,0.37143335,0.17816849,0.0038164936,0.00048725322,0.002903885,0.00038019987,0.27170333],"genre_scores_gemma":[0.9324373,0.008354654,0.045201506,0.0028836427,0.0044273427,0.000267467,0.00052752864,0.000068003035,0.0058325394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9031969,0.06740638,0.0060733194,0.0031985806,0.018100798,0.0020240124],"domain_scores_gemma":[0.8112263,0.12272967,0.026111327,0.010829003,0.026749238,0.0023544745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.076681845,0.0009780737,0.0007257357,0.009531143,0.002484645,0.013660517,0.0030640226,0.0031067894,0.0040814104],"category_scores_gemma":[0.12882769,0.00064685737,0.001036487,0.0070356806,0.0076849526,0.0155638065,0.0075245094,0.004343776,0.00055190234],"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":[0.000026586235,0.000101919635,0.012868592,0.00024437686,0.000066401255,0.00007484413,0.002493068,0.0095900865,0.00015590528,0.91289467,0.008947225,0.05253632],"study_design_scores_gemma":[0.000013209984,0.00021778054,0.032644972,0.0029644533,0.000071637456,0.00040265877,0.008067673,0.06852234,0.0017560387,0.77108634,0.11394522,0.0003076319],"about_ca_topic_score_codex":0.0075708865,"about_ca_topic_score_gemma":0.004294186,"teacher_disagreement_score":0.076681845,"about_ca_system_score_codex":0.007267434,"about_ca_system_score_gemma":0.0052278894,"threshold_uncertainty_score":0.4055372},"labels":[],"label_agreement":null},{"id":"W2896831445","doi":"10.1111/issr.12178","title":"Measuring and reporting the actuarial obligations of the Canada Pension Plan","year":2018,"lang":"en","type":"article","venue":"International Social Security Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Actua","funders":"","keywords":"Accrual; Pension; Context (archaeology); Asset (computer security); Balance sheet; Accounting; Sustainability; Business; Actuarial science; Balance (ability); Plan (archaeology); Finance; Economics","score_opus":0.08427088386283539,"score_gpt":0.34274659056135365,"score_spread":0.25847570669851827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896831445","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":[0.39942122,0.055874143,0.016185451,0.033414908,0.0011130503,0.0009697292,0.02398873,0.0005385607,0.4684942],"genre_scores_gemma":[0.9477898,0.015223622,0.008847165,0.00051137025,0.00016354516,0.000094945244,0.0040215,0.000037089998,0.023310982],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98295677,0.0014360929,0.0004645388,0.0002920094,0.013946456,0.000904186],"domain_scores_gemma":[0.9664606,0.0025715467,0.0026166763,0.0006698211,0.026429359,0.0012520973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009094636,0.00030637364,0.00019963583,0.008880475,0.0019888282,0.004502693,0.0012416862,0.00039113578,0.001758489],"category_scores_gemma":[0.027818406,0.00017073417,0.00017804487,0.007077353,0.0014256748,0.0009035889,0.00140185,0.0008058747,0.0002896948],"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":[0.0001445893,0.000081663435,0.13720275,0.0010641646,0.00017453579,0.00029529908,0.007440879,0.0122995945,0.001694204,0.110917345,0.16135575,0.5673291],"study_design_scores_gemma":[0.000015809961,0.00009148843,0.51926506,0.0021655269,0.00011496371,0.0001842663,0.007879399,0.008904854,0.003902505,0.0057142465,0.45155978,0.00020205046],"about_ca_topic_score_codex":0.97002554,"about_ca_topic_score_gemma":0.97011775,"teacher_disagreement_score":0.04377849,"about_ca_system_score_codex":0.04377849,"about_ca_system_score_gemma":0.09600993,"threshold_uncertainty_score":0.31763667},"labels":[],"label_agreement":null},{"id":"W2897257410","doi":"10.1016/s0140-6736(18)31694-5","title":"Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories","year":2018,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2960,"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":"National Cancer Institute; Bill and Melinda Gates Foundation","keywords":"Life expectancy; Cause of death; Demography; Gerontology; Medicine; Environmental health; Population; Disease; Sociology","score_opus":0.17144142440728635,"score_gpt":0.35980806922650105,"score_spread":0.1883666448192147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897257410","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":[0.9720008,0.0002523773,0.014198365,0.00071894994,0.000043731266,0.000054569184,0.009992794,0.00013689979,0.0026015472],"genre_scores_gemma":[0.9867481,0.00014795757,0.0056196935,0.00003521782,0.000010948391,0.000057392936,0.0068540536,0.000016464443,0.00051013124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955434,0.00019913686,0.000026974207,0.00010824034,0.000037811507,0.00007356311],"domain_scores_gemma":[0.9988036,0.0006435474,0.00018090547,0.00009891441,0.00017358066,0.00009945489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018557734,0.00059374725,0.0004287995,0.00067623454,0.00025971973,0.00071876403,0.0009499762,0.0010010217,0.001264849],"category_scores_gemma":[0.004246764,0.00039081243,0.0017280253,0.0009286649,0.0003434889,0.0009878926,0.00054767344,0.00089676626,0.00023847782],"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":[0.00005131445,0.000019195759,0.026145818,0.00003137301,0.00006449373,0.000078864214,0.00009663042,0.9685565,0.0001352816,0.0014946231,0.0007340754,0.002591818],"study_design_scores_gemma":[0.000025615167,0.000054487333,0.018731264,0.000043376673,0.000047303583,0.000037944486,0.0002058913,0.9767259,0.00029313273,0.0024126093,0.0013855585,0.000036983798],"about_ca_topic_score_codex":0.12751028,"about_ca_topic_score_gemma":0.07614148,"teacher_disagreement_score":0.12751028,"about_ca_system_score_codex":0.0027645205,"about_ca_system_score_gemma":0.0013384618,"threshold_uncertainty_score":0.25353616},"labels":[],"label_agreement":null},{"id":"W2899742633","doi":"10.1016/s0140-6736(18)31891-9","title":"Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017","year":2018,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1215,"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 Mental Health; National Heart, Lung, and Blood Institute; Université de Limoges; Center for International Health; Economic and Social Research Council; Medical Research Council; Weill Cornell Medicine - Qatar; Abbott Diagnostics; Injury Prevention Research Center; Cochrane South Africa; Uniwersytet Opolski; Debre Markos University; George Institute for Global Health; Frankfurt University of Applied Sciences; Kurdistan University Of Medical Sciences; H. Lundbeck A/S; Servier; Universitas Hasanuddin; Savitribai Phule Pune University; University of Thessaly; University of Zanjan; University of Hail; Pomorski Uniwersytet Medyczny W Szczecinie; National Institute on Aging; Zanjan University of Medical Sciences; Tarbiat Modares University; Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences; University of Gondar; College of Engineering, Michigan State University; Tartu Ülikool; Universitatea de Medicină şi Farmacie \"Carol Davila\" Bucureşti; University of South Africa; University of the Philippines; Arak University of Medical Sciences; Qazvin University of Medical Sciences; I.M. Sechenov First Moscow State Medical University; Hospital for Sick Children; Debre Tabor University; Shahid Beheshti University of Medical Sciences; Baqiyatallah University of Medical Sciences; University of Haifa; Kermanshah University of Medical Sciences; Shiraz University; United Arab Emirates University; Ahvaz Jundishapur University of Medical Sciences; Astellas Pharma; National Institutes of Health; Chinese University of Hong Kong; Syddansk Universitet; Jimma University; Zahedan University of Medical Sciences; Haramaya University; Sichuan University; Inyuvesi Yakwazulu-Natali; Universiti Sains Malaysia; Universiti Kebangsaan Malaysia; Tehran University of Medical Sciences and Health Services; Isfahan University of Medical Sciences; Universiti Malaya; Ain Shams University; Shiraz University of Medical Sciences; King Abdulaziz University; Sanjay Gandhi Postgraduate Institute of Medical Sciences; University of Toronto; Lunds Universitet; South African Medical Research Council; Bundesministerium für Gesundheit; National and Kapodistrian University of Athens; Cairo University; Simmons College; King Saud University; Friedrich-Schiller-Universität Jena; Queensland University of Technology; University of Technology Sydney; University College Cork; Mekelle University; Universiteit Stellenbosch; Public Health England; Université de Bordeaux; Public Health Foundation of India; Indian Council of Medical Research; National Health and Medical Research Council; Hebrew University of Jerusalem; Universidad de Extremadura; Kwame Nkrumah University of Science and Technology; Swansea University; National University of Singapore; La Trobe University; American University of Beirut; Cardiff University; McMaster University; University of Leicester; National Institute for Health and Care Research; Ministry of Health and Medical Education; Mazandaran University of Medical Sciences; University of Glasgow; Korea Health Industry Development Institute; York University; Korea University; Keimyung University; University of Canberra; Dilla University; Maragheh University of Medical Sciences; Universitat Pompeu Fabra; Amgen; Public Health Agency of Canada; Chinese Center for Disease Control and Prevention; New York University Abu Dhabi; Novavax; Johns Hopkins University; Wellcome Trust; Uniwersytet Jagielloński Collegium Medicum; Karl-Franzens-Universität Graz; Seattle Children's Research Institute; U.S. Department of Veterans Affairs; Rafsanjan University of Medical Sciences; Ahmadu Bello University; Samara University; National Cerebral and Cardiovascular Center; Jordan University of Science and Technology; University of Oxford; Public Health Agency; McGill University; University of Cape Town; Horizon Pharmaceuticals; Birzeit University; National Cancer Institute; Seoul National University; Burnet Institute; International Centre for Diarrhoeal Disease Research, Bangladesh; Washington University in St. Louis; Universidad de Chile; University of Pittsburgh; Iran University of Medical Sciences; Universidad de la República Uruguay; Tulane University; Addis Ababa University; Danone; Università degli Studi di Firenze; Bill and Melinda Gates Foundation; Madras Diabetes Research Foundation; Pfizer; Högskolan Dalarna; University of Washington; Universität Ulm; Chalmers Tekniska Högskola; Alzheimer's Association; Norwegian Institute of Public Health; Michigan State University; Weill Cornell Medical College; University of Pennsylvania; Birmingham City University; Uniwersytet Łódzki; Helsingin Yliopisto; Ball State University; Karolinska Institutet; United Nations Population Fund; Vital Strategies; Damon Runyon Cancer Research Foundation; Children's Hospital of Philadelphia; University of West Florida; Student Research Committee, Tabriz University of Medical Sciences; Medizinische Universität Graz; Bahir Dar University; Bayer; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Mashhad University of Medical Sciences; Gilead Sciences; GlaxoSmithKline; Mansoura University; AstraZeneca; Universita degli Studi di Bari Aldo Moro; Hamadan University of Medical Sciences; South Australian Health and Medical Research Institute; Damietta University; Sanofi; Hawassa University; Kuwait University; Vetenskapsrådet; Oklahoma State University; Northwestern University; United States Agency for International Development","keywords":"Life expectancy; Disease; Demography; Medicine; Gerontology; MEDLINE; Burden of disease; Environmental health; Biology; Pathology; Population","score_opus":0.1229583341961662,"score_gpt":0.38249767505645854,"score_spread":0.25953934086029234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899742633","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":[0.07198474,0.7849075,0.0036778082,0.0014520676,0.00045839752,0.000982542,0.13445155,0.00015825834,0.0019272055],"genre_scores_gemma":[0.43665582,0.4750113,0.00791722,0.0014330075,0.00048506947,0.004635392,0.07303754,0.0001598316,0.0006647498],"study_design_codex":"observational","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99365646,0.0018649427,0.0018985833,0.00097327586,0.0013148695,0.00029185915],"domain_scores_gemma":[0.9878862,0.0043317596,0.0035536776,0.00082555803,0.0031099534,0.00029290508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011644908,0.0010834638,0.003024194,0.015120109,0.000441826,0.0011776278,0.00082792336,0.0005768583,0.0018892131],"category_scores_gemma":[0.02433458,0.0008189556,0.010763355,0.019835945,0.0006644321,0.0011774533,0.0021051993,0.0009978466,0.00039579943],"study_design_candidate":"meta_analysis","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":[0.000922634,0.000041071096,0.61742175,0.13720661,0.09108168,0.00025872764,0.0015080867,0.001914486,0.00032707394,0.0016698907,0.043411832,0.1042362],"study_design_scores_gemma":[0.00049099815,0.0002710156,0.7103509,0.11694623,0.10701268,0.0008817636,0.0015981483,0.0014601154,0.00037167626,0.0017927564,0.058624733,0.00019900917],"about_ca_topic_score_codex":0.03767597,"about_ca_topic_score_gemma":0.06302839,"teacher_disagreement_score":0.03767597,"about_ca_system_score_codex":0.0019859418,"about_ca_system_score_gemma":0.0077251573,"threshold_uncertainty_score":0.07491338},"labels":[],"label_agreement":null},{"id":"W2899749914","doi":"10.11564/32-2-1215","title":"Is technical demography becoming less relevant? Two decade review of published articles in selected demography journals","year":2018,"lang":"en","type":"article","venue":"African Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","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":"Demography; Geography; Demographic change; Population; Sociology","score_opus":0.07964657586640704,"score_gpt":0.4098733407285758,"score_spread":0.33022676486216873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899749914","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007003259,0.9834487,0.00026097303,0.0054051587,0.001697016,0.000052006835,0.0004949461,0.000016291133,0.0016215941],"genre_scores_gemma":[0.04668599,0.94476885,0.0009193566,0.0041882778,0.0022322189,0.00008053038,0.00057904434,0.000015474267,0.0005302938],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9897675,0.0026436155,0.0038625621,0.00094417465,0.0024769988,0.00030531318],"domain_scores_gemma":[0.8431047,0.093720935,0.03549024,0.0021717716,0.023130585,0.0023816514],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01630449,0.00052053365,0.0012339642,0.032909937,0.00072050974,0.0039725094,0.00094921846,0.0011982665,0.0035646695],"category_scores_gemma":[0.077156104,0.0004856981,0.0016412359,0.024750583,0.001362677,0.0035240548,0.0011722947,0.0009109636,0.00044939722],"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":[0.00045068207,0.00007643703,0.04239799,0.3587815,0.0032071264,0.0011146397,0.0035977028,0.00027060596,0.0016488724,0.0037254107,0.04626219,0.53846693],"study_design_scores_gemma":[0.000068738875,0.00030674966,0.15785219,0.39312345,0.0077561117,0.0038376811,0.0064480444,0.0002060337,0.0011675588,0.002192979,0.4269495,0.00009095789],"about_ca_topic_score_codex":0.0037042848,"about_ca_topic_score_gemma":0.01003829,"teacher_disagreement_score":0.9836955,"about_ca_system_score_codex":0.0038854545,"about_ca_system_score_gemma":0.010960689,"threshold_uncertainty_score":0.08622742},"labels":[],"label_agreement":null},{"id":"W2900710720","doi":"10.23889/ijpds.v3i1.730","title":"Vasectomy reversal and prostate cancer risk: A multi-centre collaborative demonstration project of the Intentional Population Data Linkage Network","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Insurance, Mortality, Demography, Risk Management","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":"Vasectomy; Prostate cancer; Linkage (software); Vasectomy reversal; Record linkage; Medicine; Population; Gynecology; Cancer; Family planning; Environmental health; Research methodology; Internal medicine; Genetics; Biology","score_opus":0.06235565051988018,"score_gpt":0.4160999437490778,"score_spread":0.35374429322919765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900710720","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":[0.9181132,0.0016451319,0.035282172,0.015608239,0.0002735422,0.010109062,0.012884366,0.00025294372,0.005831328],"genre_scores_gemma":[0.8681647,0.0009468321,0.103674054,0.0014671376,0.00013849158,0.012622015,0.011348503,0.00012132466,0.001516909],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.90641874,0.07873641,0.0024105683,0.0031747296,0.0063684294,0.0028911594],"domain_scores_gemma":[0.86244804,0.06599819,0.011128271,0.032214236,0.01710366,0.011107648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13349408,0.0005526888,0.0009706666,0.0016916083,0.0023189753,0.0022984697,0.0028145744,0.0016562586,0.0012698397],"category_scores_gemma":[0.09549542,0.0007044234,0.0013013986,0.0041843457,0.0014438857,0.0019297962,0.0094428575,0.0025600563,0.00023547909],"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":[0.0059915544,0.0041061095,0.7607879,0.0014969147,0.002059216,0.0011855977,0.023566078,0.007740356,0.0036498297,0.0075932364,0.019095708,0.16272752],"study_design_scores_gemma":[0.0030409594,0.0056191212,0.9180876,0.00093823025,0.0011705948,0.0009917363,0.010483638,0.013246584,0.003414964,0.004540712,0.038124956,0.00034099878],"about_ca_topic_score_codex":0.092500396,"about_ca_topic_score_gemma":0.08034545,"teacher_disagreement_score":0.13349408,"about_ca_system_score_codex":0.004553878,"about_ca_system_score_gemma":0.02489853,"threshold_uncertainty_score":0.7059926},"labels":[],"label_agreement":null},{"id":"W2901412332","doi":"10.1080/10920277.2019.1570469","title":"Management of Portfolio Depletion Risk through Optimal Life Cycle Asset Allocation","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Asset allocation; Portfolio; Expected shortfall; Stochastic control; Value at risk; Actuarial science; Asset (computer security); Margin (machine learning); Economics; Risk management; Target date fund; Computer science; Econometrics; Finance; Optimal control; Mathematical optimization; Mathematics","score_opus":0.010034536753511215,"score_gpt":0.28164227263488223,"score_spread":0.271607735881371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901412332","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":[0.40018085,0.0005181189,0.5920105,0.0004998018,0.000027239477,0.00022459596,0.00009826662,0.00023326672,0.0062073586],"genre_scores_gemma":[0.9803171,0.000103661354,0.018578662,0.00003637485,0.000006017248,0.00007924968,0.00004105522,0.000016226284,0.00082169037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999191,0.00032232996,0.000030720017,0.0001147557,0.00016610505,0.00017497654],"domain_scores_gemma":[0.997292,0.0015279999,0.0005416848,0.00014834541,0.0002914065,0.0001986111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026749962,0.0007977533,0.0010209185,0.0008579656,0.00036140723,0.001393006,0.0010163389,0.0008380992,0.001408305],"category_scores_gemma":[0.006277662,0.0005698886,0.00048843434,0.0004899778,0.0007874408,0.0011470218,0.0010644287,0.00075415586,0.00011963496],"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":[0.00004769108,0.000050158356,0.00070654356,0.000012832612,0.000019011984,0.000021878945,0.000019614665,0.98706716,0.00050234067,0.0032646186,0.00014376073,0.008144406],"study_design_scores_gemma":[0.000013066582,0.000073871095,0.00027815453,0.000004413331,0.00001008533,0.0000085163665,0.000012672443,0.9961151,0.0003005234,0.0030498605,0.00012816841,0.00000552187],"about_ca_topic_score_codex":0.004532451,"about_ca_topic_score_gemma":0.0025075164,"teacher_disagreement_score":0.004532451,"about_ca_system_score_codex":0.0015007928,"about_ca_system_score_gemma":0.0018876106,"threshold_uncertainty_score":0.014146924},"labels":[],"label_agreement":null},{"id":"W2901789526","doi":"10.1080/03461238.2018.1546224","title":"Modeling cause-of-death mortality using hierarchical Archimedean copula","year":2018,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":29,"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 Manitoba","funders":"","keywords":"Life expectancy; Copula (linguistics); Longevity risk; Econometrics; Mortality rate; Pension; Cohort; Longevity; Statistics; Actuarial science; Economics; Medicine; Mathematics; Population; Gerontology; Environmental health","score_opus":0.10086324293507372,"score_gpt":0.3918964452063165,"score_spread":0.2910332022712428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901789526","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":[0.23666555,0.001386257,0.7512508,0.0010602494,0.0001272914,0.00020097553,0.0018417757,0.00032004746,0.0071469476],"genre_scores_gemma":[0.9398688,0.0012518836,0.05020382,0.00017383293,0.00009525904,0.00026605304,0.0010636192,0.000086520864,0.0069902684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885666,0.00059410353,0.00004083761,0.00020410589,0.00014507465,0.00015930946],"domain_scores_gemma":[0.99603784,0.0025218348,0.00066431134,0.00027700924,0.00035261063,0.00014632486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003549936,0.00087879994,0.00089483435,0.001314843,0.00045444822,0.0014505894,0.0016534077,0.001133684,0.0029076599],"category_scores_gemma":[0.009046966,0.0005753546,0.0013838606,0.0015309295,0.0006144049,0.0011781371,0.0010690587,0.0014330299,0.00053309853],"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":[0.000036245787,0.00006707972,0.012044007,0.000075402284,0.00020519916,0.00020713186,0.00016717994,0.9267559,0.0004313336,0.046295274,0.0017482621,0.011966908],"study_design_scores_gemma":[0.0000055909677,0.0000214385,0.0026111715,0.000009974442,0.00002535563,0.000035436806,0.000027549379,0.98532146,0.000077517885,0.011363194,0.00048912986,0.000012136586],"about_ca_topic_score_codex":0.021606661,"about_ca_topic_score_gemma":0.013348428,"teacher_disagreement_score":0.021606661,"about_ca_system_score_codex":0.0011774399,"about_ca_system_score_gemma":0.0013199552,"threshold_uncertainty_score":0.042961776},"labels":[],"label_agreement":null},{"id":"W2905143327","doi":"10.9778/cmajo.20170125","title":"The Canadian Forces Cancer and Mortality Study II: a longitudinal record-linkage study protocol","year":2018,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Veterans Affairs Canada; Pierre Elliott Trudeau Foundation; Department of National Defence","funders":"","keywords":"Medicine; Cohort; Record linkage; Military personnel; Demography; Cancer; Military service; Incidence (geometry); Linkage (software); Mortality rate; Cohort study; Cancer incidence; Environmental health; Surgery; Population; Political science; Internal medicine; Law","score_opus":0.08476106782963512,"score_gpt":0.4311120545289567,"score_spread":0.3463509866993216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905143327","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068516578,0.0010856825,0.010789424,0.0013781115,0.00035270973,0.76820314,0.19992769,0.0003855524,0.01102604],"genre_scores_gemma":[0.013832156,0.0010320002,0.01918335,0.0015133839,0.00010700757,0.8883144,0.07185355,0.00007540168,0.004088809],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98119867,0.006556481,0.0035891912,0.0026198663,0.0043880073,0.001647811],"domain_scores_gemma":[0.9774908,0.0018504342,0.001800486,0.0031580592,0.014047234,0.0016530383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025383351,0.00273827,0.0029375972,0.005898264,0.008680055,0.0033670445,0.006061103,0.0029954796,0.032587767],"category_scores_gemma":[0.029191654,0.0025459828,0.0018085067,0.009827322,0.0016355406,0.0022599043,0.0019879162,0.0036053276,0.0076924325],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011158126,0.0025788764,0.054526787,0.0141720595,0.0009803647,0.0011966944,0.003976948,0.0026841252,0.0017082021,0.011727271,0.8221531,0.073137425],"study_design_scores_gemma":[0.0185758,0.0020515781,0.27190468,0.008369292,0.0012213413,0.00076928927,0.0036176504,0.0051421104,0.0019118515,0.006888076,0.6787839,0.0007644373],"about_ca_topic_score_codex":0.5641838,"about_ca_topic_score_gemma":0.68620837,"teacher_disagreement_score":0.9762924,"about_ca_system_score_codex":0.023707613,"about_ca_system_score_gemma":0.11174708,"threshold_uncertainty_score":0.87676567},"labels":[],"label_agreement":null},{"id":"W2910533161","doi":"10.1017/s1357321718000235","title":"Assessing basis risk for longevity transactions – phase 2 presented by Dr Jackie Li and Dr Chong It Tan, IFoA Longevity Basis Risk Working Group ‐ Abstract of the London Discussion","year":2018,"lang":"en","type":"article","venue":"British Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"The Institute and Faculty of Actuaries; University of Waterloo; Macquarie University","keywords":"Longevity; Swap (finance); Actuarial science; Annals; Longevity risk; Economics; Gerontology; Demography; Psychology; Sociology; Medicine; History; Finance; Classics","score_opus":0.028234479388293914,"score_gpt":0.3316416413203712,"score_spread":0.3034071619320773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910533161","genre_codex":"commentary","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":[0.043890167,0.036443006,0.027585637,0.7867568,0.0390346,0.0043049576,0.011938702,0.0003252267,0.04972082],"genre_scores_gemma":[0.3696195,0.059870783,0.057032384,0.14571522,0.08682805,0.010196188,0.022586709,0.0016007811,0.24655043],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917277,0.004887468,0.0005407473,0.0005369205,0.001932948,0.00037427657],"domain_scores_gemma":[0.96664685,0.014547965,0.002682235,0.0018020516,0.01041931,0.0039015645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03873687,0.0005346941,0.00068605633,0.0011973914,0.00095579977,0.0028207265,0.0011462435,0.0021057352,0.029911265],"category_scores_gemma":[0.080681,0.00036109917,0.0009283662,0.0008529352,0.0006700372,0.0019550778,0.0039599654,0.004544985,0.009576367],"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":[0.001173846,0.00012606368,0.013576854,0.0005726761,0.00012165049,0.00023955989,0.0011643934,0.0007782365,0.00045660048,0.003993356,0.7559794,0.22181733],"study_design_scores_gemma":[0.0003456405,0.0008322079,0.01322032,0.0027881975,0.00010872058,0.00054185244,0.00207853,0.0018055901,0.0018409116,0.008997811,0.96735,0.00009023247],"about_ca_topic_score_codex":0.0028980249,"about_ca_topic_score_gemma":0.0032339215,"teacher_disagreement_score":0.03873687,"about_ca_system_score_codex":0.0021248336,"about_ca_system_score_gemma":0.005450637,"threshold_uncertainty_score":0.2048626},"labels":[],"label_agreement":null},{"id":"W2911568234","doi":"10.4236/ojs.2019.91006","title":"Analysis of Hospital Mortality Data: The Role of DRG’s","year":2019,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Gee; Generalized estimating equation; Medicine; Logistic regression; Generalized linear model; Statistics; Cluster analysis; Statistical model; Linear regression; Regression analysis; Nuisance parameter; Econometrics; Mathematics; Internal medicine","score_opus":0.03274084228201806,"score_gpt":0.3591191087906706,"score_spread":0.32637826650865254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911568234","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":[0.53213954,0.006627037,0.44194353,0.004059726,0.0003316972,0.0005263126,0.008584455,0.0007355296,0.0050521917],"genre_scores_gemma":[0.9075727,0.0009747297,0.08811616,0.0003119449,0.00011314522,0.00024217542,0.0021531375,0.00013413973,0.00038185169],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9554357,0.035408713,0.0023601924,0.0025240933,0.0038365335,0.0004347967],"domain_scores_gemma":[0.8281479,0.14513768,0.012816344,0.008552072,0.0046885135,0.00065757247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030718472,0.0007199775,0.0009977501,0.0026166853,0.0003463097,0.0014465565,0.001147271,0.00045648712,0.0017937287],"category_scores_gemma":[0.084476486,0.00018580027,0.0015756651,0.004701792,0.001093232,0.0010844609,0.0011230904,0.0014212149,0.00036094498],"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":[0.0009548596,0.00019069234,0.71729994,0.0016582459,0.0027622518,0.0002658943,0.0008234607,0.02059105,0.0019171798,0.011547109,0.004299887,0.23768954],"study_design_scores_gemma":[0.000113796006,0.0014484915,0.7347652,0.0012041645,0.0010763048,0.0015695726,0.0014162947,0.17686896,0.0076060984,0.055648334,0.018120896,0.00016194371],"about_ca_topic_score_codex":0.0024039655,"about_ca_topic_score_gemma":0.0023452232,"teacher_disagreement_score":0.030718472,"about_ca_system_score_codex":0.00142143,"about_ca_system_score_gemma":0.0020533262,"threshold_uncertainty_score":0.16245675},"labels":[],"label_agreement":null},{"id":"W2912160920","doi":"10.3390/risks7010014","title":"Changes of Relation in Multi-Population Mortality Dependence: An Application of Threshold VECM","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Error correction model; Econometrics; Longevity; Population; Statistics; Variable (mathematics); Mathematics; Multivariate statistics; Forcing (mathematics); Mortality rate; Contrast (vision); Economics; Demography; Cointegration; Medicine; Computer science; Internal medicine; Gerontology","score_opus":0.08248672557423722,"score_gpt":0.3922807216623483,"score_spread":0.3097939960881111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912160920","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":[0.32959396,0.00029863336,0.6656851,0.00059619657,0.00005909341,0.00005158173,0.00022460228,0.0002466736,0.0032441425],"genre_scores_gemma":[0.9589169,0.00019459451,0.038649548,0.000070748305,0.000033129036,0.00004465062,0.00016332255,0.00004617372,0.0018808406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929357,0.00033113154,0.000042200794,0.00017343427,0.00008699673,0.00007276499],"domain_scores_gemma":[0.9973393,0.0016552785,0.00046346022,0.00022773218,0.00023597303,0.000078272344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020057687,0.00040497514,0.00050634093,0.00083831244,0.00035840727,0.0009171714,0.000909861,0.00077592063,0.0018319112],"category_scores_gemma":[0.009236502,0.00029580112,0.000846999,0.0009749802,0.00057360897,0.0012605902,0.0009922987,0.0011620335,0.00011692124],"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":[0.000055508943,0.000084149586,0.056642726,0.00006861693,0.00020764075,0.00056148914,0.0003544856,0.7616042,0.0013149822,0.13060674,0.0010861269,0.04741337],"study_design_scores_gemma":[0.0000019882038,0.000012965658,0.0021029403,0.0000042595275,0.000008612743,0.000026570435,0.000028676222,0.9872493,0.000108169384,0.010193375,0.0002566264,0.000006485374],"about_ca_topic_score_codex":0.013744971,"about_ca_topic_score_gemma":0.0066080177,"teacher_disagreement_score":0.013744971,"about_ca_system_score_codex":0.0007976084,"about_ca_system_score_gemma":0.00078315026,"threshold_uncertainty_score":0.027329922},"labels":[],"label_agreement":null},{"id":"W2913702103","doi":"10.3390/books978-3-03842-823-7","title":"Ageing Population Risks","year":2018,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Universiti Teknologi MARA; Ministry of Higher Education, Malaysia; Macquarie University","keywords":"Ageing; Population ageing; Population; Environmental health; Medicine","score_opus":0.05892464565132823,"score_gpt":0.3596905916167624,"score_spread":0.3007659459654342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913702103","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":[0.002987737,0.2588785,0.028448923,0.06921042,0.02560171,0.00012469894,0.001668194,0.00065789453,0.61242193],"genre_scores_gemma":[0.039374307,0.20489892,0.010484075,0.016692989,0.021892052,0.00019745996,0.0014001407,0.00042715302,0.70463294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936324,0.00013158385,0.000025867805,0.00008866963,0.00035714442,0.00003347217],"domain_scores_gemma":[0.99890137,0.0005660723,0.00008287865,0.000125037,0.00023518737,0.000089438676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010792629,0.0008506074,0.0005218981,0.0010228062,0.00062457623,0.0031162256,0.0007725838,0.0021152615,0.02881669],"category_scores_gemma":[0.0039244466,0.00024218782,0.0006024658,0.001064237,0.0011521258,0.0033806346,0.0014662734,0.0032523538,0.014179311],"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":[0.00001370781,0.00002475488,0.0003366824,0.00032390642,0.00001932031,0.00009336372,0.00032443166,0.0014668942,0.00024273612,0.29756,0.5683432,0.13125104],"study_design_scores_gemma":[0.0000022239967,0.000014689358,0.00029702156,0.00027445023,0.0000053033655,0.00027421792,0.000059509664,0.00036494422,0.00007038282,0.09688046,0.9017482,0.000008565646],"about_ca_topic_score_codex":0.00085261464,"about_ca_topic_score_gemma":0.0010439528,"teacher_disagreement_score":0.02881669,"about_ca_system_score_codex":0.0011723065,"about_ca_system_score_gemma":0.001071425,"threshold_uncertainty_score":0.09640145},"labels":[],"label_agreement":null},{"id":"W2918068461","doi":"10.3138/cpp.2018-003","title":"Effects of Population Aging on Gross Domestic Product per Capita in the Canadian Provinces: Could Productivity Growth Provide an Offset?","year":2019,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Gross domestic product; Per capita; Productivity; Economics; Offset (computer science); Population; Population ageing; Population growth; Demographic economics; Agricultural economics; Geography; Development economics; Economic growth; Demography","score_opus":0.013133623149543291,"score_gpt":0.28780320470166476,"score_spread":0.27466958155212146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918068461","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":[0.93362474,0.0053510955,0.0019197288,0.012414883,0.00014270778,0.00008702935,0.007313536,0.00007924007,0.039066996],"genre_scores_gemma":[0.992896,0.0021823146,0.000895853,0.00038091725,0.00002728238,0.000010594303,0.00071259454,0.000012134108,0.0028824047],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99791557,0.0003053947,0.00008045968,0.00014235219,0.00052287895,0.0010334617],"domain_scores_gemma":[0.99706954,0.0004792921,0.00048402147,0.00013958721,0.0013536336,0.00047399334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019850754,0.0004872606,0.00048641418,0.0013985564,0.0012524573,0.001442243,0.0010015335,0.0005175183,0.00270634],"category_scores_gemma":[0.007155024,0.00017925633,0.00089305366,0.0024430463,0.0011846506,0.00060717657,0.001063054,0.00091653113,0.0001513978],"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":[0.0008510558,0.00018062157,0.76105076,0.0003286762,0.00056721794,0.000624544,0.0013929049,0.04522877,0.0018097642,0.06806153,0.009827762,0.1100764],"study_design_scores_gemma":[0.000045577322,0.00014542049,0.9639285,0.00010451834,0.00035937654,0.00011514387,0.0015776042,0.011765055,0.0012525202,0.0047383993,0.015913917,0.000054039723],"about_ca_topic_score_codex":0.98258775,"about_ca_topic_score_gemma":0.9864287,"teacher_disagreement_score":0.04059409,"about_ca_system_score_codex":0.04059409,"about_ca_system_score_gemma":0.041853238,"threshold_uncertainty_score":0.29453206},"labels":[],"label_agreement":null},{"id":"W2919180597","doi":"10.1016/j.insmatheco.2019.02.011","title":"A forecast reconciliation approach to cause-of-death mortality modeling","year":2019,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":30,"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 Manitoba","funders":"","keywords":"Life expectancy; Mortality rate; Actuarial science; Computer science; Econometrics; Demography; Economics; Medicine; Population; Environmental health","score_opus":0.069441951179061,"score_gpt":0.29340074089315543,"score_spread":0.22395878971409444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919180597","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":[0.014905806,0.00021031253,0.98196214,0.0007056886,0.000078858546,0.000037216443,0.0002426729,0.0002940024,0.001563279],"genre_scores_gemma":[0.78823227,0.00039493683,0.2053897,0.00034774054,0.00044098007,0.00022243956,0.0011675013,0.00017047877,0.0036339683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735236,0.0016798482,0.00012702537,0.00036028467,0.000339625,0.00014079628],"domain_scores_gemma":[0.99134594,0.006668669,0.00046920235,0.0005528112,0.0007865495,0.00017675346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065152054,0.0007425192,0.0017895706,0.0015103739,0.0009713452,0.0019152645,0.0028690079,0.001685428,0.0027059063],"category_scores_gemma":[0.019206177,0.0007989046,0.0015165379,0.0014977262,0.00082325825,0.0022394708,0.0020603372,0.0017166015,0.00035494356],"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":[0.000055122564,0.000025140382,0.001095383,0.00002152712,0.000087884895,0.0000656799,0.00007866172,0.94730914,0.00010563108,0.03352418,0.001017358,0.016614348],"study_design_scores_gemma":[0.0000041771486,0.0000065929885,0.00007948208,0.000002737466,0.00000960432,0.0000060509146,0.000005890762,0.98205805,0.00004905101,0.017489519,0.00028440158,0.000004547776],"about_ca_topic_score_codex":0.012183529,"about_ca_topic_score_gemma":0.007276815,"teacher_disagreement_score":0.012183529,"about_ca_system_score_codex":0.0012869702,"about_ca_system_score_gemma":0.0018529773,"threshold_uncertainty_score":0.034456134},"labels":[],"label_agreement":null},{"id":"W2919738624","doi":"10.1017/asb.2019.3","title":"JOINT LIFE INSURANCE PRICING USING EXTENDED MARSHALL–OLKIN MODELS","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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":"Copula (linguistics); Life insurance; Econometrics; Joint probability distribution; Residual; Actuarial science; Joint (building); Economics; Computer science; Statistics; Mathematics; Engineering","score_opus":0.0402189907330563,"score_gpt":0.2781090556575537,"score_spread":0.23789006492449744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919738624","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":[0.10991935,0.00071272074,0.87760055,0.0007965667,0.00009500432,0.00007645447,0.00019542128,0.00013050021,0.010473424],"genre_scores_gemma":[0.96148485,0.00048380825,0.027816508,0.00010258697,0.000117598145,0.000119041,0.000112384936,0.000044299188,0.0097188875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986083,0.00067315047,0.00005047334,0.00023138999,0.00023052328,0.00020631049],"domain_scores_gemma":[0.9974827,0.0012765888,0.00054588483,0.00023669444,0.00025057726,0.00020756079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027510985,0.00084824517,0.0011036703,0.00093328493,0.000501956,0.002273294,0.0021691788,0.0012937813,0.004995122],"category_scores_gemma":[0.007410159,0.00046489324,0.0015148873,0.0011385613,0.0011346055,0.0028763807,0.0013961177,0.0020357186,0.00037807232],"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":[0.00004234927,0.00006901449,0.002134358,0.000030216632,0.00008927986,0.00025832656,0.00019902897,0.5634454,0.00045069796,0.42427987,0.0011557972,0.007845652],"study_design_scores_gemma":[0.0000065468034,0.000017555963,0.0004729443,0.0000077033965,0.000013196194,0.000034783392,0.000034619843,0.9257051,0.00005861841,0.072968744,0.00066479895,0.000015378939],"about_ca_topic_score_codex":0.007244551,"about_ca_topic_score_gemma":0.004297937,"teacher_disagreement_score":0.007244551,"about_ca_system_score_codex":0.0012591856,"about_ca_system_score_gemma":0.00078324205,"threshold_uncertainty_score":0.016710281},"labels":[],"label_agreement":null},{"id":"W2920061562","doi":"10.1080/10920277.2019.1650285","title":"Constructing Out-of-the-Money Longevity Hedges Using Parametric Mortality Indexes","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","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":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hedge; Longevity risk; Parametric statistics; Downside risk; Economics; Econometrics; Moneyness; Construct (python library); Longevity; Actuarial science; Financial economics; Computer science; Mathematics; Statistics; Finance; Medicine","score_opus":0.03602321087939863,"score_gpt":0.3270904358140427,"score_spread":0.29106722493464404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920061562","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":[0.31422368,0.00030192366,0.67920107,0.00014633333,0.000039538478,0.00007247902,0.000060723192,0.00013870028,0.005815581],"genre_scores_gemma":[0.9258567,0.000175864,0.07265686,0.000034946373,0.000017109678,0.000037495025,0.000058631307,0.000019883644,0.0011424017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999516,0.00018323943,0.000052838666,0.0000764906,0.00012817892,0.00004327592],"domain_scores_gemma":[0.9979145,0.0010051312,0.00039800978,0.00036432146,0.00024558723,0.00007250951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022058433,0.0003732419,0.0004051719,0.0008577516,0.00032400526,0.0011598488,0.00037964378,0.0005143336,0.0010412163],"category_scores_gemma":[0.007587827,0.00021337079,0.0006115102,0.0006052263,0.0007762871,0.001914459,0.001168202,0.0005759046,0.00010524154],"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":[0.00020658765,0.00017528627,0.016297607,0.00010522152,0.000114909344,0.00047663855,0.0007573681,0.31939438,0.0120276045,0.42097467,0.001049019,0.22842072],"study_design_scores_gemma":[0.000025257461,0.00022248484,0.0038792589,0.00003677463,0.000040279785,0.00022836709,0.00013913745,0.8632391,0.0070587336,0.12235648,0.0027149138,0.00005921711],"about_ca_topic_score_codex":0.00063911773,"about_ca_topic_score_gemma":0.0005802402,"teacher_disagreement_score":0.0022058433,"about_ca_system_score_codex":0.00043339597,"about_ca_system_score_gemma":0.0006314944,"threshold_uncertainty_score":0.0116657615},"labels":[],"label_agreement":null},{"id":"W2922476419","doi":"10.1371/journal.pone.0212345","title":"Human lifespan records are not remarkable but their durations are","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":25,"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":"Persistence (discontinuity); Historical record; Focus (optics); Quarter (Canadian coin); Archaeological record; Genealogy; Demography; History; Archaeology; Biography; Sociology; Engineering","score_opus":0.06394813750641604,"score_gpt":0.27407658709843136,"score_spread":0.21012844959201532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922476419","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":[0.8483345,0.022036565,0.04583629,0.009552248,0.0008160328,0.000047626923,0.016950531,0.00043637326,0.055989996],"genre_scores_gemma":[0.9882052,0.0027047552,0.003936801,0.00026034395,0.00028130997,0.00002355211,0.0027307083,0.000046155074,0.0018110537],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989693,0.0002703031,0.00015534909,0.00026157682,0.0002822018,0.0000612208],"domain_scores_gemma":[0.9816482,0.0075746863,0.0064236387,0.002327716,0.0015134066,0.0005122873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002321016,0.00016638936,0.00028526734,0.0016661634,0.0006938094,0.0016317228,0.00048386288,0.00047179146,0.0036582714],"category_scores_gemma":[0.029072542,0.00022687177,0.00023414342,0.0038173345,0.0014194727,0.00381796,0.001113654,0.00091303943,0.00095975643],"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":[0.00029368,0.000041975745,0.5448939,0.0010861033,0.00045654524,0.00032921718,0.008810637,0.002241814,0.002587605,0.1053039,0.015424152,0.3185304],"study_design_scores_gemma":[0.000010304623,0.00019991922,0.79573286,0.00052635185,0.00018404813,0.0016374507,0.00669124,0.0015300231,0.0013695753,0.07976257,0.11224924,0.00010641521],"about_ca_topic_score_codex":0.0020281486,"about_ca_topic_score_gemma":0.0022780003,"teacher_disagreement_score":0.0036582714,"about_ca_system_score_codex":0.0004564277,"about_ca_system_score_gemma":0.00043004294,"threshold_uncertainty_score":0.012274861},"labels":[],"label_agreement":null},{"id":"W2923417146","doi":"10.1515/apjri-2018-0013","title":"Actuarial Modeling and Analysis of the Hong Kong Life Annuity Scheme","year":2019,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Education, India; Ministry of Earth Sciences","keywords":"Life annuity; Annuity; Actuarial science; Cash flow; Lump sum; Business; Economics; Payment; Finance; Pension","score_opus":0.010361174459968751,"score_gpt":0.25171470874688706,"score_spread":0.2413535342869183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923417146","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":[0.6879681,0.0009945986,0.2813488,0.0019990464,0.00017914867,0.00018155608,0.0015578243,0.00021965416,0.025551332],"genre_scores_gemma":[0.9832599,0.00027109837,0.004669911,0.000054412725,0.000027026572,0.000057439844,0.00027766044,0.000019086188,0.0113634365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996118,0.00015363621,0.000017101795,0.00006544119,0.000060152066,0.00009179576],"domain_scores_gemma":[0.99812084,0.0009255706,0.0003611554,0.000101991136,0.00032316233,0.00016724304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021979995,0.00053281046,0.0005820645,0.0006944874,0.00042672458,0.001260383,0.001257837,0.0011321483,0.004210135],"category_scores_gemma":[0.0036332118,0.00040324795,0.00073395047,0.0004317364,0.00078021246,0.000827214,0.00074752874,0.0011022132,0.00040703377],"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":[0.000018512486,0.000019452478,0.0033419465,0.000009579326,0.000015085407,0.000062701634,0.000039718343,0.9809399,0.0001278189,0.013089211,0.000509024,0.0018270168],"study_design_scores_gemma":[0.0000017376234,0.000006080956,0.00048055378,0.000002612714,0.0000033330361,0.0000050462922,0.0000089932355,0.9984875,0.000018757872,0.0008003145,0.00018165527,0.0000034665297],"about_ca_topic_score_codex":0.081565626,"about_ca_topic_score_gemma":0.023304068,"teacher_disagreement_score":0.081565626,"about_ca_system_score_codex":0.0020444782,"about_ca_system_score_gemma":0.0014494129,"threshold_uncertainty_score":0.16218174},"labels":[],"label_agreement":null},{"id":"W2924813464","doi":"","title":"Japan's ageing population points to our global future","year":2014,"lang":"en","type":"article","venue":"The New Scientist","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fell; Quarter (Canadian coin); Population ageing; Demography; Population; Economic stagnation; Population growth; Economic history; Demographic economics; History; Global population; Economics; Development economics; Geography; Socioeconomics; Political science; Sociology; Law; Archaeology; Cartography","score_opus":0.010658425901184968,"score_gpt":0.3073939961057574,"score_spread":0.2967355702045724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924813464","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0229343,0.062875286,0.0012144657,0.8448543,0.025144594,0.000010361636,0.00066368945,0.00024353772,0.042059515],"genre_scores_gemma":[0.40779996,0.17502399,0.005336837,0.2713542,0.059439305,0.000060032173,0.00123486,0.0002444052,0.07950653],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00013219802,0.00003234762,0.000078826655,0.0001645366,0.00014295337],"domain_scores_gemma":[0.9979752,0.00022323424,0.00022774159,0.000097150456,0.0007058568,0.0007709319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013958213,0.0006129853,0.00039540327,0.0007537661,0.002159964,0.0033412343,0.00037928188,0.0025771519,0.010632273],"category_scores_gemma":[0.0026030666,0.00016976279,0.0005061214,0.0011477142,0.0021117155,0.004135759,0.0020856992,0.0044670696,0.002986666],"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":[0.00009009686,0.000031535386,0.014716265,0.00043296427,0.00005671148,0.00070987514,0.003814769,0.000114750816,0.0009148767,0.015774116,0.85620606,0.10713798],"study_design_scores_gemma":[0.000011367358,0.000075095006,0.039680067,0.00038549534,0.000082692706,0.0006916882,0.0078447675,0.00013423951,0.00017302787,0.012378877,0.93848544,0.000057362657],"about_ca_topic_score_codex":0.01663417,"about_ca_topic_score_gemma":0.03520469,"teacher_disagreement_score":0.01663417,"about_ca_system_score_codex":0.0018343122,"about_ca_system_score_gemma":0.0030069593,"threshold_uncertainty_score":0.035568535},"labels":[],"label_agreement":null},{"id":"W2925259354","doi":"10.1088/1748-9326/ab0843","title":"The impact of climate change on fertility*","year":2019,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":77,"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 Waterloo","funders":"Canada Research Chairs; European Commission; Fund for Innovative Climate and Energy Research","keywords":"Fertility; Agriculture; Climate change; Economics; Scarcity; Natural resource economics; Development economics; Geography; Population; Ecology","score_opus":0.05139352773324046,"score_gpt":0.38340217840976193,"score_spread":0.33200865067652147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925259354","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":[0.905141,0.0028046255,0.017001245,0.010337472,0.00019431407,0.000043398988,0.0026532144,0.00010938363,0.06171536],"genre_scores_gemma":[0.9972908,0.00060259143,0.0003692644,0.00013798922,0.0000397235,0.0000050697763,0.000082941355,0.0000044723556,0.0014670816],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997464,0.00011430664,0.0000076134143,0.000032509266,0.000032417716,0.00006677298],"domain_scores_gemma":[0.9990102,0.00047120816,0.00025328435,0.000067902394,0.00009230736,0.00010504839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005877768,0.00014556233,0.00020727073,0.00033310533,0.00034050058,0.0009294429,0.0003271274,0.0006517293,0.006030606],"category_scores_gemma":[0.0025260227,0.000116090036,0.0005093529,0.00044700468,0.0006557438,0.00050150184,0.0005545264,0.00046330126,0.00029788233],"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":[0.00026905644,0.00022338302,0.44032922,0.00034845062,0.00042337258,0.0006989783,0.000686296,0.2479772,0.0044855676,0.24619459,0.005974344,0.052389577],"study_design_scores_gemma":[0.000046965182,0.0005483719,0.62177604,0.000180049,0.00026856712,0.00070308073,0.0014692656,0.17388919,0.002144495,0.16253139,0.03633264,0.000109951274],"about_ca_topic_score_codex":0.00923491,"about_ca_topic_score_gemma":0.006981238,"teacher_disagreement_score":0.00923491,"about_ca_system_score_codex":0.0010956243,"about_ca_system_score_gemma":0.00050488784,"threshold_uncertainty_score":0.020174444},"labels":[],"label_agreement":null},{"id":"W2928628092","doi":"10.1017/s1357321718000260","title":"A stochastic implementation of the APCI model for mortality projections","year":2019,"lang":"en","type":"article","venue":"British Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"The Institute and Faculty of Actuaries","keywords":"Econometrics; Computer science; Value (mathematics); Economics; Statistics; Mathematics","score_opus":0.02783591114619141,"score_gpt":0.34868856320037717,"score_spread":0.3208526520541858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928628092","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":[0.039123844,0.00012934771,0.94953394,0.001286165,0.000095034724,0.000075623975,0.0008900199,0.0006602596,0.008205764],"genre_scores_gemma":[0.8450171,0.00027073984,0.14458793,0.00028261216,0.00010716023,0.00024877535,0.0009795798,0.00016333548,0.0083427625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895144,0.00047511217,0.00005067578,0.00017663042,0.00023488265,0.00011118883],"domain_scores_gemma":[0.99682367,0.0017798437,0.00031423225,0.0002570708,0.0006599817,0.00016520484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003527801,0.00044771793,0.0007041429,0.0006075488,0.00052901584,0.0013592059,0.0019747363,0.0012632441,0.0048226286],"category_scores_gemma":[0.009722827,0.0005831632,0.0007701967,0.0008857686,0.0005721197,0.0009856466,0.0013026388,0.0022466122,0.00059913483],"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":[0.000018423752,0.000014004239,0.0008795275,0.000012150003,0.000013493638,0.000031409483,0.000026224394,0.9670054,0.00015040119,0.025565106,0.0007499859,0.00553393],"study_design_scores_gemma":[0.0000037785173,0.00000928735,0.00015052455,0.000003786959,0.0000032167684,0.000011854145,0.000004724327,0.9949124,0.000050055813,0.00443643,0.00040801684,0.000005931729],"about_ca_topic_score_codex":0.027688226,"about_ca_topic_score_gemma":0.0115770195,"teacher_disagreement_score":0.027688226,"about_ca_system_score_codex":0.0011843087,"about_ca_system_score_gemma":0.0018529458,"threshold_uncertainty_score":0.05505413},"labels":[],"label_agreement":null},{"id":"W2932962584","doi":"10.1108/jdqs-02-2009-b0002","title":"A Study on the Market Price of Weather Risk in Pricing Weather Derivatives","year":2009,"lang":"en","type":"article","venue":"Journal of Derivatives and Quantitative Studies 선물연구","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Futures contract; Extreme weather; Market price; Risk management; Econometrics; Economics; Financial economics; Climate change; Finance","score_opus":0.05999383581902356,"score_gpt":0.3723058587382398,"score_spread":0.3123120229192162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932962584","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":[0.49909845,0.006642363,0.4632819,0.0023033118,0.0002592593,0.00011798677,0.00011575414,0.00009138595,0.028089637],"genre_scores_gemma":[0.97375023,0.0023156013,0.019559504,0.00013415638,0.0003130637,0.00003950503,0.00007212513,0.00002853359,0.0037873124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9991505,0.00046710743,0.00004060803,0.0001071989,0.00017368766,0.000060895138],"domain_scores_gemma":[0.98888105,0.009550246,0.0005381036,0.0003477516,0.0005209443,0.00016183383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026224111,0.00056185294,0.00077129545,0.00082615524,0.0005484013,0.0017441531,0.00085317134,0.0013797593,0.003438277],"category_scores_gemma":[0.018907499,0.00038544432,0.0010259458,0.0012461559,0.0011430396,0.0045473007,0.00068298436,0.0020813835,0.00019313322],"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":[0.00021118058,0.0002762348,0.015257039,0.00029328009,0.00016418568,0.0014554344,0.00052856933,0.41405466,0.007069577,0.5129962,0.0019252524,0.045768395],"study_design_scores_gemma":[0.000014685883,0.00009284243,0.0029636496,0.000020914513,0.000038837767,0.00022349422,0.00005723954,0.9572684,0.0007008288,0.0375558,0.0010372642,0.000026105447],"about_ca_topic_score_codex":0.003364231,"about_ca_topic_score_gemma":0.0015753335,"teacher_disagreement_score":0.003438277,"about_ca_system_score_codex":0.0008012124,"about_ca_system_score_gemma":0.00051960564,"threshold_uncertainty_score":0.013868809},"labels":[],"label_agreement":null},{"id":"W2937941984","doi":"10.1017/asb.2019.6","title":"ECONOMIC SCENARIO GENERATOR AND PARAMETER UNCERTAINTY: A BAYESIAN APPROACH","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Markov chain Monte Carlo; Econometrics; Bayesian probability; Portfolio; Context (archaeology); Monte Carlo method; Statistics; Inflation (cosmology); Economics; Mathematics; Physics","score_opus":0.010020464837451283,"score_gpt":0.24297283194683608,"score_spread":0.2329523671093848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937941984","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":[0.019953456,0.00038249264,0.9729637,0.0013568498,0.00003163193,0.0000450847,0.00013321466,0.000076489625,0.0050570355],"genre_scores_gemma":[0.8021965,0.0013020665,0.1917719,0.0003232583,0.00030263286,0.00024264716,0.00034807343,0.000117766554,0.0033951115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596137,0.0027524927,0.00010407585,0.00036053904,0.0006509482,0.00017047364],"domain_scores_gemma":[0.9679851,0.02787794,0.0017095435,0.0009800674,0.0010435402,0.0004038272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00984596,0.00091199565,0.0015126092,0.0030818882,0.0009402636,0.0031957044,0.0025018752,0.0022824046,0.0038718106],"category_scores_gemma":[0.043473937,0.0011772588,0.0012084082,0.0019448283,0.0029335553,0.0057714074,0.0022971423,0.0030263728,0.0003914437],"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":[0.000023895447,0.00003289741,0.0009914902,0.000032497774,0.000057677094,0.00013583485,0.000103482394,0.71081936,0.00014378455,0.2773995,0.00046217116,0.00979732],"study_design_scores_gemma":[0.0000073473147,0.000009699658,0.00025084263,0.00002337193,0.000012027603,0.00005130947,0.000028616392,0.72344244,0.00006700613,0.27540532,0.00068159826,0.000020560688],"about_ca_topic_score_codex":0.0041359905,"about_ca_topic_score_gemma":0.0036357553,"teacher_disagreement_score":0.00984596,"about_ca_system_score_codex":0.0019303877,"about_ca_system_score_gemma":0.0016283015,"threshold_uncertainty_score":0.052071035},"labels":[],"label_agreement":null},{"id":"W2940118792","doi":"10.1007/s11579-018-0228-1","title":"Increasing risk aversion and life-cycle investing","year":2018,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"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 British Columbia","funders":"","keywords":"Mathematical finance; Risk aversion (psychology); Economics; Financial economics; Actuarial science; Expected utility hypothesis","score_opus":0.01429346115735343,"score_gpt":0.23687617985741866,"score_spread":0.22258271870006524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940118792","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":[0.9767163,0.0009636239,0.015304726,0.0014373681,0.000021089834,0.0000070945575,0.00007302426,0.000021203658,0.0054556318],"genre_scores_gemma":[0.99786514,0.00029264396,0.0005141035,0.000037958103,0.000014890215,0.0000026168245,0.000021400765,0.0000016537412,0.0012496768],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998374,0.00006532376,0.000009432038,0.000030858293,0.00001754624,0.000039433402],"domain_scores_gemma":[0.9965193,0.0018385925,0.00091122196,0.00017086821,0.00015811199,0.00040188208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011158893,0.00020282657,0.00026218608,0.0004892006,0.00023875333,0.0010477997,0.00029809258,0.00065574603,0.00303681],"category_scores_gemma":[0.0065815267,0.00011500419,0.00023538391,0.00048401835,0.0010264311,0.0009772042,0.0005033483,0.0007603267,0.00012852764],"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":[0.00033849454,0.00040154156,0.27803487,0.00015590293,0.00020274964,0.001027133,0.0023454246,0.04460448,0.002978721,0.6020302,0.0016966518,0.06618387],"study_design_scores_gemma":[0.000032364944,0.00014604902,0.16706589,0.0000394013,0.00009642945,0.00073774933,0.0011729517,0.039341558,0.0003439249,0.7881551,0.00282841,0.000040306528],"about_ca_topic_score_codex":0.001480948,"about_ca_topic_score_gemma":0.0016283883,"teacher_disagreement_score":0.00303681,"about_ca_system_score_codex":0.00047502108,"about_ca_system_score_gemma":0.0002668959,"threshold_uncertainty_score":0.010159075},"labels":[],"label_agreement":null},{"id":"W2940790754","doi":"10.1007/s42650-019-00004-9","title":"Modelling Fertility Schedules of India","year":2019,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"Fertility; Computer science; Geography; Population; Demography; Sociology","score_opus":0.0632873659629816,"score_gpt":0.346467025375711,"score_spread":0.2831796594127294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940790754","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":[0.9496184,0.00040955897,0.011725533,0.001829643,0.00010417895,0.000087738605,0.0075935265,0.00050104706,0.028130492],"genre_scores_gemma":[0.9893209,0.00014448294,0.002000461,0.000062735955,0.000016898426,0.000039319148,0.0013618509,0.000089226756,0.0069641564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999647,0.00012296569,0.0000115507155,0.00005847104,0.000031356623,0.00012862592],"domain_scores_gemma":[0.9966376,0.0021752573,0.00021446488,0.0001783004,0.00041421683,0.00038021928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141551,0.00043719664,0.0007481813,0.0011313392,0.0010551588,0.0016686249,0.002809946,0.0015606312,0.0075709904],"category_scores_gemma":[0.0062757344,0.0009354116,0.0010500122,0.0020784135,0.0012902644,0.0005932958,0.0007815928,0.0014502883,0.00063679967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005167735,0.000019425244,0.0038222594,0.00001759382,0.000016555901,0.00006583093,0.00013111935,0.98614943,0.000074725795,0.006756923,0.0018626188,0.0010319279],"study_design_scores_gemma":[0.000038744325,0.000015779051,0.00435162,0.00001100225,0.00002149402,0.000022882099,0.00022542206,0.99123645,0.0000716645,0.0022866775,0.0016889098,0.000029339335],"about_ca_topic_score_codex":0.7768203,"about_ca_topic_score_gemma":0.6455339,"teacher_disagreement_score":0.7768203,"about_ca_system_score_codex":0.011207151,"about_ca_system_score_gemma":0.0057393415,"threshold_uncertainty_score":0.44898808},"labels":[],"label_agreement":null},{"id":"W2946357294","doi":"10.1111/rssa.12473","title":"A Bayesian Approach to Developing a Stochastic Mortality Model for China","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Computer science; Bayesian probability; Econometrics; Stochastic modelling; Range (aeronautics); Set (abstract data type); Statistics; Mathematics; Artificial intelligence","score_opus":0.02336082914448603,"score_gpt":0.30813801428998916,"score_spread":0.28477718514550315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946357294","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":[0.031125233,0.00029528487,0.96342576,0.0008068825,0.000030279989,0.000059366754,0.000559644,0.00012538934,0.0035721448],"genre_scores_gemma":[0.7166187,0.0013344265,0.26714438,0.00031005597,0.00017855194,0.0007425368,0.0017468538,0.00013511514,0.011789318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929285,0.00034361647,0.00003980359,0.00011431364,0.00014600175,0.00006334565],"domain_scores_gemma":[0.99853027,0.00092056964,0.00019171601,0.00004697146,0.0002481572,0.00006226067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031504012,0.0005531737,0.00073123537,0.0012439324,0.0004383921,0.0009887264,0.0015801296,0.0011370032,0.0026158914],"category_scores_gemma":[0.0057188314,0.00065930415,0.0011321709,0.0012495404,0.00069385336,0.0008846384,0.0012424197,0.0010793261,0.00034474308],"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":[0.00000861767,0.000008825398,0.00074736145,0.000018486351,0.000018582188,0.000057510093,0.000043758715,0.9605996,0.00015989445,0.03318706,0.0004936235,0.0046566697],"study_design_scores_gemma":[0.0000030611775,0.0000047752505,0.00017250322,0.0000051152456,0.0000044137237,0.000008030283,0.0000067791725,0.98838145,0.00003124093,0.0109767215,0.0003995488,0.000006476534],"about_ca_topic_score_codex":0.039003827,"about_ca_topic_score_gemma":0.025825687,"teacher_disagreement_score":0.039003827,"about_ca_system_score_codex":0.0018287845,"about_ca_system_score_gemma":0.002833051,"threshold_uncertainty_score":0.07755357},"labels":[],"label_agreement":null},{"id":"W2949430994","doi":"10.48550/arxiv.1304.1821","title":"Optimal initiation of a GLWB in a variable annuity: no arbitrage approach","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bequest; Economics; Payment; Arbitrage; Asset (computer security); Consumption (sociology); Volatility (finance); Variable (mathematics); Annuity; Actuarial science; Microeconomics; Financial economics; Life annuity; Finance; Computer science","score_opus":0.060475945827508246,"score_gpt":0.21249132798934275,"score_spread":0.15201538216183452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949430994","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":[0.06697467,0.0020133362,0.89480346,0.003184273,0.00024814674,0.00023281435,0.00018660208,0.00019046929,0.032166235],"genre_scores_gemma":[0.89730954,0.0012527585,0.07876142,0.00046303959,0.00023007314,0.00027516412,0.000114917435,0.00016783013,0.02142521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982849,0.00081031857,0.000067224835,0.00038134202,0.00018024992,0.00027587768],"domain_scores_gemma":[0.9950541,0.0033918866,0.0006341802,0.00019122122,0.00026071272,0.00046788168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005116088,0.0013862012,0.0030074653,0.0009108346,0.00077443547,0.003029495,0.0028821086,0.0033576728,0.011353249],"category_scores_gemma":[0.0139109,0.0017326494,0.0019247119,0.0006080452,0.003016988,0.0040087774,0.0026379605,0.005402363,0.0006873154],"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":[0.00036032195,0.00015580439,0.00081598456,0.00025906143,0.00008948544,0.00056655763,0.00020038962,0.71282685,0.001977982,0.26825544,0.0022348587,0.012257226],"study_design_scores_gemma":[0.000095489806,0.0001492428,0.0003059543,0.00004943756,0.00005170646,0.00007947737,0.00006907439,0.8953149,0.0005164257,0.10121728,0.0021065546,0.00004452787],"about_ca_topic_score_codex":0.0026189077,"about_ca_topic_score_gemma":0.0018106065,"teacher_disagreement_score":0.011353249,"about_ca_system_score_codex":0.0020497444,"about_ca_system_score_gemma":0.0025174506,"threshold_uncertainty_score":0.037980437},"labels":[],"label_agreement":null},{"id":"W2950285971","doi":"10.1016/j.jedc.2008.09.004","title":"Valuation of mortality risk via the instantaneous Sharpe ratio: Applications to life annuities","year":2008,"lang":"en","type":"preprint","venue":"Journal of Economic Dynamics and Control","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Sharpe ratio; Valuation (finance); Martingale (probability theory); Life annuity; Actuarial science; Economics; Annuity; Econometrics; Mathematics; Mathematical economics; Upper and lower bounds; Statistics; Financial economics; Portfolio; Finance; Pension","score_opus":0.023402557484545054,"score_gpt":0.29358891589756625,"score_spread":0.2701863584130212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950285971","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":[0.057368573,0.009637042,0.9185836,0.0033668242,0.00024517198,0.000048754482,0.00009839625,0.00012817416,0.010523458],"genre_scores_gemma":[0.8921629,0.012418726,0.083940186,0.00035819347,0.0015014575,0.000094961084,0.00011449862,0.00013456681,0.009274354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986607,0.00088170654,0.00005290489,0.00012724599,0.00018585475,0.00009170448],"domain_scores_gemma":[0.9891037,0.008451071,0.00084631355,0.00048211834,0.0006766642,0.0004400703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077760136,0.0014834624,0.0019970872,0.002396024,0.0005319998,0.0042222366,0.0019983628,0.0031439357,0.0034865309],"category_scores_gemma":[0.029634932,0.0007957742,0.001278176,0.00220173,0.003924678,0.0072301,0.0023295616,0.0041594366,0.00023400794],"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":[0.000048075985,0.000057327557,0.0013823481,0.000087351254,0.0000795365,0.000208373,0.0002181753,0.1248771,0.00074692053,0.8520061,0.0016114032,0.018677324],"study_design_scores_gemma":[0.000023564757,0.000030365396,0.00065966864,0.00002736246,0.000027272841,0.00011473286,0.00007443473,0.37485722,0.00020198387,0.6227808,0.0011686101,0.000034139073],"about_ca_topic_score_codex":0.0013899469,"about_ca_topic_score_gemma":0.0007803478,"teacher_disagreement_score":0.0077760136,"about_ca_system_score_codex":0.0016358832,"about_ca_system_score_gemma":0.0008461044,"threshold_uncertainty_score":0.041123927},"labels":[],"label_agreement":null},{"id":"W2950427483","doi":"10.1016/j.insmatheco.2014.07.003","title":"Purchasing life insurance to reach a bequest goal","year":2014,"lang":"en","type":"preprint","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"Society of Actuaries; National Science Foundation","keywords":"Bequest; Life insurance; Life annuity; Purchasing; Actuarial science; Pension; Consumption (sociology); Economics; Cash; Annuity; Business; Beneficiary; Key person insurance; General insurance; Insurance policy; Finance; Marketing","score_opus":0.028128942648759446,"score_gpt":0.2863181780447802,"score_spread":0.2581892353960208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950427483","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":[0.6269371,0.0011922736,0.20635042,0.025929505,0.00045390223,0.00016163127,0.0006087087,0.00035941275,0.1380072],"genre_scores_gemma":[0.9640417,0.00034262665,0.017329594,0.0005178498,0.00021488171,0.000046451536,0.00020031603,0.000040620558,0.017266002],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994199,0.00020831662,0.000027189268,0.00009736182,0.00013379606,0.00011343998],"domain_scores_gemma":[0.99775094,0.001108361,0.00026923884,0.00014687864,0.0002807539,0.00044382064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019229339,0.0004257285,0.00082347443,0.00043428614,0.0006245965,0.002137551,0.00054685236,0.0025631313,0.009060576],"category_scores_gemma":[0.010038807,0.00026023085,0.00047525144,0.0003564857,0.0005867025,0.0028244185,0.0012096456,0.0025154466,0.0011219863],"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":[0.000381945,0.00088497886,0.019835383,0.00018437252,0.0001690728,0.0008484421,0.0010041982,0.035477232,0.005913928,0.84832126,0.016400727,0.07057848],"study_design_scores_gemma":[0.000058207377,0.00034560397,0.0063857157,0.000054415043,0.000049666807,0.00037597958,0.00052885344,0.13715169,0.0013741081,0.84588975,0.007760791,0.000025247638],"about_ca_topic_score_codex":0.00069109286,"about_ca_topic_score_gemma":0.00054052396,"teacher_disagreement_score":0.009060576,"about_ca_system_score_codex":0.0005675069,"about_ca_system_score_gemma":0.0011107763,"threshold_uncertainty_score":0.030310571},"labels":[],"label_agreement":null},{"id":"W2950598571","doi":"10.48550/arxiv.1508.06378","title":"Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Logarithm; Generalized linear model; Poisson distribution; Econometrics; Data mining; Mathematics; Statistics; Machine learning","score_opus":0.1074023590198188,"score_gpt":0.22531397770132547,"score_spread":0.11791161868150667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950598571","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":[0.059555992,0.00042559684,0.9371476,0.0005122508,0.00008799707,0.000051408428,0.00033381782,0.0010678219,0.0008175571],"genre_scores_gemma":[0.7003334,0.0003766411,0.2925655,0.00036252046,0.00026415786,0.00016462708,0.0011833084,0.00030489356,0.004444908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993455,0.00031716932,0.000027724416,0.00012363438,0.00011628439,0.00006967285],"domain_scores_gemma":[0.99559563,0.003044679,0.00039771287,0.00029631628,0.00047457629,0.0001910427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037165724,0.0006151924,0.0014020577,0.0012598316,0.00044279697,0.0010232223,0.0020879724,0.0014407446,0.0029582179],"category_scores_gemma":[0.012441442,0.0005615944,0.00096172077,0.0008847653,0.00051581336,0.0015466028,0.0009937505,0.0018189583,0.0009870426],"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":[0.00023501982,0.00013950476,0.008635601,0.00010118069,0.000106911124,0.0001556717,0.00010998615,0.85682315,0.0013359628,0.016216736,0.0058135777,0.11032661],"study_design_scores_gemma":[0.0000053971535,0.0000056948356,0.00016093197,0.000002691256,0.0000031477205,0.000005999471,0.0000016352179,0.99663526,0.00007797859,0.0029463794,0.00015152933,0.0000033881015],"about_ca_topic_score_codex":0.0049663717,"about_ca_topic_score_gemma":0.0055761724,"teacher_disagreement_score":0.0049663717,"about_ca_system_score_codex":0.0006938914,"about_ca_system_score_gemma":0.0008513113,"threshold_uncertainty_score":0.019655347},"labels":[],"label_agreement":null},{"id":"W2951426729","doi":"10.48550/arxiv.1610.01946","title":"Efficient Valuation of SCR via a Neural Network Approach","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Solvency; Computer science; Artificial neural network; Valuation (finance); Portfolio; Project portfolio management; Performance metric; Metric (unit); Capital requirement; Mathematical optimization; Finance; Economics; Artificial intelligence; Engineering; Mathematics; Systems engineering; Project management","score_opus":0.09309209953132842,"score_gpt":0.2252103949970183,"score_spread":0.13211829546568987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951426729","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":[0.07281183,0.0003932988,0.9216734,0.00069636165,0.00003949411,0.000045072255,0.000120662065,0.00032061563,0.0038992395],"genre_scores_gemma":[0.9009029,0.0003181624,0.09436778,0.00013259055,0.000069581656,0.0000965772,0.00019332366,0.00004550747,0.0038736057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999432,0.0002530187,0.000030516898,0.00010892332,0.00010558268,0.00006996108],"domain_scores_gemma":[0.9974474,0.001815191,0.00021820994,0.0001323591,0.00030481128,0.00008218309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001551398,0.0006968895,0.0010167587,0.0010292213,0.00035102823,0.0012980817,0.0011026253,0.0013769587,0.002580833],"category_scores_gemma":[0.006601644,0.00055319,0.00048088015,0.00083292276,0.000716144,0.0023544847,0.001044901,0.0013600859,0.00024184214],"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":[0.000040493956,0.00001956575,0.00056133367,0.00001442915,0.000016899286,0.000037054277,0.000011600097,0.978437,0.00029109418,0.007476506,0.00023576358,0.0128582865],"study_design_scores_gemma":[0.0000012147801,0.000001968737,0.000041815856,9.359507e-7,9.432339e-7,0.0000026114058,8.022754e-7,0.99762577,0.000044413522,0.0022530735,0.000025080099,0.0000013000538],"about_ca_topic_score_codex":0.010302732,"about_ca_topic_score_gemma":0.0062342356,"teacher_disagreement_score":0.010302732,"about_ca_system_score_codex":0.0015858276,"about_ca_system_score_gemma":0.0009470288,"threshold_uncertainty_score":0.02048552},"labels":[],"label_agreement":null},{"id":"W2952031295","doi":"10.48550/arxiv.1606.07831","title":"A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Computer science; Valuation (finance); Granularity; Artificial neural network; Portfolio; Monte Carlo method; Key (lock); Interpolation (computer graphics); Mathematical optimization; Multivariate interpolation; Data mining; Algorithm; Machine learning; Artificial intelligence; Mathematics; Economics; Finance; Statistics","score_opus":0.07254820299640366,"score_gpt":0.23028682814531617,"score_spread":0.15773862514891251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952031295","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":[0.03764118,0.00039261422,0.95690346,0.00048010395,0.00003036504,0.000022686432,0.000067650806,0.00011759003,0.0043443763],"genre_scores_gemma":[0.8320626,0.0005465702,0.16269754,0.00009709777,0.00008112838,0.000084969775,0.00009680412,0.00003316926,0.0043001375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996655,0.00013225769,0.000018245913,0.00006463785,0.00008415064,0.000035240926],"domain_scores_gemma":[0.99931085,0.0004283933,0.00007121891,0.00004130775,0.00011814091,0.000029979119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009890411,0.0005224613,0.00059094693,0.0007409115,0.00035246264,0.0009860331,0.0010909074,0.0011062857,0.0022405558],"category_scores_gemma":[0.0031002038,0.00036636266,0.00039206757,0.00093696144,0.00064176955,0.0018077131,0.0010131598,0.0013510128,0.00015863228],"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":[0.000018950921,0.000012825588,0.00032085966,0.000014812097,0.000011920517,0.000026595999,0.000015094663,0.9689766,0.00032937105,0.01543704,0.00021533774,0.014620536],"study_design_scores_gemma":[0.0000012162543,0.0000028856812,0.000034057113,0.0000015073907,0.0000010034544,0.0000033729407,0.0000014063324,0.9956086,0.00006955373,0.004211416,0.00006364568,0.0000013832663],"about_ca_topic_score_codex":0.0080853235,"about_ca_topic_score_gemma":0.006538357,"teacher_disagreement_score":0.0080853235,"about_ca_system_score_codex":0.0014505715,"about_ca_system_score_gemma":0.00072438834,"threshold_uncertainty_score":0.016076505},"labels":[],"label_agreement":null},{"id":"W2952161056","doi":"10.1080/00324728.2019.1618480","title":"Tracking progress in mean longevity: The Lagged Cohort Life Expectancy (LCLE) approach","year":2019,"lang":"en","type":"article","venue":"Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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":"Ryerson University","keywords":"Longevity; Life expectancy; Cohort; Tracking (education); Demography; Econometrics; Statistics; Gerontology; Psychology; Economics; Medicine; Mathematics; Sociology","score_opus":0.0583140758102215,"score_gpt":0.3618689646424663,"score_spread":0.3035548888322448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952161056","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":[0.21002969,0.006145278,0.766194,0.0021948493,0.00036130135,0.0001857436,0.0073998924,0.00032541103,0.0071639195],"genre_scores_gemma":[0.8948933,0.0024222007,0.09692528,0.00036061887,0.0001888525,0.00022941205,0.002770768,0.000033418608,0.0021761234],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984664,0.00086748454,0.00007900868,0.00035861513,0.00015909618,0.0000693616],"domain_scores_gemma":[0.99110514,0.0054522185,0.0013851747,0.001185122,0.000664282,0.00020812376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074038343,0.00047498738,0.0005063207,0.0019952366,0.00046512546,0.0011751849,0.00097037584,0.0007156289,0.001332277],"category_scores_gemma":[0.020073308,0.00024188758,0.00058043347,0.0018030385,0.00037470326,0.0018046176,0.0012921497,0.0013601105,0.00026596236],"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":[0.00029016891,0.00012465403,0.5293474,0.0005882933,0.0009653559,0.00036397105,0.0020012187,0.074771896,0.0017636466,0.07234587,0.0055943932,0.3118432],"study_design_scores_gemma":[0.00009029079,0.0012490806,0.33108518,0.0011332085,0.0010483013,0.0018797098,0.0020588792,0.40581918,0.0034537923,0.19137877,0.060460933,0.00034267726],"about_ca_topic_score_codex":0.0065258406,"about_ca_topic_score_gemma":0.0076248315,"teacher_disagreement_score":0.0074038343,"about_ca_system_score_codex":0.00054327596,"about_ca_system_score_gemma":0.00091899926,"threshold_uncertainty_score":0.03915572},"labels":[],"label_agreement":null},{"id":"W2952540627","doi":"10.1017/asb.2016.19","title":"EQUITABLE RETIREMENT INCOME TONTINES: MIXING COHORTS WITHOUT DISCRIMINATING","year":2016,"lang":"en","type":"preprint","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; University of New South Wales; Macquarie University","keywords":"Pooling; Pension; Economics; Actuarial science; Value (mathematics); Longevity risk; Microeconomics; Public economics; Labour economics; Finance","score_opus":0.029850926507352615,"score_gpt":0.3193995007490674,"score_spread":0.2895485742417148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952540627","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":[0.41572925,0.0004340261,0.5368974,0.0013401508,0.00015653545,0.00048760764,0.00024038261,0.00020912367,0.044505462],"genre_scores_gemma":[0.94973785,0.000109742185,0.04271312,0.00022512395,0.00005009536,0.0001777948,0.00007900514,0.00002510394,0.006882144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621636,0.0020878122,0.00017054347,0.00061402004,0.00051549,0.0003957651],"domain_scores_gemma":[0.99389195,0.0016702228,0.0010162535,0.002445791,0.0004481676,0.000527588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073278686,0.000634259,0.0006636082,0.0006117104,0.00080410816,0.0017108113,0.0014596538,0.00092422444,0.008114578],"category_scores_gemma":[0.018847942,0.00027337845,0.00057936594,0.00055586285,0.001480886,0.0023999321,0.0043260083,0.001099577,0.0008148603],"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":[0.0012110387,0.0007719594,0.021024685,0.00019996549,0.0002198552,0.00035390432,0.0018617391,0.101696536,0.0070671774,0.56311905,0.00401724,0.29845688],"study_design_scores_gemma":[0.0003074332,0.0022773372,0.015332787,0.000344479,0.00019574321,0.00063837256,0.0013274042,0.22501168,0.009448816,0.709822,0.035163615,0.00013043736],"about_ca_topic_score_codex":0.0005662107,"about_ca_topic_score_gemma":0.0006784012,"teacher_disagreement_score":0.008114578,"about_ca_system_score_codex":0.00086126366,"about_ca_system_score_gemma":0.0008679533,"threshold_uncertainty_score":0.038753927},"labels":[],"label_agreement":null},{"id":"W2952845213","doi":"10.48550/arxiv.1205.3686","title":"Valuation and hedging of the ruin-contingent life annuity (RCLA)","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life annuity; Actuarial science; Economics; Valuation (finance); Life insurance; Scrutiny; Arbitrage; Equity (law); Stochastic game; Longevity risk; Pension; Financial economics; Finance; Microeconomics","score_opus":0.11566093183762281,"score_gpt":0.2302641134920913,"score_spread":0.11460318165446849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952845213","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":[0.43468437,0.002463692,0.53597033,0.0019708013,0.0001791206,0.00008567728,0.0001914701,0.000072146824,0.024382278],"genre_scores_gemma":[0.98034406,0.0004912891,0.014377808,0.000039883602,0.00007177686,0.00002637478,0.00004339509,0.000012800118,0.0045927116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996183,0.00020724522,0.00001412599,0.00006225764,0.00005553826,0.00004247088],"domain_scores_gemma":[0.99856645,0.0008209832,0.0002473871,0.00012714132,0.000095995376,0.00014209335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018135096,0.00046653935,0.00047637147,0.000504574,0.00044225855,0.0016430949,0.0009246199,0.0016825997,0.0021755705],"category_scores_gemma":[0.005923315,0.00030270315,0.0005475675,0.0003358318,0.0016501154,0.0025196918,0.00096745184,0.0013268839,0.00011163489],"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":[0.00006543415,0.00006502904,0.0041910815,0.000055349028,0.00004605461,0.00042551797,0.00023863022,0.19864586,0.0029280642,0.7798182,0.0009436113,0.012577221],"study_design_scores_gemma":[0.0000121260355,0.00006985423,0.0022005169,0.000032413343,0.000023467444,0.00021617893,0.0001098585,0.7707239,0.00072963594,0.22342174,0.0024302246,0.000030059748],"about_ca_topic_score_codex":0.0009814823,"about_ca_topic_score_gemma":0.00083247124,"teacher_disagreement_score":0.0021755705,"about_ca_system_score_codex":0.0010980729,"about_ca_system_score_gemma":0.0005099684,"threshold_uncertainty_score":0.009590924},"labels":[],"label_agreement":null},{"id":"W2952871067","doi":"10.1017/s1474747219000040","title":"Swimming with wealthy sharks: longevity, volatility and the value of risk pooling","year":2019,"lang":"en","type":"preprint","venue":"Journal of Pensions Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Longevity; Life expectancy; Longevity risk; Life annuity; Pooling; Annuity; Economics; Volatility (finance); Actuarial science; Centenarian; Value (mathematics); Demographic economics; Pension; Demography; Financial economics; Finance; Gerontology; Medicine; Sociology","score_opus":0.011079051922245023,"score_gpt":0.2523383774281401,"score_spread":0.24125932550589507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952871067","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":[0.98108023,0.00070369686,0.0029778476,0.004053578,0.000034080924,0.0000064813307,0.000062546176,0.000008989877,0.011072527],"genre_scores_gemma":[0.9986254,0.00016021023,0.00014328785,0.00008999356,0.000031277174,0.0000016708822,0.00000987544,0.0000013369161,0.0009368382],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997607,0.00010256305,0.000010002482,0.000036781377,0.000039416802,0.000050469218],"domain_scores_gemma":[0.99584574,0.0020455155,0.0012947149,0.00019804262,0.00018652884,0.00042949937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013641332,0.00015208422,0.00031012937,0.00046028156,0.0005377582,0.001735431,0.00026891116,0.00093733025,0.005649597],"category_scores_gemma":[0.008137238,0.0001476984,0.00019689213,0.00031164632,0.0014861248,0.0016760349,0.0012654949,0.000651815,0.0002687228],"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":[0.0012510334,0.0006535571,0.6110273,0.00022102635,0.0003950683,0.0019671393,0.006970793,0.032966916,0.0032413336,0.21068864,0.008230601,0.1223866],"study_design_scores_gemma":[0.000064496126,0.00051517325,0.35364136,0.00020856208,0.00015342575,0.0007825954,0.011015703,0.05642015,0.000676562,0.57153547,0.004902285,0.00008432362],"about_ca_topic_score_codex":0.0016933131,"about_ca_topic_score_gemma":0.0018077521,"teacher_disagreement_score":0.005649597,"about_ca_system_score_codex":0.000484975,"about_ca_system_score_gemma":0.000235902,"threshold_uncertainty_score":0.018899798},"labels":[],"label_agreement":null},{"id":"W2953122516","doi":"10.48550/arxiv.1305.0113","title":"Divergence in age-patterns of mortality change drives international divergence in lifespan inequality","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Life expectancy; Inequality; Demography; Divergence (linguistics); Longevity; Demographic economics; Economics; Gerontology; Population; Medicine; Sociology","score_opus":0.15802367796766179,"score_gpt":0.27492565008960107,"score_spread":0.11690197212193928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953122516","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":[0.9917304,0.00012929409,0.0029517307,0.00021844286,0.000006018782,0.0000044483363,0.00035084842,0.0000124673425,0.0045964587],"genre_scores_gemma":[0.9991111,0.000052603173,0.00037249067,0.000017895143,0.0000026014134,0.0000022358656,0.00017905497,0.0000032853975,0.00025870517],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99974054,0.00006733918,0.000014428141,0.00008456219,0.000032546515,0.0000606113],"domain_scores_gemma":[0.9989992,0.00027437584,0.00028888613,0.00017898847,0.00015956028,0.0000989601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007266714,0.00010985834,0.00020761686,0.00071000867,0.00039904055,0.00079537026,0.00023061904,0.00024895443,0.0023426842],"category_scores_gemma":[0.0045542885,0.0000764921,0.00018859755,0.0008577675,0.00061816466,0.00061780977,0.0007786932,0.00049236114,0.00022712347],"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":[0.00010684053,0.000040068946,0.9296661,0.0000389309,0.00008718723,0.00019367477,0.003920228,0.004570217,0.0021170967,0.024894793,0.0013373696,0.033027485],"study_design_scores_gemma":[0.0000035347834,0.000021012751,0.98223716,0.000021144662,0.000016221762,0.00009133766,0.0014168129,0.0055054226,0.00040933644,0.008644597,0.0016207352,0.000012603593],"about_ca_topic_score_codex":0.013639625,"about_ca_topic_score_gemma":0.015597816,"teacher_disagreement_score":0.013639625,"about_ca_system_score_codex":0.0006749739,"about_ca_system_score_gemma":0.00030316814,"threshold_uncertainty_score":0.027120471},"labels":[],"label_agreement":null},{"id":"W2966070562","doi":"10.5539/ijsp.v8n5p13","title":"Assessing Guaranteed Minimum Income Benefits and Rationality of Exercising Reset Options in Variable Annuities","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Annuity; Payment; Variable (mathematics); Reset (finance); Present value; Interest rate; Life annuity","score_opus":0.028098164178636652,"score_gpt":0.3335560869270907,"score_spread":0.30545792274845407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966070562","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":[0.9642864,0.00034752992,0.029115148,0.00031170377,0.000011402334,0.00009958984,0.00012419902,0.000023094604,0.0056809178],"genre_scores_gemma":[0.9944728,0.00012765531,0.004902679,0.000017625716,0.0000071719364,0.000022051148,0.000059032813,0.0000055723785,0.00038547514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931803,0.0034945854,0.00046751596,0.00067286607,0.001539774,0.0006449782],"domain_scores_gemma":[0.8157001,0.15856414,0.015673766,0.0044376827,0.0036937033,0.0019305978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018518653,0.0005270746,0.0008481034,0.0010685632,0.0004330637,0.0026785594,0.0011819572,0.0016313018,0.002268989],"category_scores_gemma":[0.10488678,0.0003827014,0.00093555206,0.0005629864,0.0026481396,0.0032524837,0.0016029136,0.0021564348,0.00022256824],"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":[0.0032230031,0.0007466236,0.17381957,0.00026774095,0.00036188864,0.0006124242,0.0013522062,0.6487732,0.0044482113,0.12432183,0.0008446596,0.041228697],"study_design_scores_gemma":[0.000084925,0.0011364834,0.06471808,0.00015460896,0.00013085402,0.00030204267,0.001490434,0.8489894,0.0039190524,0.07805438,0.0008808248,0.00013892086],"about_ca_topic_score_codex":0.0044505172,"about_ca_topic_score_gemma":0.002298803,"teacher_disagreement_score":0.018518653,"about_ca_system_score_codex":0.0017803378,"about_ca_system_score_gemma":0.0015654952,"threshold_uncertainty_score":0.09793717},"labels":[],"label_agreement":null},{"id":"W2968057380","doi":"10.2139/ssrn.3209392","title":"Coherent Mortality Forecasting for Less Developed Countries","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Concordia University; University of Guelph","funders":"","keywords":"Econometrics; Geography; Economics","score_opus":0.06630451265341182,"score_gpt":0.3439649448472999,"score_spread":0.2776604321938881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968057380","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":[0.9896041,0.0006911135,0.0053439965,0.0010038299,0.00003751244,0.000007937776,0.0016032486,0.00008216406,0.0016260961],"genre_scores_gemma":[0.9974324,0.00016161168,0.0008599096,0.000023408562,0.000020539488,0.0000035047287,0.0012637785,0.000007899655,0.00022687351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976057,0.000092221955,0.00002079226,0.000065691565,0.000017577786,0.000043306412],"domain_scores_gemma":[0.9981198,0.000856626,0.00044116488,0.00019358446,0.00022567422,0.0001630832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013327219,0.00037318928,0.0006341714,0.001469142,0.00032813693,0.0016417737,0.0004010956,0.0009853091,0.0012034631],"category_scores_gemma":[0.004780324,0.00027698427,0.00050869543,0.002179094,0.00025809844,0.0013292358,0.00078303873,0.00077291956,0.00018129428],"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":[0.00033564633,0.00007691796,0.3485383,0.000065341344,0.00030190745,0.00029097634,0.00031463878,0.61209726,0.00041846602,0.007817132,0.005259039,0.024484402],"study_design_scores_gemma":[0.000037641134,0.00006139659,0.13815264,0.00004313277,0.00009069199,0.000031777945,0.00034541512,0.8512717,0.00026220857,0.008146357,0.001524442,0.000032597593],"about_ca_topic_score_codex":0.047260392,"about_ca_topic_score_gemma":0.028444977,"teacher_disagreement_score":0.047260392,"about_ca_system_score_codex":0.0009821502,"about_ca_system_score_gemma":0.0006448372,"threshold_uncertainty_score":0.09397066},"labels":[],"label_agreement":null},{"id":"W2969652138","doi":"10.7202/1062109ar","title":"Mesure de l’évolution de la mortalité sur la base de la fonction des survivants","year":2019,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humanities; Art","score_opus":0.007796738454756309,"score_gpt":0.2674728184416868,"score_spread":0.2596760799869305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969652138","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":[0.74565756,0.0011554653,0.23545553,0.00069813244,0.00012893244,0.00009196811,0.0047592064,0.002808209,0.009245014],"genre_scores_gemma":[0.8992614,0.0007421595,0.0885886,0.00006837996,0.00003383295,0.00009725706,0.0027525858,0.0003279879,0.008127756],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99930036,0.00018648796,0.00003584894,0.00025501236,0.00015100815,0.000071249415],"domain_scores_gemma":[0.9955751,0.0025802262,0.00042328922,0.0005517032,0.00075023837,0.00011953463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018914586,0.00086010614,0.0005633971,0.0016473408,0.00029500184,0.0012334542,0.0005663322,0.0006658083,0.005053365],"category_scores_gemma":[0.0062013594,0.00047143584,0.0011167198,0.0013604389,0.00041429637,0.00091405003,0.0005604658,0.0008873601,0.0015914479],"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":[0.0015924993,0.00014877903,0.30686915,0.0010650395,0.00070885423,0.00027184447,0.0032344058,0.21661296,0.07583204,0.007094301,0.004951114,0.38161898],"study_design_scores_gemma":[0.00005508609,0.0011313386,0.58022434,0.00032700563,0.00037148464,0.0004537362,0.0019762781,0.32899323,0.052076135,0.0059235073,0.0282317,0.00023615207],"about_ca_topic_score_codex":0.014706971,"about_ca_topic_score_gemma":0.013841846,"teacher_disagreement_score":0.014706971,"about_ca_system_score_codex":0.00044665183,"about_ca_system_score_gemma":0.00059937587,"threshold_uncertainty_score":0.029242754},"labels":[],"label_agreement":null},{"id":"W2970202282","doi":"10.1080/10920277.2019.1614463","title":"Bühlmann Credibility-Based Approaches to Modeling Mortality Rates for Multiple Populations","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credibility; Econometrics; Statistics; Mortality rate; Population; Measure (data warehouse); Computer science; Construct (python library); Stochastic modelling; Demography; Mathematics; Data mining","score_opus":0.21125976184026826,"score_gpt":0.3713053437838701,"score_spread":0.16004558194360183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970202282","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":[0.024617936,0.0005484007,0.9710569,0.0007111836,0.0000846877,0.000049203434,0.00015134407,0.00010637524,0.0026740285],"genre_scores_gemma":[0.88831186,0.0013721011,0.10366299,0.00020623868,0.00026609033,0.00025085115,0.00029858027,0.00006463474,0.0055665625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688137,0.0016532874,0.00015422514,0.000538137,0.0005469775,0.00022601779],"domain_scores_gemma":[0.9889572,0.007876304,0.0014772725,0.00046810549,0.0009252685,0.00029587292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007919945,0.0011310165,0.0012584033,0.0021992256,0.00067047385,0.0017936236,0.0029481873,0.002099632,0.0024927834],"category_scores_gemma":[0.029142587,0.00064262765,0.0016585167,0.0018620143,0.002012292,0.0039132396,0.0017882133,0.0028881992,0.00030200498],"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":[0.000035511417,0.000014608491,0.0015871733,0.000036766884,0.000059022994,0.000114009184,0.00018065222,0.860201,0.00018848419,0.12746744,0.0005542136,0.0095611615],"study_design_scores_gemma":[0.0000068711024,0.000014318081,0.00025378945,0.000012125632,0.000013588575,0.00002585154,0.000023397411,0.954169,0.000077279125,0.044948295,0.00043673534,0.000018658206],"about_ca_topic_score_codex":0.012803229,"about_ca_topic_score_gemma":0.0055769547,"teacher_disagreement_score":0.012803229,"about_ca_system_score_codex":0.0021756534,"about_ca_system_score_gemma":0.0012941764,"threshold_uncertainty_score":0.041885197},"labels":[],"label_agreement":null},{"id":"W2970219383","doi":"10.1007/s13524-019-00801-6","title":"Bounding Analyses of Age-Period-Cohort Effects","year":2019,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":96,"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 Toronto","funders":"","keywords":"Bounding overwatch; Econometrics; Cohort effect; Computer science; Demography; Population; Variety (cybernetics); Incidence (geometry); Statistics; Psychology; Mathematics; Artificial intelligence; Sociology","score_opus":0.0217779170806576,"score_gpt":0.3398652623045318,"score_spread":0.31808734522387416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970219383","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":[0.030911576,0.0070932936,0.9506961,0.0025440098,0.00034208412,0.00022655264,0.0006804228,0.00034305506,0.007162849],"genre_scores_gemma":[0.6373161,0.007048642,0.33969733,0.0028083613,0.0010643211,0.0015969806,0.0014679377,0.00051973446,0.00848061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9379033,0.050826672,0.0012797695,0.004812051,0.003840995,0.0013372373],"domain_scores_gemma":[0.41447744,0.5572202,0.0077945916,0.01631599,0.0031228703,0.0010689407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12300886,0.0020566776,0.0031419832,0.004286586,0.0015522528,0.0049563837,0.004260886,0.0028584322,0.0055969986],"category_scores_gemma":[0.29797566,0.001225818,0.0051666107,0.0032794632,0.004498352,0.005748668,0.008317394,0.007906566,0.0005107095],"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":[0.00036364843,0.00014558184,0.10487271,0.0010524002,0.005247123,0.0008452605,0.002444047,0.11299595,0.0018304193,0.6656426,0.004257561,0.10030262],"study_design_scores_gemma":[0.00005096528,0.00028402495,0.03241495,0.0007079774,0.0013039337,0.0003812843,0.0006872942,0.34379202,0.0027030194,0.5952148,0.022315057,0.00014467986],"about_ca_topic_score_codex":0.007631936,"about_ca_topic_score_gemma":0.0051803146,"teacher_disagreement_score":0.12300886,"about_ca_system_score_codex":0.002969852,"about_ca_system_score_gemma":0.0028054044,"threshold_uncertainty_score":0.6505408},"labels":[],"label_agreement":null},{"id":"W2972076680","doi":"10.55016/ojs/sppp.v12i1.69011","title":"Social Policy Trends: Canada and U.S. Fertility Rates,1920-2018","year":2019,"lang":"en","type":"article","venue":"The School of Public Policy Publications","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fertility; Total fertility rate; Demography; Sub-replacement fertility; Birth rate; Population; Mortality rate; Geography; Economics; Family planning; Research methodology; Sociology","score_opus":0.03514324135745414,"score_gpt":0.33834189555406574,"score_spread":0.3031986541966116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972076680","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":[0.12995552,0.012937011,0.00037229393,0.009956345,0.0006407703,0.00014343364,0.7605133,0.0004688803,0.08501248],"genre_scores_gemma":[0.5656205,0.029362442,0.0019485154,0.0018040768,0.00031103575,0.00019735972,0.33744356,0.00019583975,0.06311664],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987895,0.000028921426,0.000057096084,0.00007901315,0.00062151265,0.00042389863],"domain_scores_gemma":[0.9959401,0.00010495573,0.00033398086,0.00004476646,0.0031020315,0.00047419296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076711655,0.0004805145,0.00031993267,0.006543731,0.0022939034,0.0020811707,0.00097173627,0.00045590484,0.010631345],"category_scores_gemma":[0.0044202246,0.00025911155,0.000700374,0.013365526,0.00055633456,0.0006845728,0.0008775272,0.0014580674,0.0015127317],"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":[0.00018164798,0.000075040996,0.2871844,0.0007324725,0.00014844052,0.00017282776,0.0023258359,0.0017047867,0.00018896868,0.013004385,0.612033,0.0822482],"study_design_scores_gemma":[0.000026017673,0.000018726034,0.7537049,0.00047749587,0.000040261348,0.00011599321,0.0022079786,0.0006955254,0.00016741201,0.00029127923,0.2422115,0.000042970692],"about_ca_topic_score_codex":0.9962224,"about_ca_topic_score_gemma":0.9967417,"teacher_disagreement_score":0.04134271,"about_ca_system_score_codex":0.04134271,"about_ca_system_score_gemma":0.061890874,"threshold_uncertainty_score":0.2999637},"labels":[],"label_agreement":null},{"id":"W2974645000","doi":"10.1080/10920277.2019.1625789","title":"Hedging Mortality/Longevity Risks for Multiple Years","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan","keywords":"Longevity risk; Longevity; Actuarial science; Econometrics; Economics; Gerontology; Medicine","score_opus":0.048002977506233606,"score_gpt":0.35548984123289806,"score_spread":0.30748686372666445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974645000","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":[0.31306738,0.0012192742,0.67844754,0.00060018466,0.00008913797,0.00007329632,0.00011090046,0.00013901865,0.0062532276],"genre_scores_gemma":[0.9616601,0.00032986913,0.034685552,0.000047786103,0.000044268745,0.000030190791,0.000045158573,0.000012436398,0.0031446838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994041,0.00018345441,0.00003319006,0.00012841377,0.00017032548,0.00008046841],"domain_scores_gemma":[0.99897516,0.0004930809,0.00023607916,0.00013843163,0.000075858465,0.00008143852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020796172,0.00078753417,0.00067959575,0.00039972947,0.00029093315,0.0012491489,0.0010684625,0.00089126086,0.002752429],"category_scores_gemma":[0.0038673037,0.00038796122,0.000701669,0.00031812265,0.00042323064,0.0018365976,0.0012614345,0.0011280505,0.00016993527],"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":[0.00025673132,0.0001675155,0.009534547,0.00007571275,0.00020641832,0.0004466463,0.00016451377,0.81895393,0.007201702,0.06214572,0.0006273938,0.100219294],"study_design_scores_gemma":[0.000025557063,0.0002872333,0.003305665,0.00002321263,0.0000868267,0.00013990226,0.00007414997,0.9577838,0.002373831,0.034573406,0.0012917914,0.000034588596],"about_ca_topic_score_codex":0.0011608369,"about_ca_topic_score_gemma":0.0015003842,"teacher_disagreement_score":0.002752429,"about_ca_system_score_codex":0.0007661095,"about_ca_system_score_gemma":0.0006912885,"threshold_uncertainty_score":0.010998189},"labels":[],"label_agreement":null},{"id":"W2976244068","doi":"10.1016/j.insmatheco.2019.09.005","title":"Optimal investment strategies and risk-sharing arrangements for a hybrid pension plan","year":2019,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Pension; Pension plan; Investment strategy; Stochastic control; Actuarial science; Economics; Asset (computer security); Investment (military); Microeconomics; Time horizon; Plan (archaeology); Optimal control; Finance; Computer science; Mathematical optimization; Mathematics","score_opus":0.02792031214634864,"score_gpt":0.26335023011729397,"score_spread":0.23542991797094534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976244068","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":[0.76640904,0.0004784417,0.21262145,0.0017415643,0.00004252483,0.00019895539,0.00036773804,0.00011606238,0.018024275],"genre_scores_gemma":[0.9788038,0.00019796658,0.013696063,0.000044785378,0.00001739273,0.00009774268,0.000082595696,0.000018854153,0.007040712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99933773,0.00033832146,0.000033597702,0.000081844366,0.00007438702,0.00013420472],"domain_scores_gemma":[0.9979888,0.0013158352,0.00021352664,0.000091900445,0.00010609349,0.00028381342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027284538,0.0008414356,0.0011440794,0.0010515312,0.00061102514,0.0028774329,0.0013060861,0.002021834,0.005775004],"category_scores_gemma":[0.006371339,0.0007840609,0.0007372997,0.0006688034,0.0013674201,0.0029864898,0.0015807917,0.0011874858,0.00029033906],"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":[0.00048031443,0.00016474507,0.0025767246,0.0000694047,0.00009096187,0.0002755528,0.00024512698,0.7452525,0.0022709067,0.23299642,0.001152254,0.01442508],"study_design_scores_gemma":[0.00006912805,0.0001378337,0.00094041636,0.000021463617,0.000035713096,0.000050181636,0.00015077558,0.9139781,0.0003072717,0.08372554,0.0005624339,0.000021156804],"about_ca_topic_score_codex":0.0029821228,"about_ca_topic_score_gemma":0.002028221,"teacher_disagreement_score":0.005775004,"about_ca_system_score_codex":0.0023502638,"about_ca_system_score_gemma":0.0013516801,"threshold_uncertainty_score":0.019319296},"labels":[],"label_agreement":null},{"id":"W2979692409","doi":"10.1177/0033354919878158","title":"Black and White Differences in Life Expectancy in 4 US States, 1969-2013","year":2019,"lang":"en","type":"article","venue":"Public Health Reports","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"White (mutation); Life expectancy; Gerontology; Demography; Medicine; Psychology; Sociology; Biology; Population; Genetics","score_opus":0.03688200738090473,"score_gpt":0.3092855489565888,"score_spread":0.272403541575684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979692409","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":[0.98975813,0.00033908052,0.0002463842,0.00016773239,0.000021525877,0.000016319711,0.0077322084,0.000015483392,0.0017031499],"genre_scores_gemma":[0.99200445,0.00017578808,0.0002046007,0.00006453254,0.000017872884,0.000030923184,0.0070028477,0.0000046644304,0.00049432216],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997216,0.00004417673,0.0000324428,0.00007761521,0.00006242663,0.00006173035],"domain_scores_gemma":[0.99923766,0.000080233425,0.00038208038,0.000036145866,0.00016613689,0.00009770652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007335498,0.00017915246,0.00020044952,0.00094550283,0.00038903454,0.0003026942,0.00027596945,0.0001858809,0.0012411423],"category_scores_gemma":[0.0017160795,0.00014159337,0.00046001136,0.0010048533,0.00021958479,0.00036103412,0.0006221116,0.00033702268,0.00019465123],"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":[0.000085574735,0.000030870666,0.9938638,0.000022760212,0.000113109134,0.00002197215,0.0004052398,0.0003205448,0.00011866595,0.00017837499,0.0011684856,0.003670686],"study_design_scores_gemma":[0.0000023835162,0.00001041994,0.998944,0.000009340133,0.000015829706,0.000012627585,0.00017642656,0.00014686225,0.000032469175,0.00003204655,0.00061506964,0.0000025152103],"about_ca_topic_score_codex":0.08170133,"about_ca_topic_score_gemma":0.15459017,"teacher_disagreement_score":0.08170133,"about_ca_system_score_codex":0.00069095654,"about_ca_system_score_gemma":0.0005763411,"threshold_uncertainty_score":0.16245157},"labels":[],"label_agreement":null},{"id":"W2982364494","doi":"10.1016/s2468-2667(19)30177-x","title":"Trends in life expectancy and age-specific mortality in England and Wales, 1970–2016, in comparison with a set of 22 high-income countries: an analysis of vital statistics data","year":2019,"lang":"en","type":"article","venue":"The Lancet Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":124,"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":"Life expectancy; Demography; Context (archaeology); Mortality rate; Medicine; Gerontology; Geography; Population; Sociology","score_opus":0.09643758117760659,"score_gpt":0.37644694020482805,"score_spread":0.28000935902722146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982364494","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":[0.96960497,0.0040873424,0.00028968096,0.00041605593,0.000052065174,0.0000330759,0.024151577,0.000017040778,0.0013483176],"genre_scores_gemma":[0.97568977,0.0020129695,0.00026300942,0.00013939262,0.000027836655,0.00007548567,0.0213405,0.000008232503,0.00044281906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986376,0.00026588357,0.0004097667,0.0002354805,0.0002590676,0.0001922044],"domain_scores_gemma":[0.9974068,0.00033498413,0.0011768399,0.00011963515,0.0007265656,0.00023509542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016936735,0.0002429538,0.00051788765,0.0025172564,0.00020798302,0.0006642865,0.0002996692,0.00022263509,0.0010863614],"category_scores_gemma":[0.0048101065,0.0002297125,0.0009169976,0.0036116175,0.00026268323,0.0009052732,0.0010744587,0.0003708252,0.0003563153],"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":[0.00013120178,0.00001137507,0.99218035,0.00034135967,0.00040556915,0.00013076335,0.00037377447,0.00023950441,0.00016348179,0.00013920086,0.0016479668,0.0042354823],"study_design_scores_gemma":[0.000006488901,0.000036611927,0.9972554,0.000084614476,0.00006820714,0.000121646764,0.0004134529,0.0001601932,0.00004904116,0.000027469803,0.0017692429,0.000007680281],"about_ca_topic_score_codex":0.051386088,"about_ca_topic_score_gemma":0.06857653,"teacher_disagreement_score":0.051386088,"about_ca_system_score_codex":0.0010387582,"about_ca_system_score_gemma":0.0010380588,"threshold_uncertainty_score":0.102173984},"labels":[],"label_agreement":null},{"id":"W2983272885","doi":"10.1093/geroni/igz038.2905","title":"INCREASED PHYSIOLOGICAL VARIABILITY PREDICTS DECLINING HEALTH AND CRITICAL TRANSITIONS IN HEMODIALYSIS PATIENTS","year":2019,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Sherbrooke","funders":"","keywords":"Medicine; Hemodialysis; Quantile; Multivariate statistics; Hazard ratio; Biomarker; Mahalanobis distance; Internal medicine; Emergency medicine; Statistics; Confidence interval; Mathematics","score_opus":0.029004423637888445,"score_gpt":0.33892699561580686,"score_spread":0.3099225719779184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983272885","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":[0.99879116,0.00009313405,0.0005027625,0.00003851112,0.0000042709325,0.000006258717,0.00034542367,0.0000067752167,0.00021168575],"genre_scores_gemma":[0.9996698,0.000012774542,0.00011338991,0.000006782923,0.0000029218036,0.0000025640395,0.00016062222,6.935526e-7,0.000030545125],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971,0.00009185513,0.000027587832,0.000061238396,0.00005984052,0.000049519203],"domain_scores_gemma":[0.9980033,0.00057303585,0.0007448583,0.00015774394,0.0002481949,0.0002728752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008912431,0.0002789539,0.00028959292,0.00059373246,0.00028030158,0.00058881246,0.00025485136,0.00030268557,0.0008885747],"category_scores_gemma":[0.0031957272,0.000089823465,0.00034797157,0.00058250374,0.00023611578,0.00023529728,0.00044515685,0.00071984716,0.00009964713],"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":[0.000070591115,0.000014294062,0.99808973,0.000004201364,0.00003780236,0.000016955704,0.00003103272,0.0002116402,0.00020012865,0.000011366967,0.000056778983,0.0012554098],"study_design_scores_gemma":[0.0000010976897,0.000030515714,0.99835974,0.0000021677226,0.00000716837,0.000027889047,0.000048968748,0.0013850417,0.000060265913,0.00003432594,0.00003989154,0.0000030261224],"about_ca_topic_score_codex":0.01346089,"about_ca_topic_score_gemma":0.012593721,"teacher_disagreement_score":0.01346089,"about_ca_system_score_codex":0.00039444963,"about_ca_system_score_gemma":0.00034733454,"threshold_uncertainty_score":0.026765108},"labels":[],"label_agreement":null},{"id":"W2987181516","doi":"10.1136/bmjopen-2019-029936","title":"Recent adverse mortality trends in Scotland: comparison with other high-income countries","year":2019,"lang":"en","type":"article","venue":"BMJ Open","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":86,"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":"Life expectancy; Demography; Medicine; Mortality rate; Geography; Quarter (Canadian coin); Socioeconomics; Population; Environmental health","score_opus":0.05701176087420947,"score_gpt":0.4108009169261054,"score_spread":0.3537891560518959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987181516","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":[0.9803916,0.004128474,0.00024780034,0.0010330196,0.00013022457,0.00003729392,0.010066349,0.00002940905,0.0039358707],"genre_scores_gemma":[0.9905434,0.002224417,0.00017877248,0.00022473079,0.00010294226,0.000035539946,0.006453827,0.000009410649,0.00022696606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99889416,0.00019501464,0.00021151266,0.0001472886,0.00024187981,0.00031027079],"domain_scores_gemma":[0.9968899,0.00023614129,0.0016710609,0.000084799445,0.00070495234,0.00041314017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014147465,0.0002828772,0.0004298556,0.0030693964,0.0004268672,0.0010517992,0.00038666636,0.00026319842,0.0017659578],"category_scores_gemma":[0.004097389,0.00014148175,0.0011583174,0.0037776956,0.00029577402,0.0008066377,0.0011706197,0.00034301833,0.00029540795],"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":[0.00015215146,0.000022459664,0.98826027,0.00024872876,0.00023927404,0.00022408883,0.00041048508,0.00018025629,0.00013378657,0.00020836284,0.0017650224,0.008155228],"study_design_scores_gemma":[0.000006532052,0.00005651583,0.9973865,0.000078834695,0.000039870065,0.00012315853,0.00054564717,0.00008658157,0.000027519645,0.00002716559,0.0016147761,0.0000069582547],"about_ca_topic_score_codex":0.042724483,"about_ca_topic_score_gemma":0.046576325,"teacher_disagreement_score":0.042724483,"about_ca_system_score_codex":0.0010001141,"about_ca_system_score_gemma":0.0011595854,"threshold_uncertainty_score":0.08495164},"labels":[],"label_agreement":null},{"id":"W2989718300","doi":"10.1007/s11579-019-00252-y","title":"Quantile hedging in models with dividends and application to equity-linked life insurance contracts","year":2019,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life insurance; Dividend; Jump diffusion; Equity (law); Mathematical finance; Actuarial science; Economics; Valuation (finance); Financial economics; Insurance policy; Econometrics; Business; Jump; Finance","score_opus":0.019030513540789874,"score_gpt":0.26799906948918495,"score_spread":0.24896855594839506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989718300","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":[0.1280514,0.0055028223,0.85578394,0.0034174644,0.0003058068,0.00010531581,0.00025742836,0.00024945842,0.006326417],"genre_scores_gemma":[0.92228866,0.0039545326,0.055563193,0.00038190428,0.0004675319,0.00016834358,0.00036511253,0.00013338058,0.01667741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983575,0.0010493058,0.00007441937,0.00019578378,0.00013628446,0.00018674623],"domain_scores_gemma":[0.98153704,0.015421993,0.0008361096,0.00054406194,0.000789504,0.00087134173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010580472,0.0012948896,0.0037273208,0.0018044723,0.0012417855,0.004173719,0.0032137134,0.004372156,0.0050273477],"category_scores_gemma":[0.035092607,0.0013904892,0.002274615,0.002632261,0.003646744,0.004384108,0.003540395,0.005075477,0.00026718536],"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":[0.00007285844,0.00013266376,0.0029014093,0.00010028618,0.00012830835,0.00024941954,0.00033713825,0.6505044,0.00027551586,0.33284792,0.0013707875,0.011079349],"study_design_scores_gemma":[0.000013453478,0.0000107734,0.0002524047,0.000011541561,0.00001658772,0.000016885002,0.000029062063,0.9346808,0.000027141314,0.06466673,0.0002614982,0.0000130848775],"about_ca_topic_score_codex":0.020278256,"about_ca_topic_score_gemma":0.011878363,"teacher_disagreement_score":0.020278256,"about_ca_system_score_codex":0.0024564269,"about_ca_system_score_gemma":0.0023466472,"threshold_uncertainty_score":0.05595553},"labels":[],"label_agreement":null},{"id":"W2989899942","doi":"10.1080/10920277.2019.1672566","title":"New Solutions to an Age-Old Problem: Innovative Strategies for Managing Pension and Longevity Risk","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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":"Pension; Longevity risk; Longevity; Actuarial science; Business; Capital market; Capital (architecture); Database transaction; Economics; Finance; Medicine","score_opus":0.02489064441465308,"score_gpt":0.30878931991263175,"score_spread":0.28389867549797865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989899942","genre_codex":"commentary","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":[0.026805494,0.15659934,0.26102558,0.42255712,0.0069357003,0.00029127725,0.00019967568,0.0008234694,0.124762304],"genre_scores_gemma":[0.4321626,0.16358098,0.25337863,0.047885444,0.008530676,0.00053390476,0.00029228767,0.00027480233,0.09336073],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857223,0.00044948558,0.00006350949,0.00019483229,0.0005801178,0.00013974747],"domain_scores_gemma":[0.99676514,0.0011853239,0.00036435848,0.00034738018,0.0008553713,0.00048243813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054631853,0.00069912896,0.00064015226,0.0014220299,0.0013216396,0.004692948,0.0017040872,0.0038298247,0.009785636],"category_scores_gemma":[0.007174215,0.0002448843,0.0006520101,0.00076851516,0.0032975134,0.010658794,0.0035238725,0.003867221,0.002746449],"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":[0.00011627247,0.00038100578,0.0018739612,0.00072501396,0.00009737729,0.00031862076,0.0021193915,0.0028391618,0.0030678012,0.3377756,0.09002438,0.56066144],"study_design_scores_gemma":[0.000065957785,0.00030663057,0.0010934152,0.0009294627,0.00007277984,0.00052369206,0.0034288634,0.008463246,0.0016538781,0.43105632,0.55231714,0.000088613226],"about_ca_topic_score_codex":0.00071214634,"about_ca_topic_score_gemma":0.0014425088,"teacher_disagreement_score":0.009785636,"about_ca_system_score_codex":0.0011922177,"about_ca_system_score_gemma":0.0026932547,"threshold_uncertainty_score":0.032736182},"labels":[],"label_agreement":null},{"id":"W2990059041","doi":"10.1080/10920277.2019.1650283","title":"Longevity Greeks: What Do Insurers and Capital Market Investors Need to Know?","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Greeks; Longevity risk; Longevity; Hedge; Economics; Equity (law); Actuarial science; Issuer; Financial economics; Life insurance; Volatility (finance); Pension; Business; Finance; Political science","score_opus":0.007338441077066187,"score_gpt":0.25716460992644385,"score_spread":0.24982616884937767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990059041","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":[0.3620056,0.106004044,0.027167078,0.47625586,0.0014564082,0.00009391892,0.00055559876,0.00011431071,0.026347147],"genre_scores_gemma":[0.9117898,0.049151104,0.008033473,0.024683759,0.0025082289,0.00005950275,0.00027443323,0.000024250321,0.003475458],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991009,0.00035893457,0.0000890332,0.00010984879,0.00022711285,0.000114064984],"domain_scores_gemma":[0.98890173,0.004242461,0.0023217923,0.00055926543,0.0016644228,0.0023103883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005740109,0.00035236354,0.0009034889,0.0012483769,0.0008939255,0.0029415945,0.00061920716,0.0030091258,0.00294452],"category_scores_gemma":[0.028256068,0.0002713121,0.00037660843,0.000620934,0.002641737,0.01470241,0.0018953705,0.0028659783,0.00076327886],"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":[0.0004919781,0.00030162223,0.25889233,0.00051812234,0.0002253514,0.0009300352,0.0064264913,0.0019367588,0.0013492707,0.058797605,0.031610347,0.63852006],"study_design_scores_gemma":[0.00011307286,0.00088483706,0.19228464,0.0030241795,0.00025082627,0.0050242026,0.043018844,0.011007152,0.0012887128,0.60153496,0.14131866,0.00024999448],"about_ca_topic_score_codex":0.0022608945,"about_ca_topic_score_gemma":0.002647345,"teacher_disagreement_score":0.005740109,"about_ca_system_score_codex":0.00094984315,"about_ca_system_score_gemma":0.0012923528,"threshold_uncertainty_score":0.030356944},"labels":[],"label_agreement":null},{"id":"W2990388147","doi":"10.2139/ssrn.3211009","title":"How Does Consumption Habit Affect the Household's Demand for Life-Contingent Claims?","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Affect (linguistics); Habit; Consumption (sociology); Economics; Microeconomics; Psychology; Social psychology; Sociology","score_opus":0.0221888086770356,"score_gpt":0.2889848840769735,"score_spread":0.2667960753999379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990388147","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":[0.9965759,0.00020614485,0.00029781097,0.000801103,0.0000132380055,0.0000064220267,0.00048383675,0.000006468359,0.0016091588],"genre_scores_gemma":[0.99885345,0.00008606433,0.000043647113,0.00006573369,0.000012352584,0.000002760619,0.00019791766,0.000002818092,0.00073517347],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968576,0.00012215518,0.00002080316,0.00006517056,0.000026885753,0.00007913618],"domain_scores_gemma":[0.99603647,0.0019306338,0.0009543468,0.00027185451,0.00020463626,0.0006022211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008001659,0.00016839115,0.00035884403,0.000518469,0.00025803962,0.0013801726,0.00040741873,0.0012483995,0.007340015],"category_scores_gemma":[0.004561909,0.00027289958,0.0006999602,0.0008452905,0.00053998793,0.00069358724,0.0004311072,0.0007805887,0.0008708448],"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":[0.00017315334,0.00016687954,0.99377596,0.000012295126,0.00020635941,0.00015060532,0.00029941593,0.00059954583,0.00027985175,0.0005326728,0.00028942115,0.0035138472],"study_design_scores_gemma":[0.0000059354634,0.00005879372,0.99618274,0.0000058393816,0.00005073974,0.000078986304,0.00056134356,0.0017997468,0.00008169319,0.00083701103,0.00032845177,0.0000087105],"about_ca_topic_score_codex":0.014042895,"about_ca_topic_score_gemma":0.013585333,"teacher_disagreement_score":0.014042895,"about_ca_system_score_codex":0.00039449098,"about_ca_system_score_gemma":0.0002619317,"threshold_uncertainty_score":0.027922273},"labels":[],"label_agreement":null},{"id":"W2993099696","doi":"10.55221/2572-7478.1591","title":"Mathis' \"The fabric of hope: An Irish family legacy\" (Book Review)","year":2017,"lang":"en","type":"article","venue":"The Christian librarian:/The Christian librarian","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institute for Christian Studies","funders":"","keywords":"Irish; Genealogy; History; Art; Psychoanalysis; Philosophy; Psychology; Linguistics","score_opus":0.028156756633827846,"score_gpt":0.2905557377695078,"score_spread":0.26239898113568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993099696","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":[0.0002641052,0.8699605,0.00019023039,0.07758591,0.01318013,0.000024818091,0.000105126055,0.00003045466,0.038658727],"genre_scores_gemma":[0.006418883,0.7692002,0.00074576115,0.09562644,0.009367,0.0001396001,0.00019563654,0.000114225724,0.118192285],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992112,0.00027637213,0.00004600341,0.000057003188,0.0003177346,0.00009178085],"domain_scores_gemma":[0.9983663,0.001039877,0.000091954666,0.000043674238,0.00029202853,0.00016615164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002040873,0.0006007582,0.0008876468,0.0019782446,0.0013243929,0.006111741,0.0013901037,0.0031704863,0.025967872],"category_scores_gemma":[0.006097027,0.00035793043,0.00044406086,0.0028049292,0.0035981557,0.0048629204,0.0025491598,0.00580582,0.005754195],"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":[0.000014210888,0.000009928077,0.000064707405,0.0008352043,0.000008492525,0.000045971497,0.0005053322,0.000016943028,0.000015469941,0.011361022,0.8967506,0.09037218],"study_design_scores_gemma":[0.0000062656004,0.000005013101,0.00019858018,0.0015066768,0.0000063308844,0.00013637105,0.0004060657,0.0000055493574,0.000015778525,0.0013101731,0.9963971,0.000006091011],"about_ca_topic_score_codex":0.023901436,"about_ca_topic_score_gemma":0.061854526,"teacher_disagreement_score":0.025967872,"about_ca_system_score_codex":0.0048524365,"about_ca_system_score_gemma":0.0067739524,"threshold_uncertainty_score":0.08687115},"labels":[],"label_agreement":null},{"id":"W2996711368","doi":"10.1080/10920277.2019.1658607","title":"An Efficient Method for Mitigating Longevity Value-at-Risk","year":2019,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Longevity risk; Solvency; Hedge; Econometrics; Value at risk; Variance (accounting); Time horizon; Position (finance); Value (mathematics); Actuarial science; Economics; Computer science; Mathematics; Statistics; Risk management; Finance","score_opus":0.011135215907141352,"score_gpt":0.32831456344060894,"score_spread":0.3171793475334676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996711368","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":[0.0042083985,0.00008853426,0.9946983,0.00006477731,0.000021772903,0.000034675573,0.000017290373,0.00009744389,0.0007687539],"genre_scores_gemma":[0.1931934,0.00026310765,0.80173326,0.00008073649,0.00008957825,0.00019923541,0.00011806987,0.00007076147,0.004251882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946064,0.00015545424,0.00003106142,0.00007561942,0.00023509811,0.000042108943],"domain_scores_gemma":[0.99872154,0.00074589095,0.00013414488,0.00011571567,0.00023951226,0.000043154923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018974888,0.00050520274,0.00074790145,0.0010092988,0.00039887094,0.00077999907,0.0008224886,0.0008973958,0.0029781796],"category_scores_gemma":[0.0054245344,0.00034823737,0.0005401194,0.0006403231,0.0004825065,0.00095211284,0.0010128547,0.00097579753,0.00033387094],"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":[0.00012361136,0.00010809773,0.0018048793,0.00017733107,0.000076889024,0.00015997076,0.0001454625,0.52093154,0.014356321,0.08697226,0.0036660987,0.37147754],"study_design_scores_gemma":[0.000012246018,0.000051226245,0.0002706356,0.000015307958,0.000010574239,0.000078456884,0.000012567906,0.9825068,0.0018212196,0.013494929,0.0017134231,0.000012630773],"about_ca_topic_score_codex":0.0013126001,"about_ca_topic_score_gemma":0.0020424197,"teacher_disagreement_score":0.0029781796,"about_ca_system_score_codex":0.00055549026,"about_ca_system_score_gemma":0.001525822,"threshold_uncertainty_score":0.010034978},"labels":[],"label_agreement":null},{"id":"W2997613370","doi":"10.1017/asb.2019.38","title":"NATURAL HEDGES WITH IMMUNIZATION STRATEGIES OF MORTALITY AND INTEREST RATES","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Longevity risk; Hedge; Convexity; Interest rate; Portfolio; Econometrics; Interest rate risk; Life annuity; Life insurance; Economics; Actuarial science; Annuity; Matching (statistics); Mortality rate; Mathematics; Statistics; Financial economics; Finance; Demography; Pension","score_opus":0.048833520615444916,"score_gpt":0.31198820114179127,"score_spread":0.26315468052634633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997613370","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":[0.23496534,0.00026906776,0.7512836,0.00038270993,0.00003338324,0.00007015646,0.000063879765,0.00010414724,0.012827707],"genre_scores_gemma":[0.97230756,0.00014102187,0.024018256,0.00005653738,0.000017080505,0.000049316546,0.00001968014,0.000014248287,0.0033763028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958867,0.00016893081,0.00002130704,0.00007419461,0.00008854394,0.000058381316],"domain_scores_gemma":[0.9986958,0.00067406165,0.0003196777,0.00012523822,0.00009613158,0.00008915323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019054109,0.0004499515,0.0003730267,0.00043925902,0.00023851397,0.0010061896,0.0006819246,0.00065315975,0.002763083],"category_scores_gemma":[0.007195746,0.0003113781,0.00054515584,0.00022598187,0.0009401852,0.0016037684,0.00085129176,0.000787371,0.00017797103],"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":[0.000091249894,0.00010152234,0.0032257596,0.00006407356,0.000057593905,0.00021395102,0.00022909198,0.49292108,0.007580785,0.45390338,0.0009645997,0.040646985],"study_design_scores_gemma":[0.00002336266,0.000121837395,0.0018711932,0.000020681082,0.000029582272,0.00011251345,0.00007049861,0.87537545,0.001844747,0.11915058,0.0013573997,0.000022203945],"about_ca_topic_score_codex":0.0010632524,"about_ca_topic_score_gemma":0.0007067874,"teacher_disagreement_score":0.002763083,"about_ca_system_score_codex":0.00096752646,"about_ca_system_score_gemma":0.00073898886,"threshold_uncertainty_score":0.01007688},"labels":[],"label_agreement":null},{"id":"W2999448625","doi":"10.1016/j.insmatheco.2020.01.002","title":"Fast and efficient nested simulation for large variable annuity portfolios: A surrogate modeling approach","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Nested set model; Nested loop join; Computer science; Surrogate model; Portfolio; Population; Mathematical optimization; Algorithm; Mathematics; Data mining; Finance","score_opus":0.040107480139438365,"score_gpt":0.2773103546660203,"score_spread":0.2372028745265819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999448625","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":[0.01396771,0.00011714274,0.98386014,0.00016608852,0.000033309258,0.000043202137,0.00006770585,0.00030180166,0.0014430174],"genre_scores_gemma":[0.5572381,0.00022959702,0.43727458,0.0002210305,0.00008783396,0.0004244838,0.0005105429,0.00039549777,0.0036183207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985776,0.0007380184,0.00006439888,0.00012298675,0.00033713513,0.00015992917],"domain_scores_gemma":[0.990473,0.0067298147,0.0004992401,0.0007268532,0.0010181254,0.0005529242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00429658,0.00086937164,0.0021526376,0.00094795635,0.0009445767,0.0015623531,0.0025294262,0.0024201262,0.0040366626],"category_scores_gemma":[0.016281385,0.0012532318,0.0015976575,0.00093798555,0.0011418707,0.0018132067,0.0029325094,0.0027043936,0.00072039885],"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":[0.00006491007,0.000046297955,0.0006216883,0.000026456848,0.000028720555,0.00007107579,0.0000372105,0.9722492,0.00041094027,0.019349528,0.00037406458,0.0067199636],"study_design_scores_gemma":[0.000004645768,0.000003473598,0.000015266744,0.0000018266509,0.0000012869301,0.0000050611216,0.000001588733,0.99708194,0.000040969233,0.0027759455,0.00006660162,0.0000013973598],"about_ca_topic_score_codex":0.006614077,"about_ca_topic_score_gemma":0.005780011,"teacher_disagreement_score":0.006614077,"about_ca_system_score_codex":0.0009968836,"about_ca_system_score_gemma":0.002211961,"threshold_uncertainty_score":0.02272278},"labels":[],"label_agreement":null},{"id":"W3000338135","doi":"10.1098/rsos.202097","title":"Human mortality at extreme age","year":2021,"lang":"en","type":"preprint","venue":"Royal Society Open Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Longevity; Demography; Sampling frame; Mortality rate; Medicine; Gerontology; Population; Sociology","score_opus":0.09291704062273762,"score_gpt":0.38462776429891615,"score_spread":0.29171072367617856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000338135","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":[0.8583619,0.0014082293,0.12628709,0.0006061862,0.00006049843,0.000038094004,0.002033134,0.00030467674,0.010900193],"genre_scores_gemma":[0.9930455,0.00027719216,0.0051289233,0.000043113774,0.000052646767,0.000028692424,0.00088348426,0.00001477413,0.00052563316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998517,0.0007938123,0.000045657915,0.0002140263,0.00024240308,0.00018707644],"domain_scores_gemma":[0.9968503,0.0014454076,0.00069410965,0.0005689376,0.00023670774,0.00020450291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022206118,0.00028115025,0.0003951342,0.0020932944,0.0004309427,0.00093732314,0.00037443027,0.00047022293,0.0013336329],"category_scores_gemma":[0.009791307,0.00007906782,0.0007795636,0.0015185702,0.0008268912,0.00036463246,0.00091726624,0.00045621936,0.00034938398],"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":[0.00038509024,0.00007541747,0.68165994,0.0002142749,0.0004663758,0.0014150846,0.0024095953,0.08518713,0.003106491,0.0805128,0.006669836,0.13789797],"study_design_scores_gemma":[0.00002943604,0.00031944402,0.75092065,0.000094790186,0.00013060303,0.0018955967,0.0007033123,0.09720287,0.0015879024,0.13686578,0.010151209,0.00009841214],"about_ca_topic_score_codex":0.004309025,"about_ca_topic_score_gemma":0.003231827,"teacher_disagreement_score":0.004309025,"about_ca_system_score_codex":0.0006098784,"about_ca_system_score_gemma":0.00036459303,"threshold_uncertainty_score":0.011743903},"labels":[],"label_agreement":null},{"id":"W3004588335","doi":"","title":"A tale of two pension plans: Measuring pension plan risk from an economic capital perspective","year":2019,"lang":"en","type":"article","venue":"Kent Academic Repository (University of Kent)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"University of Waterloo","keywords":"Pension; Actuarial science; Valuation (finance); Life expectancy; Context (archaeology); Business; Finance; Economics; Population","score_opus":0.016650676356395364,"score_gpt":0.2496486872780283,"score_spread":0.23299801092163294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004588335","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":[0.9845812,0.0004499088,0.0074809417,0.0008119978,0.000028330953,0.000050039474,0.0007125968,0.00006949599,0.005815477],"genre_scores_gemma":[0.9959234,0.000092361865,0.0030511252,0.00004981656,0.000007478176,0.000021555858,0.00031733248,0.000008662453,0.0005282936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921525,0.00042948476,0.000034783574,0.00011181343,0.00012901585,0.00007965988],"domain_scores_gemma":[0.9951857,0.0032649552,0.00049182493,0.000520841,0.00029540947,0.00024133208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025049401,0.00042687633,0.00069236784,0.0012258278,0.0004110209,0.0014675676,0.00064549176,0.00077084795,0.0026027767],"category_scores_gemma":[0.012295127,0.00022704835,0.00056916336,0.0010988687,0.00084819715,0.0020486328,0.0013779957,0.0012415408,0.00017532079],"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":[0.00045849205,0.0001704378,0.07191604,0.00006241872,0.00027458556,0.00013047621,0.00020687585,0.88676846,0.0007418987,0.022669725,0.0021866884,0.01441385],"study_design_scores_gemma":[0.000100521305,0.000533476,0.030894749,0.000034628556,0.00008417761,0.00009055346,0.00043755502,0.94167906,0.0014335026,0.0231299,0.0015321377,0.00004975022],"about_ca_topic_score_codex":0.012601415,"about_ca_topic_score_gemma":0.006622645,"teacher_disagreement_score":0.012601415,"about_ca_system_score_codex":0.0014443541,"about_ca_system_score_gemma":0.0005636383,"threshold_uncertainty_score":0.025056124},"labels":[],"label_agreement":null},{"id":"W3005202186","doi":"10.1080/10920277.2020.1716808","title":"Drivers of Mortality Dynamics: Identifying Age/Period/Cohort Components of Historical U.S. Mortality Improvements","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Decomposition; Mortality rate; Cohort; Demography; Cohort effect; Statistics; Econometrics; Computer science; Mathematics; Medicine; Surgery; Biology","score_opus":0.04794933407826303,"score_gpt":0.3121226363357556,"score_spread":0.2641733022574926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005202186","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":[0.97949207,0.00023471145,0.015093757,0.00031547752,0.0000246535,0.00007702291,0.0017667817,0.00004734806,0.0029482187],"genre_scores_gemma":[0.9902553,0.0002520721,0.0065876255,0.000028696135,0.000018055052,0.000024616533,0.0016542663,0.000018087952,0.0011614085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997898,0.00006667476,0.000015423342,0.000048773367,0.000037532966,0.00004190972],"domain_scores_gemma":[0.99915004,0.00022840317,0.00026061732,0.00009615825,0.00018934006,0.00007542439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018058539,0.00046071125,0.00024429662,0.0018692333,0.00029901738,0.00089676754,0.00025554505,0.00025418255,0.0017005379],"category_scores_gemma":[0.0030641842,0.00023493636,0.0008688714,0.001264048,0.00018996015,0.0007944378,0.00066553836,0.000580236,0.0002024724],"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":[0.000053795116,0.000046559522,0.9476439,0.000022534907,0.00014293988,0.000099429526,0.00036295797,0.023058487,0.00072586926,0.0052761952,0.0007571713,0.021810148],"study_design_scores_gemma":[0.0000049803507,0.00007987018,0.9011774,0.000022442795,0.000085461004,0.000081357444,0.00070157787,0.091176465,0.0005157508,0.0031820394,0.0029443668,0.000028193497],"about_ca_topic_score_codex":0.027220039,"about_ca_topic_score_gemma":0.031096593,"teacher_disagreement_score":0.027220039,"about_ca_system_score_codex":0.0006669883,"about_ca_system_score_gemma":0.0011731096,"threshold_uncertainty_score":0.054123223},"labels":[],"label_agreement":null},{"id":"W3005382281","doi":"","title":"Can Unconventional Oil and Gas Reduce the Rural Mortality Penalty? A Study of U.S. Counties","year":2019,"lang":"en","type":"article","venue":"Journal of rural and community development","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Boom; Rural development; Geography; Humanities; Political science; Welfare economics; Economics; Environmental science; Environmental engineering","score_opus":0.028495397018468836,"score_gpt":0.2955217172339262,"score_spread":0.26702632021545736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005382281","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":[0.9977864,0.00013293611,0.00017963971,0.0007086486,0.0000072927123,0.0000080880245,0.00016188674,0.0000032761889,0.0010119089],"genre_scores_gemma":[0.99900323,0.00014408008,0.00010416181,0.00011153711,0.000008030128,0.000014357237,0.00014688833,0.0000020495609,0.00046577337],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927765,0.00041767885,0.000012737312,0.00008186896,0.000054082,0.00015600123],"domain_scores_gemma":[0.99843806,0.0006194333,0.00040617693,0.00008035668,0.00019907445,0.0002568382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006972638,0.00018666967,0.00022300647,0.00048304975,0.001025633,0.000716015,0.00059964356,0.00033081722,0.0023539036],"category_scores_gemma":[0.0033259792,0.00011741832,0.00039873118,0.0012940925,0.0005411096,0.0006528735,0.001086337,0.000734316,0.00017336206],"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":[0.00023422949,0.0008102243,0.9618272,0.0000683221,0.00020547291,0.0004022502,0.0063360413,0.0036081916,0.00028813933,0.005027725,0.006432312,0.014759854],"study_design_scores_gemma":[0.000039159495,0.0003500852,0.9459224,0.00007813193,0.00010685104,0.00008818881,0.037811894,0.006204293,0.00013870171,0.0013612163,0.00788152,0.000017508566],"about_ca_topic_score_codex":0.10946115,"about_ca_topic_score_gemma":0.1910715,"teacher_disagreement_score":0.10946115,"about_ca_system_score_codex":0.0013990978,"about_ca_system_score_gemma":0.0012520559,"threshold_uncertainty_score":0.21764803},"labels":[],"label_agreement":null},{"id":"W3005661482","doi":"10.1186/s12889-020-8307-7","title":"A probabilistic approach for economic evaluation of occupational health and safety interventions: a case study of silica exposure reduction interventions in the construction sector","year":2020,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Occupational Cancer Research Centre; McMaster University; Institute for Work & Health","funders":"Workplace Safety and Insurance Board; Cancer Care Ontario","keywords":"Psychological intervention; Medicine; Cost–benefit analysis; Environmental health; Economic evaluation","score_opus":0.31061920992818476,"score_gpt":0.44690581649476147,"score_spread":0.1362866065665767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005661482","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":[0.41485578,0.0066896803,0.51701,0.006455657,0.00029369257,0.0050704824,0.003482466,0.00018513811,0.045957148],"genre_scores_gemma":[0.8953013,0.0025792222,0.09294131,0.00027368192,0.000114385235,0.0029890283,0.00047463752,0.000047829555,0.005278555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9876781,0.009898666,0.00029171086,0.0006145339,0.0007358323,0.0007810963],"domain_scores_gemma":[0.9570005,0.03973848,0.0013421994,0.00049298274,0.0009729329,0.0004529061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016433148,0.0027157618,0.0027181944,0.003985962,0.0014908633,0.0035810424,0.0030618168,0.005113516,0.00938576],"category_scores_gemma":[0.031672116,0.0016124708,0.005059274,0.0030620263,0.0018830792,0.0023264838,0.0026750648,0.003963983,0.00031232953],"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":[0.00041372998,0.00047896488,0.00408366,0.00046599942,0.00040862645,0.0012635079,0.00018178817,0.9398688,0.0003707322,0.0410855,0.0009365843,0.010442174],"study_design_scores_gemma":[0.00020991494,0.00038286054,0.001753038,0.00013668157,0.00030481772,0.00034482614,0.00027286186,0.9742246,0.00018600095,0.020685969,0.0014308977,0.000067459834],"about_ca_topic_score_codex":0.024474489,"about_ca_topic_score_gemma":0.013997347,"teacher_disagreement_score":0.024474489,"about_ca_system_score_codex":0.007123843,"about_ca_system_score_gemma":0.005784304,"threshold_uncertainty_score":0.08690786},"labels":[],"label_agreement":null},{"id":"W3006504846","doi":"10.1080/10920277.2020.1716809","title":"Dynamic Bayesian Ratemaking: A Markov Chain Approximation Approach","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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 Manitoba","funders":"Université Paris 13; Agence Nationale de la Recherche","keywords":"Bayesian probability; Markov chain Monte Carlo; Markov chain; Computer science; Parametric statistics; Mathematical optimization; Markov process; Random effects model; Variable-order Bayesian network; Markov decision process; Bayesian inference; Econometrics; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.016533569982236113,"score_gpt":0.2773901862383029,"score_spread":0.2608566162560668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006504846","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":[0.0030410897,0.00017243097,0.9937071,0.00028645503,0.000035894253,0.000030244215,0.000032197775,0.00007520921,0.0026193226],"genre_scores_gemma":[0.49596253,0.0017046296,0.48296204,0.00045579005,0.00035779993,0.00047321612,0.000326552,0.00032146933,0.01743597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772424,0.0010857177,0.000072109135,0.00029057296,0.0005929757,0.00023441564],"domain_scores_gemma":[0.9914739,0.0067837113,0.0004810431,0.0004150348,0.0006117454,0.00023455262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006234877,0.0010153832,0.0017133908,0.0011920223,0.0008171308,0.0026575828,0.0038480232,0.00219208,0.008457095],"category_scores_gemma":[0.02284532,0.0009547958,0.0014025264,0.0015723708,0.0018253182,0.0041277753,0.0020493378,0.004138926,0.0012586778],"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":[0.000034630837,0.00005994576,0.0007267982,0.00005168283,0.00003087324,0.00012925286,0.0001723974,0.5918893,0.00029942568,0.38098052,0.0016644417,0.023960698],"study_design_scores_gemma":[0.000006139905,0.000007231472,0.000047014666,0.000010215054,0.000005704407,0.000025305997,0.000010557433,0.94668597,0.00006965434,0.05238337,0.0007404869,0.0000083491395],"about_ca_topic_score_codex":0.009835018,"about_ca_topic_score_gemma":0.0058472576,"teacher_disagreement_score":0.009835018,"about_ca_system_score_codex":0.0026948852,"about_ca_system_score_gemma":0.0025145758,"threshold_uncertainty_score":0.032973528},"labels":[],"label_agreement":null},{"id":"W3006741465","doi":"10.2139/ssrn.3481182","title":"Calibrating Gompertz in Reverse: Mortality-adjusted (Biological) Ages around the World","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Gompertz function; Demography; Geography; Statistics; Mathematics; Sociology","score_opus":0.02733987517985756,"score_gpt":0.2989442548525254,"score_spread":0.2716043796726678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006741465","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":[0.86511266,0.0011652057,0.106428474,0.0011555974,0.00029030498,0.00009833017,0.009396078,0.0011077231,0.015245771],"genre_scores_gemma":[0.9712177,0.00024016575,0.019736893,0.00016805044,0.000038005797,0.00005438765,0.0060970266,0.0002723021,0.0021754345],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984139,0.0006044426,0.00008056798,0.00062351784,0.00016837125,0.00010911851],"domain_scores_gemma":[0.9950128,0.0015671947,0.0009548528,0.0014444409,0.0008795904,0.00014103096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046886513,0.0005468086,0.00050336507,0.0024863582,0.00039582356,0.0014368589,0.0012713233,0.0011247328,0.0031978486],"category_scores_gemma":[0.024095345,0.00036716647,0.00096626906,0.0029497114,0.00062013237,0.0013265831,0.001477084,0.001242128,0.0019124929],"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":[0.00016674216,0.000058813675,0.8855155,0.00009667245,0.0006752517,0.00017143242,0.0007473649,0.039120816,0.00097404973,0.011043241,0.0061614173,0.05526858],"study_design_scores_gemma":[0.000039373364,0.00011160558,0.891384,0.00014514056,0.00046542368,0.0004037084,0.0010171583,0.06582535,0.002546134,0.017185662,0.020801838,0.00007465797],"about_ca_topic_score_codex":0.03572612,"about_ca_topic_score_gemma":0.025981126,"teacher_disagreement_score":0.03572612,"about_ca_system_score_codex":0.0005750977,"about_ca_system_score_gemma":0.0006241933,"threshold_uncertainty_score":0.07103634},"labels":[],"label_agreement":null},{"id":"W3006809263","doi":"10.1080/10920277.2019.1703753","title":"The Valuation of a Guaranteed Minimum Maturity Benefit under a Regime-Switching Framework","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geometric Brownian motion; Equity (law); Affine transformation; Economics; Valuation (finance); Econometrics; Interest rate; Stock (firearms); Actuarial science; Stock market index; Markov chain; Portfolio; Financial economics; Stock market; Mathematics; Finance; Statistics","score_opus":0.034420154805092314,"score_gpt":0.3057460260811649,"score_spread":0.2713258712760726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006809263","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":[0.23779869,0.00094015844,0.7476629,0.0015460607,0.00009121362,0.000079941405,0.00016377994,0.00012570912,0.011591497],"genre_scores_gemma":[0.9790678,0.00040235266,0.01685593,0.00005641239,0.00009491557,0.000055081637,0.00005649692,0.000021428787,0.0033896067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980209,0.0011414349,0.0000606561,0.00020755516,0.00035293106,0.00021660987],"domain_scores_gemma":[0.9946748,0.0038185124,0.0005543198,0.00025839344,0.0002920365,0.00040187125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068066125,0.00072023686,0.0009722308,0.00084163324,0.0005110903,0.0023482868,0.0013728825,0.0022477445,0.0033335974],"category_scores_gemma":[0.011648876,0.0005495572,0.0012168891,0.00058577186,0.0020279516,0.002719084,0.0014158161,0.0022566682,0.0001991552],"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":[0.00014378481,0.000084499174,0.002927346,0.000069346956,0.00009471205,0.00025138978,0.00022694524,0.5231345,0.0021265692,0.45693815,0.00082564075,0.013177047],"study_design_scores_gemma":[0.000007734135,0.000039865696,0.00046194857,0.00000879627,0.000010593876,0.000023769164,0.000016683713,0.9529282,0.00009144733,0.046196647,0.00020176217,0.0000125405295],"about_ca_topic_score_codex":0.0024759562,"about_ca_topic_score_gemma":0.0014718658,"teacher_disagreement_score":0.0068066125,"about_ca_system_score_codex":0.0021668742,"about_ca_system_score_gemma":0.0013894797,"threshold_uncertainty_score":0.035997212},"labels":[],"label_agreement":null},{"id":"W3008326625","doi":"10.1073/pnas.1922723117","title":"Reply to Li et al.: Human societies began to play a significant role in global sediment transfer 4,000 years ago","year":2020,"lang":"en","type":"letter","venue":"Proceedings of the National Academy of Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal; McGill University","funders":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Universität Bremen; Universidade Nova de Lisboa; AXA Research Fund; Institut national de la recherche scientifique; McGill University","keywords":"Reproduction; Fertility; Human reproduction; Psychology; Demography; Economics; Biology; Ecology; Sociology","score_opus":0.037963686634241524,"score_gpt":0.33321501921317764,"score_spread":0.2952513325789361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008326625","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019767456,0.0005470275,0.000026360276,0.9875566,0.010361748,0.000004532092,0.000078821555,0.000020873307,0.0012063759],"genre_scores_gemma":[0.0016663827,0.00048045485,0.000077362296,0.98132014,0.011529418,0.000022987044,0.000041120595,0.000016714055,0.0048454306],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982674,0.00048427543,0.00023785948,0.00020517835,0.0005596293,0.0002455443],"domain_scores_gemma":[0.9923679,0.0034021465,0.0005720676,0.0002753175,0.002000714,0.001381854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031029787,0.00058020424,0.0009767988,0.00066550943,0.0030392825,0.0025465302,0.00137133,0.037631366,0.0089548575],"category_scores_gemma":[0.020708596,0.00057620986,0.0007810727,0.00072133593,0.0018025408,0.0032339816,0.0015297106,0.033069562,0.010230244],"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":[0.000029756577,0.000010277627,0.00042934946,0.000020446872,0.000004215971,0.00022682939,0.000068862515,0.0000114283175,0.000056229568,0.00046309523,0.9964,0.002279512],"study_design_scores_gemma":[0.000096594624,0.00006189937,0.0018546821,0.00028236935,0.000022455657,0.0012633862,0.00078951207,0.00017914064,0.0002767579,0.00462502,0.9904822,0.000065895874],"about_ca_topic_score_codex":0.0071071475,"about_ca_topic_score_gemma":0.011014547,"teacher_disagreement_score":0.037631366,"about_ca_system_score_codex":0.0026761857,"about_ca_system_score_gemma":0.004167152,"threshold_uncertainty_score":0.029956996},"labels":[],"label_agreement":null},{"id":"W3012085502","doi":"","title":"The Law of Options","year":2002,"lang":"en","type":"article","venue":"Dalhousie law journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Law; Political science","score_opus":0.02771607847675601,"score_gpt":0.2841747365592504,"score_spread":0.2564586580824944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012085502","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":[0.009461827,0.0059626754,0.007908676,0.07565456,0.0005655858,0.000044213455,0.0002414508,0.000039247643,0.90012175],"genre_scores_gemma":[0.72139883,0.0037783128,0.004074449,0.03738293,0.0023814854,0.00042472692,0.00013362356,0.00009895894,0.23032662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706787,0.0009630647,0.00011835233,0.00061531976,0.000798708,0.00043669235],"domain_scores_gemma":[0.9975303,0.0014415766,0.00015597724,0.00038396622,0.00032049074,0.0001676591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041000517,0.0005105255,0.0008350859,0.0008991769,0.0061232597,0.009963884,0.0012890766,0.0107839815,0.014329156],"category_scores_gemma":[0.010410903,0.0007359213,0.0007235411,0.00093683874,0.019948985,0.007536552,0.0029747144,0.012465334,0.0014278772],"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":[0.0000025232048,0.0000030920257,0.000035236706,0.0000024147619,0.0000013527113,0.0000082790075,0.00007520332,0.000027685343,0.0000085562915,0.99632806,0.003008819,0.00049876794],"study_design_scores_gemma":[0.000013981962,0.0000041554813,0.000117511045,0.000031328433,0.000005278542,0.000020762394,0.00009435013,0.0002530043,0.000034624012,0.9492217,0.050196413,0.0000068761638],"about_ca_topic_score_codex":0.026557712,"about_ca_topic_score_gemma":0.022061117,"teacher_disagreement_score":0.026557712,"about_ca_system_score_codex":0.006689656,"about_ca_system_score_gemma":0.0051569487,"threshold_uncertainty_score":0.05280626},"labels":[],"label_agreement":null},{"id":"W3014062970","doi":"10.5539/ijsp.v9n3p13","title":"Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in Nigeria","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"UNICEF","keywords":"Autoregressive integrated moving average; Markov chain; Autoregressive model; Econometrics; Statistics; Moving average; Closing (real estate); SETAR; Mathematics; Markov model; Time series; STAR model; Economics","score_opus":0.030122695807124955,"score_gpt":0.30812319432878893,"score_spread":0.278000498521664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014062970","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":[0.9318582,0.0010402336,0.06421034,0.00055240805,0.000108325796,0.00008024932,0.00053563924,0.00018411504,0.0014305054],"genre_scores_gemma":[0.9877713,0.00045014743,0.010560695,0.00003370428,0.000030059422,0.000042878164,0.00068619195,0.000015294032,0.0004098045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992761,0.0003747594,0.00005560524,0.00014221454,0.00007468715,0.00007665061],"domain_scores_gemma":[0.994626,0.004209385,0.00032912,0.00014768327,0.0005084782,0.00017933064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033482302,0.0004943723,0.0005228638,0.0013601402,0.00028358688,0.0008538573,0.00046683068,0.00050556957,0.001181816],"category_scores_gemma":[0.0091594085,0.00027180248,0.00075045344,0.0006059376,0.00019486349,0.0009941715,0.00045250345,0.00074016134,0.0001961877],"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":[0.00073982496,0.00031676295,0.43605804,0.0001511486,0.00044336228,0.00018459938,0.00037658162,0.47900835,0.0006580923,0.0040098056,0.0013941692,0.076659314],"study_design_scores_gemma":[0.00001346333,0.00008959027,0.013907916,0.000038007118,0.000053144096,0.000022112828,0.00009217818,0.9838696,0.00020533486,0.001461104,0.00023344233,0.000014049872],"about_ca_topic_score_codex":0.03141075,"about_ca_topic_score_gemma":0.018613609,"teacher_disagreement_score":0.03141075,"about_ca_system_score_codex":0.000544887,"about_ca_system_score_gemma":0.001298499,"threshold_uncertainty_score":0.062455833},"labels":[],"label_agreement":null},{"id":"W3015605702","doi":"10.1016/j.insmatheco.2020.03.009","title":"Calibrating Gompertz in reverse: What is your longevity-risk-adjusted global age?","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":32,"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":"Schulich School of Business, York University","keywords":"Longevity risk; Longevity; Salience (neuroscience); Gompertz function; Metric (unit); Pension; Hazard; Demography; Actuarial science; Salient; Hazard ratio; Gerontology; Economics; Psychology; Medicine; Computer science; Biology; Sociology; Statistics; Finance; Operations management; Mathematics; Cognitive psychology; Artificial intelligence","score_opus":0.046054441797611055,"score_gpt":0.2824059208799021,"score_spread":0.236351479082291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015605702","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":[0.19461603,0.001222484,0.78814167,0.0042647487,0.00036213663,0.00006109022,0.0010762964,0.00039517082,0.009860286],"genre_scores_gemma":[0.8694533,0.0008306034,0.12574977,0.0005225821,0.00016870935,0.000075705706,0.0008550826,0.00014800084,0.0021962747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99938333,0.0002721929,0.000023673767,0.00020615188,0.00006454324,0.00005003303],"domain_scores_gemma":[0.99690586,0.0019728544,0.00040000814,0.000375571,0.00022626748,0.00011943842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039125374,0.00047139157,0.00061427697,0.0012902189,0.00039745646,0.0017834405,0.0011766786,0.0010512392,0.0027435252],"category_scores_gemma":[0.023470182,0.00027599392,0.0007528899,0.00097265694,0.0011829388,0.0032243626,0.0018202064,0.0016608711,0.00048140026],"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":[0.00011791894,0.00006265932,0.08406861,0.0001221991,0.00016579729,0.00019564548,0.0008946277,0.46203417,0.00050569174,0.33982605,0.0058846483,0.10612198],"study_design_scores_gemma":[0.000026729398,0.00008206769,0.016095167,0.00014072064,0.000052267933,0.00024098204,0.0005553865,0.47347152,0.0007566197,0.49453598,0.0139618665,0.00008070586],"about_ca_topic_score_codex":0.0062686694,"about_ca_topic_score_gemma":0.0045792907,"teacher_disagreement_score":0.0062686694,"about_ca_system_score_codex":0.00083424844,"about_ca_system_score_gemma":0.00075020385,"threshold_uncertainty_score":0.020691752},"labels":[],"label_agreement":null},{"id":"W3019463389","doi":"10.1177/0049124120914928","title":"Joint Modeling of Multivariate Survival Data With an Application to Retirement","year":2020,"lang":"en","type":"article","venue":"Sociological Methods & Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University; University of Victoria","funders":"","keywords":"Univariate; Multivariate statistics; Proportional hazards model; Covariate; Event (particle physics); Econometrics; Survival analysis; Variety (cybernetics); Multivariate analysis; Event data; Computer science; Statistics; Actuarial science; Psychology; Mathematics; Economics","score_opus":0.5795516871879395,"score_gpt":0.581134355203404,"score_spread":0.0015826680154644723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019463389","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":[0.01101257,0.00056885224,0.9866693,0.0006319026,0.000064877015,0.000051316852,0.0002252047,0.00019253007,0.00058341335],"genre_scores_gemma":[0.5163275,0.0029703425,0.46736026,0.00033555334,0.0005872263,0.0009236614,0.0011003099,0.00024439138,0.010150782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940414,0.0043844036,0.00023044055,0.0005604896,0.00052319455,0.0002600857],"domain_scores_gemma":[0.9750718,0.020089053,0.002134096,0.0014218931,0.0009040693,0.0003790628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014374436,0.0011144419,0.0015750082,0.0016914989,0.00063266547,0.0017462266,0.0022199915,0.0014420816,0.003242206],"category_scores_gemma":[0.028292306,0.0008581059,0.0028400521,0.0027585025,0.0015644662,0.0018761917,0.0031365305,0.0028585952,0.00043432714],"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":[0.00014297497,0.00012281435,0.014183832,0.00018942496,0.0004386548,0.0003253473,0.0007074147,0.5834932,0.00046690108,0.3442662,0.0022118594,0.053451404],"study_design_scores_gemma":[0.000014695814,0.00007230644,0.0015410736,0.000028018194,0.000045628243,0.000063654916,0.000061157865,0.9151382,0.00010009444,0.08077292,0.0021302914,0.000031843687],"about_ca_topic_score_codex":0.013990253,"about_ca_topic_score_gemma":0.0110651245,"teacher_disagreement_score":0.014374436,"about_ca_system_score_codex":0.0011300826,"about_ca_system_score_gemma":0.0020688805,"threshold_uncertainty_score":0.07602018},"labels":[],"label_agreement":null},{"id":"W3021305677","doi":"10.1017/s174849952000010x","title":"Asymmetry in mortality volatility and its implications on index-based longevity hedging","year":2020,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Longevity; Volatility (finance); Hedge; Heteroscedasticity; Econometrics; Longevity risk; Autoregressive conditional heteroskedasticity; Economics; Index (typography); Financial economics; Biology; Computer science","score_opus":0.17216841679727518,"score_gpt":0.4195325013111974,"score_spread":0.24736408451392222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021305677","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":[0.85283726,0.0010079239,0.13574526,0.001159855,0.00010223227,0.000029890498,0.00028148762,0.00011115128,0.008724856],"genre_scores_gemma":[0.99747556,0.0001007507,0.0020914772,0.000024291596,0.000022900815,0.0000033376061,0.000036904832,0.000004092723,0.0002406658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999181,0.00029523013,0.00007815124,0.000125206,0.00023955412,0.00008089221],"domain_scores_gemma":[0.9944722,0.0026027923,0.0015544072,0.00072005036,0.00048043067,0.00017004074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044677034,0.0003216297,0.0005978394,0.0009248798,0.00024662074,0.0014210619,0.0005957888,0.0008067279,0.0013128838],"category_scores_gemma":[0.012934218,0.00015891888,0.00041348772,0.0006193624,0.00075201085,0.001507046,0.0013240232,0.0008682937,0.000091619106],"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":[0.0005988251,0.00018538356,0.11944605,0.00023949177,0.00033370857,0.0011456151,0.0006938729,0.39185852,0.01463861,0.35193056,0.0022004484,0.1167289],"study_design_scores_gemma":[0.000036853275,0.0002536541,0.056601938,0.00011021774,0.00009663024,0.0006148944,0.00030168312,0.68679965,0.004440519,0.24888785,0.0017551618,0.00010089745],"about_ca_topic_score_codex":0.00051950465,"about_ca_topic_score_gemma":0.00028507158,"teacher_disagreement_score":0.0044677034,"about_ca_system_score_codex":0.00061881455,"about_ca_system_score_gemma":0.00030003913,"threshold_uncertainty_score":0.023627818},"labels":[],"label_agreement":null},{"id":"W3021729611","doi":"","title":"Alternative Pasts, Possible Futures: A \"What If\" Study of the Effects of Fertility on the Canadian Population and Labour Force","year":2002,"lang":"en","type":"article","venue":"Social and Economic Dimensions of an Aging Population Research Papers","topic":"Insurance, Mortality, Demography, Risk Management","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":"Bust; Boom; Futures contract; Dependency ratio; Counterfactual thinking; Population; Economics; Baby boom; Fertility; Shadow (psychology); Population ageing; Population growth; Development economics; Demographic economics; Economy; Demography; Financial economics; Sociology; Engineering","score_opus":0.04274939589824917,"score_gpt":0.33783645777690996,"score_spread":0.2950870618786608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021729611","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":[0.8504209,0.006975765,0.03340884,0.032613344,0.00056210207,0.00021986617,0.004458611,0.00011270716,0.07122785],"genre_scores_gemma":[0.9934117,0.0014585378,0.0025345634,0.0003572094,0.000039797193,0.000031637388,0.00039689592,0.000010752065,0.0017588777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979431,0.0011728324,0.000059492795,0.00032753882,0.0002496665,0.00024729068],"domain_scores_gemma":[0.99493355,0.002994797,0.00077334774,0.00052214664,0.00055085076,0.00022542606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057587503,0.0004939488,0.00063408253,0.0010119217,0.0026626696,0.004434323,0.0015584684,0.0013330056,0.0049214032],"category_scores_gemma":[0.014985418,0.00038002254,0.0010018647,0.0012993228,0.0033704722,0.004035836,0.0012716885,0.0016424091,0.00022071386],"study_design_candidate":"simulation_or_modeling","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":[0.0010860955,0.00019089146,0.07906444,0.00032800133,0.0006833714,0.0010710246,0.004286641,0.062499035,0.00050438696,0.8160806,0.005016705,0.029188884],"study_design_scores_gemma":[0.00033098733,0.00041094612,0.10946505,0.0002797021,0.0006892515,0.0005435654,0.0138323475,0.18708533,0.0008837497,0.6427612,0.043371297,0.00034661742],"about_ca_topic_score_codex":0.45939153,"about_ca_topic_score_gemma":0.5601353,"teacher_disagreement_score":0.54060847,"about_ca_system_score_codex":0.011998009,"about_ca_system_score_gemma":0.004032407,"threshold_uncertainty_score":0.91343516},"labels":[],"label_agreement":null},{"id":"W3023030581","doi":"10.1002/9781118445112.stat03571","title":"Longevity Risk and Life Annuities","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Centre for Infectious Diseases; York University","funders":"","keywords":"Longevity; Longevity risk; Perspective (graphical); Argument (complex analysis); Actuarial science; Life insurance; Economics; Gerontology; Medicine; Computer science","score_opus":0.029104900335117898,"score_gpt":0.31874480352405576,"score_spread":0.28963990318893784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023030581","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":[0.038915053,0.21652417,0.011955159,0.06068897,0.0020378064,0.000050896717,0.0021893538,0.0001119739,0.66752654],"genre_scores_gemma":[0.73836243,0.16524999,0.0036607992,0.00462977,0.0065858075,0.000055703746,0.0011434441,0.000060165177,0.08025196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99933773,0.0002693383,0.000033688568,0.00006880534,0.00023441057,0.000055970733],"domain_scores_gemma":[0.9968104,0.0017399921,0.0006418584,0.00015282331,0.00042685523,0.00022805095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009605276,0.00027655813,0.0002833007,0.0014736258,0.0004070053,0.0022378392,0.00030415217,0.0011211359,0.022422],"category_scores_gemma":[0.006505989,0.00006596735,0.00020102928,0.0020506226,0.0012229081,0.0013866865,0.0010372363,0.0015920633,0.0018287435],"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":[0.000040323714,0.00004140538,0.006223161,0.00027753506,0.000023094959,0.0001602139,0.00041908064,0.0014443459,0.00013502534,0.85652125,0.03885894,0.095855564],"study_design_scores_gemma":[0.000007401686,0.000059132937,0.014264935,0.0007740965,0.000019697212,0.0007541861,0.0005853826,0.0014884381,0.00017733495,0.6683766,0.31346336,0.00002936864],"about_ca_topic_score_codex":0.0015465815,"about_ca_topic_score_gemma":0.0013226533,"teacher_disagreement_score":0.022422,"about_ca_system_score_codex":0.00078716135,"about_ca_system_score_gemma":0.00071617676,"threshold_uncertainty_score":0.07500899},"labels":[],"label_agreement":null},{"id":"W3024880065","doi":"10.1017/9781108784184.019","title":"Estimating survival models","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actuarial science; Life insurance; Cash flow; Computer science; Finance; Economics","score_opus":0.04140841057448921,"score_gpt":0.24530510875998954,"score_spread":0.20389669818550032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024880065","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":[0.015823314,0.0023931144,0.9673651,0.0007487249,0.00024344772,0.00015276951,0.004531452,0.0014575673,0.0072845155],"genre_scores_gemma":[0.42862925,0.008877494,0.48376545,0.000504861,0.00091288274,0.0012950677,0.032923087,0.0010344246,0.042057462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99750537,0.0014026173,0.00012082251,0.0005172302,0.00028816488,0.00016583022],"domain_scores_gemma":[0.9863085,0.0113614,0.0004762242,0.0009896201,0.00070277345,0.00016156484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007260269,0.0014827393,0.0017522891,0.0032815526,0.00062144996,0.0030230512,0.0020371345,0.001786045,0.027878996],"category_scores_gemma":[0.026956486,0.001039068,0.002511804,0.0033284097,0.0006894755,0.0029310922,0.0020115746,0.0026078583,0.008934454],"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":[0.00020613696,0.00012820409,0.018695567,0.00051014224,0.00049945764,0.00022260976,0.0004209726,0.4262316,0.00042414365,0.12383179,0.028127542,0.40070185],"study_design_scores_gemma":[0.000025657417,0.000087325156,0.0029325762,0.00022821214,0.00009361708,0.0001649276,0.00016990383,0.8139213,0.00030965952,0.16540563,0.016617924,0.000043397948],"about_ca_topic_score_codex":0.0062406794,"about_ca_topic_score_gemma":0.0056127263,"teacher_disagreement_score":0.027878996,"about_ca_system_score_codex":0.0012783549,"about_ca_system_score_gemma":0.0014213889,"threshold_uncertainty_score":0.09326452},"labels":[],"label_agreement":null},{"id":"W3025591180","doi":"10.1017/s1748499521000178","title":"Impact of the choice of risk assessment time horizons on defined benefit pension schemes","year":2021,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; The Institute and Faculty of Actuaries; University of Waterloo","keywords":"Bond; Asset allocation; Pension; Economics; Time horizon; Equity (law); Incentive; Risk premium; Horizon; Actuarial science; Econometrics; Financial economics; Finance; Microeconomics; Portfolio; Mathematics","score_opus":0.04832944782587889,"score_gpt":0.3971130355113609,"score_spread":0.348783587685482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025591180","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":[0.9933561,0.0005606023,0.0016393259,0.00032811196,0.00003232258,0.00003635893,0.00016480341,0.000013443649,0.0038689622],"genre_scores_gemma":[0.99897575,0.00008498814,0.00043551475,0.000033728014,0.0000099346835,0.0000113069655,0.00003488362,0.0000029169255,0.0004110391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99523723,0.002755908,0.00022890615,0.00029588662,0.0007065786,0.0007754411],"domain_scores_gemma":[0.92799646,0.057750188,0.008595325,0.0014802336,0.0015715563,0.0026062985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014089509,0.00045941712,0.00056211004,0.00065314857,0.0003705169,0.0024654474,0.00091323437,0.0013343234,0.005007668],"category_scores_gemma":[0.043697976,0.00020381858,0.00087469607,0.0005647207,0.00074809534,0.0012555479,0.0011953117,0.001734175,0.0002449212],"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":[0.03427754,0.0037188546,0.15895833,0.00063935836,0.0010215398,0.0013998165,0.0006615488,0.6119293,0.02577505,0.04500493,0.0019837467,0.11462999],"study_design_scores_gemma":[0.0016699596,0.029241873,0.38833734,0.0005582693,0.0024530352,0.0007819665,0.003563948,0.4921097,0.03736384,0.03460914,0.008793826,0.0005172136],"about_ca_topic_score_codex":0.0020887244,"about_ca_topic_score_gemma":0.0016597284,"teacher_disagreement_score":0.014089509,"about_ca_system_score_codex":0.0013638111,"about_ca_system_score_gemma":0.0009176881,"threshold_uncertainty_score":0.074513316},"labels":[],"label_agreement":null},{"id":"W3029513928","doi":"10.2139/ssrn.3045430","title":"Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"Society of Actuaries","keywords":"Econometrics; Variable (mathematics); Mathematics; Statistics; Computer science; Economics","score_opus":0.020246836135443237,"score_gpt":0.33124574967894715,"score_spread":0.3109989135435039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029513928","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":[0.04757026,0.00012222481,0.9473688,0.00025583856,0.00005129205,0.0001217994,0.0002039396,0.00052929024,0.003776514],"genre_scores_gemma":[0.77624714,0.00012435578,0.21639784,0.00017324947,0.00006405079,0.00042083886,0.00065524864,0.00041658222,0.0055006323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977228,0.0012562751,0.00008714633,0.00022990584,0.00035678473,0.00034699458],"domain_scores_gemma":[0.9718693,0.022857774,0.0010289854,0.001468675,0.0015603146,0.001214906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072160023,0.0009430346,0.0025629848,0.0013308205,0.0011961072,0.002097559,0.0035540692,0.003080476,0.008574805],"category_scores_gemma":[0.033523243,0.0015281738,0.0017340422,0.0011280242,0.0026665346,0.0026185801,0.0035317168,0.0030484798,0.0009056743],"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":[0.000121349876,0.00008221631,0.0011442382,0.000032244952,0.00003159041,0.00011890644,0.0000910576,0.9364977,0.00028488968,0.0580021,0.0004061093,0.003187575],"study_design_scores_gemma":[0.00000996179,0.0000048583374,0.000031201795,0.000003007447,0.0000021225235,0.0000061515316,0.0000045495926,0.9925014,0.000055053886,0.0073195295,0.000059047525,0.0000030083365],"about_ca_topic_score_codex":0.016449533,"about_ca_topic_score_gemma":0.011522744,"teacher_disagreement_score":0.016449533,"about_ca_system_score_codex":0.0021424885,"about_ca_system_score_gemma":0.0033715344,"threshold_uncertainty_score":0.03816235},"labels":[],"label_agreement":null},{"id":"W3029803082","doi":"10.1080/10920277.2020.1737495","title":"Advances in Predictive Analytics","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Predictive analytics; Analytics; Data science; Computer science; Range (aeronautics); Predictive value; Engineering; Medicine; Internal medicine","score_opus":0.017480640621440186,"score_gpt":0.2985119088576981,"score_spread":0.28103126823625796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029803082","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":[0.0021941937,0.5852568,0.11448439,0.11268764,0.13055442,0.00013089096,0.0019126029,0.0026420096,0.050137002],"genre_scores_gemma":[0.030071974,0.6156511,0.054272726,0.01839294,0.2522119,0.00013287982,0.0028169197,0.0010508623,0.025398692],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907302,0.0019290509,0.00074191106,0.001165068,0.005040018,0.00039379235],"domain_scores_gemma":[0.9350554,0.047075488,0.0017082018,0.003739267,0.0104313195,0.0019903234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011121441,0.0016637454,0.0017637426,0.0070176814,0.0011441316,0.00904083,0.002054201,0.0027902965,0.01453704],"category_scores_gemma":[0.05061328,0.0008048536,0.0016393977,0.0074109305,0.0026214435,0.010519398,0.0039722356,0.00954766,0.00851084],"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":[0.000057188146,0.00008045701,0.0013868859,0.0013202211,0.0001653226,0.00012472637,0.00019150022,0.0025304938,0.00034621242,0.06474831,0.5285087,0.40053993],"study_design_scores_gemma":[0.000014493828,0.000049290225,0.0009717579,0.0012530941,0.000054874272,0.00035650327,0.00015782742,0.0075775916,0.00034681012,0.110931076,0.8782209,0.00006580995],"about_ca_topic_score_codex":0.0017559562,"about_ca_topic_score_gemma":0.0015571639,"teacher_disagreement_score":0.01453704,"about_ca_system_score_codex":0.001824975,"about_ca_system_score_gemma":0.0036464185,"threshold_uncertainty_score":0.058816493},"labels":[],"label_agreement":null},{"id":"W3033540916","doi":"10.71781/13012","title":"Age, period, and cohort effects on adult mortality due to extrinsic causes of death","year":2019,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Period (music); Cohort; Cohort effect; Demography; Medicine; Gerontology; Internal medicine","score_opus":0.007482293942913708,"score_gpt":0.22434104174697694,"score_spread":0.21685874780406322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033540916","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":[0.9892881,0.0050949245,0.0004024283,0.00039674147,0.00015731096,0.000034744793,0.0014394562,0.000018479113,0.0031677734],"genre_scores_gemma":[0.99298567,0.0018957985,0.0002240234,0.00011870049,0.00008112786,0.000028882921,0.0005594236,0.000013033063,0.00409349],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99920696,0.00026260348,0.000047010868,0.00015653725,0.00006995553,0.00025695906],"domain_scores_gemma":[0.9943235,0.0020408873,0.0012060958,0.0008969779,0.00038892354,0.0011436567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032099888,0.0004266357,0.00029654318,0.00055523566,0.00037079677,0.0007856959,0.00043062645,0.00056497054,0.0055087935],"category_scores_gemma":[0.00796574,0.0002735088,0.0018255545,0.00044779602,0.00026599027,0.00053321716,0.0009963879,0.0006632181,0.00041760187],"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":[0.0032233791,0.000079946956,0.9854854,0.00008358967,0.00094044267,0.00015542185,0.00038374632,0.00033871806,0.00058740575,0.00041823328,0.00048791568,0.007815821],"study_design_scores_gemma":[0.000025943407,0.00050994556,0.9969494,0.00004229065,0.00046914673,0.000076455,0.00019960034,0.00026693943,0.00011418529,0.00013406682,0.0012009899,0.000011103643],"about_ca_topic_score_codex":0.016144212,"about_ca_topic_score_gemma":0.019555112,"teacher_disagreement_score":0.016144212,"about_ca_system_score_codex":0.00034913642,"about_ca_system_score_gemma":0.0011209404,"threshold_uncertainty_score":0.0321005},"labels":[],"label_agreement":null},{"id":"W3033556067","doi":"10.2139/ssrn.3421296","title":"Optimal Insurance Contracts with Limited Commitment and Unobservable Disability","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Calgary","funders":"","keywords":"Unobservable; Actuarial science; Business; Disability insurance; Economics; Econometrics","score_opus":0.009276308143697913,"score_gpt":0.2533204523539158,"score_spread":0.2440441442102179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033556067","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":[0.79820776,0.002587921,0.13407786,0.018235818,0.000382444,0.0006134205,0.0034105792,0.0005031326,0.041981094],"genre_scores_gemma":[0.9798043,0.00061365,0.0065911617,0.00034237746,0.00017400774,0.0001974135,0.00032618953,0.00004856093,0.011902356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99374086,0.0032650707,0.00034098435,0.00068374944,0.00046695257,0.0015023622],"domain_scores_gemma":[0.9311894,0.054042023,0.0067312047,0.002220764,0.0010741493,0.0047424864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010491601,0.0016736988,0.005658489,0.0019198266,0.0012545072,0.005387541,0.0032226036,0.0063225226,0.013256867],"category_scores_gemma":[0.05053652,0.0029255922,0.0013125094,0.0016910923,0.00376907,0.0055713407,0.0040915306,0.0048517413,0.0010311577],"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":[0.0034365025,0.002366727,0.018171584,0.00072949915,0.0005383151,0.0015382516,0.0011103877,0.48899418,0.0014909566,0.43418315,0.009173479,0.038266964],"study_design_scores_gemma":[0.0013575094,0.0007308053,0.006997546,0.00020328745,0.00019265791,0.000382025,0.0008488368,0.4891982,0.0003662777,0.49703443,0.0025730385,0.00011538862],"about_ca_topic_score_codex":0.006752405,"about_ca_topic_score_gemma":0.004117177,"teacher_disagreement_score":0.013256867,"about_ca_system_score_codex":0.004298996,"about_ca_system_score_gemma":0.004720607,"threshold_uncertainty_score":0.055485547},"labels":[],"label_agreement":null},{"id":"W3035255910","doi":"10.15173/mumj.v16i1.2020","title":"Is There a Limit to Human Life Expectancy?","year":2019,"lang":"en","type":"article","venue":"McMaster University Medical Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Life expectancy; Prosperity; Welfare; Pension; Expectancy theory; Psychology; Economics; Political science; Sociology; Economic growth; Social psychology; Demography; Population; Law","score_opus":0.025126265302710112,"score_gpt":0.28196450830780045,"score_spread":0.25683824300509034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035255910","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013543603,0.06591301,0.0009671697,0.9200429,0.0070138024,0.0000033054087,0.000043615855,0.000009627562,0.0046522073],"genre_scores_gemma":[0.13473314,0.18754242,0.0017110095,0.5758841,0.09589096,0.0000664394,0.000111268244,0.000052979605,0.0040076496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930963,0.0030366678,0.00047933788,0.0012822902,0.0016914172,0.00041402294],"domain_scores_gemma":[0.97700125,0.018517975,0.0015282313,0.00046889743,0.0019276614,0.0005559837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0087230485,0.0005083891,0.0015580211,0.0013737371,0.0024127765,0.0058703623,0.0035135064,0.012243357,0.0031582743],"category_scores_gemma":[0.03266543,0.00027467718,0.0006997702,0.001175073,0.026025563,0.0101702195,0.0026311786,0.014737112,0.0010503588],"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":[0.00014865388,0.000040628896,0.003623159,0.0014287352,0.00010797033,0.00082809274,0.0065741935,0.00050866173,0.00026943834,0.7091824,0.1924925,0.08479552],"study_design_scores_gemma":[0.00002365166,0.00007607333,0.0037426597,0.003503761,0.00004869942,0.0005253961,0.0052623046,0.00040068926,0.00019577601,0.5176089,0.4685277,0.00008437528],"about_ca_topic_score_codex":0.010656527,"about_ca_topic_score_gemma":0.00986391,"teacher_disagreement_score":0.012243357,"about_ca_system_score_codex":0.005248024,"about_ca_system_score_gemma":0.0044038286,"threshold_uncertainty_score":0.046132445},"labels":[],"label_agreement":null},{"id":"W3043082045","doi":"","title":"Demography and Growth | Bulletin – June Quarter 2010","year":2010,"lang":"en","type":"article","venue":"Philadelphia Museum of Art Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Geography; Demography; Sociology","score_opus":0.009515544524924263,"score_gpt":0.2447885530921432,"score_spread":0.23527300856721894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043082045","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":[0.04086039,0.09104951,0.00067981926,0.13300356,0.036224354,0.00017088528,0.10155096,0.0014226127,0.59503794],"genre_scores_gemma":[0.07690218,0.037205905,0.00067765394,0.0027485783,0.008322208,0.000111292895,0.03865531,0.00021613554,0.83516073],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99985147,0.000021468903,0.000012683612,0.000020391315,0.000071178314,0.00002288775],"domain_scores_gemma":[0.99917406,0.00012930343,0.00011551192,0.0000589503,0.0003798298,0.00014223013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046239857,0.00060601707,0.00032694812,0.002303586,0.0008053427,0.0017584999,0.00030855008,0.00063842855,0.080020145],"category_scores_gemma":[0.0019971442,0.00028475432,0.000185242,0.003085748,0.00033465135,0.0010358967,0.00065408007,0.0012679121,0.018800473],"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":[0.00002619246,0.000018551693,0.0027960495,0.000031068175,0.000004853618,0.000015104441,0.000037322723,0.00003400869,0.00002857362,0.00033435426,0.97589964,0.020774355],"study_design_scores_gemma":[0.00001303591,0.000019731433,0.045355354,0.00009761849,0.000010426659,0.00005700274,0.00016947211,0.000060480124,0.00009662559,0.00020873414,0.95390475,0.0000067727583],"about_ca_topic_score_codex":0.05363443,"about_ca_topic_score_gemma":0.15780798,"teacher_disagreement_score":0.080020145,"about_ca_system_score_codex":0.0018852117,"about_ca_system_score_gemma":0.0013446606,"threshold_uncertainty_score":0.26769406},"labels":[],"label_agreement":null},{"id":"W3043471198","doi":"","title":"Abstract for Demography and Growth | Bulletin – June Quarter 2010","year":2010,"lang":"en","type":"article","venue":"Philadelphia Museum of Art Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Demography; Gerontology; Geography; Medicine; Sociology","score_opus":0.01333214434509777,"score_gpt":0.26245055030627623,"score_spread":0.24911840596117846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043471198","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":[0.008240015,0.0285583,0.0017113885,0.047227383,0.06865577,0.0007954439,0.17828292,0.00262597,0.6639028],"genre_scores_gemma":[0.008292303,0.0058550574,0.00063371786,0.0012113458,0.0069210595,0.00021607622,0.038826328,0.000298431,0.9377457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996793,0.000038932056,0.00003392794,0.000040878105,0.00015221961,0.000054784894],"domain_scores_gemma":[0.99706656,0.00043983193,0.00021738381,0.00021979949,0.0016330505,0.00042345285],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007083484,0.0013359982,0.0009617882,0.0041413675,0.0010810972,0.0021239188,0.0008970137,0.0010627675,0.38577554],"category_scores_gemma":[0.0032970684,0.0005402566,0.0005240491,0.0034004753,0.00029619507,0.0014033788,0.0010050965,0.0021476173,0.15665701],"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":[0.000039545954,0.000024491683,0.00040330237,0.000045956036,0.0000034023851,0.00000739038,0.000008443669,0.000028818265,0.0000287049,0.0001782849,0.9852865,0.013945091],"study_design_scores_gemma":[0.000039753373,0.0000500132,0.013908279,0.00015938743,0.000012251318,0.00002703581,0.00004739288,0.00011530863,0.00014377321,0.00027102686,0.9852164,0.000009279674],"about_ca_topic_score_codex":0.022695795,"about_ca_topic_score_gemma":0.048884567,"teacher_disagreement_score":0.38577554,"about_ca_system_score_codex":0.0016851063,"about_ca_system_score_gemma":0.0015821515,"threshold_uncertainty_score":0.87611663},"labels":[],"label_agreement":null},{"id":"W3045266887","doi":"10.1007/s11160-020-09613-z","title":"Living until proven dead: addressing mortality in acoustic telemetry research","year":2020,"lang":"en","type":"article","venue":"Reviews in Fish Biology and Fisheries","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":120,"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 Windsor; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Telemetry; Biotelemetry; Ecology; Fishery; Telecommunications; Engineering","score_opus":0.2238857617962672,"score_gpt":0.43601179822463965,"score_spread":0.21212603642837244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045266887","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":[0.0055713165,0.88363653,0.0052705347,0.09771347,0.004588523,0.000030357627,0.0001473838,0.00003200163,0.003009933],"genre_scores_gemma":[0.06486698,0.89560634,0.0074754157,0.016043045,0.014098426,0.000120783,0.00016383822,0.000025703044,0.0015995663],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99191064,0.0041498425,0.0008802325,0.0007735589,0.0019804144,0.00030540585],"domain_scores_gemma":[0.91901225,0.061787892,0.0060863257,0.0013733408,0.009867509,0.001872692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031010814,0.0013563042,0.0020219388,0.0045333826,0.0010761659,0.0038550284,0.002187016,0.004612415,0.0034854575],"category_scores_gemma":[0.086228825,0.00052635814,0.0009766835,0.004693545,0.0060004294,0.0099405665,0.0035235023,0.0054331394,0.0005616808],"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":[0.000093803625,0.0001658758,0.023777654,0.00862811,0.0002604307,0.00014805987,0.0012689698,0.0012191158,0.0003015396,0.023133105,0.028893653,0.9121097],"study_design_scores_gemma":[0.000104444385,0.0008350003,0.20429762,0.08240013,0.0012626098,0.0019226813,0.009691749,0.007806136,0.0008220634,0.26687258,0.42360076,0.00038427836],"about_ca_topic_score_codex":0.01373261,"about_ca_topic_score_gemma":0.021933474,"teacher_disagreement_score":0.031010814,"about_ca_system_score_codex":0.0032050493,"about_ca_system_score_gemma":0.007295868,"threshold_uncertainty_score":0.16400284},"labels":[],"label_agreement":null},{"id":"W3046122054","doi":"","title":"The age-trajectory of mortality for french-canadian centenarians","year":2016,"lang":"en","type":"article","venue":"Gerontologie et societe","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Demography; Life expectancy; Mortality rate; Population; Gerontology; Geography; Medicine; Sociology","score_opus":0.08469706449399059,"score_gpt":0.37260929744460064,"score_spread":0.2879122329506101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046122054","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":[0.9678886,0.0027545332,0.0005595539,0.0009910675,0.000037158352,0.00003305654,0.019900639,0.000059651717,0.007775801],"genre_scores_gemma":[0.98706126,0.0010929991,0.00048396213,0.000083451756,0.000012976437,0.000018536,0.008736333,0.000015542213,0.0024949806],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993394,0.00006710026,0.00003462845,0.00010823585,0.00015896201,0.00029170912],"domain_scores_gemma":[0.99746895,0.00017488917,0.00038493768,0.00012748307,0.0015204028,0.00032348145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009698985,0.00052540173,0.00035039466,0.0041999756,0.0019154396,0.0014185342,0.00076075067,0.00052045006,0.0032847247],"category_scores_gemma":[0.004191911,0.00013072467,0.0008565289,0.004602788,0.00041053083,0.00036224906,0.0007058151,0.000489271,0.0004627132],"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":[0.00020614706,0.000024221115,0.9744091,0.000045372857,0.00011824896,0.00016845335,0.0016023981,0.0007600985,0.0002722888,0.00079422793,0.0045703333,0.017029053],"study_design_scores_gemma":[0.000005052804,0.00002022186,0.9914556,0.000038912396,0.000033233802,0.000099129655,0.0011166491,0.0007995971,0.00008977877,0.00009204974,0.0062226416,0.000027005108],"about_ca_topic_score_codex":0.9894131,"about_ca_topic_score_gemma":0.99186945,"teacher_disagreement_score":0.015807185,"about_ca_system_score_codex":0.015807185,"about_ca_system_score_gemma":0.011765312,"threshold_uncertainty_score":0.11468965},"labels":[],"label_agreement":null},{"id":"W3046535124","doi":"10.3390/risks8030080","title":"The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Hedge; Longevity risk; Bootstrapping (finance); Econometrics; Bivariate analysis; Index (typography); Basis risk; Portfolio; Longevity; Selection (genetic algorithm); Actuarial science; Computer science; Statistics; Mathematics; Economics; Artificial intelligence","score_opus":0.10681548433332545,"score_gpt":0.36082785378888416,"score_spread":0.2540123694555587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046535124","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":[0.9173162,0.00038467313,0.08016712,0.00037709257,0.000026042175,0.0000401818,0.00021314633,0.000100999496,0.001374591],"genre_scores_gemma":[0.99299026,0.000058528112,0.0067290836,0.000026589572,0.0000057568836,0.00001138151,0.00010232035,0.000010631077,0.00006536079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954855,0.0030311767,0.0002364202,0.00041559688,0.0005880159,0.00024331184],"domain_scores_gemma":[0.9191865,0.068659924,0.004587043,0.0049064895,0.00216768,0.0004923016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01745236,0.0007877286,0.00091169856,0.00096295355,0.00043787816,0.0016410406,0.0009038398,0.0009906348,0.00075645663],"category_scores_gemma":[0.07261853,0.0003832505,0.0008113937,0.0010142752,0.0009630687,0.0025036132,0.0014419662,0.0013615828,0.000054663196],"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":[0.00032917093,0.00008128863,0.03690571,0.00007344642,0.00022548091,0.00015000998,0.00014062625,0.9406762,0.0012590985,0.0093888985,0.00017641882,0.010593784],"study_design_scores_gemma":[0.000014309965,0.00015154699,0.007858052,0.000024946721,0.00004972616,0.000060980874,0.00007455575,0.9855656,0.0016214311,0.004428006,0.00011357232,0.00003718185],"about_ca_topic_score_codex":0.004970808,"about_ca_topic_score_gemma":0.002737412,"teacher_disagreement_score":0.01745236,"about_ca_system_score_codex":0.00087319757,"about_ca_system_score_gemma":0.00067744276,"threshold_uncertainty_score":0.09229797},"labels":[],"label_agreement":null},{"id":"W3047639159","doi":"10.2139/ssrn.3009489","title":"Optimal Longevity Risk Transfer and Investment Strategies","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Longevity; Longevity risk; Investment (military); Economics; Business; Actuarial science; Financial economics; Natural resource economics; Medicine; Political science; Gerontology","score_opus":0.01343591305096419,"score_gpt":0.29274871197866703,"score_spread":0.27931279892770283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047639159","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":[0.8590173,0.0012221193,0.08964406,0.006354182,0.00007054864,0.00013101808,0.00035522177,0.00017989715,0.0430257],"genre_scores_gemma":[0.989205,0.00023676644,0.0019878526,0.00010771203,0.000026089865,0.000030128329,0.000033584176,0.000009910238,0.008362904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951637,0.00020768822,0.000022191429,0.00007142225,0.000045215977,0.0001372535],"domain_scores_gemma":[0.99651426,0.0022645886,0.0004991952,0.00013400626,0.00017867713,0.00040937163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019559416,0.00057391165,0.0007916775,0.0007633934,0.0003513567,0.0021139714,0.00071208354,0.0024842715,0.008266597],"category_scores_gemma":[0.013457921,0.0004423998,0.0003603327,0.000450935,0.0008267412,0.0028905184,0.0011687,0.0012732429,0.00061654276],"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":[0.0010892173,0.00097390084,0.022452341,0.00027403698,0.00022002489,0.0008760483,0.0009161979,0.3474455,0.004363636,0.512914,0.007457265,0.10101784],"study_design_scores_gemma":[0.00017100401,0.00048058294,0.015544075,0.00010850496,0.00011057181,0.00043057575,0.00094563654,0.35620746,0.0010260368,0.6218759,0.0030546533,0.000045105466],"about_ca_topic_score_codex":0.0009559095,"about_ca_topic_score_gemma":0.0008022036,"teacher_disagreement_score":0.008266597,"about_ca_system_score_codex":0.001413476,"about_ca_system_score_gemma":0.0010442153,"threshold_uncertainty_score":0.027654529},"labels":[],"label_agreement":null},{"id":"W3048804622","doi":"10.1017/asb.2020.26","title":"EFFICIENT DYNAMIC HEDGING FOR LARGE VARIABLE ANNUITY PORTFOLIOS WITH MULTIPLE UNDERLYING ASSETS","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"","keywords":"Portfolio; Replicating portfolio; Actuarial science; Economics; Liability; Econometrics; Computer science; Portfolio optimization; Finance","score_opus":0.026872897716586817,"score_gpt":0.29617866980283125,"score_spread":0.26930577208624445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048804622","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":[0.18598458,0.0001827721,0.81091964,0.00013547114,0.000020758755,0.00004630361,0.0000727174,0.0004551105,0.0021826155],"genre_scores_gemma":[0.893966,0.000053953336,0.10475895,0.000033918303,0.000008767999,0.000042352338,0.00012325085,0.0000464496,0.00096636004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974483,0.00008110476,0.0000149785965,0.00004524322,0.000071114606,0.000042692598],"domain_scores_gemma":[0.99915826,0.00051770743,0.00007144754,0.000076761244,0.0001014726,0.00007432169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000944467,0.00038981746,0.0010465507,0.00039121986,0.0004005068,0.00090665946,0.00072478433,0.00064830284,0.0019115547],"category_scores_gemma":[0.0022346517,0.00035045028,0.0005335623,0.00037178074,0.0003912851,0.0006475979,0.0009046523,0.00065224466,0.00019599605],"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":[0.000052298063,0.000023888146,0.0010604903,0.000015229535,0.000012657,0.000059395676,0.000029476438,0.9834003,0.0012718772,0.0027067694,0.00013809354,0.011229529],"study_design_scores_gemma":[0.0000022590718,0.0000047632243,0.000042671723,8.6550733e-7,9.383227e-7,0.00000401349,0.0000024919705,0.99921787,0.00012065286,0.000556423,0.000046265017,8.291732e-7],"about_ca_topic_score_codex":0.00457307,"about_ca_topic_score_gemma":0.0032419576,"teacher_disagreement_score":0.00457307,"about_ca_system_score_codex":0.0006582901,"about_ca_system_score_gemma":0.00083921116,"threshold_uncertainty_score":0.009092927},"labels":[],"label_agreement":null},{"id":"W3049612868","doi":"10.1007/s40747-020-00185-w","title":"Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest","year":2020,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"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":"Autoregressive integrated moving average; Life expectancy; Random forest; Artificial neural network; Mortality rate; Population; Expectancy theory; Econometrics; Computer science; Actuarial science; Statistics; Economics; Time series; Artificial intelligence; Demography; Machine learning; Mathematics","score_opus":0.2514610604083913,"score_gpt":0.35225629617313303,"score_spread":0.10079523576474175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049612868","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":[0.8856822,0.00088977884,0.10815931,0.0005938099,0.00013645153,0.00008500391,0.0012026491,0.00030188856,0.0029488942],"genre_scores_gemma":[0.98904604,0.00018357667,0.009389051,0.000028726794,0.00002813487,0.00003433039,0.00045087282,0.000005041279,0.0008341952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977726,0.000064490465,0.000017191036,0.00005079924,0.00004140198,0.000048905593],"domain_scores_gemma":[0.99940777,0.00031879882,0.00007864244,0.000019463458,0.00014279582,0.000032551638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008176223,0.0005480332,0.00048492374,0.0010187285,0.0002256133,0.0005036545,0.00057880435,0.00063305785,0.0006031831],"category_scores_gemma":[0.0013509986,0.00017626582,0.0007262601,0.0006774857,0.00013214804,0.00051004154,0.00021771644,0.0004926367,0.00010799383],"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":[0.00006901219,0.00008340741,0.016604861,0.000033555967,0.000053726297,0.000090025176,0.000025695763,0.9658413,0.00061627297,0.0005257968,0.00058137235,0.015475011],"study_design_scores_gemma":[0.0000021466446,0.000018901545,0.0016813357,0.0000039584784,0.0000058159844,0.00000769679,0.0000092472,0.99792385,0.000113035894,0.0001479576,0.00008167037,0.0000044039143],"about_ca_topic_score_codex":0.042815454,"about_ca_topic_score_gemma":0.026224982,"teacher_disagreement_score":0.042815454,"about_ca_system_score_codex":0.0005148032,"about_ca_system_score_gemma":0.0006081524,"threshold_uncertainty_score":0.08513248},"labels":[],"label_agreement":null},{"id":"W3084084021","doi":"10.11647/obp.0251","title":"Human Evolutionary Demography","year":2024,"lang":"en","type":"book","venue":"Open Book Publishers","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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","funders":"Natural Environment Research Council; Danmarks Frie Forskningsfond; Wellcome Trust","keywords":"Demography; Geography; Evolutionary biology; Biology; Sociology","score_opus":0.028004753892283202,"score_gpt":0.3264122312677413,"score_spread":0.29840747737545814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084084021","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":[0.01674335,0.05602326,0.024858922,0.025707223,0.004626566,0.0001722897,0.005167368,0.00045975804,0.8662413],"genre_scores_gemma":[0.42511597,0.094772115,0.047997378,0.0072013014,0.0044445884,0.00036349884,0.0068686237,0.00045242388,0.41278407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99959713,0.0002007498,0.000016606493,0.00008278032,0.00006314391,0.000039651935],"domain_scores_gemma":[0.99972135,0.00012264971,0.00002255072,0.000042450196,0.000059706435,0.000031298005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006554496,0.00036821453,0.00028006622,0.0016035929,0.00092602253,0.0023263686,0.00037552664,0.00073415437,0.02865403],"category_scores_gemma":[0.0014428227,0.00012363555,0.00025414306,0.001814518,0.0013565884,0.002185861,0.0013112944,0.0011217523,0.003906155],"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":[0.000017440054,0.000008234556,0.0025380019,0.0003115955,0.000021172134,0.00017203609,0.0049893004,0.0009894689,0.00022286702,0.554923,0.22675644,0.20905039],"study_design_scores_gemma":[0.0000016027003,0.000010468775,0.004096821,0.00020501296,0.0000038452245,0.00028805912,0.0011169884,0.00029233325,0.000058298607,0.049934186,0.9439825,0.000009971367],"about_ca_topic_score_codex":0.003922939,"about_ca_topic_score_gemma":0.0052679963,"teacher_disagreement_score":0.02865403,"about_ca_system_score_codex":0.0012842255,"about_ca_system_score_gemma":0.0007953653,"threshold_uncertainty_score":0.09585732},"labels":[],"label_agreement":null},{"id":"W3084378721","doi":"10.1017/asb.2020.28","title":"AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"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","funders":"","keywords":"Boosting (machine learning); Computer science; Portfolio; Econometrics; Valuation (finance); Machine learning; Annuity; Monte Carlo method; Bootstrap aggregating; Artificial intelligence; Actuarial science; Economics; Mathematics; Statistics; Financial economics; Finance","score_opus":0.043611882694605426,"score_gpt":0.35207749822477835,"score_spread":0.30846561553017293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084378721","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":[0.037179038,0.0007136319,0.9603898,0.00021775255,0.00009349117,0.00007857977,0.00007003801,0.0005258966,0.00073180615],"genre_scores_gemma":[0.6157366,0.00043526286,0.3805637,0.00027037945,0.00016662391,0.00019804202,0.00036907673,0.00011444755,0.0021458936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987452,0.00058524933,0.000059743328,0.00014748928,0.0003612208,0.00010105651],"domain_scores_gemma":[0.99436164,0.0036479433,0.00035865986,0.00036512708,0.0010430468,0.0002235654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052337153,0.0007965257,0.0017671881,0.0018526776,0.0007001454,0.0014134982,0.0019522494,0.001502003,0.0024652106],"category_scores_gemma":[0.011317734,0.0005155577,0.00073758775,0.0015783183,0.0006633464,0.0016657297,0.0011631481,0.0015687101,0.00047548115],"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":[0.00020611088,0.00017495173,0.0038328357,0.00008565856,0.00010149057,0.000086815526,0.000088341585,0.75768745,0.0016763258,0.012085067,0.0023539343,0.22162095],"study_design_scores_gemma":[0.000004520334,0.000012832953,0.00017846219,0.0000072094895,0.0000052444198,0.0000073875485,0.00000384226,0.9968924,0.00016154883,0.0025373206,0.0001846838,0.000004506105],"about_ca_topic_score_codex":0.004673129,"about_ca_topic_score_gemma":0.0033642394,"teacher_disagreement_score":0.0052337153,"about_ca_system_score_codex":0.00096861576,"about_ca_system_score_gemma":0.0011334108,"threshold_uncertainty_score":0.027678907},"labels":[],"label_agreement":null},{"id":"W3087164092","doi":"10.2139/ssrn.3537368","title":"A DSA Algorithm for Mortality Forecasting","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Computer science; Econometrics; Algorithm; Economics","score_opus":0.0512558257093354,"score_gpt":0.3157046319230709,"score_spread":0.2644488062137355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087164092","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":[0.008683713,0.00035649154,0.98855084,0.00020763866,0.0001333375,0.00004388031,0.00017114212,0.00086946174,0.0009835798],"genre_scores_gemma":[0.19538943,0.0004325279,0.798376,0.00020077502,0.0002006001,0.00022826617,0.00083163957,0.00013252637,0.0042082528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952555,0.00015584005,0.000054937653,0.00013187218,0.000095320815,0.000036518653],"domain_scores_gemma":[0.99828255,0.00096227124,0.0000605382,0.0001375991,0.0004937501,0.00006323069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015517687,0.0007446429,0.0012717848,0.0013283645,0.00067067833,0.0010830108,0.0012857609,0.0012979389,0.0047546765],"category_scores_gemma":[0.005583088,0.0005454489,0.0006916404,0.0013371679,0.00034299525,0.0010802848,0.00093304086,0.001388208,0.0019166496],"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":[0.00016668884,0.000076895274,0.0018081666,0.000055129636,0.00008847081,0.00006012823,0.00004253325,0.43945345,0.0019136223,0.006987813,0.0055842777,0.5437628],"study_design_scores_gemma":[0.000008622276,0.000010447264,0.000117266376,0.000003607834,0.0000064331257,0.000012913265,0.0000037475704,0.9964179,0.000258511,0.002380284,0.0007763917,0.000003830767],"about_ca_topic_score_codex":0.010082416,"about_ca_topic_score_gemma":0.0068649324,"teacher_disagreement_score":0.010082416,"about_ca_system_score_codex":0.0006344649,"about_ca_system_score_gemma":0.0015102905,"threshold_uncertainty_score":0.020047486},"labels":[],"label_agreement":null},{"id":"W3087932553","doi":"10.1007/978-3-030-51434-1_7","title":"Modeling Human Longevity and Life Tables","year":2020,"lang":"en","type":"book-chapter","venue":"Use R!","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Longevity; Longevity risk; Life table; Randomness; Human life; Demography; Population; Life course approach; Actuarial science; Gerontology; Psychology; Sociology; Statistics; Mathematics; Medicine; Economics; Political science; Developmental psychology","score_opus":0.07986858777346396,"score_gpt":0.2983933228238241,"score_spread":0.21852473505036013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087932553","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":[0.013410582,0.004018301,0.87249523,0.0032083865,0.00045202783,0.00006231916,0.010154229,0.010701126,0.08549777],"genre_scores_gemma":[0.30204105,0.012299104,0.49978444,0.0014232257,0.00069171574,0.0005701642,0.015899276,0.0063308217,0.16096008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996786,0.00017383251,0.000010258597,0.000054861714,0.000067993846,0.000014470846],"domain_scores_gemma":[0.99794465,0.001633493,0.00007723676,0.00019236672,0.000111334266,0.000040899664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013625291,0.0006501442,0.0004947364,0.00066210213,0.0002076729,0.0010853553,0.0010808263,0.0008541014,0.025186146],"category_scores_gemma":[0.008195813,0.0005333384,0.0007218193,0.0010708403,0.0003193783,0.0017315602,0.00073151285,0.00085070473,0.011125737],"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":[0.000031278192,0.00004249326,0.003383796,0.00028348906,0.00014475339,0.00008519925,0.00016304421,0.48600852,0.00047722145,0.1843472,0.15661012,0.16842292],"study_design_scores_gemma":[0.000019578023,0.000019928839,0.0013234481,0.00010959953,0.000056003275,0.00009310165,0.000042663516,0.5616535,0.0004990261,0.3137659,0.12238329,0.0000340029],"about_ca_topic_score_codex":0.01079933,"about_ca_topic_score_gemma":0.009259878,"teacher_disagreement_score":0.025186146,"about_ca_system_score_codex":0.00062811974,"about_ca_system_score_gemma":0.00064161816,"threshold_uncertainty_score":0.08425605},"labels":[],"label_agreement":null},{"id":"W3087952799","doi":"10.36334/modsim.2011.d2.chan","title":"Time-Simultaneous Fan Charts: Applications to stochastic life table forecasting","year":2011,"lang":"en","type":"article","venue":"Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Chinese University of Hong Kong","keywords":"Table (database); Computer science; Operations research; Engineering; Data mining","score_opus":0.08426268225648907,"score_gpt":0.312141885339146,"score_spread":0.2278792030826569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087952799","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":[0.033200953,0.0033431128,0.9566304,0.00070599915,0.00030264497,0.00008314526,0.0005482529,0.0013898163,0.003795577],"genre_scores_gemma":[0.76665634,0.0044860924,0.22272252,0.00014889668,0.0004967532,0.00019443026,0.0011007925,0.00024764962,0.003946602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918824,0.00037088315,0.000057830883,0.00011072605,0.00020913572,0.00006311592],"domain_scores_gemma":[0.99042237,0.007238976,0.00061836484,0.00035302856,0.0010466137,0.00032069455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039377967,0.0011358664,0.0014546876,0.0021269054,0.0006903358,0.0016901545,0.0014248398,0.0014067495,0.0033075698],"category_scores_gemma":[0.01853112,0.0005433982,0.000878573,0.0030018373,0.0007712197,0.001355032,0.001458916,0.0015314003,0.00038917008],"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":[0.00006261982,0.000030652787,0.0018264297,0.000048396745,0.000028402023,0.00010163707,0.000071850365,0.9283891,0.00019912957,0.023129184,0.0014749045,0.0446377],"study_design_scores_gemma":[0.000003398409,0.000005140673,0.00009265171,0.000005629316,0.0000024508236,0.0000074475333,0.000004234966,0.99230886,0.00003780211,0.0070888908,0.00043822793,0.0000053066683],"about_ca_topic_score_codex":0.018842917,"about_ca_topic_score_gemma":0.0076116375,"teacher_disagreement_score":0.018842917,"about_ca_system_score_codex":0.0011040589,"about_ca_system_score_gemma":0.0009536067,"threshold_uncertainty_score":0.037466466},"labels":[],"label_agreement":null},{"id":"W3088485711","doi":"","title":"Still Watching Trees Grow: A Multispecies and Cross-Scale Examination of the Radial Growth, Climate, and Carbon Interface","year":2020,"lang":"en","type":"dissertation","venue":"University Library (University of Saskatchewan)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Interface (matter); Scale (ratio); Carbon fibers; Climate change; Environmental science; Ecology; Environmental resource management; Geography; Meteorology; Materials science; Biology; Cartography","score_opus":0.006893701664115369,"score_gpt":0.20249851275176542,"score_spread":0.19560481108765004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088485711","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":[0.99650866,0.00032363573,0.00041431122,0.000093552706,0.00000989034,0.0000075345147,0.00016743212,0.000010556434,0.002464481],"genre_scores_gemma":[0.9977441,0.00030580472,0.0009823873,0.00009070312,0.0000105323215,0.000012789036,0.00021494718,0.000011164418,0.0006275767],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997814,0.00004963002,0.000008119993,0.000060680693,0.000049566584,0.000050615974],"domain_scores_gemma":[0.9991393,0.00019207095,0.00019831104,0.000075509655,0.00016166532,0.00023315732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012235368,0.00017956136,0.00019735562,0.0011024121,0.00097573386,0.0012554013,0.00032256532,0.00034347695,0.00089567713],"category_scores_gemma":[0.0013407737,0.00021079821,0.0002616282,0.0010612372,0.00047907067,0.0017940763,0.0010221141,0.0006263505,0.00013686123],"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":[0.000046390025,0.000067969275,0.96164465,0.000040390772,0.000086863416,0.00012887889,0.01042638,0.00011094161,0.0065616965,0.00039038173,0.0005602762,0.01993534],"study_design_scores_gemma":[3.9037909e-7,0.000027298533,0.99406284,0.000009700102,0.000010349276,0.00004653132,0.0046311826,0.00016903738,0.00010930832,0.00008342571,0.00084492745,0.0000050195345],"about_ca_topic_score_codex":0.018841797,"about_ca_topic_score_gemma":0.08484818,"teacher_disagreement_score":0.018841797,"about_ca_system_score_codex":0.00048058163,"about_ca_system_score_gemma":0.00042741912,"threshold_uncertainty_score":0.03746426},"labels":[],"label_agreement":null},{"id":"W3088854618","doi":"10.1111/jori.12327","title":"Wishart‐gamma random effects models with applications to nonlife insurance","year":2020,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Wishart distribution; Econometrics; Censoring (clinical trials); Multivariate statistics; Random effects model; Context (archaeology); Diagonal; Computer science; Statistics; Actuarial science; Mathematics; Economics; Medicine","score_opus":0.017766701925013298,"score_gpt":0.27715390201494816,"score_spread":0.2593872000899349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088854618","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":[0.01470053,0.0011307321,0.9805735,0.0008240668,0.00009470457,0.000056022374,0.00029309792,0.00020644348,0.0021210571],"genre_scores_gemma":[0.77731997,0.0045887115,0.18844621,0.00057874375,0.00053446577,0.000608837,0.000947689,0.0002750272,0.026700292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976006,0.0015798487,0.000106420695,0.0002862722,0.0002415341,0.00018527507],"domain_scores_gemma":[0.9753456,0.019816937,0.0019963228,0.0011714682,0.0011686463,0.00050106295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009581913,0.001431077,0.0017166442,0.0016677601,0.0005322037,0.0022520926,0.0027320373,0.0023220433,0.0056111086],"category_scores_gemma":[0.024108743,0.0012055099,0.0023069144,0.0022054624,0.0023584766,0.0021482827,0.00186322,0.003821188,0.000880524],"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":[0.00004838074,0.0000623562,0.002374903,0.00007613842,0.00015148894,0.0004358746,0.00017507472,0.57187676,0.00026088866,0.41076115,0.0018514276,0.011925687],"study_design_scores_gemma":[0.000013104478,0.000023451035,0.0004605104,0.000022437398,0.000026939646,0.000051433624,0.00003371868,0.8874994,0.00009247526,0.11064648,0.0011051166,0.000024805706],"about_ca_topic_score_codex":0.016983207,"about_ca_topic_score_gemma":0.012754184,"teacher_disagreement_score":0.016983207,"about_ca_system_score_codex":0.0015343407,"about_ca_system_score_gemma":0.001454247,"threshold_uncertainty_score":0.050674558},"labels":[],"label_agreement":null},{"id":"W3089518909","doi":"10.1007/978-3-030-42472-5_3","title":"Using Expert Elicitation to Build Long-Term Projection Assumptions","year":2020,"lang":"en","type":"book-chapter","venue":"The Springer series on demographic methods and population analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"Universitetet i Oslo","keywords":"Expert elicitation; Probabilistic logic; Randomness; Projection (relational algebra); Computer science; Term (time); Protocol (science); Population; Artificial intelligence; Data science; Management science; Statistics; Mathematics; Engineering; Algorithm","score_opus":0.08768141678458508,"score_gpt":0.4119517408774807,"score_spread":0.32427032409289563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089518909","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":[0.026099024,0.00025778834,0.9155762,0.00379749,0.00012090642,0.0020315393,0.0022095148,0.0005089896,0.04939855],"genre_scores_gemma":[0.30852786,0.00048393948,0.6786236,0.00093880796,0.0000737744,0.0038085305,0.0032476205,0.00019856708,0.0040973234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9138486,0.066588625,0.004265467,0.0033613846,0.010883925,0.0010520474],"domain_scores_gemma":[0.6635058,0.2707825,0.008034405,0.023277096,0.032956645,0.0014435403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08587313,0.0012217631,0.00067546905,0.0027251886,0.002042408,0.0055647716,0.0035454673,0.0022249995,0.015103496],"category_scores_gemma":[0.21801789,0.0009746978,0.0010004896,0.0024580879,0.0032339657,0.0075044874,0.008796815,0.0043709874,0.002572665],"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":[0.00068599737,0.00047538933,0.007073941,0.0023756186,0.00025709195,0.001059596,0.03294061,0.14014272,0.005550817,0.4411491,0.045409795,0.32287925],"study_design_scores_gemma":[0.00011552016,0.00019661177,0.0030758965,0.00292417,0.00008623888,0.00020554259,0.009135993,0.25354898,0.007556508,0.6340231,0.08888651,0.0002450807],"about_ca_topic_score_codex":0.0059987656,"about_ca_topic_score_gemma":0.0074011325,"teacher_disagreement_score":0.08587313,"about_ca_system_score_codex":0.006482592,"about_ca_system_score_gemma":0.007617431,"threshold_uncertainty_score":0.4541459},"labels":[],"label_agreement":null},{"id":"W3089835371","doi":"10.24095/hpcdp.33.4.01f","title":"Mortalité par cause en fonction du niveau de compétence professionnelle au Canada : une étude de suivi sur 16 ans","year":2013,"lang":"fr","type":"article","venue":"Maladies chroniques et blessures au Canada","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; University of Ottawa; Statistics Canada","funders":"","keywords":"Political science; Humanities; Gynecology; Medicine; Art","score_opus":0.010939837520285108,"score_gpt":0.24968800793390952,"score_spread":0.23874817041362442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089835371","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":[0.9702545,0.0042766514,0.0007471099,0.0009518977,0.000048516344,0.0001443633,0.018319095,0.000041781004,0.00521622],"genre_scores_gemma":[0.9847987,0.0035541696,0.0010057143,0.000313229,0.000021457312,0.0001625609,0.00559076,0.00002464959,0.004528678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986534,0.00010634238,0.00008209215,0.000213375,0.0005642372,0.00038035633],"domain_scores_gemma":[0.9967102,0.0003701624,0.0006227091,0.000104113584,0.0016924859,0.00050040334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015800263,0.0004290749,0.0007265097,0.002354281,0.0022775214,0.0016053542,0.0012062656,0.00042875865,0.0032636884],"category_scores_gemma":[0.004988254,0.00034819156,0.0010816251,0.0055945576,0.00086957286,0.0005954882,0.0011223326,0.0008605574,0.00026909634],"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":[0.00010391771,0.0000204606,0.98483366,0.00018635532,0.00015987101,0.0000685697,0.0029399202,0.00017888559,0.00017088145,0.0003506808,0.0020679703,0.0089187985],"study_design_scores_gemma":[0.0000061054047,0.00002634743,0.9953229,0.00016394595,0.00007179016,0.000032500837,0.0018634495,0.00019923062,0.00007405737,0.000065870176,0.0021578914,0.000015883285],"about_ca_topic_score_codex":0.9952447,"about_ca_topic_score_gemma":0.9951507,"teacher_disagreement_score":0.024773108,"about_ca_system_score_codex":0.024773108,"about_ca_system_score_gemma":0.042406455,"threshold_uncertainty_score":0.17974228},"labels":[],"label_agreement":null},{"id":"W3093066829","doi":"10.1080/02664763.2020.1833183","title":"The GLM framework of the Lee–Carter model: a multi-country study","year":2020,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Universiti Malaya","keywords":"Deviance (statistics); Negative binomial distribution; Generalized linear model; Statistics; Poisson distribution; Econometrics; Mathematics; Count data; Poisson regression; Binomial distribution; Statistical model; Deviance information criterion; Demography; Sociology; Population; Bayesian probability; Bayesian inference","score_opus":0.028181942462116884,"score_gpt":0.31734128641478293,"score_spread":0.28915934395266607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093066829","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":[0.88768035,0.0022825554,0.0987042,0.001158101,0.00013472338,0.00032390305,0.0024987527,0.00018216272,0.0070351753],"genre_scores_gemma":[0.9741634,0.000732488,0.022157174,0.0001287778,0.00005002745,0.00026965726,0.0011955138,0.00006723665,0.001235746],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921766,0.0066000386,0.00014386192,0.00048705356,0.00025649802,0.00033591696],"domain_scores_gemma":[0.9844173,0.011842537,0.0014834863,0.0011564008,0.0008206078,0.00027967955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010654456,0.0006603908,0.0013258853,0.0025229573,0.0006969825,0.0014036382,0.0012319156,0.0009373182,0.0046369596],"category_scores_gemma":[0.01911528,0.00027688342,0.0022354373,0.0046092966,0.000688759,0.0013736344,0.0018141053,0.0018648214,0.00051478826],"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":[0.0012657836,0.0006790596,0.5622464,0.0007264543,0.0044996217,0.0031261237,0.0026419405,0.2840353,0.0007117749,0.05912633,0.009986372,0.07095492],"study_design_scores_gemma":[0.00024515556,0.0017574405,0.23222232,0.00043723834,0.0017928089,0.0015431289,0.0066195005,0.70584345,0.00057654077,0.032470133,0.016210059,0.00028218658],"about_ca_topic_score_codex":0.021758746,"about_ca_topic_score_gemma":0.012859395,"teacher_disagreement_score":0.021758746,"about_ca_system_score_codex":0.00093105476,"about_ca_system_score_gemma":0.0009108342,"threshold_uncertainty_score":0.056346774},"labels":[],"label_agreement":null},{"id":"W3093581254","doi":"10.5430/jnep.v11n2p13","title":"A prospective evaluation of the Flacker-Kiely One Year Mortality Score and the added value of NT-proBNP","year":2020,"lang":"en","type":"article","venue":"Journal of Nursing Education and Practice","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Universitetet i Bergen","keywords":"Medicine; Norwegian; Youden's J statistic; Index (typography); Cohort; Prospective cohort study; Internal medicine; Heart failure; Cohort study; Cardiology; Predictive value","score_opus":0.17078367649799517,"score_gpt":0.4456690749318441,"score_spread":0.27488539843384896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093581254","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":[0.99865687,0.0001861343,0.00025640128,0.00001845343,0.000012861308,0.000028296134,0.00047814962,0.0000051368775,0.00035774015],"genre_scores_gemma":[0.9989221,0.000051336356,0.00034759875,0.000007204422,0.000017400162,0.000022380465,0.00048020048,0.0000017910852,0.00014994851],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990151,0.00032255732,0.00010152562,0.0001901867,0.00026834928,0.00010231351],"domain_scores_gemma":[0.9965945,0.00069662696,0.0012407472,0.00021762763,0.0006253385,0.0006250911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026772462,0.00075762573,0.00045726713,0.0011140885,0.0003036382,0.0006593311,0.00045742732,0.0004940044,0.0010280821],"category_scores_gemma":[0.005993343,0.00020419943,0.0005678583,0.00056760455,0.00027960466,0.0007053411,0.00053360994,0.00050463266,0.00022465829],"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":[0.00078214717,0.000058406906,0.996454,0.000012402066,0.00007589525,0.00005643306,0.000050899023,0.0001335789,0.00017142679,0.000014628084,0.00006727398,0.0021230297],"study_design_scores_gemma":[0.0000336838,0.0010391447,0.9975567,0.000007889361,0.0000638553,0.00013634408,0.00006536834,0.0008262659,0.000104265324,0.0000205227,0.0001359189,0.0000099332765],"about_ca_topic_score_codex":0.003914113,"about_ca_topic_score_gemma":0.0044394583,"teacher_disagreement_score":0.003914113,"about_ca_system_score_codex":0.00047271923,"about_ca_system_score_gemma":0.00043413587,"threshold_uncertainty_score":0.014158785},"labels":[],"label_agreement":null},{"id":"W3097364423","doi":"10.1007/s12546-020-09247-9","title":"Modal lifespan and disparity at older ages by leading causes of death: a Canada-U.S. comparison","year":2020,"lang":"en","type":"article","venue":"Journal of Population Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; AXA Research Fund; Agence Nationale de la Recherche","keywords":"Gerontology; Modal; Older people; Demography; Medicine; Psychology; Sociology","score_opus":0.12080431620910495,"score_gpt":0.42863211783209165,"score_spread":0.30782780162298673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097364423","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":[0.9717381,0.002503823,0.0004024482,0.0006443548,0.000032609234,0.000047610778,0.01770458,0.000025551817,0.006900959],"genre_scores_gemma":[0.99392575,0.00068430137,0.00019197921,0.00006870436,0.0000065721006,0.000010021973,0.0046402123,0.000005203086,0.00046736447],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928916,0.00005000685,0.000041576306,0.00012238571,0.00021510929,0.00028171553],"domain_scores_gemma":[0.99759126,0.00018188155,0.00040186514,0.00008119064,0.0012986552,0.00044528427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008148891,0.00032033707,0.00040048012,0.0036351837,0.0014926454,0.001017488,0.0007256565,0.00036362978,0.0032174797],"category_scores_gemma":[0.0027720726,0.00013822364,0.00080410123,0.008099548,0.0004499839,0.00040840797,0.0013346678,0.0005772923,0.00017861142],"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":[0.00018760182,0.000026407182,0.9817488,0.00008639445,0.00023201175,0.0001293392,0.0006940312,0.00057977636,0.00016636807,0.0012343092,0.0034384257,0.011476496],"study_design_scores_gemma":[0.0000061095834,0.000017326316,0.9968022,0.000035904417,0.00005683914,0.000047653437,0.001046839,0.0004806898,0.000043800123,0.000071421484,0.0013796162,0.00001158206],"about_ca_topic_score_codex":0.98918134,"about_ca_topic_score_gemma":0.99294406,"teacher_disagreement_score":0.011513211,"about_ca_system_score_codex":0.011513211,"about_ca_system_score_gemma":0.015606165,"threshold_uncertainty_score":0.08353466},"labels":[],"label_agreement":null},{"id":"W3099175442","doi":"10.1038/s41598-020-76827-3","title":"Generating synthetic aging trajectories with a weighted network model using cross-sectional data","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"Canadian Institutes of Health Research; Dalhousie University; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Dalhousie Medical Research Foundation","keywords":"Observational study; Set (abstract data type); Data set; Computer science; Cross-sectional study; Network model; Econometrics; Artificial intelligence; Statistics; Mathematics","score_opus":0.08051314197824462,"score_gpt":0.3332853367914737,"score_spread":0.2527721948132291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099175442","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":[0.8897305,0.00016533278,0.10502381,0.000790839,0.00006424599,0.000120669,0.00250434,0.00020936629,0.0013907739],"genre_scores_gemma":[0.9758572,0.00012372252,0.02022135,0.0001071008,0.000018060906,0.00020888225,0.002291867,0.00002365584,0.0011481734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965954,0.00017993044,0.000017529841,0.00008253976,0.000028775501,0.00003177964],"domain_scores_gemma":[0.9945116,0.0039908853,0.0005127879,0.0004044232,0.00039370335,0.00018657069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017459309,0.0004819928,0.00043843113,0.0007676654,0.0002860149,0.00048747245,0.0011815779,0.0011037724,0.0019176048],"category_scores_gemma":[0.010090848,0.00036791386,0.0006264049,0.0008224204,0.00047930953,0.000922653,0.0006280865,0.0008740845,0.00020116502],"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":[0.00005460016,0.000054504537,0.007802763,0.000017539689,0.000038080318,0.000057023277,0.000057826936,0.9851134,0.00016945317,0.003581613,0.00044702628,0.0026062108],"study_design_scores_gemma":[0.000016186612,0.000018759509,0.0008964546,0.0000046604596,0.0000072232806,0.00000879763,0.000013815058,0.99617296,0.00009278562,0.0026097114,0.0001537641,0.0000048804136],"about_ca_topic_score_codex":0.01747851,"about_ca_topic_score_gemma":0.014952612,"teacher_disagreement_score":0.01747851,"about_ca_system_score_codex":0.0010640307,"about_ca_system_score_gemma":0.00067895395,"threshold_uncertainty_score":0.03475356},"labels":[],"label_agreement":null},{"id":"W3109344025","doi":"10.1186/s12874-020-01159-9","title":"Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models","year":2020,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saskatchewan Health; University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Autoregressive integrated moving average; Exponential smoothing; Statistics; Mean squared error; Time series; Artificial neural network; Mean absolute percentage error; Moving average; Autoregressive model; Linear regression; Econometrics; Mathematics; Computer science; Artificial intelligence","score_opus":0.3938727386782235,"score_gpt":0.5010691567098738,"score_spread":0.10719641803165036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109344025","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":[0.9735449,0.003225158,0.018632937,0.0006654205,0.00013190413,0.000054873504,0.00047299816,0.00014375013,0.0031280492],"genre_scores_gemma":[0.99392724,0.0010524571,0.0039192494,0.000028658693,0.000025210658,0.000027334116,0.00042154954,0.000008372815,0.0005898631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995926,0.00015300146,0.00004700695,0.000084532665,0.00007810288,0.00004481817],"domain_scores_gemma":[0.99827635,0.0011744745,0.00015750686,0.00004528974,0.0002932737,0.00005313725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023689487,0.0007828671,0.00062140526,0.0011300176,0.0002646019,0.0009177969,0.00056467875,0.00066109764,0.00075337116],"category_scores_gemma":[0.004244444,0.00020426059,0.0007130342,0.00072490727,0.00017085372,0.0009505846,0.00037245677,0.0006717786,0.00012112063],"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":[0.00061144197,0.000382275,0.08590313,0.00039680203,0.00036989863,0.00026664455,0.00018100272,0.83163536,0.0007953698,0.001305664,0.001420376,0.076732025],"study_design_scores_gemma":[0.000007581252,0.00011189335,0.009583844,0.000051643397,0.000059653572,0.000014786207,0.000100323035,0.9892257,0.00026758114,0.0003629541,0.00020469977,0.000009317304],"about_ca_topic_score_codex":0.023690281,"about_ca_topic_score_gemma":0.013633341,"teacher_disagreement_score":0.023690281,"about_ca_system_score_codex":0.00069348747,"about_ca_system_score_gemma":0.0008036275,"threshold_uncertainty_score":0.047104776},"labels":[],"label_agreement":null},{"id":"W3110143632","doi":"10.5287/ora-gakpvedme","title":"On the potential future effects of population structure on financial stability","year":2018,"lang":"en","type":"article","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Economics; Consumption (sociology); Population; Debt; Boom; Consumer debt; Supply and demand; Sustainability; Demographic economics; Finance; Macroeconomics","score_opus":0.015234846757509732,"score_gpt":0.2588305547107359,"score_spread":0.24359570795322616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110143632","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":[0.9625203,0.00085068605,0.011027226,0.0051028486,0.000046431556,0.000028885022,0.0008161646,0.000040108083,0.01956725],"genre_scores_gemma":[0.99828124,0.0004478399,0.00042709004,0.00009164613,0.000014198227,0.0000079005895,0.00007425396,0.000005972818,0.0006498697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994443,0.00031815533,0.000015809552,0.00005901834,0.000052266816,0.000110379246],"domain_scores_gemma":[0.9962851,0.0025976934,0.0004916989,0.00014173223,0.00028416372,0.00019961997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020401564,0.00025183064,0.0002868765,0.000605556,0.00046196175,0.0017017493,0.00046336022,0.0011470617,0.0045913523],"category_scores_gemma":[0.011587123,0.00019523878,0.0004783401,0.00055205147,0.0011307234,0.0017565966,0.0010495336,0.0008902013,0.0002848325],"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":[0.00027243557,0.00014719806,0.25456482,0.00012510017,0.00010941608,0.0010960276,0.0011310721,0.5270309,0.0017494815,0.16476212,0.0030798689,0.045931667],"study_design_scores_gemma":[0.000037788563,0.00039850193,0.23399729,0.00019485854,0.00010063035,0.0004047343,0.002165669,0.6366234,0.0010200652,0.11751506,0.007416593,0.0001254196],"about_ca_topic_score_codex":0.011880367,"about_ca_topic_score_gemma":0.010676509,"teacher_disagreement_score":0.011880367,"about_ca_system_score_codex":0.001628045,"about_ca_system_score_gemma":0.0005427687,"threshold_uncertainty_score":0.023622453},"labels":[],"label_agreement":null},{"id":"W3110725171","doi":"10.1007/978-3-030-49970-9_2","title":"The International Database on Longevity: Data Resource Profile","year":2020,"lang":"en","type":"book-chapter","venue":"Demographic research monographs","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Longevity; Database; Population; Resource (disambiguation); Computer science; Demography; Geography; Medicine; Gerontology","score_opus":0.22945473817051923,"score_gpt":0.4179825232172756,"score_spread":0.18852778504675635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110725171","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":[0.00018301395,0.00042008734,0.00080846215,0.00016858813,0.000037446363,0.0002455951,0.99502605,0.00094762805,0.0021631122],"genre_scores_gemma":[0.0012833299,0.0010828314,0.0028548078,0.00022639484,0.000060262173,0.0015264946,0.99147123,0.0005038809,0.0009907673],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9925891,0.0012286558,0.0035769478,0.00083827245,0.0013584968,0.0004085055],"domain_scores_gemma":[0.9671618,0.01337883,0.005937131,0.0050669773,0.0064019007,0.0020534757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062896335,0.001578183,0.0032199689,0.013206326,0.0006416613,0.0056489487,0.0023799366,0.0018358871,0.14224248],"category_scores_gemma":[0.04156169,0.0010780452,0.0010733232,0.031872787,0.000516135,0.003998164,0.0034267092,0.0027113934,0.11275758],"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":[0.0002523862,0.0000460934,0.0027881896,0.0072608935,0.00009613885,0.00009854334,0.00019610192,0.00045281113,0.00036378464,0.0044508786,0.9517798,0.032214455],"study_design_scores_gemma":[0.0002816629,0.000038576243,0.012132137,0.0025007948,0.000082072766,0.00023631603,0.00014336246,0.0004059738,0.0004154621,0.0040252134,0.97963697,0.000101487065],"about_ca_topic_score_codex":0.0043226886,"about_ca_topic_score_gemma":0.004024387,"teacher_disagreement_score":0.14224248,"about_ca_system_score_codex":0.0019519224,"about_ca_system_score_gemma":0.006224079,"threshold_uncertainty_score":0.4758485},"labels":[],"label_agreement":null},{"id":"W3111826016","doi":"10.1007/978-3-030-49970-9_9","title":"Supercentenarians and Semi-supercentenarians in France","year":2020,"lang":"en","type":"book-chapter","venue":"Demographic research monographs","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal","funders":"","keywords":"Life expectancy; Demography; Nominative case; Sample (material); Statistics; Geography; Genealogy; History; Computer science; Mathematics; Population; Artificial intelligence; Sociology","score_opus":0.07751678691433608,"score_gpt":0.354177348710913,"score_spread":0.27666056179657694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111826016","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":[0.99500024,0.00038080825,0.00037403833,0.00016434214,0.000010312156,0.0000089090945,0.0016654497,0.000013420362,0.002382644],"genre_scores_gemma":[0.9964134,0.00021861955,0.00026494375,0.00005790842,0.000013407687,0.00001191804,0.0016487861,0.000006929141,0.0013640529],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848336,0.0004900539,0.00008864295,0.00034683626,0.0002985042,0.000292617],"domain_scores_gemma":[0.9975364,0.00088577846,0.0007332647,0.00021209555,0.00050221855,0.00013031783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014874678,0.00025130104,0.00029550656,0.0022025388,0.000509797,0.00071643374,0.00044772238,0.00039702308,0.0044785775],"category_scores_gemma":[0.0042410274,0.000108456996,0.00026761292,0.001315928,0.00039367503,0.00034042416,0.00059763686,0.0002497842,0.0005755238],"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":[0.0001188242,0.000019821304,0.9598336,0.000054567336,0.00006166623,0.00048736276,0.0045866044,0.00057183404,0.0006279551,0.0012347306,0.0019126653,0.030490298],"study_design_scores_gemma":[0.0000064042342,0.00006712582,0.9826826,0.000044048305,0.000016411374,0.00042783382,0.0034969137,0.0008463851,0.0002846701,0.00020415707,0.011908037,0.000015492196],"about_ca_topic_score_codex":0.16359611,"about_ca_topic_score_gemma":0.15847164,"teacher_disagreement_score":0.16359611,"about_ca_system_score_codex":0.0012841871,"about_ca_system_score_gemma":0.00078636245,"threshold_uncertainty_score":0.3252877},"labels":[],"label_agreement":null},{"id":"W3112321584","doi":"10.1186/s12874-020-01181-x","title":"Handling coarsened age information in the analysis of emergency department presentations","year":2020,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta Hospital; Simon Fraser University; University of Alberta","funders":"Canadian Statistical Sciences Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Alberta Health Services","keywords":"Missing data; Emergency department; Computer science; Statistics; Event (particle physics); Regression analysis; Psychology; Medicine; Medical emergency; Psychiatry; Machine learning; Mathematics","score_opus":0.5513198400258864,"score_gpt":0.5748121554512873,"score_spread":0.023492315425400867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112321584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2524322,0.0044364696,0.7331725,0.0013372386,0.0003399122,0.0007234896,0.003992653,0.0009466471,0.002618959],"genre_scores_gemma":[0.6992312,0.0011930709,0.29548547,0.00037505268,0.00020889143,0.0003293034,0.0023735422,0.00010529124,0.0006982964],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907725,0.00551769,0.00078410516,0.0015800986,0.0010462416,0.00029942053],"domain_scores_gemma":[0.9288263,0.05456201,0.005664841,0.0069857053,0.003318857,0.00064240914],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016006188,0.0007955942,0.0012510224,0.004499255,0.0005886186,0.00162735,0.0016426586,0.0010312643,0.001599497],"category_scores_gemma":[0.094009325,0.00065281463,0.001571859,0.0033479198,0.00094074034,0.0023453115,0.0025600025,0.0022534365,0.00043653575],"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":[0.0008306018,0.00021753667,0.38807818,0.0012952558,0.0012867202,0.0009914355,0.0026929765,0.2624724,0.002452584,0.022846857,0.0062495074,0.31058595],"study_design_scores_gemma":[0.00016118892,0.00061643176,0.27133155,0.00090012315,0.00071223563,0.0011596994,0.0022118893,0.5893003,0.004240411,0.10490097,0.024121305,0.00034383754],"about_ca_topic_score_codex":0.020503497,"about_ca_topic_score_gemma":0.021709204,"teacher_disagreement_score":0.9839938,"about_ca_system_score_codex":0.001094273,"about_ca_system_score_gemma":0.0022168136,"threshold_uncertainty_score":0.0846498},"labels":[],"label_agreement":null},{"id":"W3112942004","doi":"10.1007/978-3-030-49970-9_12","title":"Extreme Longevity in Quebec: Factors and Characteristics","year":2020,"lang":"en","type":"book-chapter","venue":"Demographic research monographs","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Intégré de Santé et de Services Sociaux des Laurentides; Santé Montérégie","funders":"","keywords":"Centenarian; Census; Demography; Longevity; Population; Geography; Gerontology; Population ageing; Medicine; Sociology","score_opus":0.14387471862046142,"score_gpt":0.35915543793582233,"score_spread":0.2152807193153609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112942004","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":[0.9699201,0.0023189005,0.00036754948,0.0013609978,0.000032154363,0.00007186664,0.014434478,0.000036245678,0.011457746],"genre_scores_gemma":[0.9946202,0.0005460788,0.00014959092,0.00009755637,0.000012621705,0.000020397254,0.0022660883,0.0000064482024,0.0022809815],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99953794,0.00005848551,0.000027114897,0.00007808543,0.00014978247,0.00014851979],"domain_scores_gemma":[0.99756974,0.00019466062,0.00059735565,0.00007787639,0.0010129366,0.000547375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074658,0.00025045025,0.00023283131,0.0017479389,0.002098247,0.0014577848,0.00083786086,0.00035323348,0.005941452],"category_scores_gemma":[0.0020941931,0.000097193784,0.00034802168,0.004850432,0.0006069922,0.00043571528,0.00059317873,0.00049068197,0.00035971505],"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":[0.000028351114,0.000020544814,0.9886646,0.000026123103,0.000044315013,0.000110877954,0.00049955625,0.00022054667,0.00012762401,0.00046421587,0.0027889274,0.0070043057],"study_design_scores_gemma":[0.0000015249966,0.000010462029,0.99600464,0.000030388414,0.000010048692,0.00006204145,0.00060098816,0.0002398381,0.000023880271,0.000057288526,0.0029518448,0.000006963142],"about_ca_topic_score_codex":0.9818158,"about_ca_topic_score_gemma":0.98716867,"teacher_disagreement_score":0.018184185,"about_ca_system_score_codex":0.016896669,"about_ca_system_score_gemma":0.010924903,"threshold_uncertainty_score":0.122594476},"labels":[],"label_agreement":null},{"id":"W3115683849","doi":"10.3390/risks9010004","title":"Machine Learning in P&amp;C Insurance: A Review for Pricing and Reserving","year":2020,"lang":"en","type":"review","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":46,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Machine learning; Artificial intelligence; Computer science; Artificial neural network; Field (mathematics); State (computer science); Work (physics); Data science; Algorithm; Engineering; Mathematics","score_opus":0.17190130679763913,"score_gpt":0.44687395260805907,"score_spread":0.2749726458104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115683849","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":[0.00006464523,0.99866176,0.00024168729,0.00037677036,0.00012959518,0.000002782127,0.000014917758,0.0000039190713,0.00050399033],"genre_scores_gemma":[0.0006095269,0.9984818,0.0002463947,0.00018583972,0.00023794935,0.0000044982717,0.000021458702,0.0000016892456,0.00021084202],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996493,0.000090629226,0.000055460372,0.00006646143,0.000115799085,0.000022431235],"domain_scores_gemma":[0.9982644,0.0012684801,0.00013176157,0.00003290651,0.0002518555,0.000050711096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013323049,0.000835148,0.0014519922,0.0028036747,0.00031718868,0.0011836523,0.00094174297,0.0015704086,0.004272697],"category_scores_gemma":[0.002816688,0.0003554569,0.0008649955,0.004286773,0.000570617,0.0018522622,0.0006707464,0.0018199753,0.0018602357],"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":[0.00005213573,0.00007466332,0.00028873215,0.020421972,0.00012052825,0.00009188818,0.000052647258,0.0010344856,0.00030441652,0.011161817,0.032562762,0.93383396],"study_design_scores_gemma":[0.000018023477,0.00012624382,0.0013224687,0.014221331,0.00024065086,0.000500868,0.00006788438,0.00068353646,0.00022454583,0.009048856,0.97350514,0.000040365107],"about_ca_topic_score_codex":0.0022444308,"about_ca_topic_score_gemma":0.002843333,"teacher_disagreement_score":0.004272697,"about_ca_system_score_codex":0.00082221517,"about_ca_system_score_gemma":0.0016125307,"threshold_uncertainty_score":0.014293611},"labels":[],"label_agreement":null},{"id":"W3116968961","doi":"10.21203/rs.3.rs-118237/v1","title":"The evolutionary landscape of primate longevity","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Calgary","funders":"National Institute on Aging; Syddansk Universitet","keywords":"Longevity; Primate; Evolutionary biology; Biology; Ecology; Geography; Genetics","score_opus":0.08795532295057286,"score_gpt":0.43864097706900074,"score_spread":0.3506856541184279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116968961","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":[0.9903527,0.0020913314,0.0027648718,0.0009977174,0.0000056099634,0.0000040204322,0.00060398533,0.00002584026,0.00315392],"genre_scores_gemma":[0.99836046,0.00033188032,0.00071833626,0.00006124274,0.000011874168,0.0000042896595,0.00042110472,0.000009106568,0.000081695434],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99825615,0.0010608827,0.00004350315,0.0004155978,0.00014846845,0.00007534175],"domain_scores_gemma":[0.9966731,0.0018335913,0.0005449699,0.00045707924,0.00031736123,0.00017383395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003142394,0.00017421893,0.00054025126,0.0028096614,0.00078870205,0.0018718729,0.00038339978,0.0004601596,0.0018410876],"category_scores_gemma":[0.007460827,0.00016453887,0.00024323518,0.0021790068,0.0013252578,0.0010815627,0.0013004668,0.0006207935,0.00016185541],"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":[0.0004187815,0.000077003926,0.75776136,0.0004553341,0.0014748041,0.0006421147,0.003822517,0.03451324,0.018878253,0.05297432,0.0032881652,0.12569416],"study_design_scores_gemma":[0.000019639449,0.0001288926,0.8671484,0.00010934341,0.00014911519,0.0006703912,0.0015862394,0.035661712,0.0008780877,0.08030131,0.013279533,0.00006731315],"about_ca_topic_score_codex":0.0013666794,"about_ca_topic_score_gemma":0.0016726418,"teacher_disagreement_score":0.003142394,"about_ca_system_score_codex":0.00047952918,"about_ca_system_score_gemma":0.0002129243,"threshold_uncertainty_score":0.016618729},"labels":[],"label_agreement":null},{"id":"W3117791828","doi":"10.3934/mfc.2021001","title":"The uses and abuses of an age-period-cohort method: On the linear algebra and statistical properties of intrinsic and related estimators","year":2020,"lang":"en","type":"article","venue":"Mathematical Foundations of Computing","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of British Columbia","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Aging","keywords":"Estimator; Cohort; Period (music); Exposition (narrative); Econometrics; Statistics; Mathematical proof; Style (visual arts); Cohort effect; Mathematics; Computer science; History","score_opus":0.0455255089250547,"score_gpt":0.3339745974608645,"score_spread":0.2884490885358098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117791828","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":[0.007823863,0.015740994,0.96036965,0.0067859516,0.0006050162,0.00003437889,0.00016012578,0.00006989282,0.008410143],"genre_scores_gemma":[0.3853271,0.033820067,0.5651629,0.0043371688,0.005206539,0.00050812337,0.00029390067,0.00038575818,0.0049584056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9636746,0.02807147,0.0014826296,0.002672936,0.0037489838,0.00034936657],"domain_scores_gemma":[0.8070509,0.16171747,0.0078056203,0.016379463,0.0060967337,0.0009497957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06932318,0.0008638729,0.0013444141,0.0033813592,0.0014388723,0.0059470567,0.0024060637,0.0019629071,0.0024297682],"category_scores_gemma":[0.16563231,0.0005651103,0.001663096,0.0038609637,0.016971812,0.0119298175,0.004769775,0.0059514386,0.00058325427],"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":[0.000007281084,0.0000052406485,0.0008389825,0.00009549156,0.000027649809,0.000020352258,0.00028335987,0.00085203483,0.00005630661,0.98556733,0.0005096333,0.0117363585],"study_design_scores_gemma":[0.000009422373,0.000063780295,0.0011979106,0.00035514132,0.000042981796,0.00018627917,0.00017318543,0.014185827,0.00034301262,0.9654476,0.01794992,0.000044834145],"about_ca_topic_score_codex":0.0016752955,"about_ca_topic_score_gemma":0.0010272474,"teacher_disagreement_score":0.06932318,"about_ca_system_score_codex":0.0019297079,"about_ca_system_score_gemma":0.002190341,"threshold_uncertainty_score":0.36662042},"labels":[],"label_agreement":null},{"id":"W3118476658","doi":"10.1155/2021/8829122","title":"The Effects of Age, Period, and Cohort on the Mortality of Cervical Cancer in Three High‐Income Countries: Canada, Korea, and Italy","year":2021,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Shanxi Datong University; National Natural Science Foundation of China","keywords":"Cohort effect; Medicine; Cohort; Demography; Cervical cancer; Mortality rate; Cohort study; Cancer; Surgery; Internal medicine","score_opus":0.026110458152923996,"score_gpt":0.3629479064964454,"score_spread":0.3368374483435214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118476658","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":[0.99626774,0.0015076647,0.00014230143,0.00016773988,0.000011348987,0.000018412653,0.0010734291,0.0000073223987,0.0008040672],"genre_scores_gemma":[0.99774915,0.00071492745,0.00017345994,0.000037627276,0.000005617042,0.000008301434,0.0010342485,0.0000033434476,0.00027332653],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99932134,0.00011770176,0.00004217806,0.00015006767,0.00014936348,0.00021937785],"domain_scores_gemma":[0.9976877,0.00032288718,0.00056010555,0.00020299962,0.0007408682,0.00048555588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405741,0.00040381475,0.00028054995,0.0013767056,0.00084798335,0.0007870938,0.000594958,0.00030296503,0.0007463725],"category_scores_gemma":[0.0029527743,0.00015548208,0.0010581319,0.001991843,0.00051001646,0.00028426579,0.0008690013,0.0004486929,0.000047425925],"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":[0.000116945426,0.0000059160843,0.9970636,0.000015751195,0.000119324206,0.000062185885,0.0001185044,0.00017976621,0.000064187596,0.000073065006,0.00014559578,0.002035151],"study_design_scores_gemma":[0.0000029748394,0.000013677842,0.99913293,0.00000877028,0.00008179451,0.00002896613,0.00019196057,0.00027755834,0.000026259691,0.000014987398,0.00021549636,0.0000046497653],"about_ca_topic_score_codex":0.8629186,"about_ca_topic_score_gemma":0.9017013,"teacher_disagreement_score":0.13708138,"about_ca_system_score_codex":0.0044156793,"about_ca_system_score_gemma":0.0083145555,"threshold_uncertainty_score":0.27577734},"labels":[],"label_agreement":null},{"id":"W3119553566","doi":"10.1161/strokeaha.120.032028","title":"Average Lifespan Shortened due to Stroke in Canada","year":2021,"lang":"en","type":"article","venue":"Stroke","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"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 Alberta; University of Calgary; Alberta Health; Alberta Cancer Foundation; Alberta Health Services","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Stroke (engine); Life expectancy; Demography; Population; Years of potential life lost; Mortality rate; Gerontology; Epidemiology; Pediatrics; Surgery; Internal medicine","score_opus":0.013225288394706606,"score_gpt":0.26120353383830175,"score_spread":0.24797824544359515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119553566","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":[0.8442355,0.013966278,0.0013290892,0.007266743,0.00031987287,0.00020898292,0.10487758,0.0001535504,0.027642487],"genre_scores_gemma":[0.97839487,0.0041789,0.0008270856,0.00066664396,0.00003424676,0.00006224502,0.013300363,0.000018838045,0.0025167041],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998609,0.00007952239,0.00010330482,0.0001627842,0.000613315,0.00043210783],"domain_scores_gemma":[0.9954855,0.00013429923,0.0005185967,0.0000935097,0.0030989496,0.00066915515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011159074,0.0003428815,0.0004092332,0.0024779672,0.0026084029,0.0012029433,0.0015098397,0.00031113715,0.0025857943],"category_scores_gemma":[0.0047717243,0.00018143418,0.0007419958,0.005775466,0.0005738102,0.0006370552,0.0011961163,0.000848793,0.0002312715],"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":[0.00024830014,0.000034084715,0.90857404,0.00039990633,0.00026894468,0.00037966418,0.0015549724,0.00121716,0.00018382004,0.0018348098,0.028551089,0.056753144],"study_design_scores_gemma":[0.000015583017,0.00003232906,0.98153245,0.0002628809,0.000102970094,0.00026007235,0.001373226,0.0012700543,0.00017082592,0.00043369105,0.0144953625,0.000050628267],"about_ca_topic_score_codex":0.9956934,"about_ca_topic_score_gemma":0.9973857,"teacher_disagreement_score":0.050066046,"about_ca_system_score_codex":0.050066046,"about_ca_system_score_gemma":0.06601839,"threshold_uncertainty_score":0.36325628},"labels":[],"label_agreement":null},{"id":"W3120148263","doi":"10.2139/ssrn.3664433","title":"Tail Index-Linked Annuity: A Longevity Risk Sharing Retirement Plan","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Annuity; Longevity; Life annuity; Actuarial science; Longevity risk; Index (typography); Business; Economics; Pension; Finance; Gerontology; Medicine; Computer science","score_opus":0.028324641972418945,"score_gpt":0.28302731754081756,"score_spread":0.2547026755683986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120148263","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":[0.8161922,0.0005542145,0.10874103,0.0030735086,0.00043129988,0.0006553101,0.001967278,0.0028390412,0.065546006],"genre_scores_gemma":[0.96671116,0.00010473912,0.0149243,0.00020908973,0.0000822302,0.000070994436,0.00045341832,0.00004841792,0.01739558],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999688,0.0001013688,0.000012337876,0.000044396682,0.00008248364,0.000071320705],"domain_scores_gemma":[0.99935216,0.00018196889,0.00005697237,0.00012828434,0.00007802179,0.00020266089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011265508,0.00026927114,0.00043133335,0.000549563,0.00057937397,0.0009087273,0.00094355986,0.0008449704,0.016351085],"category_scores_gemma":[0.0025241578,0.00015071407,0.00045053003,0.00043728066,0.00032864162,0.0006648014,0.0011364769,0.0007344371,0.002169125],"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":[0.0076528946,0.0076497495,0.049006287,0.0001225046,0.00019530268,0.0013252413,0.0003908101,0.13565269,0.0082839,0.06889219,0.053109743,0.6677187],"study_design_scores_gemma":[0.002174246,0.0057598273,0.0318633,0.00006353269,0.00047660831,0.0010245915,0.00037679638,0.85879683,0.0056352695,0.06204837,0.031631105,0.00014955898],"about_ca_topic_score_codex":0.0016980998,"about_ca_topic_score_gemma":0.0024288113,"teacher_disagreement_score":0.016351085,"about_ca_system_score_codex":0.00047076627,"about_ca_system_score_gemma":0.0009792924,"threshold_uncertainty_score":0.05469978},"labels":[],"label_agreement":null},{"id":"W3121286583","doi":"10.1016/j.insmatheco.2014.12.002","title":"Assessing the solvency of insurance portfolios via a continuous-time cohort model","year":2014,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Solvency; Actuarial science; Business; Cohort; Econometrics; Economics; Statistics; Mathematics; Finance","score_opus":0.015446219031210595,"score_gpt":0.2710113495313326,"score_spread":0.25556513050012203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121286583","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":[0.9320717,0.00031206745,0.06393441,0.0014435482,0.000038193448,0.00006146614,0.0008840575,0.00006742217,0.001187067],"genre_scores_gemma":[0.9914711,0.00025516984,0.004975583,0.00006444718,0.00003947366,0.000044643428,0.00046256874,0.000008570288,0.0026784972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987691,0.00054803153,0.000053696414,0.00027517104,0.00009834757,0.00025561388],"domain_scores_gemma":[0.9743509,0.019834906,0.0024050933,0.0010277439,0.0006877131,0.0016936167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008447205,0.0009128506,0.0016840181,0.0017652098,0.00066978694,0.0026892999,0.0023759764,0.0029196383,0.0039595594],"category_scores_gemma":[0.02618267,0.0008260219,0.0015578935,0.0011807064,0.0012828734,0.002139246,0.0017461934,0.0018657341,0.00037711105],"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":[0.00048489845,0.0004219666,0.11059945,0.00006269139,0.0005251314,0.0003516603,0.0002654246,0.83702254,0.00046918882,0.038856413,0.0011011999,0.009839381],"study_design_scores_gemma":[0.0000418416,0.00009519903,0.007569237,0.000008647391,0.00005423874,0.00004344295,0.00005698228,0.98257154,0.000062734034,0.0093168765,0.00015987159,0.000019349878],"about_ca_topic_score_codex":0.045396164,"about_ca_topic_score_gemma":0.022190891,"teacher_disagreement_score":0.045396164,"about_ca_system_score_codex":0.0017837528,"about_ca_system_score_gemma":0.002170489,"threshold_uncertainty_score":0.09026384},"labels":[],"label_agreement":null},{"id":"W3121299065","doi":"10.1007/s13385-012-0047-3","title":"A subordinated Markov model for stochastic mortality","year":2012,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"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 Toronto; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valuation (finance); Queueing theory; Econometrics; Stochastic modelling; Markov chain; Mathematics; Applied probability; Markov process; Mathematical finance; Computer science; Actuarial science; Mathematical economics; Applied mathematics; Statistics; Economics; Finance","score_opus":0.05514871508794552,"score_gpt":0.3388253726754683,"score_spread":0.2836766575875228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121299065","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":[0.13317339,0.001268753,0.84610325,0.0033993472,0.0003205957,0.00012833046,0.0014107812,0.0004303965,0.0137650715],"genre_scores_gemma":[0.9204557,0.0015163587,0.040424008,0.00057338603,0.0005678124,0.00036637342,0.0010680887,0.00017040386,0.03485786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759763,0.0011486828,0.00012512878,0.00040558973,0.00033593478,0.00038699096],"domain_scores_gemma":[0.9911514,0.0061057406,0.00073990977,0.0004436663,0.00070161093,0.000857736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006271587,0.001375929,0.0040285424,0.002018741,0.0012469753,0.0032877503,0.0052892384,0.0036975949,0.0100261],"category_scores_gemma":[0.013030275,0.0016039335,0.0021130121,0.0017539291,0.0033821673,0.0044265976,0.0037004873,0.0039859978,0.0011359552],"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":[0.00010949895,0.00007479802,0.0012770327,0.00008130143,0.000062066305,0.00024055081,0.00029193205,0.38915923,0.00050135073,0.60309416,0.0016412686,0.003466756],"study_design_scores_gemma":[0.000033696007,0.000027636177,0.00022142314,0.0000132922305,0.000021365728,0.000038918064,0.00002060381,0.91787636,0.000038397622,0.08114773,0.0005413629,0.000019169509],"about_ca_topic_score_codex":0.018720383,"about_ca_topic_score_gemma":0.010913573,"teacher_disagreement_score":0.018720383,"about_ca_system_score_codex":0.0036332961,"about_ca_system_score_gemma":0.003215584,"threshold_uncertainty_score":0.037222862},"labels":[],"label_agreement":null},{"id":"W3121445907","doi":"10.2139/ssrn.2862159","title":"The Joint Mortality of Couples in Continuous Time","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Joint (building); Geography; Engineering; Structural engineering","score_opus":0.012282855903119304,"score_gpt":0.27247145958590197,"score_spread":0.2601886036827827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121445907","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":[0.9972843,0.00034119427,0.0008103,0.0003553158,0.00002908241,0.000006167333,0.0006837582,0.000009197839,0.00048070285],"genre_scores_gemma":[0.99875295,0.00014984407,0.000105630344,0.000019797957,0.000027931153,0.0000070878355,0.00052385224,0.000001988897,0.0004109012],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986565,0.0006207555,0.000109044,0.00018233746,0.0001421856,0.00028920238],"domain_scores_gemma":[0.991816,0.0034961344,0.0019280403,0.0007859726,0.00045535655,0.001518506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003058727,0.00030984893,0.0006369991,0.00148102,0.00038709235,0.0014691917,0.00073643937,0.0014429978,0.0028671874],"category_scores_gemma":[0.016073242,0.00042319312,0.0009091673,0.0018403594,0.0006454979,0.0016312506,0.0015773185,0.0013850314,0.00044622796],"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":[0.00031337628,0.00009795924,0.99305844,0.000017734414,0.00027275618,0.00023242597,0.00028381124,0.0018086487,0.000095029405,0.00058755226,0.00020129951,0.0030310564],"study_design_scores_gemma":[0.000016028596,0.00027256555,0.9816929,0.000020560821,0.00013226633,0.0003740396,0.00092385453,0.014564986,0.00006356578,0.0016457107,0.0002660985,0.000027492042],"about_ca_topic_score_codex":0.009425621,"about_ca_topic_score_gemma":0.007261544,"teacher_disagreement_score":0.009425621,"about_ca_system_score_codex":0.00040982346,"about_ca_system_score_gemma":0.00059388956,"threshold_uncertainty_score":0.018741488},"labels":[],"label_agreement":null},{"id":"W3121618178","doi":"10.1017/s1474747211000333","title":"Lifetime ruin minimization: should retirees hedge inflation or just worry about it?","year":2011,"lang":"en","type":"article","venue":"Journal of Pensions Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Economics; Hedge; Inflation (cosmology); Bond; Population; Portfolio; Investment (military); Consumption (sociology); Asset (computer security); Econometrics; Monetary economics; Financial economics; Finance","score_opus":0.08626099548269046,"score_gpt":0.30579916803609375,"score_spread":0.2195381725534033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121618178","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":[0.55286,0.003974964,0.40757003,0.009405446,0.00014144617,0.00012730624,0.0002319028,0.00013812742,0.025550786],"genre_scores_gemma":[0.97799647,0.0004395977,0.017760964,0.00015137033,0.000058972706,0.000033230885,0.000034448967,0.000021665197,0.0035033214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99967396,0.00018400619,0.000008047812,0.00004284284,0.000029783116,0.000061319864],"domain_scores_gemma":[0.99884415,0.00075450714,0.00017321843,0.000056492892,0.000056739427,0.0001147881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014242993,0.00033656,0.000949945,0.00026668707,0.00026526974,0.0008647561,0.00069466553,0.0009867642,0.0032822937],"category_scores_gemma":[0.0063139936,0.00021556842,0.0003026365,0.00021082595,0.0005160224,0.0011367698,0.0006761537,0.0009113815,0.00016880367],"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":[0.00041787807,0.00028924516,0.010264788,0.00025869368,0.00013584511,0.00033504757,0.0002774051,0.7199622,0.0022355313,0.15062304,0.0088229645,0.10637734],"study_design_scores_gemma":[0.00006180419,0.00034299088,0.0030714239,0.00010001787,0.00005752862,0.00014901368,0.0003413539,0.8894587,0.0010053451,0.1014411,0.003949769,0.000020948735],"about_ca_topic_score_codex":0.0024146077,"about_ca_topic_score_gemma":0.0020511437,"teacher_disagreement_score":0.0032822937,"about_ca_system_score_codex":0.0005915753,"about_ca_system_score_gemma":0.00079270336,"threshold_uncertainty_score":0.010980308},"labels":[],"label_agreement":null},{"id":"W3121683133","doi":"","title":"Optimal Multivariate Quota-Share Reinsurance: A Nonparametric Mean-CVaR Framework","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"CVAR; Reinsurance; Nonparametric statistics; Risk measure; Expected shortfall; Estimator; Econometrics; Mathematical optimization; Multivariate statistics; Coherent risk measure; Mathematics; Computer science; Statistics; Risk management; Economics; Actuarial science; Finance","score_opus":0.02092928721819888,"score_gpt":0.3283957424759001,"score_spread":0.3074664552577012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121683133","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":[0.010539366,0.00032943278,0.98760694,0.00034014514,0.000025746609,0.00002701633,0.00007193953,0.000114917064,0.00094448583],"genre_scores_gemma":[0.7772791,0.0014235975,0.21463014,0.00028976027,0.00022496437,0.00029906034,0.00038421337,0.0002172018,0.0052519417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99504673,0.003038656,0.00017401631,0.0006620804,0.0006944826,0.00038407103],"domain_scores_gemma":[0.9859445,0.01091735,0.0012942468,0.00076130894,0.00083201076,0.0002505338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012584232,0.0013649033,0.0029674005,0.0013560177,0.00047235392,0.0022326335,0.0031806796,0.0018199389,0.0030504947],"category_scores_gemma":[0.026100183,0.0011167177,0.0017629244,0.0015318372,0.0022987723,0.004361672,0.002065075,0.0028713115,0.00027111894],"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":[0.00007746893,0.00007245248,0.0011032334,0.00009065608,0.000093108414,0.000087790926,0.000087416054,0.7726914,0.00052220444,0.20648308,0.00078367774,0.017907482],"study_design_scores_gemma":[0.0000075945422,0.000034856454,0.0002879173,0.000012215853,0.000017957389,0.000026088432,0.000016271322,0.95041275,0.00019999634,0.04858925,0.00037453446,0.000020640879],"about_ca_topic_score_codex":0.0031347626,"about_ca_topic_score_gemma":0.0022496467,"teacher_disagreement_score":0.012584232,"about_ca_system_score_codex":0.0015520244,"about_ca_system_score_gemma":0.0022500246,"threshold_uncertainty_score":0.06655252},"labels":[],"label_agreement":null},{"id":"W3121846663","doi":"","title":"Downside Risk Management of a Defined Benefit Plan Considering Longevity Basis Risk","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"CVAR; Downside risk; Longevity risk; Risk management; Actuarial science; Basis risk; Expected shortfall; Economics; Pension; Longevity; Control (management); Tail risk; Risk analysis (engineering); Business; Portfolio; Financial economics; Finance; Capital asset pricing model; Medicine","score_opus":0.01122258420018014,"score_gpt":0.24673856019136445,"score_spread":0.23551597599118432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121846663","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":[0.5587507,0.001203793,0.42677867,0.0012579497,0.00010226186,0.00013466601,0.00023731221,0.00016049187,0.011374127],"genre_scores_gemma":[0.99124885,0.00016487908,0.0061674416,0.000029741403,0.000013802495,0.000025771264,0.00003237982,0.0000099998715,0.0023071247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956506,0.00016572143,0.000018606004,0.00007292177,0.000075948235,0.000101791375],"domain_scores_gemma":[0.999022,0.0004440607,0.00023850374,0.000056881956,0.00009387895,0.00014468495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016131081,0.00065995665,0.00069881516,0.0003791667,0.00027358808,0.0013321515,0.00081040314,0.0010209812,0.0022568526],"category_scores_gemma":[0.0029128762,0.00033826294,0.00051648787,0.0002479467,0.00049974886,0.001052366,0.00096696755,0.0011274527,0.000117677926],"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":[0.00013657003,0.00006216078,0.002655728,0.00005792862,0.00004749173,0.0002730769,0.00008747503,0.9402638,0.0025262497,0.03661311,0.0007515121,0.016524995],"study_design_scores_gemma":[0.00001696086,0.00009705925,0.0012398297,0.000013256063,0.000031477997,0.00004509789,0.00003865746,0.9885634,0.0004192058,0.009018598,0.00050298264,0.000013479487],"about_ca_topic_score_codex":0.0023252654,"about_ca_topic_score_gemma":0.0012508247,"teacher_disagreement_score":0.0023252654,"about_ca_system_score_codex":0.00085657294,"about_ca_system_score_gemma":0.0008835706,"threshold_uncertainty_score":0.008531034},"labels":[],"label_agreement":null},{"id":"W3121923523","doi":"10.4054/mpidr-wp-2001-031","title":"Small effects of selective migration and selective survival in retrospective studies of fertility","year":2001,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Queen's University","funders":"","keywords":"Fertility; Demography; Population; Sociology","score_opus":0.03822741550589839,"score_gpt":0.32696594742032026,"score_spread":0.2887385319144219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121923523","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":[0.85268825,0.020555988,0.10195754,0.004022537,0.0009671648,0.0009178685,0.0019603346,0.0003929434,0.016537366],"genre_scores_gemma":[0.98701453,0.0011772242,0.008070264,0.0010231149,0.00056052767,0.00028170215,0.0006715414,0.000089022236,0.0011121887],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.6670801,0.27747923,0.019472646,0.018331746,0.014229604,0.0034066401],"domain_scores_gemma":[0.10787751,0.76385456,0.05090117,0.0693265,0.006634008,0.0014061986],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23771547,0.0010746784,0.0016119635,0.0034418083,0.0016203083,0.0023768062,0.002091156,0.0016901385,0.0023162873],"category_scores_gemma":[0.6035123,0.0011108298,0.0023761888,0.004388595,0.0066639422,0.0030814647,0.00502817,0.0019821094,0.0005086593],"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":[0.0018116377,0.00008609403,0.9496112,0.000552283,0.0038953268,0.00071666593,0.0024700605,0.0026836467,0.00051664695,0.0033574565,0.0008837646,0.03341517],"study_design_scores_gemma":[0.00012535696,0.0010141011,0.9710961,0.00035046393,0.0028670044,0.002238771,0.0009725594,0.005944334,0.0023980981,0.0072494405,0.005637407,0.000106375985],"about_ca_topic_score_codex":0.006042671,"about_ca_topic_score_gemma":0.008063425,"teacher_disagreement_score":0.23771547,"about_ca_system_score_codex":0.0009716368,"about_ca_system_score_gemma":0.0011639782,"threshold_uncertainty_score":0.94003254},"labels":[],"label_agreement":null},{"id":"W3121937590","doi":"10.1080/1351847x.2015.1029590","title":"A bootstrap-based comparison of portfolio insurance strategies","year":2015,"lang":"en","type":"article","venue":"European Journal of Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"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":"University of Lethbridge","keywords":"Downside risk; Portfolio insurance; Portfolio; Actuarial science; Probabilistic logic; Econometrics; Portfolio optimization; Economics; Replicating portfolio; Computer science; Financial economics; Artificial intelligence","score_opus":0.08417739293616057,"score_gpt":0.3559230577346019,"score_spread":0.27174566479844137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121937590","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":[0.9103915,0.0010081838,0.08132334,0.0000970754,0.00005111968,0.00026192938,0.0008142574,0.00019206014,0.005860546],"genre_scores_gemma":[0.9783895,0.00018595131,0.019938162,0.00003125653,0.00001482999,0.00016312298,0.0009101263,0.000026430964,0.0003405853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941168,0.0036719143,0.00042155635,0.00040742202,0.0011819277,0.00020042103],"domain_scores_gemma":[0.96149474,0.03207853,0.0013478519,0.00269413,0.002134969,0.00024980734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011821506,0.00045174966,0.00078627974,0.0020661848,0.00020427207,0.0007038795,0.00065016345,0.0008668028,0.002331017],"category_scores_gemma":[0.04263163,0.000147906,0.0007175075,0.00095027714,0.0003927261,0.0011209014,0.00089259265,0.0005374956,0.00036447938],"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":[0.009055712,0.0024422342,0.12409641,0.001456847,0.0045080967,0.00043049944,0.00060384086,0.2905358,0.025263838,0.041363757,0.004631652,0.4956113],"study_design_scores_gemma":[0.000782867,0.018800806,0.13563405,0.00024619573,0.0009656273,0.00086987385,0.0008886808,0.78780884,0.021753414,0.025625031,0.006426057,0.00019859677],"about_ca_topic_score_codex":0.0003199973,"about_ca_topic_score_gemma":0.0002602693,"teacher_disagreement_score":0.011821506,"about_ca_system_score_codex":0.00035578595,"about_ca_system_score_gemma":0.00042439034,"threshold_uncertainty_score":0.062518835},"labels":[],"label_agreement":null},{"id":"W3121945553","doi":"","title":"Estimation of Risk Contributions with MCMC","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Markov chain Monte Carlo; Estimator; Econometrics; Monte Carlo method; Computer science; Value at risk; Consistency (knowledge bases); Statistics; Mathematics; Risk management; Finance; Economics; Artificial intelligence","score_opus":0.028612443607647666,"score_gpt":0.36845796454894575,"score_spread":0.3398455209412981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121945553","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":[0.02256217,0.00032587867,0.9743887,0.00021786617,0.000055747973,0.000109898036,0.0002564057,0.0007863206,0.0012970244],"genre_scores_gemma":[0.43201926,0.00048550384,0.56088585,0.0003392925,0.00017404062,0.00058598333,0.0016491961,0.00036202438,0.0034987875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998487,0.0006369061,0.00010353856,0.0002465262,0.00039266382,0.00013329423],"domain_scores_gemma":[0.9850482,0.01128342,0.0009014649,0.0012914409,0.0011856105,0.0002898278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045598974,0.0011968926,0.0015756943,0.0024345003,0.00093326886,0.0014865764,0.0025724801,0.0019490819,0.0052854815],"category_scores_gemma":[0.026505856,0.0012971794,0.0013348052,0.0020595314,0.0012234556,0.0017653779,0.0017380111,0.0028850287,0.00090609124],"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":[0.00011988169,0.000062655585,0.0043722326,0.00009704205,0.00013092301,0.000122062076,0.00007847395,0.9220781,0.00079561846,0.027805943,0.0014965367,0.04284055],"study_design_scores_gemma":[0.000008677155,0.000004923428,0.00022013202,0.000009680416,0.0000067449364,0.000011395065,0.0000044383028,0.99188,0.00021523847,0.007363858,0.00026784357,0.0000071617565],"about_ca_topic_score_codex":0.018307228,"about_ca_topic_score_gemma":0.019642085,"teacher_disagreement_score":0.018307228,"about_ca_system_score_codex":0.0015481396,"about_ca_system_score_gemma":0.0030352888,"threshold_uncertainty_score":0.03640133},"labels":[],"label_agreement":null},{"id":"W3122129901","doi":"10.1017/s1748499514000232","title":"Trends in disguise","year":2014,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Deflation; Inflation (cosmology); Economics; Phenomenon; Longevity; Positive economics; Keynesian economics; Econometrics; Monetary policy; Philosophy; Epistemology","score_opus":0.06041949593874586,"score_gpt":0.39620342351131754,"score_spread":0.3357839275725717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122129901","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":[0.46130094,0.022104422,0.120919146,0.13377222,0.006328863,0.00014837616,0.007479317,0.0016331383,0.24631363],"genre_scores_gemma":[0.9763591,0.0035072563,0.0101957675,0.0025561997,0.0011008956,0.000047160935,0.00078632904,0.00018328024,0.0052640634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99616385,0.0017493849,0.00032477293,0.000628333,0.0009158825,0.00021777708],"domain_scores_gemma":[0.9796704,0.0069783297,0.005950217,0.0029513047,0.0039604837,0.00048932596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005348289,0.0003494964,0.00030961886,0.0030549995,0.000857249,0.0045933686,0.0005412971,0.0010235914,0.007552229],"category_scores_gemma":[0.03142024,0.00021794034,0.00031641728,0.003334859,0.005152127,0.006911283,0.0023018573,0.002979711,0.00091453566],"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":[0.0003098441,0.000035406385,0.058533277,0.00045116845,0.000099151664,0.00028652258,0.020379245,0.002200826,0.0022564575,0.7029285,0.043224663,0.16929486],"study_design_scores_gemma":[0.000014365383,0.0002243839,0.07727191,0.0009621459,0.00006686002,0.0011683498,0.012598152,0.006710379,0.0021910707,0.46560812,0.43306983,0.0001144587],"about_ca_topic_score_codex":0.002344804,"about_ca_topic_score_gemma":0.0022513673,"teacher_disagreement_score":0.007552229,"about_ca_system_score_codex":0.0017516462,"about_ca_system_score_gemma":0.0007034138,"threshold_uncertainty_score":0.028284848},"labels":[],"label_agreement":null},{"id":"W3122211064","doi":"","title":"CDF Formulation for Solving an Optimal Reinsurance Problem","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinsurance; Mathematical optimization; Maximization; Cumulative prospect theory; Stochastic programming; Cumulative distribution function; Dynamic programming; Set (abstract data type); Computer science; Probability density function; Mathematics; Expected utility hypothesis; Mathematical economics; Economics; Actuarial science; Statistics","score_opus":0.012302265448851749,"score_gpt":0.2920544488709321,"score_spread":0.27975218342208036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122211064","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":[0.0012607274,0.00030231313,0.99364084,0.0004666285,0.00007191263,0.000029524119,0.000074433374,0.00004450872,0.0041091237],"genre_scores_gemma":[0.37285507,0.003376302,0.59945357,0.00086116843,0.0006524806,0.0009018377,0.0005325439,0.0002792447,0.02108781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992065,0.00032726926,0.000038518094,0.00011797895,0.00021421004,0.00009560562],"domain_scores_gemma":[0.9982222,0.0012881323,0.00007193297,0.00004714223,0.00030592497,0.00006476089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026674096,0.0012617512,0.0012520512,0.0011624896,0.00039273774,0.0014894635,0.0011148667,0.0020640437,0.0076722177],"category_scores_gemma":[0.005066025,0.0005819033,0.0011268116,0.0011341431,0.0008438862,0.0012625373,0.0012051729,0.0024455648,0.0005812941],"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":[0.00002236717,0.000040911113,0.00030440377,0.00015820282,0.000026558582,0.0001415509,0.00007049848,0.72854936,0.0007433764,0.24349894,0.005186692,0.021257143],"study_design_scores_gemma":[0.000011510643,0.00001524309,0.000075427866,0.000026310334,0.000009838075,0.00004446151,0.00001894466,0.9567095,0.00018902853,0.040494684,0.0023960073,0.000008998648],"about_ca_topic_score_codex":0.0047142147,"about_ca_topic_score_gemma":0.0030529888,"teacher_disagreement_score":0.0076722177,"about_ca_system_score_codex":0.0013892931,"about_ca_system_score_gemma":0.0029290218,"threshold_uncertainty_score":0.025666118},"labels":[],"label_agreement":null},{"id":"W3122724187","doi":"10.48550/arxiv.2006.15384","title":"Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Asset allocation; Computer science; Benchmark (surveying); Portfolio; Robustness (evolution); Mathematical optimization; Stochastic control; Portfolio optimization; Asset (computer security); Optimal control; Economics; Finance; Mathematics","score_opus":0.0890370472669609,"score_gpt":0.242192834313488,"score_spread":0.1531557870465271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122724187","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":[0.1838666,0.0002527551,0.81129354,0.00046288525,0.000027610975,0.000069933594,0.00009123226,0.00018137974,0.0037540898],"genre_scores_gemma":[0.96839035,0.00007156406,0.030402401,0.00008266839,0.000013808503,0.00006551724,0.00004929129,0.000017925415,0.0009064755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992618,0.0002707688,0.0000403256,0.0001727709,0.00015716693,0.00009717501],"domain_scores_gemma":[0.99838364,0.00088779646,0.00030809647,0.000101215206,0.00022358302,0.00009553918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002498145,0.000607481,0.0009948324,0.0006057197,0.00021923459,0.00096202875,0.0008545659,0.00093186094,0.0011256953],"category_scores_gemma":[0.0064382306,0.00032846307,0.00036375667,0.00034042334,0.0007770964,0.0012603994,0.0008402782,0.0008084298,0.00012238587],"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":[0.00004140149,0.000025594976,0.00058404705,0.000019733048,0.000025210566,0.000022414317,0.000012364594,0.98418856,0.0010172656,0.0060126213,0.0001555196,0.007895313],"study_design_scores_gemma":[0.000004331005,0.0000226777,0.00014084726,0.000002895126,0.0000033218814,0.000004935994,0.000002345146,0.9967795,0.00034330296,0.0026232712,0.000069582034,0.0000030977783],"about_ca_topic_score_codex":0.002161394,"about_ca_topic_score_gemma":0.0015851567,"teacher_disagreement_score":0.002498145,"about_ca_system_score_codex":0.0010861441,"about_ca_system_score_gemma":0.0011965357,"threshold_uncertainty_score":0.013211608},"labels":[],"label_agreement":null},{"id":"W3122782882","doi":"10.34989/swp-2006-43","title":"Efficient Hedging and Pricing of Equity-Linked Life Insurance Contracts on Several Risky Assets","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Bank of Canada; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Life insurance; Equity (law); Actuarial science; Imperfect; Maturity (psychological); Economics; Probabilistic logic; Expected utility hypothesis; Insurance policy; Business; Financial economics; Computer science","score_opus":0.046920974023536496,"score_gpt":0.3687990200702674,"score_spread":0.32187804604673087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122782882","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":[0.0974143,0.0008349563,0.8944881,0.0003934893,0.00006265542,0.0000911112,0.000056189132,0.00004734881,0.0066117765],"genre_scores_gemma":[0.90959144,0.0007885926,0.08284204,0.00007971851,0.00007566089,0.00010077623,0.000088620014,0.000043952252,0.0063890805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989097,0.0005506597,0.000053360392,0.00011426589,0.00024222616,0.00012978954],"domain_scores_gemma":[0.99730647,0.0018533625,0.00030973417,0.00019084063,0.0001825091,0.00015715737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003422836,0.0012290084,0.0014971558,0.0009745975,0.00045399857,0.0019215406,0.001188476,0.0017585233,0.0031700176],"category_scores_gemma":[0.010287985,0.0008895955,0.0013169583,0.0008447668,0.0017810676,0.0026761198,0.001666031,0.0019049884,0.00016060064],"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":[0.000074639,0.00010903891,0.0008540805,0.00008931373,0.000089671325,0.00020037357,0.00013837153,0.68013155,0.002379865,0.29563618,0.0005003338,0.019796504],"study_design_scores_gemma":[0.000020942689,0.00005537083,0.00024883117,0.000014546614,0.000018480167,0.000036590798,0.000016713664,0.91495216,0.0005549607,0.08367958,0.00038862065,0.00001330801],"about_ca_topic_score_codex":0.0013740937,"about_ca_topic_score_gemma":0.0009927157,"teacher_disagreement_score":0.003422836,"about_ca_system_score_codex":0.0017688888,"about_ca_system_score_gemma":0.0011275278,"threshold_uncertainty_score":0.01810193},"labels":[],"label_agreement":null},{"id":"W3122996412","doi":"10.48550/arxiv.1811.09932","title":"The implied longevity curve: How long does the market think you are\\n going to live?","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Longevity; Life expectancy; Implied volatility; Economics; Longevity risk; Yield curve; Life annuity; Volatility (finance); Actuarial science; Econometrics; Financial economics; Demography; Interest rate; Population; Medicine; Finance; Pension; Gerontology","score_opus":0.04972280204316543,"score_gpt":0.2255743321813413,"score_spread":0.17585153013817587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122996412","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":[0.9177386,0.0011672287,0.044496767,0.0065512704,0.00016746901,0.000060495368,0.00479122,0.00017789415,0.02484901],"genre_scores_gemma":[0.99119896,0.0004481774,0.0027855875,0.00024310984,0.00016169251,0.0000394632,0.0022303462,0.000025814781,0.0028668873],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930346,0.00020116163,0.000049256832,0.00016232021,0.00021683695,0.0000670518],"domain_scores_gemma":[0.98527414,0.0077069616,0.00435347,0.00086337136,0.0013301731,0.00047192362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002770731,0.00032376568,0.00039368498,0.0010049198,0.00024454948,0.0020232776,0.00066237827,0.0011265572,0.009840615],"category_scores_gemma":[0.03506604,0.0001619505,0.0004282709,0.00097247167,0.0005898252,0.0032365292,0.00061895524,0.0014024442,0.0016634205],"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":[0.00085616315,0.00036020746,0.6601066,0.0002444122,0.00028905223,0.0005622416,0.0017999333,0.04452936,0.0024502901,0.06724802,0.012442403,0.20911136],"study_design_scores_gemma":[0.00007983637,0.0006731021,0.4795739,0.00022844586,0.00020273277,0.00078976754,0.0020515432,0.31543458,0.0026687316,0.17352699,0.024483208,0.000287159],"about_ca_topic_score_codex":0.0024240734,"about_ca_topic_score_gemma":0.0015406646,"teacher_disagreement_score":0.009840615,"about_ca_system_score_codex":0.0006785991,"about_ca_system_score_gemma":0.00037271925,"threshold_uncertainty_score":0.032920122},"labels":[],"label_agreement":null},{"id":"W3123046357","doi":"10.1016/s0167-6687(01)00093-2","title":"Mortality derivatives and the option to annuitise","year":2001,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":330,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Mitacs","keywords":"Actuarial science; Interest rate; Life insurance; Bond; Coupon; Annuity; Economics; Portfolio; Life annuity; Mortality rate; Value (mathematics); Financial economics; Mathematics; Finance; Statistics; Medicine","score_opus":0.02704878833499688,"score_gpt":0.28835896279232903,"score_spread":0.2613101744573322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123046357","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":[0.7044285,0.016813453,0.14105342,0.049187914,0.000901397,0.00003172928,0.0005479874,0.0002032124,0.08683238],"genre_scores_gemma":[0.9799399,0.0024007324,0.0024862296,0.00026104847,0.00040910608,0.000014009057,0.00007166726,0.00001860662,0.014398756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995278,0.00021960384,0.000026025567,0.000060921386,0.000092135786,0.00007360066],"domain_scores_gemma":[0.99597317,0.002173608,0.00067067344,0.00027877878,0.00029703282,0.0006067174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020796738,0.0004434961,0.0005928162,0.0009933203,0.00068832986,0.0025652924,0.0008500933,0.0021420983,0.0061944285],"category_scores_gemma":[0.012555132,0.0002533935,0.0004897341,0.00070905156,0.0031633556,0.004258237,0.0012741964,0.0027028918,0.0003145387],"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":[0.000073179595,0.000034501063,0.002316462,0.000028383536,0.000018620023,0.0001887367,0.00018170357,0.009008707,0.00023382278,0.97707224,0.0013816835,0.0094619915],"study_design_scores_gemma":[0.000017171742,0.000018842533,0.0017258305,0.000014207446,0.000009900214,0.00015623312,0.000069067355,0.018609209,0.000070996735,0.97684515,0.0024509644,0.0000124192375],"about_ca_topic_score_codex":0.0015999661,"about_ca_topic_score_gemma":0.0011934101,"teacher_disagreement_score":0.0061944285,"about_ca_system_score_codex":0.0011177326,"about_ca_system_score_gemma":0.0007100337,"threshold_uncertainty_score":0.020722449},"labels":[],"label_agreement":null},{"id":"W3123060020","doi":"","title":"Efficient Hedging and Pricing of Equity-Linked Life Insurance Contracts on Several Risky Assets","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Alberta; Bank of Canada; Government of Canada","funders":"","keywords":"Life insurance; Equity (law); Hedge; Actuarial science; Imperfect; Maturity (psychological); Economics; Business; Financial economics","score_opus":0.02134067152585875,"score_gpt":0.3007929212645729,"score_spread":0.2794522497387142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123060020","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":[0.2607024,0.0006260776,0.73294777,0.0003569367,0.00003051996,0.000091157584,0.000050632476,0.00004537203,0.0051491517],"genre_scores_gemma":[0.96684074,0.00030314742,0.030655988,0.000025851537,0.000023712751,0.000047906007,0.00003736831,0.000012458035,0.0020527805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884903,0.0006081024,0.000055106357,0.00011923913,0.0002397643,0.00012864856],"domain_scores_gemma":[0.99745256,0.0017018649,0.000368691,0.00017698374,0.00013360045,0.0001662415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003521514,0.0008335758,0.0010854339,0.0006829007,0.00036303105,0.001628072,0.001012559,0.001359153,0.0019789378],"category_scores_gemma":[0.010812169,0.00069418707,0.0007133142,0.0005940792,0.001601825,0.0024500766,0.0013145718,0.0013333979,0.00011780658],"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":[0.00009515833,0.00008087513,0.0013548809,0.00005445869,0.000065117565,0.00022260174,0.00011988838,0.8026934,0.0022675027,0.17446837,0.0002205335,0.018357124],"study_design_scores_gemma":[0.000025809917,0.00007974738,0.00047095382,0.000012623236,0.000017974735,0.000044761924,0.000019295463,0.93060845,0.000713483,0.06772168,0.00027177628,0.000013392448],"about_ca_topic_score_codex":0.00096373464,"about_ca_topic_score_gemma":0.0007355132,"teacher_disagreement_score":0.003521514,"about_ca_system_score_codex":0.0014316638,"about_ca_system_score_gemma":0.0009408699,"threshold_uncertainty_score":0.01862377},"labels":[],"label_agreement":null},{"id":"W3123288161","doi":"10.2143/ast.42.2.2182808","title":"Are Flexible Premium Variable Annuities Under-Priced?","year":2012,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; China Scholarship Council; University of Toronto","keywords":"Annuity; Valuation (finance); Variable (mathematics); Economics; Actuarial science; Life annuity; Mutual fund; Econometrics; Mathematical economics; Finance; Mathematics; Pension","score_opus":0.034597155003804225,"score_gpt":0.29646271593871704,"score_spread":0.2618655609349128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123288161","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":[0.75549746,0.0055640354,0.13924588,0.013282501,0.00076168525,0.00013812445,0.0006793558,0.00023046124,0.084600516],"genre_scores_gemma":[0.99010825,0.0008842757,0.002970619,0.00030322105,0.0002172809,0.000021083833,0.00010906301,0.000033538374,0.005352665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979971,0.00046545497,0.00011977836,0.0003836407,0.00057620794,0.0004578491],"domain_scores_gemma":[0.9934174,0.0022136304,0.0022880693,0.0008276996,0.00081268506,0.00044054107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028553295,0.00032527331,0.0008557323,0.00070681435,0.00068201916,0.0034701433,0.0016093013,0.0017084561,0.00946417],"category_scores_gemma":[0.020060195,0.00027311637,0.0005808898,0.0009703966,0.0020739557,0.004681918,0.0011583875,0.0015386392,0.0009060308],"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":[0.0006376527,0.00019261937,0.048436586,0.00021374339,0.00011030272,0.0024971769,0.0013022773,0.015640821,0.0016867394,0.6862779,0.013067039,0.2299371],"study_design_scores_gemma":[0.00012001738,0.0003488073,0.024363033,0.00023320423,0.000070407106,0.0037444767,0.0033863469,0.055437762,0.0018956307,0.85918295,0.051130485,0.00008682632],"about_ca_topic_score_codex":0.0019705626,"about_ca_topic_score_gemma":0.000926662,"teacher_disagreement_score":0.00946417,"about_ca_system_score_codex":0.001017049,"about_ca_system_score_gemma":0.0007117243,"threshold_uncertainty_score":0.031660795},"labels":[],"label_agreement":null},{"id":"W3123307269","doi":"10.2139/ssrn.3192132","title":"A Data Driven Neural Network Approach to Optimal Asset Allocation for Target Based Defined Contribution Pension Plans","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Pension; Asset allocation; Artificial neural network; Asset (computer security); Computer science; Pension plan; Actuarial science; Business; Economics; Finance; Artificial intelligence; Portfolio; Computer security","score_opus":0.029564869344312655,"score_gpt":0.30855031547796324,"score_spread":0.2789854461336506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123307269","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":[0.08553043,0.00082539686,0.90734166,0.0012254894,0.00010130585,0.00010513297,0.0002891831,0.00025242515,0.004328812],"genre_scores_gemma":[0.91589427,0.0003940718,0.074692994,0.00014365446,0.00010348612,0.0002485061,0.00031659048,0.000062481544,0.008143925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965227,0.0001491951,0.00001886124,0.000072211216,0.000058415604,0.000049094797],"domain_scores_gemma":[0.9980178,0.0015982938,0.00007756131,0.000029958264,0.00021383607,0.00006261972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016913885,0.0006204217,0.0012822957,0.0006672922,0.00037783256,0.0013020446,0.0015270632,0.0018969624,0.004100313],"category_scores_gemma":[0.005011449,0.0008915667,0.0005513129,0.00070010533,0.00066290936,0.0012157104,0.001079573,0.0017854585,0.00024665392],"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":[0.000017921606,0.000015589312,0.00016214579,0.000011842069,0.000011679843,0.000015984791,0.000009957312,0.99240816,0.00005734772,0.0024971962,0.0001769069,0.0046152757],"study_design_scores_gemma":[0.000001800848,0.0000023354364,0.000021010412,0.0000012768294,0.0000011874737,8.488597e-7,0.0000013933803,0.9989593,0.000012868982,0.0009719903,0.000025043977,8.452971e-7],"about_ca_topic_score_codex":0.023616962,"about_ca_topic_score_gemma":0.0188505,"teacher_disagreement_score":0.023616962,"about_ca_system_score_codex":0.0017955423,"about_ca_system_score_gemma":0.0016052504,"threshold_uncertainty_score":0.046958983},"labels":[],"label_agreement":null},{"id":"W3123402379","doi":"","title":"Asymptotic Normality for Weighted Sums of Linear Processes","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Economic and Social Research Council; National Science Foundation","keywords":"Mathematics; Asymptotic distribution; Martingale (probability theory); Estimator; Applied mathematics; Autoregressive conditional heteroskedasticity; Covariance; Weak convergence; Kernel (algebra); Econometrics; Statistics; Discrete mathematics; Queue; Computer science","score_opus":0.05347312601431871,"score_gpt":0.3720708856145507,"score_spread":0.318597759600232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123402379","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":[0.03710121,0.0031986411,0.9268349,0.0031912187,0.0006536516,0.00025113928,0.0011482877,0.00081268774,0.026808344],"genre_scores_gemma":[0.76335114,0.01058353,0.106429376,0.0020925833,0.0027928317,0.0032697394,0.0056822454,0.0010215638,0.10477689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99257904,0.003339208,0.00040268316,0.0013214257,0.001607678,0.0007499388],"domain_scores_gemma":[0.9223011,0.059088323,0.0047532655,0.0054302122,0.0068369657,0.0015902426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016855562,0.002244577,0.0032366414,0.0038205506,0.0010170257,0.004320875,0.0032837398,0.003202529,0.024561105],"category_scores_gemma":[0.08492613,0.0012517316,0.0023986483,0.0034173245,0.0063886563,0.008630043,0.0039337254,0.006326775,0.004177526],"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":[0.00020009068,0.00009650669,0.0024020034,0.0006629415,0.00020778638,0.00032248054,0.00042060696,0.049775794,0.001122625,0.9064687,0.009712373,0.02860819],"study_design_scores_gemma":[0.000044479693,0.000069733345,0.0014147263,0.00014725149,0.00004839819,0.00015093881,0.00011022781,0.26536494,0.00044466183,0.72920376,0.002948616,0.0000522772],"about_ca_topic_score_codex":0.0036623962,"about_ca_topic_score_gemma":0.0022099307,"teacher_disagreement_score":0.024561105,"about_ca_system_score_codex":0.0025817894,"about_ca_system_score_gemma":0.0021792452,"threshold_uncertainty_score":0.089141846},"labels":[],"label_agreement":null},{"id":"W3123754507","doi":"","title":"Valuation and Hedging of the Ruin-Contingent Life Annuity (RCLA)","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life annuity; Actuarial science; Valuation (finance); Economics; Scrutiny; Arbitrage; Life insurance; Equity (law); Stochastic game; Pension; Financial economics; Finance; Microeconomics","score_opus":0.05637400023504379,"score_gpt":0.3277543455971999,"score_spread":0.2713803453621561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123754507","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":[0.34694386,0.0055950126,0.5926726,0.001755149,0.0003695886,0.00013824287,0.00021905506,0.000099859986,0.052206773],"genre_scores_gemma":[0.97751266,0.0007426575,0.014595547,0.00003907785,0.000098359735,0.0000257805,0.000055446722,0.000018899933,0.0069115283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995695,0.00020730785,0.000016759052,0.00006906048,0.0000776779,0.000059682792],"domain_scores_gemma":[0.9986298,0.00079555565,0.00022200658,0.000108042914,0.00012789975,0.00011673069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017879027,0.00058255595,0.0005295789,0.00049545,0.00054345076,0.002238998,0.0009871963,0.0018486503,0.0025066063],"category_scores_gemma":[0.0054139816,0.00038245923,0.0006942046,0.0003668117,0.001527629,0.0031672947,0.000916694,0.0016261782,0.00017500545],"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":[0.00012801775,0.00008665518,0.0033364485,0.00009317443,0.000044520886,0.00072862365,0.00018947605,0.3682348,0.0064572273,0.60472816,0.0013685602,0.014604386],"study_design_scores_gemma":[0.0000106448415,0.00010614213,0.0013266285,0.000038684633,0.000023296416,0.00026880362,0.00006976109,0.9023196,0.0011005485,0.09244147,0.0022597464,0.000034675493],"about_ca_topic_score_codex":0.0013478909,"about_ca_topic_score_gemma":0.00095215504,"teacher_disagreement_score":0.0025066063,"about_ca_system_score_codex":0.0012505143,"about_ca_system_score_gemma":0.00056945346,"threshold_uncertainty_score":0.009455442},"labels":[],"label_agreement":null},{"id":"W3123768761","doi":"","title":"Approximate Derivative Pricing for Large Classes of Homogeneous Assets with Systematic Risk","year":2010,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation du Risque; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Unobservable; Homogeneous; Econometrics; Actuarial science; Class (philosophy); Derivative (finance); Economics; Longevity risk; Capital asset pricing model; Order (exchange); Mathematics; Credit risk; Financial economics; Finance; Computer science; Pension","score_opus":0.02819583998701782,"score_gpt":0.33901286547491044,"score_spread":0.31081702548789264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123768761","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":[0.14253503,0.0009235961,0.85215485,0.0004954977,0.000048763173,0.00004145109,0.00010180745,0.00016193926,0.0035370667],"genre_scores_gemma":[0.9309497,0.0010206024,0.062337145,0.00014031658,0.00012471744,0.00008059686,0.00020179665,0.00008359243,0.005061642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99920005,0.00031217982,0.000040189392,0.00010155982,0.00021732315,0.00012880441],"domain_scores_gemma":[0.9943299,0.0040167654,0.0004477929,0.00056260696,0.000396261,0.00024657257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032552194,0.00087245647,0.0013056321,0.0009257399,0.00036167898,0.0021265582,0.0013556721,0.0013189228,0.0018415467],"category_scores_gemma":[0.016807854,0.00047856974,0.0009881755,0.0007414745,0.0016392401,0.0030561688,0.0012366223,0.0013910249,0.00021017308],"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":[0.00011943039,0.00008331791,0.0037487661,0.00011655415,0.00008076534,0.00052600895,0.0002093755,0.6452011,0.002689236,0.32578716,0.001151862,0.020286484],"study_design_scores_gemma":[0.0000047196027,0.000006490247,0.00016232862,0.0000033383576,0.0000038400854,0.000020782361,0.0000073421425,0.9720052,0.0001385823,0.027534733,0.000108608445,0.0000040720224],"about_ca_topic_score_codex":0.0040713423,"about_ca_topic_score_gemma":0.0025124766,"teacher_disagreement_score":0.0040713423,"about_ca_system_score_codex":0.0011882914,"about_ca_system_score_gemma":0.0007179015,"threshold_uncertainty_score":0.01721543},"labels":[],"label_agreement":null},{"id":"W3124138303","doi":"","title":"Bayesian Semiparametric Stochastic Volatility Modeling","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"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":"Social Sciences and Humanities Research Council of Canada","keywords":"Stochastic volatility; Markov chain Monte Carlo; Econometrics; Kurtosis; Bayesian probability; Semiparametric model; Parametric statistics; Volatility (finance); Skewness; Nonparametric statistics; Computer science; Bayesian inference; Posterior probability; Semiparametric regression; Mathematics; Statistics","score_opus":0.05628013914585686,"score_gpt":0.35582087014960667,"score_spread":0.2995407310037498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124138303","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":[0.036473613,0.0007021381,0.95300573,0.0012082603,0.000060649778,0.000050719464,0.0005548962,0.00030108826,0.007642921],"genre_scores_gemma":[0.93804604,0.00087732467,0.0521078,0.00028044696,0.00014684697,0.00014455433,0.00061204715,0.000097956625,0.007687018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973706,0.0014377002,0.00010054096,0.00032521572,0.0005984554,0.00016749451],"domain_scores_gemma":[0.9911889,0.00603346,0.0010014552,0.0007487256,0.00081667193,0.00021081268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040419204,0.00074445945,0.0012080024,0.0010043513,0.00037873752,0.0027445506,0.00203837,0.0018434655,0.0056209667],"category_scores_gemma":[0.016564522,0.0006254063,0.00091159344,0.0011931065,0.0013918921,0.0024145942,0.0020184473,0.0018610938,0.0007399296],"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":[0.00006773332,0.000058822654,0.0022901515,0.000100181685,0.00010165136,0.00014704451,0.0001586914,0.63497317,0.0005771945,0.33867952,0.0025066116,0.020339258],"study_design_scores_gemma":[0.0000106901725,0.000011137817,0.00050807814,0.000022249147,0.000011862229,0.00005687911,0.000013258082,0.8482686,0.000107584165,0.14965785,0.0013160969,0.000015764886],"about_ca_topic_score_codex":0.0036298074,"about_ca_topic_score_gemma":0.0023446053,"teacher_disagreement_score":0.0056209667,"about_ca_system_score_codex":0.0012075037,"about_ca_system_score_gemma":0.00081912155,"threshold_uncertainty_score":0.021376014},"labels":[],"label_agreement":null},{"id":"W3124245730","doi":"10.2139/ssrn.2919884","title":"Pension Risk Management with Funding and Buyout Options","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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 Manitoba","funders":"","keywords":"Pension; Business; Leveraged buyout; Risk management; Actuarial science; Finance; Private equity","score_opus":0.015120644149183349,"score_gpt":0.29398184819365464,"score_spread":0.2788612040444713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124245730","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":[0.89972955,0.004221025,0.018279737,0.008244386,0.00017725569,0.0001343693,0.00042479055,0.00014146024,0.068647355],"genre_scores_gemma":[0.9930757,0.0002611781,0.0012830241,0.000056612666,0.000063177074,0.000013193926,0.000050646027,0.0000039622378,0.005192468],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803215,0.0008884315,0.0001330409,0.00019893849,0.0003951598,0.00035230874],"domain_scores_gemma":[0.99264973,0.0037636454,0.0016158649,0.0004444315,0.00053009734,0.0009962225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004406522,0.00051179744,0.00053683337,0.0008214046,0.00063866685,0.0044324836,0.00068370695,0.0021001967,0.008053504],"category_scores_gemma":[0.021417925,0.00023147445,0.0005000879,0.00061893987,0.0008200934,0.0027268426,0.0014695444,0.0013874024,0.00055115845],"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":[0.0029085106,0.003089401,0.29769903,0.00032164677,0.00058541965,0.0017505494,0.0019166069,0.088772096,0.00259772,0.144184,0.0097111715,0.44646382],"study_design_scores_gemma":[0.0005116674,0.0037008037,0.25925452,0.00047220202,0.0008216145,0.0015173382,0.005243383,0.20067778,0.0035726803,0.48610657,0.037901465,0.00022002817],"about_ca_topic_score_codex":0.002097068,"about_ca_topic_score_gemma":0.0029978228,"teacher_disagreement_score":0.008053504,"about_ca_system_score_codex":0.0011644069,"about_ca_system_score_gemma":0.0015869613,"threshold_uncertainty_score":0.026941657},"labels":[],"label_agreement":null},{"id":"W3124276902","doi":"","title":"Measuring Longevity Risk for a Canadian Pension Fund","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Pension; Life expectancy; Beneficiary; Actuarial science; Longevity risk; Business; Payment; Pension fund; Life annuity; Hedge; Life insurance; Longevity; Economics; Finance; Population","score_opus":0.13674299010149255,"score_gpt":0.3537906038560874,"score_spread":0.21704761375459486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124276902","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":[0.95181096,0.0015709979,0.010935959,0.0013607398,0.000041916297,0.00015067167,0.0076396894,0.00019233726,0.026296841],"genre_scores_gemma":[0.9833211,0.00044078042,0.010570264,0.000030050514,0.000007798619,0.000028569148,0.0022906598,0.000012901561,0.0032979166],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989267,0.00008409783,0.00003432258,0.00011417818,0.0006741356,0.00016660949],"domain_scores_gemma":[0.99860567,0.00018406355,0.00024004972,0.00009724596,0.00071817153,0.00015488091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012415792,0.00053179346,0.00029480195,0.004026438,0.0015574989,0.0017973155,0.0009172693,0.0005622684,0.0019223382],"category_scores_gemma":[0.0069581163,0.00014233953,0.0005002555,0.0037644515,0.00047200397,0.000715029,0.0011914689,0.00042484718,0.00018299944],"study_design_candidate":"simulation_or_modeling","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":[0.0004759364,0.00014779635,0.5989297,0.00021624829,0.00039928543,0.00044944207,0.0033001676,0.12120486,0.0036813219,0.05076217,0.01201814,0.20841491],"study_design_scores_gemma":[0.000037791026,0.00013779987,0.7974394,0.00013579136,0.00021435691,0.00027640292,0.0028773842,0.15022703,0.0031259938,0.007960951,0.037358202,0.0002089051],"about_ca_topic_score_codex":0.9606413,"about_ca_topic_score_gemma":0.9559026,"teacher_disagreement_score":0.039358675,"about_ca_system_score_codex":0.026041878,"about_ca_system_score_gemma":0.012483602,"threshold_uncertainty_score":0.18894798},"labels":[],"label_agreement":null},{"id":"W3124295207","doi":"10.59962/9780774852111-003","title":"How Old Is Old? Revising the Definition Based on Life Table Criteria","year":2007,"lang":"en","type":"book-chapter","venue":"University of British Columbia Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Table (database); Point (geometry); Genealogy; History; Epistemology; Computer science; Mathematics; Philosophy; Database","score_opus":0.04702915842076675,"score_gpt":0.2352853650478158,"score_spread":0.18825620662704906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124295207","genre_codex":"other","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":[0.025396325,0.058190297,0.10577691,0.14099206,0.0107891,0.00016951618,0.0019819576,0.00019578854,0.65650797],"genre_scores_gemma":[0.66581583,0.0587367,0.102007106,0.025017776,0.005508936,0.0003174615,0.0024760182,0.00046962936,0.13965051],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99795544,0.00075183064,0.00015602035,0.00022157756,0.00069514697,0.00021999732],"domain_scores_gemma":[0.9965771,0.001501997,0.00015010324,0.00011341252,0.0013581736,0.000299301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044824593,0.0005684182,0.00058286585,0.003666202,0.0019854524,0.004697509,0.0017526728,0.0011963008,0.0026471661],"category_scores_gemma":[0.0064592254,0.00016855245,0.00035527497,0.0040568225,0.011107512,0.0071546272,0.0014873439,0.0038498738,0.00074127555],"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":[0.0000071403265,0.000006094272,0.0012364015,0.00006124316,0.0000036038082,0.000080603786,0.005418635,0.00028756217,0.00006746796,0.9129906,0.04667053,0.033170078],"study_design_scores_gemma":[0.000004193443,0.000011734093,0.003731985,0.000409835,0.00000811618,0.00024727656,0.0064926073,0.00094819913,0.00012391066,0.46207753,0.52590775,0.000036835932],"about_ca_topic_score_codex":0.156288,"about_ca_topic_score_gemma":0.17477632,"teacher_disagreement_score":0.156288,"about_ca_system_score_codex":0.010571576,"about_ca_system_score_gemma":0.0073557184,"threshold_uncertainty_score":0.31075662},"labels":[],"label_agreement":null},{"id":"W3124350828","doi":"10.2139/ssrn.3138280","title":"The Utility Value of Longevity Risk Pooling: Analytic Insights","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Longevity; Pooling; Longevity risk; Value (mathematics); Actuarial science; Value at risk; Econometrics; Economics; Risk analysis (engineering); Statistics; Mathematics; Medicine; Computer science; Risk management; Gerontology; Artificial intelligence","score_opus":0.0101865860793774,"score_gpt":0.2884859157356611,"score_spread":0.27829932965628373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124350828","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":[0.3253888,0.010012307,0.5972326,0.013846809,0.00013719058,0.00047156325,0.0011583015,0.000264437,0.051488012],"genre_scores_gemma":[0.97855955,0.0020240932,0.015483288,0.00030122785,0.00026869954,0.0001349934,0.00015681869,0.000038408012,0.0030330159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99311674,0.004786274,0.00019696627,0.0005219518,0.0008157107,0.0005623529],"domain_scores_gemma":[0.86904806,0.12227843,0.0026516598,0.0037011544,0.0015714942,0.0007492251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018646313,0.0013296017,0.003012385,0.0034974127,0.0009761058,0.0059769605,0.003102337,0.0023016722,0.008520442],"category_scores_gemma":[0.10743064,0.0009560128,0.0018738153,0.004277469,0.0044067334,0.010401671,0.0037555117,0.0034553653,0.00036089166],"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":[0.00027112232,0.00021595399,0.014455569,0.0002931534,0.000346139,0.00038859466,0.00095481845,0.07395843,0.00038248158,0.85869944,0.0033424983,0.04669185],"study_design_scores_gemma":[0.000030569103,0.000092957096,0.003336766,0.00011698549,0.0002183739,0.00028916795,0.00049791334,0.2130798,0.00018644854,0.7808329,0.0012700114,0.000048065078],"about_ca_topic_score_codex":0.003949533,"about_ca_topic_score_gemma":0.0018501714,"teacher_disagreement_score":0.018646313,"about_ca_system_score_codex":0.0030242226,"about_ca_system_score_gemma":0.0017130784,"threshold_uncertainty_score":0.09861231},"labels":[],"label_agreement":null},{"id":"W3124431941","doi":"10.1017/s1474747218000069","title":"Robust hedging in incomplete markets","year":2018,"lang":"en","type":"article","venue":"Journal of Pensions Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Global Risk Institute in Financial Services","funders":"","keywords":"Robustness (evolution); Pension; Hedge fund; Equity (law); Economics; Incomplete markets; Hedge; Investment strategy; Pension fund; Actuarial science; Econometrics; Finance; Microeconomics; Market liquidity","score_opus":0.03292686327766851,"score_gpt":0.26644645604469414,"score_spread":0.23351959276702564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124431941","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":[0.42925167,0.002000534,0.5530779,0.0021537167,0.00013498189,0.000086230364,0.00038057475,0.000113314185,0.012801042],"genre_scores_gemma":[0.9884022,0.00030528108,0.007469657,0.000056820187,0.000052395753,0.000041810803,0.000062750245,0.000015520432,0.0035934418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99899,0.0004542188,0.00006081734,0.00018749897,0.00015026663,0.00015707115],"domain_scores_gemma":[0.9950382,0.0029112708,0.0010919968,0.00024252968,0.00032442732,0.00039152178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044799233,0.0012251269,0.0020030616,0.0007804095,0.00045800392,0.0022984857,0.0011961408,0.0023237937,0.0024017284],"category_scores_gemma":[0.009622385,0.0007115873,0.0013153001,0.00045772284,0.0021198925,0.002433243,0.0016160342,0.0016234398,0.00010576106],"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":[0.00007577187,0.000040777624,0.0008517969,0.000072318915,0.00009920286,0.0004289384,0.000076628574,0.8748122,0.0009839478,0.119351506,0.0002992262,0.0029077232],"study_design_scores_gemma":[0.00002667024,0.00004775142,0.0003302681,0.000015141576,0.00002212188,0.00003182329,0.00002227169,0.9437343,0.0002215058,0.055217315,0.00031512603,0.000015685071],"about_ca_topic_score_codex":0.004028948,"about_ca_topic_score_gemma":0.0013490295,"teacher_disagreement_score":0.0044799233,"about_ca_system_score_codex":0.0015847221,"about_ca_system_score_gemma":0.0009651028,"threshold_uncertainty_score":0.023692429},"labels":[],"label_agreement":null},{"id":"W3124450192","doi":"","title":"Eliciting Subjective Survival Curves: Lessons from Partial Identification","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","funders":"","keywords":"Life expectancy; Rounding; Construct (python library); Consistency (knowledge bases); Econometrics; Point (geometry); Inference; Parametric statistics; Mathematics; Expectancy theory; Identification (biology); Statistics; Psychology; Computer science; Social psychology; Artificial intelligence; Medicine; Discrete mathematics","score_opus":0.09422028548523298,"score_gpt":0.4081399816353739,"score_spread":0.3139196961501409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124450192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037336074,0.00045802238,0.9546695,0.0015722163,0.00004157778,0.00010503746,0.000540603,0.00024779458,0.005029257],"genre_scores_gemma":[0.77831626,0.0010681042,0.21506779,0.0007664588,0.00014051731,0.0004644009,0.0014597666,0.00026228768,0.0024545027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97868973,0.016115416,0.0010217327,0.0017879083,0.0019421782,0.00044296906],"domain_scores_gemma":[0.5663377,0.37440926,0.012678848,0.038940758,0.0063030273,0.001330358],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.048704002,0.0016173841,0.0021864064,0.0019443247,0.00084747974,0.0049095736,0.0029014165,0.003185356,0.006123817],"category_scores_gemma":[0.32455072,0.0012999232,0.002391789,0.0026018622,0.0039224806,0.008442417,0.0061685005,0.0054396465,0.0009000547],"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":[0.0003285637,0.0001679793,0.024626737,0.0005594757,0.00042205193,0.00038011908,0.003827047,0.24201348,0.000584743,0.61283445,0.0035324497,0.110722944],"study_design_scores_gemma":[0.000032425218,0.00009267985,0.0029810043,0.0001655503,0.00003743359,0.00010364094,0.00040277018,0.2980257,0.0006969789,0.69468594,0.0027075626,0.00006829954],"about_ca_topic_score_codex":0.003828388,"about_ca_topic_score_gemma":0.0021407288,"teacher_disagreement_score":0.951296,"about_ca_system_score_codex":0.0019019453,"about_ca_system_score_gemma":0.0013174037,"threshold_uncertainty_score":0.25757444},"labels":[],"label_agreement":null},{"id":"W3124463716","doi":"10.1111/j.1475-4991.2008.00272.x/enhancedabs","title":"Lifetimes of Machinery and Equipment. Evidence from Dutch Manufacturing","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","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":"Weibull distribution; Asset (computer security); Stock (firearms); Econometrics; Electrical machinery; Service (business); Capital asset; Business; Economics; Engineering; Computer science; Statistics; Finance; Mathematics; Economy","score_opus":0.039990652941707235,"score_gpt":0.34609146884020453,"score_spread":0.3061008158984973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124463716","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":[0.96746427,0.0073587103,0.0016907045,0.00036345093,0.000023454064,0.00001371762,0.012549634,0.000013376141,0.010522606],"genre_scores_gemma":[0.98561513,0.0022517433,0.00028936804,0.000044234774,0.000013519913,0.000011726281,0.009143077,0.00001274072,0.0026185254],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99900454,0.00014512394,0.00013809551,0.0002402411,0.00035298412,0.00011898481],"domain_scores_gemma":[0.9918059,0.0018319227,0.004275131,0.000547132,0.0011766864,0.0003632458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014111546,0.00019606035,0.00025909004,0.0016333223,0.00027657714,0.0006884703,0.0005620222,0.0003671395,0.0038027382],"category_scores_gemma":[0.009926639,0.00019729616,0.00043597436,0.0028445919,0.00035526484,0.00086362014,0.00064112636,0.00025775662,0.0008689736],"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":[0.00029526028,0.0000278785,0.9461175,0.00038208775,0.00017736283,0.0005047401,0.0021736482,0.0019547876,0.0005638773,0.0026730786,0.003619874,0.04150998],"study_design_scores_gemma":[0.0000033087395,0.000033014287,0.98890686,0.00007127659,0.000030380614,0.0002884605,0.0007421272,0.0007507047,0.0003142586,0.00038165456,0.008469141,0.000008791228],"about_ca_topic_score_codex":0.075776435,"about_ca_topic_score_gemma":0.08640951,"teacher_disagreement_score":0.075776435,"about_ca_system_score_codex":0.0009182233,"about_ca_system_score_gemma":0.00054240023,"threshold_uncertainty_score":0.1506707},"labels":[],"label_agreement":null},{"id":"W3124472429","doi":"","title":"Monitoring 15 years of residential house price development in Hungary with the help of the FHB House Price Index","year":2013,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Real estate; Index (typography); Database transaction; House price; Valuation (finance); Price index; Value (mathematics); Hedonic index; Business; Market value; Economics; Transaction data; Quarter (Canadian coin); Finance; Monetary economics; Geography; Econometrics; Database; Statistics","score_opus":0.014120715419631086,"score_gpt":0.2520694262836592,"score_spread":0.2379487108640281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124472429","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":[0.99024653,0.0002841437,0.0005642757,0.000023122726,0.000013478329,0.000026738373,0.007096199,0.000024299892,0.0017212309],"genre_scores_gemma":[0.9908195,0.00026569565,0.0008880923,0.000012924119,0.000029075885,0.00003165683,0.0072696474,0.000008894942,0.0006745514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999567,0.00003792878,0.000070311144,0.00010965297,0.0001248103,0.000090351736],"domain_scores_gemma":[0.9992834,0.000063833344,0.00031724368,0.000037225924,0.00020692461,0.00009146443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049234775,0.00019915219,0.00038512645,0.0023590175,0.00015895767,0.0007688789,0.00027844054,0.0002117454,0.000944238],"category_scores_gemma":[0.0008129029,0.00013665024,0.00032003396,0.0025338323,0.00017712187,0.00050290255,0.00064702757,0.00024677906,0.00034831557],"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":[0.00020968336,0.0000758751,0.96241695,0.000112236696,0.00013682549,0.00089598645,0.0009781811,0.0011849037,0.0015407276,0.00036786153,0.0019598664,0.030120812],"study_design_scores_gemma":[0.0000018669683,0.00003854563,0.99768996,0.0000068816958,0.00001546953,0.00010466363,0.00021476464,0.00031016106,0.00032989122,0.000024394729,0.001258071,0.000005235208],"about_ca_topic_score_codex":0.0083994595,"about_ca_topic_score_gemma":0.010660142,"teacher_disagreement_score":0.0083994595,"about_ca_system_score_codex":0.0005843335,"about_ca_system_score_gemma":0.0003788695,"threshold_uncertainty_score":0.016701162},"labels":[],"label_agreement":null},{"id":"W3124493524","doi":"","title":"If we can simulate it, we can insure it: An application to longevity risk management","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Longevity risk; Longevity; Perspective (graphical); Risk analysis (engineering); Risk management; Process (computing); Computer science; Actuarial science; Economics; Business; Finance; Artificial intelligence","score_opus":0.03779084047097912,"score_gpt":0.3622695334922801,"score_spread":0.32447869302130095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124493524","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":[0.21967785,0.0004938964,0.7652151,0.002583054,0.00010447853,0.00008230324,0.00018274544,0.00045649707,0.01120408],"genre_scores_gemma":[0.89427745,0.0003073189,0.102945216,0.0001439088,0.000045574743,0.000068880814,0.00006806963,0.00006362699,0.0020797767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961543,0.0001776846,0.000018837427,0.0000526242,0.00009696617,0.000038348287],"domain_scores_gemma":[0.9981552,0.0011469219,0.00020529542,0.00023679956,0.00013563712,0.00012013713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019491768,0.00046794006,0.00049777376,0.0004603829,0.0005564022,0.00095294317,0.00088042003,0.0015435552,0.0033156106],"category_scores_gemma":[0.00725482,0.00017681324,0.00061878504,0.0003975389,0.0009225058,0.0015679768,0.0014527169,0.0014248352,0.0001659242],"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":[0.000056504294,0.00008161285,0.0035060085,0.000032747364,0.000036493468,0.00017171196,0.00019532029,0.794468,0.0010934523,0.17601506,0.0009763155,0.023366753],"study_design_scores_gemma":[0.000018299932,0.00005232072,0.00035326477,0.000013257129,0.000010760869,0.000048865713,0.000036367914,0.91254634,0.0004735092,0.08514666,0.0012831151,0.00001722188],"about_ca_topic_score_codex":0.0051903236,"about_ca_topic_score_gemma":0.002469458,"teacher_disagreement_score":0.0051903236,"about_ca_system_score_codex":0.0008098265,"about_ca_system_score_gemma":0.00072515704,"threshold_uncertainty_score":0.011091769},"labels":[],"label_agreement":null},{"id":"W3124504710","doi":"10.2139/ssrn.3180333","title":"Management of Withdrawal Risk Through Optimal Life Cycle Asset Allocation","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Asset allocation; IT asset management; Economics; Business; Actuarial science; Asset management; Financial economics; Finance","score_opus":0.008722092026762761,"score_gpt":0.2891649995024045,"score_spread":0.2804429074756417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124504710","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":[0.3867862,0.0006805823,0.5988534,0.0015896645,0.000071940296,0.00017173961,0.00013413632,0.00020537198,0.011506976],"genre_scores_gemma":[0.98807985,0.00013451693,0.009402974,0.00004217451,0.000021507869,0.000042823915,0.000030163481,0.000014712792,0.0022311881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994475,0.00025460616,0.000024924188,0.00006426472,0.000080183716,0.0001284953],"domain_scores_gemma":[0.99842334,0.00080847094,0.0002744851,0.00008097066,0.00021574044,0.00019699334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016755277,0.0005418471,0.0009368436,0.00063805236,0.0003007634,0.0015694727,0.0010707715,0.0010478941,0.0030167487],"category_scores_gemma":[0.006851139,0.0004183456,0.0002845003,0.00042220487,0.00051605725,0.0014968015,0.0014049353,0.00084672787,0.00026292528],"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":[0.0003720334,0.0003066635,0.0043415497,0.00006829152,0.00009819733,0.00011723532,0.00010050358,0.9000188,0.0035316115,0.03382022,0.0013896695,0.055835165],"study_design_scores_gemma":[0.000046212605,0.00011514647,0.0011970394,0.000011319076,0.000025094607,0.000031037573,0.000036508925,0.9680655,0.00043497825,0.029660773,0.00036386345,0.000012538303],"about_ca_topic_score_codex":0.0013543726,"about_ca_topic_score_gemma":0.0010425826,"teacher_disagreement_score":0.0030167487,"about_ca_system_score_codex":0.0007195004,"about_ca_system_score_gemma":0.0015402406,"threshold_uncertainty_score":0.01009202},"labels":[],"label_agreement":null},{"id":"W3124874316","doi":"10.2139/ssrn.3319160","title":"A Backward Simulation Method for Stochastic Optimal Control Problems","year":2019,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mathematical optimization; Stochastic control; Computer science; Bellman equation; Monte Carlo method; Selection (genetic algorithm); Optimal control; Mathematics; Artificial intelligence","score_opus":0.02120854266233264,"score_gpt":0.34577217436881263,"score_spread":0.32456363170648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124874316","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":[0.0012984042,0.00007660581,0.9969068,0.00010493044,0.000054899116,0.000034771856,0.000041466945,0.000109169196,0.0013728746],"genre_scores_gemma":[0.20521334,0.00059823686,0.7716961,0.0003283897,0.00023979173,0.0011561456,0.00045575612,0.0006700395,0.019642234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914,0.0004752173,0.00004121778,0.000073406285,0.00021483078,0.00005527855],"domain_scores_gemma":[0.99584806,0.003118586,0.00014114034,0.00017509573,0.0005206308,0.00019638201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033610573,0.0010477538,0.0019238072,0.0013244144,0.001011469,0.0010473296,0.0018994841,0.0018694059,0.00961647],"category_scores_gemma":[0.00789944,0.0010313778,0.0017929305,0.0012066835,0.0012932288,0.0012439662,0.0031615095,0.002735875,0.0014045849],"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":[0.00012833785,0.000113270595,0.0006127387,0.00014089307,0.00011602687,0.00012302292,0.00008753926,0.8064019,0.0020670963,0.14958656,0.0016266602,0.03899587],"study_design_scores_gemma":[0.000024457948,0.000014982073,0.000034551478,0.000012019735,0.000010000104,0.000010301093,0.0000032371506,0.97637844,0.00014865743,0.022308195,0.0010483854,0.0000068537606],"about_ca_topic_score_codex":0.011180656,"about_ca_topic_score_gemma":0.007917768,"teacher_disagreement_score":0.011180656,"about_ca_system_score_codex":0.0010775765,"about_ca_system_score_gemma":0.0033638137,"threshold_uncertainty_score":0.032170296},"labels":[],"label_agreement":null},{"id":"W3125352985","doi":"","title":"Securitization of Mortality Risks in Life Annuities","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Manitoba","funders":"","keywords":"Securitization; Annuity; Life annuity; Actuarial science; Bond; Life insurance; Business; Economics; Financial economics; Finance; Pension","score_opus":0.027775683900546567,"score_gpt":0.3448710888812238,"score_spread":0.31709540498067723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125352985","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":[0.6232238,0.003570735,0.3322618,0.0026833827,0.00025687195,0.00012052884,0.0008991752,0.00022881267,0.036754914],"genre_scores_gemma":[0.9807591,0.001043978,0.010908822,0.000064108885,0.00011285398,0.00004932346,0.00027889363,0.000024174664,0.006758726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991228,0.000421399,0.00006531629,0.000097055185,0.00022106293,0.0000723685],"domain_scores_gemma":[0.9951615,0.0019709128,0.0015404064,0.0005988903,0.00053662015,0.00019177698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027220452,0.0003448448,0.00050495716,0.0007395172,0.0003208227,0.0022352769,0.0006390151,0.0008875474,0.004765714],"category_scores_gemma":[0.01309429,0.0002581063,0.0004389035,0.0011362095,0.001061759,0.0030239252,0.0010641919,0.0016597363,0.00047869454],"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":[0.000110455694,0.00006647823,0.021845482,0.00013410811,0.00007010741,0.0003398257,0.00038759012,0.0691006,0.0033362987,0.86162084,0.0017708443,0.041217443],"study_design_scores_gemma":[0.000035340654,0.00026398493,0.026638335,0.00016692858,0.00006432474,0.00075991557,0.000347856,0.38268802,0.00405069,0.57287127,0.0120695885,0.000043834632],"about_ca_topic_score_codex":0.0004602019,"about_ca_topic_score_gemma":0.00041366534,"teacher_disagreement_score":0.004765714,"about_ca_system_score_codex":0.0009714553,"about_ca_system_score_gemma":0.000535391,"threshold_uncertainty_score":0.015942931},"labels":[],"label_agreement":null},{"id":"W3125387354","doi":"10.2139/ssrn.2500346","title":"Assessing the Solvency of Insurance Portfolios Via a Continuous Time Cohort Model","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Solvency; Actuarial science; Business; Econometrics; Economics; Finance; Market liquidity","score_opus":0.00819038999913584,"score_gpt":0.2835163061839902,"score_spread":0.27532591618485436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125387354","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":[0.94681495,0.00029763157,0.04928497,0.0011921601,0.00004458043,0.00007793598,0.0011275493,0.000073548385,0.0010867415],"genre_scores_gemma":[0.99026066,0.00024070615,0.0055485843,0.00006010714,0.000045045268,0.000059193557,0.0006697885,0.00000910201,0.003106621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985812,0.0006363692,0.00006363938,0.0003172845,0.0001170446,0.0002844668],"domain_scores_gemma":[0.96814126,0.025607405,0.002656765,0.0011302256,0.00076496444,0.0016994071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010063145,0.0009189371,0.0015329826,0.0018858371,0.0006463776,0.0025359544,0.0023385645,0.002633488,0.004627859],"category_scores_gemma":[0.02756034,0.0007521362,0.0017133924,0.0012613517,0.0010312633,0.0017517563,0.0017038009,0.00208998,0.00044802917],"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":[0.0007128945,0.0005675019,0.19051987,0.0000713413,0.00071812473,0.000377414,0.00029329344,0.7675896,0.00040397214,0.023903355,0.0012825427,0.013560033],"study_design_scores_gemma":[0.00005062198,0.00019196508,0.013215707,0.000011174027,0.00007726413,0.000047972007,0.00008114,0.97959274,0.00006745527,0.006440523,0.00020205505,0.000021429483],"about_ca_topic_score_codex":0.048744116,"about_ca_topic_score_gemma":0.0241006,"teacher_disagreement_score":0.048744116,"about_ca_system_score_codex":0.0015495443,"about_ca_system_score_gemma":0.002474874,"threshold_uncertainty_score":0.09692079},"labels":[],"label_agreement":null},{"id":"W3125512052","doi":"","title":"Annuities Markets Around the World: Money’s Worth and Risk Intermediation","year":2001,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Economics; Discounting; Present value; Life annuity; Population; Pension; Differential (mechanical device); Interest rate; Cash flow; Actuarial science; Business; Finance","score_opus":0.025003624087533247,"score_gpt":0.33139544285285055,"score_spread":0.3063918187653173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125512052","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":[0.85060805,0.015945446,0.0053557553,0.007160911,0.000084739746,0.00003301137,0.000879197,0.000051180916,0.11988173],"genre_scores_gemma":[0.9948206,0.0022642019,0.000373521,0.0001483466,0.000047532856,0.0000039857678,0.00011917767,0.0000062019335,0.0022163875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99980706,0.00003910755,0.000009209787,0.000035059053,0.00006600554,0.000043643056],"domain_scores_gemma":[0.9981194,0.00046964092,0.0008678036,0.00006955812,0.00030390575,0.00016974549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047070347,0.00017276766,0.0002419111,0.000989657,0.0005740498,0.003581536,0.00026177094,0.00051936577,0.0051140934],"category_scores_gemma":[0.002522806,0.00011171635,0.00021459146,0.0011038287,0.00086633384,0.00481296,0.00072947034,0.0008431206,0.00036121366],"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":[0.00030842065,0.00015368554,0.23874182,0.0004572111,0.00023368465,0.0025576241,0.004780851,0.009309475,0.002462552,0.5496094,0.013015793,0.17836952],"study_design_scores_gemma":[0.000048722795,0.00019715333,0.4188912,0.0010316146,0.0002435708,0.0019073754,0.011461379,0.023258999,0.004881042,0.3867112,0.1511592,0.00020847686],"about_ca_topic_score_codex":0.005600888,"about_ca_topic_score_gemma":0.0049706195,"teacher_disagreement_score":0.005600888,"about_ca_system_score_codex":0.00093789125,"about_ca_system_score_gemma":0.00045815366,"threshold_uncertainty_score":0.01710838},"labels":[],"label_agreement":null},{"id":"W3125712333","doi":"10.1080/10920277.2020.1806884","title":"A DSA Algorithm for Mortality Forecasting","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature selection; Mortality rate; Feature (linguistics); Population; Selection (genetic algorithm); Data mining; Artificial intelligence; Machine learning; Econometrics; Mathematics; Demography","score_opus":0.07551085995473171,"score_gpt":0.328622378801259,"score_spread":0.2531115188465273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125712333","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":[0.0025259622,0.00019571267,0.9958158,0.00014219442,0.000064583895,0.000030201694,0.00008306873,0.00022711947,0.0009153482],"genre_scores_gemma":[0.09785287,0.0004761207,0.8958903,0.0001914912,0.00015261507,0.00029463202,0.0006563145,0.00009982155,0.004385816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912816,0.0003168132,0.00008412651,0.0002350172,0.00018300768,0.000052869516],"domain_scores_gemma":[0.99867463,0.00072220765,0.000074807744,0.00011333441,0.00035863978,0.00005650378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017924861,0.0008678044,0.0010564689,0.0015171069,0.00086973445,0.0011323565,0.0014230636,0.0011165922,0.004542529],"category_scores_gemma":[0.00577464,0.00047524326,0.00080024445,0.0018563352,0.00058093056,0.0011781001,0.0013984492,0.0017290143,0.0018780186],"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":[0.00007861566,0.00004753289,0.0015882731,0.00007026761,0.00006647179,0.00006802568,0.00007467568,0.53846526,0.0015221945,0.027219784,0.0057433597,0.42505553],"study_design_scores_gemma":[0.000007701972,0.000013222044,0.00009991633,0.0000061763976,0.0000058853816,0.000020555424,0.000008543733,0.9877489,0.00026119137,0.009274741,0.0025475859,0.000005629587],"about_ca_topic_score_codex":0.0070946277,"about_ca_topic_score_gemma":0.005919346,"teacher_disagreement_score":0.0070946277,"about_ca_system_score_codex":0.0007393686,"about_ca_system_score_gemma":0.001933488,"threshold_uncertainty_score":0.015196264},"labels":[],"label_agreement":null},{"id":"W3125920698","doi":"","title":"Quadratic Stochastic Intensity and Prospective Mortality Tables","year":2007,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Quadratic equation; Mathematics; Gaussian; Kalman filter; Applied mathematics; Econometrics; Intensity (physics); Statistics","score_opus":0.046966488272527945,"score_gpt":0.37013866485334984,"score_spread":0.32317217658082187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125920698","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":[0.106343485,0.00045201986,0.8792013,0.0015133921,0.00007331778,0.000058879075,0.0009723182,0.00033754206,0.011047785],"genre_scores_gemma":[0.94739044,0.0005970488,0.033830214,0.00018995417,0.00018467347,0.00010493817,0.0010236949,0.00011540535,0.016563704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876404,0.0003820242,0.00006476497,0.00026264656,0.00030373217,0.00022279749],"domain_scores_gemma":[0.99476576,0.0029020093,0.001033064,0.0005061916,0.00055723515,0.00023563948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040752282,0.0005119965,0.0006918224,0.001035584,0.0003708908,0.0019807648,0.0019286522,0.0010800116,0.00920736],"category_scores_gemma":[0.015298322,0.0004367772,0.00091647485,0.0014682085,0.0011990012,0.002720689,0.0013253349,0.0012371499,0.0007186678],"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":[0.0000606519,0.000042819152,0.0073069967,0.00005168617,0.00003768919,0.00018322242,0.00018455328,0.26353395,0.00058216526,0.7064724,0.002577122,0.018966785],"study_design_scores_gemma":[0.000021904774,0.00004659006,0.0038201844,0.000020572883,0.000021736634,0.00010808067,0.0000372122,0.6825873,0.00022423624,0.31063464,0.002442557,0.00003488835],"about_ca_topic_score_codex":0.010000853,"about_ca_topic_score_gemma":0.005156002,"teacher_disagreement_score":0.010000853,"about_ca_system_score_codex":0.0013426597,"about_ca_system_score_gemma":0.0009306774,"threshold_uncertainty_score":0.030801713},"labels":[],"label_agreement":null},{"id":"W3126041739","doi":"10.1017/asb.2014.5","title":"AN ACTUARIAL BALANCE SHEET MODEL FOR DEFINED BENEFIT PAY-AS-YOU-GO PENSION SYSTEMS WITH DISABILITY AND RETIREMENT CONTINGENCIES","year":2014,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"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":"Solvency; Actuarial science; Pension; Social security; Balance sheet; Balance (ability); Pension plan; Asset (computer security); Economics; Disability insurance; Business; Finance; Computer science; Psychology","score_opus":0.01808491763809278,"score_gpt":0.2687883151123655,"score_spread":0.2507033974742727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126041739","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":[0.30087486,0.0007434768,0.6223264,0.004986691,0.00024940813,0.0002458744,0.002330431,0.00051797426,0.06772487],"genre_scores_gemma":[0.93933153,0.00038565247,0.02136403,0.00016345088,0.000068795736,0.00021618501,0.0006749143,0.000057466626,0.03773789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931574,0.0002672145,0.000032319378,0.00012149734,0.000113540285,0.00014974046],"domain_scores_gemma":[0.99828476,0.0009545909,0.0002730857,0.000082210514,0.00021108142,0.00019413176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024837803,0.0008546583,0.0008600393,0.0012858356,0.00077590614,0.0031821372,0.0019239802,0.002204474,0.008192698],"category_scores_gemma":[0.004720582,0.0006314158,0.00096774305,0.00095362664,0.0014840503,0.002016263,0.0012289124,0.0017548884,0.0009235052],"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":[0.000038837323,0.000031126565,0.0011497998,0.000022635795,0.00002362282,0.0001729596,0.00013522955,0.85780597,0.0002166044,0.13610846,0.0011897698,0.0031050872],"study_design_scores_gemma":[0.000010776889,0.000021608308,0.0003858504,0.000014303756,0.000011928474,0.00004274163,0.00006315791,0.9693349,0.00004575814,0.028583087,0.0014704997,0.000015353522],"about_ca_topic_score_codex":0.020976268,"about_ca_topic_score_gemma":0.008797323,"teacher_disagreement_score":0.020976268,"about_ca_system_score_codex":0.0026313823,"about_ca_system_score_gemma":0.0020180773,"threshold_uncertainty_score":0.04170835},"labels":[],"label_agreement":null},{"id":"W3126043346","doi":"10.2139/ssrn.2569675","title":"The Choice of Sample Size for Mortality Forecasting: A Bayesian Learning Approach","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Guelph","funders":"","keywords":"Econometrics; Bayesian probability; Sample (material); Sample size determination; Statistics; Computer science; Artificial intelligence; Geography; Machine learning; Economics; Mathematics","score_opus":0.029466670535258258,"score_gpt":0.3056853625337939,"score_spread":0.27621869199853566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126043346","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":[0.02381421,0.0007647864,0.9710608,0.0023859858,0.00018101775,0.00036294226,0.00015933056,0.0002831768,0.0009877976],"genre_scores_gemma":[0.50958467,0.0010670864,0.4829759,0.0014632522,0.0010390515,0.001435259,0.0004874308,0.00013497181,0.0018124662],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9851745,0.011228759,0.0007459197,0.001436863,0.0011169564,0.0002970036],"domain_scores_gemma":[0.7600624,0.22638698,0.0024657438,0.0056406865,0.004226017,0.001218175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07070063,0.0010466464,0.004392802,0.0025250942,0.0011481707,0.0022276116,0.0037108175,0.004488442,0.004007854],"category_scores_gemma":[0.19096181,0.0011996638,0.0018215298,0.0012531499,0.0020718803,0.0047132885,0.002161247,0.005186863,0.0005246536],"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":[0.0038080756,0.0011422306,0.032215863,0.0010449777,0.001370229,0.0008060813,0.0009879291,0.38860238,0.00504962,0.1423435,0.0127129005,0.40991625],"study_design_scores_gemma":[0.0004949037,0.0003608827,0.0033112937,0.00026943654,0.00026214647,0.00018690861,0.00009560698,0.8847955,0.001359284,0.10706946,0.0017373441,0.000057363934],"about_ca_topic_score_codex":0.0027808654,"about_ca_topic_score_gemma":0.0024317722,"teacher_disagreement_score":0.07070063,"about_ca_system_score_codex":0.001006822,"about_ca_system_score_gemma":0.0015057514,"threshold_uncertainty_score":0.37390512},"labels":[],"label_agreement":null},{"id":"W3126099518","doi":"10.2139/ssrn.1465104","title":"Joint Survival Analysis of Hedge Funds and Funds of Funds Using Copulas","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Hedge fund; Global assets under management; Passive management; Business; Alternative beta; Fund of funds; Commodity pool; Copula (linguistics); Actuarial science; Institutional investor; Econometrics; Financial system; Economics; Finance; Market liquidity","score_opus":0.03340747187450592,"score_gpt":0.3235840253076625,"score_spread":0.29017655343315657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126099518","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":[0.7632828,0.002142941,0.22780536,0.0014005745,0.000105202394,0.000101868645,0.0008814278,0.0002761495,0.004003751],"genre_scores_gemma":[0.9886796,0.00062747474,0.0048033535,0.000045027893,0.00011062593,0.0000625117,0.00047959402,0.000054923228,0.005136889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818796,0.0010670909,0.00008000542,0.00018187496,0.00015728128,0.00032578496],"domain_scores_gemma":[0.9653725,0.028307071,0.0028035939,0.0011023742,0.0012291182,0.001185347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011184911,0.0010088341,0.0021834737,0.0027450598,0.00051184057,0.0026567373,0.001192417,0.0012175263,0.0057801562],"category_scores_gemma":[0.043231916,0.000698071,0.0019533332,0.0018728389,0.0016032257,0.0025240064,0.0015990242,0.001704544,0.00038956376],"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":[0.000804264,0.00021153684,0.07955645,0.00023019015,0.0012913385,0.0010550021,0.0009762177,0.5768973,0.0010692104,0.280063,0.0073567405,0.05048866],"study_design_scores_gemma":[0.00004296505,0.00011072598,0.012128019,0.000046532565,0.0002472171,0.0001653219,0.00035747472,0.9235698,0.0003361918,0.062101852,0.00085134315,0.00004256541],"about_ca_topic_score_codex":0.007898487,"about_ca_topic_score_gemma":0.0049660183,"teacher_disagreement_score":0.011184911,"about_ca_system_score_codex":0.0012075516,"about_ca_system_score_gemma":0.001618519,"threshold_uncertainty_score":0.059152186},"labels":[],"label_agreement":null},{"id":"W3126100301","doi":"","title":"Greener Pastures: Resetting the Age of Eligibility for Social Security Based on Actuarial Science","year":2017,"lang":"en","type":"article","venue":"C.D. Howe Institute Commentary","topic":"Insurance, Mortality, Demography, Risk Management","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":"Social security; Life expectancy; Pension; Old Age Security; Baby boom; Dependency ratio; Context (archaeology); Retirement age; Notice; Legislation; Demographic economics; Social Security Act; Economics; Demography; Fertility; Political science; Population; Geography; Birth rate; Sociology; Finance; Law","score_opus":0.06148105730243573,"score_gpt":0.3993573106878309,"score_spread":0.3378762533853952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126100301","genre_codex":"methods","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":[0.05892779,0.010271444,0.535358,0.080395356,0.006762281,0.00056765537,0.0042391615,0.0013711824,0.30210713],"genre_scores_gemma":[0.73304677,0.009288273,0.15178145,0.008479944,0.0025642992,0.00033962907,0.0014341554,0.0007863838,0.092279054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99648076,0.0013952389,0.00009992364,0.00052719953,0.0011360002,0.0003609566],"domain_scores_gemma":[0.9903032,0.0052162544,0.00086660916,0.0014940948,0.0015973202,0.00052252284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010121068,0.0006106529,0.0005483679,0.0026364645,0.002225099,0.004265479,0.0015817055,0.0014776904,0.018667983],"category_scores_gemma":[0.036830567,0.00037969643,0.0008463847,0.0017589043,0.0037865872,0.004911142,0.0026589443,0.0047764573,0.0024652435],"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":[0.00009502167,0.00008391433,0.0113464,0.0001461333,0.00006259739,0.00011723006,0.0014463307,0.015892586,0.00021439334,0.7958979,0.0654965,0.109201],"study_design_scores_gemma":[0.00003689124,0.00012719717,0.0115676755,0.00065046706,0.00009675322,0.00016863072,0.0016546488,0.057004217,0.00089492986,0.60135067,0.326284,0.0001639824],"about_ca_topic_score_codex":0.09014349,"about_ca_topic_score_gemma":0.096289255,"teacher_disagreement_score":0.09014349,"about_ca_system_score_codex":0.0042855763,"about_ca_system_score_gemma":0.0068807076,"threshold_uncertainty_score":0.1792376},"labels":[],"label_agreement":null},{"id":"W3126144087","doi":"10.1111/j.1539-6975.2012.01483.x","title":"Managing Systematic Mortality Risk With Group Self‐Pooling and Annuitization Schemes","year":2012,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"Longevity risk; Pooling; Actuarial science; Payment; Economics; Longevity; Econometrics; Demography; Medicine; Computer science; Gerontology; Finance","score_opus":0.010446315351003377,"score_gpt":0.2683056906408364,"score_spread":0.25785937528983305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126144087","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":[0.3497172,0.0006691066,0.6381392,0.002418495,0.00014573545,0.0010847169,0.0003603645,0.00048802115,0.0069771064],"genre_scores_gemma":[0.96419585,0.00012905538,0.03388806,0.00011606486,0.000052107895,0.0003060373,0.00006390216,0.000014624511,0.0012343411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98787224,0.008417799,0.0006213061,0.0010071442,0.0011387146,0.0009427078],"domain_scores_gemma":[0.9695719,0.017500892,0.0059014303,0.004528868,0.00141103,0.0010859595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024080915,0.001022395,0.0016008051,0.0010706012,0.0009207333,0.0019981929,0.0024725713,0.0019873779,0.0030299516],"category_scores_gemma":[0.045242485,0.00054743415,0.0014329671,0.0011174632,0.0014114178,0.0030259758,0.0047612223,0.0014379834,0.00029185103],"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":[0.0008928866,0.0008398087,0.015145655,0.0002228645,0.0007663522,0.00029177536,0.0011523069,0.7430792,0.001949576,0.10268041,0.0028791276,0.13010007],"study_design_scores_gemma":[0.00039328847,0.0011177273,0.0046007256,0.000076671626,0.00032556627,0.00012844159,0.0004069971,0.8695653,0.001697283,0.11757542,0.0040041883,0.0001083108],"about_ca_topic_score_codex":0.002667836,"about_ca_topic_score_gemma":0.0018411428,"teacher_disagreement_score":0.024080915,"about_ca_system_score_codex":0.0022944948,"about_ca_system_score_gemma":0.0029590689,"threshold_uncertainty_score":0.12735361},"labels":[],"label_agreement":null},{"id":"W3128789444","doi":"10.3390/risks9020035","title":"Mortality Forecasting with an Age-Coherent Sparse VAR Model","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Vector autoregression; Econometrics; Context (archaeology); Term (time); Dimension (graph theory); Population; Computer science; Mortality rate; Statistics; Mathematics; Demography; Geography","score_opus":0.19859721979741646,"score_gpt":0.3745388889291356,"score_spread":0.17594166913171916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128789444","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":[0.048433054,0.00024788748,0.94919074,0.00040495704,0.00006176147,0.000018555054,0.0002914487,0.00018275235,0.0011688089],"genre_scores_gemma":[0.9054386,0.00088812853,0.086805,0.00022854841,0.000215539,0.00009734561,0.0010589763,0.00004878357,0.00521898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995309,0.00018092433,0.000023505576,0.00012574474,0.00008781865,0.000051184998],"domain_scores_gemma":[0.99917644,0.00041555607,0.00016377092,0.000054541153,0.00015956485,0.000030037585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012948952,0.00045719562,0.00072774687,0.000530622,0.00020807366,0.00071523275,0.0010619871,0.0007987961,0.0009481568],"category_scores_gemma":[0.0031065664,0.00042557594,0.0007106771,0.0007789551,0.0003341234,0.0010949507,0.0006453955,0.0011144398,0.0002673775],"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":[0.000029706545,0.000021460744,0.002530209,0.000024504687,0.000042005522,0.000054968277,0.00004301691,0.9538552,0.00067222194,0.020649964,0.0008418387,0.021234892],"study_design_scores_gemma":[0.0000020543096,0.0000060956586,0.00025406052,0.0000016041545,0.0000047035546,0.000006061607,0.000002659623,0.99701655,0.000046444165,0.0025174613,0.00013832713,0.000004121336],"about_ca_topic_score_codex":0.0066099553,"about_ca_topic_score_gemma":0.005683198,"teacher_disagreement_score":0.0066099553,"about_ca_system_score_codex":0.00036290995,"about_ca_system_score_gemma":0.0005680895,"threshold_uncertainty_score":0.013142943},"labels":[],"label_agreement":null},{"id":"W3132689244","doi":"10.1007/s10985-021-09518-4","title":"Information measures and design issues in the study of mortality deceleration: findings for the gamma-Gompertz model","year":2021,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":7,"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":"Max-Planck-Institut für demografische Forschung","keywords":"Fisher information; Gompertz function; Statistics; Range (aeronautics); Sample size determination; Econometrics; Parametric statistics; Variance (accounting); Limit (mathematics); Mathematics; Statistical hypothesis testing; Demography; Computer science; Economics; Engineering; Sociology","score_opus":0.11108078895825284,"score_gpt":0.37525940163514343,"score_spread":0.2641786126768906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132689244","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":[0.06645037,0.010307459,0.89043206,0.017300222,0.00040013017,0.0008882899,0.0005562408,0.00035051035,0.013314713],"genre_scores_gemma":[0.70612603,0.007503021,0.27369452,0.004054708,0.0012452279,0.002711905,0.00038795426,0.00024136183,0.0040352796],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8418784,0.1296325,0.0040602866,0.007904553,0.014033252,0.0024909133],"domain_scores_gemma":[0.10574826,0.8574316,0.015236743,0.014079439,0.0065200655,0.0009838955],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.24541168,0.0022140036,0.005841517,0.0063856305,0.0024714873,0.005485264,0.005276837,0.005497757,0.0072518117],"category_scores_gemma":[0.62957054,0.0016434072,0.007940717,0.007173532,0.01273302,0.011837336,0.0048972825,0.007988501,0.0005053819],"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":[0.00062257494,0.0002122452,0.022918893,0.0012423227,0.0014348648,0.00050657796,0.0025237682,0.044441264,0.0003724419,0.8321319,0.0031180966,0.09047497],"study_design_scores_gemma":[0.0003242571,0.0006373396,0.011780911,0.0005645017,0.00075391744,0.00024075608,0.00064180687,0.09712629,0.000485821,0.88273084,0.0045391093,0.00017439555],"about_ca_topic_score_codex":0.012318851,"about_ca_topic_score_gemma":0.0045460146,"teacher_disagreement_score":0.24541168,"about_ca_system_score_codex":0.0048950077,"about_ca_system_score_gemma":0.006502877,"threshold_uncertainty_score":0.93054175},"labels":[],"label_agreement":null},{"id":"W3135671680","doi":"10.1007/s13385-021-00269-y","title":"Correlated age-specific mortality model: an application to annuity portfolio management","year":2021,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan","keywords":"Annuity; Mathematical finance; Actuarial science; Portfolio; Business; Economics; Life annuity; Financial economics; Finance","score_opus":0.0365821483177496,"score_gpt":0.3247013997200056,"score_spread":0.288119251402256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135671680","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":[0.27009505,0.00081826665,0.7182223,0.0013378859,0.00020136028,0.00016943175,0.0016564401,0.00065843726,0.006840878],"genre_scores_gemma":[0.9187907,0.0007811581,0.06530502,0.00026459867,0.00017296513,0.0002577613,0.0012140145,0.00013388622,0.013079904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933785,0.00032297216,0.000033654593,0.00013353639,0.000088802095,0.00008315957],"domain_scores_gemma":[0.996102,0.0024642413,0.00036918966,0.00021297015,0.00060649245,0.00024513324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038254634,0.00079656363,0.0019584284,0.00079131656,0.00066714117,0.0015471072,0.0027010643,0.0024166876,0.0050656744],"category_scores_gemma":[0.007993585,0.0007870278,0.0012427612,0.0015903815,0.0006910787,0.001039008,0.0011702295,0.0022691037,0.0006758071],"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":[0.00004488131,0.000051184696,0.0025750112,0.000014866504,0.00005950079,0.00007419498,0.00002613319,0.9830815,0.00008934043,0.008939208,0.00058521447,0.0044589923],"study_design_scores_gemma":[0.000010310723,0.000008311394,0.00030520707,0.0000024917304,0.000012410897,0.000010795698,0.000003913804,0.99795216,0.000024540228,0.0015501814,0.000115226394,0.0000045041293],"about_ca_topic_score_codex":0.033957742,"about_ca_topic_score_gemma":0.02252499,"teacher_disagreement_score":0.033957742,"about_ca_system_score_codex":0.001326355,"about_ca_system_score_gemma":0.0030084976,"threshold_uncertainty_score":0.0675202},"labels":[],"label_agreement":null},{"id":"W3138284370","doi":"10.2139/ssrn.3738777","title":"Dynamic Importance Allocated Nested Simulation for Variable Annuity Risk Measurement","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Annuity; Econometrics; Actuarial science; Variable (mathematics); Computer science; Economics; Statistics; Mathematics; Life annuity; Finance; Pension","score_opus":0.025077075077585182,"score_gpt":0.2947982433876894,"score_spread":0.26972116831010423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138284370","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":[0.04888521,0.000060238814,0.94929075,0.00016248603,0.000044529115,0.00011552275,0.00006702076,0.00021464117,0.0011595549],"genre_scores_gemma":[0.72858614,0.00005291061,0.26858947,0.00011775014,0.00003913125,0.00046580657,0.00021602344,0.00011999974,0.0018126153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604803,0.002623601,0.00011583904,0.00046546475,0.0004161213,0.000330842],"domain_scores_gemma":[0.957545,0.03619138,0.0011644037,0.0021916684,0.0017501889,0.0011573489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008942294,0.0006409404,0.0018111074,0.00091732043,0.0008663772,0.0013083091,0.0030064688,0.0022477745,0.0045975023],"category_scores_gemma":[0.044250146,0.0012790413,0.0010432245,0.00080438936,0.0016952867,0.0022072177,0.003588104,0.0032698307,0.00044460746],"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":[0.00030501361,0.00016324801,0.0018752578,0.00003431184,0.000052323307,0.00007030363,0.00013573833,0.9463728,0.00033972162,0.039729103,0.00030851134,0.010613644],"study_design_scores_gemma":[0.000008266277,0.000009410408,0.000040581086,0.0000021960157,0.0000020384784,0.0000025325717,0.0000032192709,0.99657935,0.00004589174,0.0032651543,0.00003891592,0.0000024564652],"about_ca_topic_score_codex":0.008823853,"about_ca_topic_score_gemma":0.0058532087,"teacher_disagreement_score":0.008942294,"about_ca_system_score_codex":0.0014443266,"about_ca_system_score_gemma":0.0019399168,"threshold_uncertainty_score":0.047291934},"labels":[],"label_agreement":null},{"id":"W3140020526","doi":"10.1109/wsc.2008.4736099","title":"Fast simulation of equity-linked life insurance contracts with a surrender option","year":2008,"lang":"en","type":"article","venue":"2008 Winter Simulation Conference","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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 Waterloo","funders":"National Research Council Canada","keywords":"Surrender; Life insurance; Monte Carlo methods for option pricing; Monte Carlo method; Equity (law); Estimator; Actuarial science; Control variates; Computer science; Put option; Order (exchange); Importance sampling; Econometrics; Economics; Valuation of options; Markov chain Monte Carlo; Finance; Mathematics; Hybrid Monte Carlo","score_opus":0.09144684093465552,"score_gpt":0.3538910352994352,"score_spread":0.2624441943647797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140020526","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":[0.7261033,0.00039522577,0.2662769,0.0009076515,0.000088964094,0.0000776548,0.00024771274,0.00037356617,0.005528969],"genre_scores_gemma":[0.97348285,0.00010516947,0.024543552,0.00007144015,0.000013752921,0.000068621724,0.00021337203,0.000035233872,0.0014659028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993666,0.00033220334,0.000025756082,0.0000643962,0.00011261592,0.00009847231],"domain_scores_gemma":[0.992156,0.0060808393,0.0005327268,0.00027607946,0.0005396192,0.00041476422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026114178,0.00048115966,0.0009080712,0.00063679786,0.0005506612,0.001131122,0.0011208513,0.001802178,0.0030235657],"category_scores_gemma":[0.011583067,0.0005300642,0.000755271,0.00066510466,0.0014056526,0.0014231353,0.0012805741,0.0014695227,0.00018810746],"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":[0.00004558919,0.000021498672,0.001139556,0.000006232745,0.000008989527,0.000038637765,0.00002604758,0.99090475,0.00014337487,0.0068075564,0.00007427487,0.000783583],"study_design_scores_gemma":[0.000007862663,0.00000468778,0.00007120317,0.0000012868388,0.0000012067378,0.0000020922787,0.0000047182157,0.99856514,0.000049057824,0.00125616,0.00003474997,0.0000018419918],"about_ca_topic_score_codex":0.016538505,"about_ca_topic_score_gemma":0.007870613,"teacher_disagreement_score":0.016538505,"about_ca_system_score_codex":0.0011195238,"about_ca_system_score_gemma":0.0012072197,"threshold_uncertainty_score":0.03288448},"labels":[],"label_agreement":null},{"id":"W3142614828","doi":"10.2307/3079277","title":"Will Small Population Sizes Warn Us of Impending Extinctions?","year":2002,"lang":"en","type":"article","venue":"The American Naturalist","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Population; Biology; Ecology; Geography; Evolutionary biology; Demography","score_opus":0.02466422898181151,"score_gpt":0.29116722538966633,"score_spread":0.2665029964078548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142614828","genre_codex":"commentary","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":[0.2985826,0.011764652,0.043786656,0.5220424,0.0036291752,0.000081984705,0.0005497777,0.00034510664,0.11921777],"genre_scores_gemma":[0.9604233,0.0037613472,0.0019250924,0.019287813,0.0018087961,0.000053771884,0.00007178427,0.00006139912,0.012606615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894744,0.00042849674,0.00003452177,0.00024737246,0.00015658616,0.000185636],"domain_scores_gemma":[0.98679674,0.007070935,0.0023338592,0.00094132277,0.0012874139,0.0015697192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006442328,0.0003150378,0.00069896603,0.000672258,0.0011332456,0.002507175,0.0009469372,0.004024741,0.009158266],"category_scores_gemma":[0.038070664,0.00023262095,0.00039099765,0.00045598563,0.005736667,0.009267917,0.0014265011,0.0027468463,0.0017472726],"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":[0.00037544654,0.000115236675,0.10087512,0.00025572104,0.0002050414,0.0012191185,0.0019358742,0.009140421,0.001360952,0.58888084,0.07014856,0.22548765],"study_design_scores_gemma":[0.000043665437,0.00013438363,0.022819484,0.00012187252,0.000047968675,0.0009351717,0.0018360412,0.0070056026,0.0006250784,0.9047553,0.061611548,0.000063919906],"about_ca_topic_score_codex":0.002286679,"about_ca_topic_score_gemma":0.0024281046,"teacher_disagreement_score":0.009158266,"about_ca_system_score_codex":0.0014263586,"about_ca_system_score_gemma":0.0006444204,"threshold_uncertainty_score":0.03407067},"labels":[],"label_agreement":null},{"id":"W3142802259","doi":"10.3386/w25009","title":"The Life Expectancy of Older Couples And Surviving Spouses","year":2018,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Life expectancy; Spouse; Wife; Psychology; Expectancy theory; Demography; Census; Gerontology; Social psychology; Medicine; Sociology; Population; Political science","score_opus":0.2336533250199701,"score_gpt":0.5137219021328995,"score_spread":0.2800685771129294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142802259","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":[0.9910068,0.0004945787,0.00037411685,0.00008591567,0.0000068599174,0.000005962421,0.004789888,0.000009078454,0.003226784],"genre_scores_gemma":[0.99653864,0.0003006076,0.00014879735,0.00001913069,0.0000090793465,0.000008465681,0.002410486,0.0000011820932,0.0005637279],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985075,0.00003618505,0.000022644934,0.000032477205,0.000031427895,0.000026502448],"domain_scores_gemma":[0.9990532,0.00022054177,0.00043134997,0.00005669601,0.00011954772,0.0001186605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005665448,0.00008639001,0.00015887994,0.000862692,0.00017143843,0.00037518798,0.00011607652,0.00020089131,0.0024611987],"category_scores_gemma":[0.0040042945,0.0000641834,0.00030649398,0.0010599061,0.00012165216,0.00055649556,0.00034664915,0.00017006478,0.0003717985],"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":[0.000053451367,0.000017179367,0.9855583,0.000018119417,0.00007659841,0.000037303183,0.00031096084,0.0007297833,0.00009452356,0.0005103924,0.00053034595,0.012063058],"study_design_scores_gemma":[0.00000171705,0.00003983094,0.99766576,0.000009964754,0.000018400022,0.000089734305,0.00023728532,0.00046782708,0.000046629255,0.00022490503,0.0011938359,0.0000041525614],"about_ca_topic_score_codex":0.0068934984,"about_ca_topic_score_gemma":0.0067460793,"teacher_disagreement_score":0.0068934984,"about_ca_system_score_codex":0.00020777766,"about_ca_system_score_gemma":0.00010950253,"threshold_uncertainty_score":0.013706744},"labels":[],"label_agreement":null},{"id":"W3145965401","doi":"10.1080/03461238.2021.1895299","title":"A law of uniform seniority for dependent lives","year":2021,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Annuity; Mathematics; Seniority; Econometrics; Closure (psychology); Extension (predicate logic); Bilinear interpolation; Marginal distribution; Life annuity; Mathematical economics; Actuarial science; Economics; Law; Statistics; Computer science; Random variable; Political science","score_opus":0.023648220116874657,"score_gpt":0.3191372361504004,"score_spread":0.29548901603352573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145965401","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":[0.06955911,0.0010630765,0.84951097,0.0056803147,0.000544017,0.000044315733,0.000347843,0.00021624069,0.07303416],"genre_scores_gemma":[0.9111016,0.0011263569,0.055292305,0.0014765552,0.0010839741,0.00013126957,0.00019746531,0.00015321988,0.029437242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797827,0.0005584262,0.00009512752,0.000550173,0.0005554966,0.00026248372],"domain_scores_gemma":[0.9932963,0.0030539283,0.0008203107,0.0014359474,0.000782675,0.0006108034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004170541,0.00046267372,0.00072395185,0.00085769023,0.0010488934,0.0021391485,0.0013000384,0.0016480797,0.0067981435],"category_scores_gemma":[0.0161298,0.00030959025,0.0011643397,0.0006320739,0.0059341765,0.0052741845,0.002820005,0.0038838454,0.001022772],"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":[0.0000037387895,0.0000058345872,0.00016206175,0.000007099741,0.0000019365016,0.000034099987,0.000069296526,0.001070151,0.00013991557,0.99570566,0.000573986,0.0022263166],"study_design_scores_gemma":[0.0000066856564,0.000029655948,0.0004520714,0.000017733219,0.000004022532,0.00019885367,0.000038489517,0.020029088,0.00015790832,0.9728674,0.0061853183,0.000012628985],"about_ca_topic_score_codex":0.0012419606,"about_ca_topic_score_gemma":0.00076926145,"teacher_disagreement_score":0.0067981435,"about_ca_system_score_codex":0.0012723301,"about_ca_system_score_gemma":0.0010664982,"threshold_uncertainty_score":0.022742093},"labels":[],"label_agreement":null},{"id":"W3148161953","doi":"10.7202/1076125ar","title":"Coherent Mortality Forecasting for the Algerian Population","year":2021,"lang":"en","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Divergence (linguistics); Econometrics; Coherence (philosophical gambling strategy); Population; Goodness of fit; Statistics; Mortality rate; Convergence (economics); Demography; Mathematics; Economics; Sociology","score_opus":0.12341818895082247,"score_gpt":0.37033759512414777,"score_spread":0.2469194061733253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148161953","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":[0.89873767,0.0006359449,0.09756762,0.00097090774,0.00003966306,0.000026375426,0.0005093834,0.00015093769,0.0013615222],"genre_scores_gemma":[0.9903454,0.00019853021,0.008795005,0.000031251224,0.000019790326,0.000011064838,0.00035704317,0.000006428913,0.00023543937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999676,0.00016298161,0.000018685178,0.00006834733,0.00004395418,0.000030028077],"domain_scores_gemma":[0.99881124,0.00057189504,0.0002598932,0.00011388106,0.0001903847,0.00005265488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017418732,0.00044046875,0.00036968687,0.0006539701,0.0002618316,0.0007048183,0.0004501748,0.00071254617,0.00052703667],"category_scores_gemma":[0.004476101,0.00016543384,0.00041658187,0.0005334576,0.00022774322,0.000832135,0.0005840345,0.00046734538,0.00013830524],"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":[0.00006710436,0.00003347479,0.030473351,0.000031285155,0.00008190729,0.000061731596,0.00012731184,0.9297508,0.00063844235,0.003014312,0.00065477326,0.03506545],"study_design_scores_gemma":[0.0000042595434,0.000024562032,0.009625243,0.000008130244,0.000013342828,0.000012378701,0.00004151721,0.98751277,0.00015512362,0.002407339,0.0001853678,0.000009970717],"about_ca_topic_score_codex":0.021207945,"about_ca_topic_score_gemma":0.011727799,"teacher_disagreement_score":0.021207945,"about_ca_system_score_codex":0.00075523864,"about_ca_system_score_gemma":0.0006843745,"threshold_uncertainty_score":0.042169034},"labels":[],"label_agreement":null},{"id":"W3148904637","doi":"10.2139/ssrn.3740215","title":"LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"","keywords":"Statistics; Regression; Regression analysis; Econometrics; R package; Computer science; Mathematics","score_opus":0.06570594852370867,"score_gpt":0.34425787303764654,"score_spread":0.27855192451393784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148904637","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035651366,0.0002811947,0.928535,0.00023944111,0.00015504837,0.00010229675,0.012074667,0.053118877,0.0019284796],"genre_scores_gemma":[0.085256964,0.00056201534,0.8457233,0.00042962268,0.0001863472,0.0011071796,0.020128777,0.033876732,0.012729173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990274,0.00050685793,0.00007566966,0.00015338216,0.00016624691,0.0000704967],"domain_scores_gemma":[0.99447787,0.004134154,0.00033023828,0.0005677617,0.000387089,0.000102879705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034642103,0.0013689963,0.0012862956,0.0010878424,0.00031038435,0.0014327324,0.0026284168,0.0013832762,0.053816054],"category_scores_gemma":[0.019263096,0.001170448,0.0021969664,0.00074009824,0.00031368702,0.0013971878,0.0014398681,0.0025182336,0.02713759],"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":[0.00060285913,0.00020044745,0.0071503716,0.0014393291,0.0018134505,0.0005683097,0.00030738075,0.39870188,0.002333365,0.050080363,0.2812052,0.25559708],"study_design_scores_gemma":[0.00016977375,0.00007341916,0.0014240152,0.00015450058,0.00015901837,0.00032145012,0.000032573687,0.88884246,0.0018610555,0.0342775,0.07258334,0.00010088129],"about_ca_topic_score_codex":0.004672681,"about_ca_topic_score_gemma":0.0063422658,"teacher_disagreement_score":0.053816054,"about_ca_system_score_codex":0.00044827373,"about_ca_system_score_gemma":0.0011537179,"threshold_uncertainty_score":0.18003261},"labels":[],"label_agreement":null},{"id":"W3149944603","doi":"10.1080/10920277.2003.10596100","title":"“Geometric Brownian Motion Models for Assets and Liabilities: From Pension Funding to Optimal Dividends”, Hans U. Gerber and Elias S. W. Shiu, January 2003","year":2003,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Pension; Dividend; Geometric Brownian motion; Brownian motion; Actuarial science; Mathematical economics; Economics; Law and economics; Mathematics; Finance; Statistics; Diffusion process; Economy","score_opus":0.03240901258747878,"score_gpt":0.297669686337421,"score_spread":0.2652606737499422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149944603","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":[0.03511483,0.038300168,0.84088737,0.05780218,0.0030486388,0.00008928664,0.0007198366,0.00038059626,0.023657111],"genre_scores_gemma":[0.6744626,0.06974903,0.16155128,0.0044939816,0.007291194,0.00045834525,0.0015181378,0.0006944178,0.07978093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913615,0.00047801354,0.000042304386,0.00011974251,0.00013577795,0.00008809544],"domain_scores_gemma":[0.99654764,0.0023412367,0.0003224967,0.0001631011,0.00041319415,0.00021237286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004971884,0.0021758883,0.002151717,0.0011634867,0.0010248106,0.0043304143,0.0031884415,0.004232188,0.008983587],"category_scores_gemma":[0.022226673,0.0020887274,0.0020451827,0.0017569788,0.0035045191,0.008422132,0.0026523469,0.0053968085,0.001565932],"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":[0.000061987805,0.000031813117,0.0008376115,0.00010356283,0.000068375295,0.00010833601,0.0001893707,0.083814144,0.00016986075,0.8652406,0.030625928,0.018748533],"study_design_scores_gemma":[0.000029732622,0.00002066572,0.00037913956,0.00005376827,0.000036988593,0.000040853203,0.000049271697,0.10597153,0.00010314655,0.8852067,0.008078282,0.000029921499],"about_ca_topic_score_codex":0.008253054,"about_ca_topic_score_gemma":0.007676939,"teacher_disagreement_score":0.008983587,"about_ca_system_score_codex":0.002669782,"about_ca_system_score_gemma":0.0022113402,"threshold_uncertainty_score":0.03005308},"labels":[],"label_agreement":null},{"id":"W3154418169","doi":"10.1016/j.insmatheco.2021.03.028","title":"Recent declines in life expectancy: Implication on longevity risk hedging","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"Society of Actuaries","keywords":"Longevity; Life expectancy; Longevity risk; Expectancy theory; Economics; Actuarial science; Gerontology; Demography; Medicine; Sociology","score_opus":0.029072966396813102,"score_gpt":0.28962547738719596,"score_spread":0.26055251099038285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154418169","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":[0.95406675,0.008929677,0.005554655,0.016595906,0.00029499832,0.0000151958675,0.0019955914,0.000037246446,0.012510043],"genre_scores_gemma":[0.9942321,0.002711031,0.00046617404,0.0004088755,0.00036323853,0.0000044739177,0.00034814872,0.0000050466374,0.0014608188],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998518,0.00003361095,0.000017665134,0.00004256601,0.000028307226,0.000026095577],"domain_scores_gemma":[0.9963554,0.0011505004,0.0012062622,0.00024448434,0.0007050977,0.0003383086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013853038,0.0001355995,0.00020716028,0.001014011,0.00021999217,0.00090295065,0.000341695,0.00082420796,0.0060554235],"category_scores_gemma":[0.007758696,0.00007751319,0.0002654724,0.0013484836,0.0006544811,0.0011637172,0.00044510342,0.00083912,0.00037269594],"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":[0.00070582243,0.00026496133,0.6942144,0.00032658502,0.00024050116,0.00071155233,0.0024308239,0.0076644765,0.0057051107,0.075064786,0.0057658055,0.20690511],"study_design_scores_gemma":[0.000008914226,0.00016421099,0.9597811,0.000054931188,0.000041008294,0.0005362364,0.00072459085,0.0030949668,0.00039287005,0.02308866,0.012095637,0.000016877926],"about_ca_topic_score_codex":0.002744885,"about_ca_topic_score_gemma":0.002689044,"teacher_disagreement_score":0.0060554235,"about_ca_system_score_codex":0.0006670204,"about_ca_system_score_gemma":0.00020148273,"threshold_uncertainty_score":0.020257354},"labels":[],"label_agreement":null},{"id":"W3155716110","doi":"10.1146/annurev-statistics-040120-025426","title":"Is There a Cap on Longevity? A Statistical Review","year":2021,"lang":"en","type":"review","venue":"Annual Review of Statistics and Its Application","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"HEC Montréal","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Longevity; Limit (mathematics); Confidence interval; Econometrics; Statistics; Computer science; Economics; Mathematics; Gerontology; Medicine","score_opus":0.03801024001685632,"score_gpt":0.4047839851145799,"score_spread":0.3667737450977236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155716110","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":[0.000033338278,0.99748945,0.00026593727,0.0015185147,0.00035206194,0.0000041146805,0.000034298668,0.000006735496,0.00029543584],"genre_scores_gemma":[0.00047643622,0.99811304,0.00017924444,0.0005598716,0.0005380134,0.000008400869,0.000036558908,0.000004281707,0.0000841302],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710506,0.0011585392,0.0004669846,0.00031992458,0.0008535122,0.00009593518],"domain_scores_gemma":[0.97672844,0.018308688,0.0010795993,0.00046555212,0.0030036597,0.00041408246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077851205,0.0011115528,0.0031486838,0.0064713145,0.0004897619,0.0021054968,0.0020245325,0.0018599913,0.0042229686],"category_scores_gemma":[0.031508103,0.00073395326,0.001652236,0.008570169,0.0021613047,0.0035005447,0.0014426339,0.003575991,0.002013927],"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":[0.00009899239,0.000039060673,0.0005067562,0.032044444,0.00048774018,0.000093881106,0.00010401755,0.00078515895,0.00015043074,0.016362667,0.07285781,0.87646896],"study_design_scores_gemma":[0.000022566397,0.00012801841,0.0022723628,0.0339538,0.00052845,0.00057446084,0.00011761305,0.00023049113,0.00012378008,0.019289177,0.942691,0.00006817393],"about_ca_topic_score_codex":0.0045320336,"about_ca_topic_score_gemma":0.0033928666,"teacher_disagreement_score":0.0077851205,"about_ca_system_score_codex":0.002045829,"about_ca_system_score_gemma":0.006355007,"threshold_uncertainty_score":0.041172147},"labels":[],"label_agreement":null},{"id":"W3156919873","doi":"10.1016/j.insmatheco.2021.03.018","title":"Gompertz law revisited: Forecasting mortality with a multi-factor exponential model","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gompertz function; Life expectancy; Exponential function; Econometrics; Estimation; Exponential smoothing; Focus (optics); Computer science; Statistics; Population; Mathematics; Demography; Economics","score_opus":0.07905345941122027,"score_gpt":0.29047002431755237,"score_spread":0.2114165649063321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156919873","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":[0.58721054,0.0033596097,0.3916923,0.012380809,0.00042274332,0.00004404047,0.00042718305,0.00022698873,0.0042357403],"genre_scores_gemma":[0.9879524,0.001100872,0.007815901,0.0002375176,0.0002624458,0.00001720383,0.00012314897,0.00002109277,0.002469439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992017,0.0003295955,0.00004628735,0.00020602204,0.00011530434,0.00010106134],"domain_scores_gemma":[0.9901884,0.007864299,0.0007280848,0.00039044078,0.0005558811,0.00027289437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005655994,0.00072038855,0.0018359867,0.0010307805,0.00051831955,0.0025835924,0.0030079014,0.0044382317,0.0014667336],"category_scores_gemma":[0.033349495,0.0006670032,0.000962765,0.0012256468,0.001613082,0.0047732173,0.0010147372,0.003055066,0.0002508515],"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":[0.00009932613,0.00006219875,0.012735351,0.000064815824,0.00008950829,0.00027765092,0.00016262809,0.90095216,0.00035722795,0.06921541,0.0014955557,0.014488146],"study_design_scores_gemma":[0.0000068089053,0.000007963378,0.00081965583,0.000008571875,0.000010347546,0.000021204933,0.0000170293,0.98317033,0.000038295217,0.015778042,0.000112242364,0.000009467104],"about_ca_topic_score_codex":0.023258748,"about_ca_topic_score_gemma":0.010548747,"teacher_disagreement_score":0.023258748,"about_ca_system_score_codex":0.001424218,"about_ca_system_score_gemma":0.0009790359,"threshold_uncertainty_score":0.046246707},"labels":[],"label_agreement":null},{"id":"W3157197944","doi":"10.24908/iqurcp.7542","title":"5.  Attitudes on Investment in Descendants: Products of Evolution?","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Reproductive success; Welfare; Parental investment; Psychology; Demography; Economics; Biology; Population; Sociology; Pregnancy; Offspring","score_opus":0.165027179681323,"score_gpt":0.4396362484830142,"score_spread":0.2746090688016912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157197944","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":[0.98301446,0.000395873,0.0008041352,0.0017770161,0.000021417209,0.0000060675957,0.000035485675,0.000004154316,0.01394127],"genre_scores_gemma":[0.9981299,0.0001431796,0.00025172866,0.00022903898,0.0000144978685,0.0000031489767,0.000018350458,0.0000028432712,0.0012072441],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991068,0.0005075864,0.00002427042,0.000113701164,0.00013773463,0.000109862434],"domain_scores_gemma":[0.9947797,0.002228309,0.001763177,0.000298547,0.00034510004,0.0005851689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127701,0.00022118556,0.000161071,0.00048796504,0.00062542746,0.002024884,0.00028278743,0.0008474091,0.0065055913],"category_scores_gemma":[0.0067514973,0.00016339941,0.00021518236,0.00047859256,0.0019311743,0.0014000776,0.00067940605,0.0012121514,0.00030722216],"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":[0.00012170482,0.00017743142,0.93686354,0.00006269841,0.00008821806,0.00047619495,0.019037507,0.00024089926,0.0015524632,0.013669223,0.00039944577,0.027310707],"study_design_scores_gemma":[0.000004975046,0.00015158571,0.98275065,0.00005035911,0.000037642774,0.0004319721,0.008716747,0.00037608205,0.00031503604,0.0039590173,0.0031885605,0.000017345517],"about_ca_topic_score_codex":0.002113711,"about_ca_topic_score_gemma":0.0031638204,"teacher_disagreement_score":0.0065055913,"about_ca_system_score_codex":0.00050350546,"about_ca_system_score_gemma":0.00023085863,"threshold_uncertainty_score":0.021763325},"labels":[],"label_agreement":null},{"id":"W3157598368","doi":"","title":"Guaranteed Renewable Life Insurance Under Demand Uncertainty","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph","funders":"","keywords":"Unobservable; Actuarial science; Auto insurance risk selection; Insurance policy; Life insurance; Economics; Welfare; Population; Casualty insurance; Microeconomics; Business; Econometrics","score_opus":0.01370825348926688,"score_gpt":0.2844802236699091,"score_spread":0.27077197018064225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157598368","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":[0.6846352,0.001182601,0.2774345,0.0039684,0.0001026986,0.000053861222,0.0009725548,0.00018072272,0.031469494],"genre_scores_gemma":[0.9949078,0.00017998945,0.002165302,0.00006433176,0.00003726757,0.000017032113,0.00006646971,0.000011933807,0.0025499733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851054,0.00054903567,0.000051971067,0.00023289505,0.00023876,0.00041672302],"domain_scores_gemma":[0.9955586,0.0024577014,0.0010047891,0.00036159303,0.00021420777,0.00040310927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002229134,0.00048958237,0.0009968247,0.00045799822,0.0005010675,0.0017129349,0.0011605279,0.00149933,0.0035140922],"category_scores_gemma":[0.0075384667,0.0003609075,0.00059837644,0.00050336437,0.0013412016,0.0020868052,0.0016770309,0.00158943,0.00027757144],"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":[0.0002485734,0.0001563394,0.0062056123,0.00010391274,0.0000882985,0.00078570197,0.00024016468,0.5320927,0.002039405,0.43566668,0.0028067122,0.019565897],"study_design_scores_gemma":[0.00005417466,0.00018625284,0.0044286,0.000035954818,0.000039545925,0.00038410845,0.00026223913,0.6049515,0.00041880325,0.38679525,0.0023997314,0.000043836426],"about_ca_topic_score_codex":0.003052128,"about_ca_topic_score_gemma":0.0020426714,"teacher_disagreement_score":0.0035140922,"about_ca_system_score_codex":0.0015652544,"about_ca_system_score_gemma":0.00087299704,"threshold_uncertainty_score":0.011788964},"labels":[],"label_agreement":null},{"id":"W3157879576","doi":"10.1016/j.insmatheco.2021.04.006","title":"Forecasting mortality with international linkages: A global vector-autoregression approach","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Macquarie University","keywords":"Autoregressive model; Vector autoregression; Econometrics; Population; Explanatory power; Statistics; Mathematics; Demography","score_opus":0.042832342924488606,"score_gpt":0.28580542421882604,"score_spread":0.24297308129433742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157879576","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":[0.6318587,0.0020956695,0.35548112,0.0024406884,0.00028854283,0.0000361271,0.001170656,0.0004558291,0.006172646],"genre_scores_gemma":[0.9760349,0.00087085745,0.020746643,0.00007549278,0.000109388806,0.000020515401,0.0007513474,0.00003991898,0.0013509184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956113,0.00023887971,0.000022109529,0.00009928574,0.000034539225,0.000043971555],"domain_scores_gemma":[0.99865,0.0008385981,0.00020850085,0.00010415647,0.00014258023,0.000056214354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023551479,0.0009223159,0.0010197511,0.0014614752,0.00029883155,0.0017300888,0.0006038253,0.00099841,0.0011217956],"category_scores_gemma":[0.005094247,0.00041399556,0.001193427,0.0024047147,0.0004975686,0.0020010665,0.0011795228,0.0011214312,0.00020226883],"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":[0.00007831728,0.000060741753,0.04796548,0.000042751304,0.0005248157,0.00016453867,0.0001251756,0.889629,0.0003913262,0.017913328,0.0018899555,0.041214537],"study_design_scores_gemma":[0.000010553707,0.00003554211,0.012101485,0.000017298498,0.00010317135,0.00002176827,0.00007717477,0.972137,0.0001132219,0.014529276,0.00083615346,0.000017302591],"about_ca_topic_score_codex":0.014696595,"about_ca_topic_score_gemma":0.010777641,"teacher_disagreement_score":0.014696595,"about_ca_system_score_codex":0.00054359453,"about_ca_system_score_gemma":0.0005593992,"threshold_uncertainty_score":0.02922213},"labels":[],"label_agreement":null},{"id":"W3158242124","doi":"10.1177/01410768211011742","title":"What can lifespan variation reveal that life expectancy hides? Comparison of five high-income countries","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Society of Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":26,"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 Oxford","keywords":"Life expectancy; High income countries; Variation (astronomy); Computer science; Gerontology; Developing country; Data science; Medicine; Economic growth; Environmental health; Economics; Population","score_opus":0.02074486309607426,"score_gpt":0.30669095951279796,"score_spread":0.2859460964167237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158242124","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":[0.99281317,0.0021972684,0.00040850264,0.0005431864,0.00002969253,0.000008295101,0.001693408,0.000006650354,0.0022998666],"genre_scores_gemma":[0.9985648,0.0003142502,0.00009945549,0.00006596547,0.00001117456,0.000005580019,0.0008920018,0.0000020404839,0.000044812285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865675,0.0006284075,0.00013776454,0.00020985559,0.00017274634,0.00019454649],"domain_scores_gemma":[0.9968106,0.00093223463,0.0012200971,0.00031252645,0.0004313305,0.00029311303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026005718,0.00019337259,0.00045375866,0.0020639778,0.00032655313,0.0009211119,0.0003345177,0.0003089192,0.00089017925],"category_scores_gemma":[0.008836949,0.000103726044,0.00060393754,0.0022759347,0.00061934587,0.0008135395,0.0013420953,0.00032663564,0.00009908865],"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":[0.00014709134,0.000012982727,0.9841495,0.00009443473,0.00063868676,0.000101866004,0.00072515424,0.00048431547,0.00014125158,0.0012109077,0.00072262617,0.011571158],"study_design_scores_gemma":[0.0000047947237,0.000064782434,0.9970138,0.00006521051,0.00010446849,0.00008991012,0.0009819557,0.00025323447,0.0000590113,0.00030407705,0.0010494931,0.000009202767],"about_ca_topic_score_codex":0.018297821,"about_ca_topic_score_gemma":0.02246646,"teacher_disagreement_score":0.018297821,"about_ca_system_score_codex":0.0005843313,"about_ca_system_score_gemma":0.00051355176,"threshold_uncertainty_score":0.036382616},"labels":[],"label_agreement":null},{"id":"W3158703564","doi":"10.2139/ssrn.3309598","title":"Dynamic Bayesian Ratemaking: A Markov Chain Approximation Approach","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; University of Guelph","funders":"","keywords":"Bayesian probability; Markov chain; Econometrics; Markov chain Monte Carlo; Economics; Computer science; Artificial intelligence; Machine learning","score_opus":0.006263972285190232,"score_gpt":0.2594437926058406,"score_spread":0.2531798203206504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158703564","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":[0.0045478465,0.00027476935,0.99116045,0.0007165276,0.000066556546,0.000062966836,0.0001036372,0.00012569006,0.0029415896],"genre_scores_gemma":[0.5011204,0.0021608958,0.47083613,0.0006808197,0.0007365299,0.0008620501,0.00081154745,0.00050465396,0.022286924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925965,0.00440906,0.0002811963,0.0011071338,0.0009776489,0.00062845607],"domain_scores_gemma":[0.9523626,0.042701103,0.0014964764,0.0011138659,0.0016371595,0.00068891275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01700568,0.0015973656,0.005520841,0.0028352516,0.0017577008,0.0060841367,0.0071446085,0.005846487,0.012445926],"category_scores_gemma":[0.06316404,0.003912078,0.003157788,0.00371824,0.0039104214,0.008476126,0.0033030834,0.0068621757,0.0017004521],"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":[0.000059408074,0.00007105281,0.00052539934,0.00006595167,0.00007690557,0.00010998725,0.00015550738,0.7426797,0.00011068235,0.24333154,0.0018285847,0.010985235],"study_design_scores_gemma":[0.000018900402,0.00000829326,0.00005849833,0.000017984992,0.000018585228,0.000023464281,0.000015508944,0.9327456,0.000039049355,0.06659556,0.0004382152,0.000020327085],"about_ca_topic_score_codex":0.02590194,"about_ca_topic_score_gemma":0.016735073,"teacher_disagreement_score":0.02590194,"about_ca_system_score_codex":0.0049704593,"about_ca_system_score_gemma":0.005399364,"threshold_uncertainty_score":0.08993572},"labels":[],"label_agreement":null},{"id":"W3158909794","doi":"10.2139/ssrn.3550106","title":"Efficient Dynamic Hedging for Large Variable Annuity Portfolios with Multiple Underlying Assets","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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 Toronto","funders":"","keywords":"Annuity; Life annuity; Actuarial science; Econometrics; Variable (mathematics); Economics; Financial economics; Business; Mathematics; Finance; Pension","score_opus":0.017927174909144798,"score_gpt":0.2979751676255334,"score_spread":0.2800479927163886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158909794","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":[0.11497665,0.0004774263,0.8810452,0.0003578572,0.00003760409,0.000043318752,0.000091335896,0.00019663977,0.0027737727],"genre_scores_gemma":[0.90670145,0.0003475884,0.08794173,0.000053774602,0.000050575567,0.00005232163,0.00020769608,0.00005427095,0.0045905453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.00015923344,0.000029138557,0.00007592673,0.000109300716,0.00007553017],"domain_scores_gemma":[0.9976203,0.0018720304,0.0001303509,0.00017247738,0.00010832792,0.000096513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002004037,0.00053842727,0.001287441,0.00050804997,0.00032177166,0.0015435882,0.0012315554,0.0011407975,0.0029673313],"category_scores_gemma":[0.0059651276,0.0006297487,0.00053443323,0.0008586527,0.00050674775,0.001790097,0.0016950357,0.0010094912,0.0002792216],"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":[0.00012450048,0.000060703078,0.0012919448,0.00004646,0.000044514025,0.00015839507,0.000049389488,0.91874665,0.0027622604,0.022657812,0.00074147707,0.053315923],"study_design_scores_gemma":[0.000007985224,0.000014754912,0.00016972348,0.000003079471,0.000005403073,0.000024966717,0.0000064208743,0.99171025,0.00031469206,0.0076026535,0.00013675904,0.0000032911355],"about_ca_topic_score_codex":0.0016187193,"about_ca_topic_score_gemma":0.0015124442,"teacher_disagreement_score":0.0029673313,"about_ca_system_score_codex":0.00072747964,"about_ca_system_score_gemma":0.0008293529,"threshold_uncertainty_score":0.01059854},"labels":[],"label_agreement":null},{"id":"W3160845706","doi":"10.2139/ssrn.3638370","title":"Optimal Dynamic Longevity Hedge with Basis Risk","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Basis risk; Hedge; Longevity; Longevity risk; Actuarial science; Economics; Financial economics; Econometrics; Risk analysis (engineering); Business; Medicine; Biology; Capital asset pricing model; Gerontology","score_opus":0.00842643265197107,"score_gpt":0.25760089262841274,"score_spread":0.24917445997644166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160845706","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":[0.5503568,0.0015807666,0.41556183,0.004909144,0.0002609204,0.00009772555,0.0007185666,0.0004938746,0.026020426],"genre_scores_gemma":[0.98638004,0.00019451267,0.0064165625,0.0000652537,0.00006461204,0.000026302445,0.000086720174,0.000024139405,0.0067418693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994685,0.00019359683,0.00002590198,0.00012059292,0.00007571808,0.00011568577],"domain_scores_gemma":[0.9974463,0.00167211,0.00027002202,0.00017851846,0.0001597655,0.00027321512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017622798,0.00060004345,0.0015920852,0.00065605016,0.00040119045,0.0019225588,0.0008540925,0.002148795,0.004209387],"category_scores_gemma":[0.009154653,0.0005699302,0.0004906522,0.0004745233,0.0008397211,0.0021603433,0.0014734468,0.0013280641,0.00033866338],"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":[0.00065523805,0.00023426976,0.003998269,0.00016685187,0.00014847332,0.00036111116,0.00020333144,0.6861525,0.0027072749,0.24631768,0.0070304563,0.052024487],"study_design_scores_gemma":[0.00005624878,0.000116332856,0.0011788547,0.000021606835,0.000038045277,0.000101997925,0.00007396986,0.8464867,0.000288166,0.15057471,0.0010402062,0.000023064275],"about_ca_topic_score_codex":0.0016851022,"about_ca_topic_score_gemma":0.00093929033,"teacher_disagreement_score":0.004209387,"about_ca_system_score_codex":0.0010782506,"about_ca_system_score_gemma":0.0010637216,"threshold_uncertainty_score":0.014081836},"labels":[],"label_agreement":null},{"id":"W3168138641","doi":"10.1038/s41467-021-23894-3","title":"The long lives of primates and the ‘invariant rate of ageing’ hypothesis","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":91,"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":"National Institute on Aging; Syddansk Universitet","keywords":"Ageing; Life expectancy; Biology; Primate; Mortality rate; Nonhuman primate; Evolutionary biology; Demography; Ecology; Genetics","score_opus":0.023043465797518835,"score_gpt":0.3058441510820336,"score_spread":0.2828006852845148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168138641","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":[0.9656266,0.0053468347,0.015229601,0.0021328412,0.000074908516,0.000021509328,0.0018329053,0.00010963111,0.009625032],"genre_scores_gemma":[0.9973246,0.0003629156,0.0013193886,0.00021828632,0.00006996459,0.000014935995,0.00050855055,0.000021937683,0.00015937518],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972772,0.0016221441,0.0001249833,0.00058140507,0.0002741543,0.00012021417],"domain_scores_gemma":[0.9882619,0.006319828,0.0021811225,0.0023105058,0.0005206065,0.00040601328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005761025,0.0002103916,0.00058063515,0.0013580428,0.0007343272,0.0015295219,0.000540164,0.0004586853,0.0025217575],"category_scores_gemma":[0.025020786,0.00014422879,0.00043217064,0.0016045498,0.0019309279,0.0017746608,0.0012902712,0.0008035865,0.0003534202],"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":[0.0009202838,0.00007857125,0.77530015,0.00071034644,0.0015767806,0.0007660275,0.006429339,0.01404858,0.008369236,0.03876602,0.0071087675,0.14592591],"study_design_scores_gemma":[0.00004740277,0.00032897628,0.8347995,0.00017106898,0.00028713586,0.0015133197,0.0019832177,0.01600435,0.0014805445,0.12509285,0.018198371,0.00009326156],"about_ca_topic_score_codex":0.001071979,"about_ca_topic_score_gemma":0.0010242405,"teacher_disagreement_score":0.005761025,"about_ca_system_score_codex":0.00029446822,"about_ca_system_score_gemma":0.00030340382,"threshold_uncertainty_score":0.03046757},"labels":[],"label_agreement":null},{"id":"W3168507971","doi":"10.3390/jrfm14060259","title":"A Deep Learning Integrated Cairns-Blake-Dowd (CBD) Sytematic Mortality Risk Model","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"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":"Autoregressive integrated moving average; Computer science; Artificial neural network; Artificial intelligence; Machine learning; Cohort; Actuarial science; Econometrics; Economics; Statistics; Time series; Mathematics","score_opus":0.012740653640531006,"score_gpt":0.26488407524500196,"score_spread":0.25214342160447095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168507971","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":[0.22360462,0.0009969781,0.7601451,0.0026144045,0.00022635334,0.000096439355,0.001597518,0.000555664,0.010162929],"genre_scores_gemma":[0.95321226,0.0004517813,0.033745978,0.00027793186,0.000060134385,0.00013848681,0.00073670706,0.000019235435,0.011357477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997346,0.00004870913,0.000016812648,0.0000857447,0.000054519132,0.00005964838],"domain_scores_gemma":[0.9996549,0.00012058379,0.000057736626,0.000018565506,0.00012037938,0.000027874436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008519345,0.00044180301,0.0005448971,0.00048525247,0.00030712754,0.0007024549,0.001430842,0.0011496465,0.0020243418],"category_scores_gemma":[0.0016435058,0.0003094489,0.0005517446,0.00045975906,0.0004154637,0.000841825,0.0008342043,0.0013234074,0.00024268361],"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":[0.00011070573,0.00009922046,0.009235147,0.000060501006,0.000108597524,0.00015989054,0.0000834687,0.9146383,0.0012382579,0.021664793,0.0026845841,0.049916577],"study_design_scores_gemma":[0.000004945392,0.000016546051,0.0005897758,0.0000053901595,0.000012949003,0.000019896335,0.0000035591188,0.99624974,0.00012169992,0.0026371956,0.00033387452,0.000004405366],"about_ca_topic_score_codex":0.019027486,"about_ca_topic_score_gemma":0.017261162,"teacher_disagreement_score":0.019027486,"about_ca_system_score_codex":0.001254036,"about_ca_system_score_gemma":0.0015487721,"threshold_uncertainty_score":0.037833452},"labels":[],"label_agreement":null},{"id":"W3169827057","doi":"10.1201/9781003157625","title":"Risk Measures and Insurance Solvency Benchmarks","year":2021,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Solvency; Actuarial science; Business; Finance","score_opus":0.014162213137932447,"score_gpt":0.265743610157121,"score_spread":0.2515813970191886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169827057","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":[0.024365934,0.01160217,0.06503307,0.0065284087,0.00077491475,0.000053302985,0.00041250463,0.00029557623,0.8909341],"genre_scores_gemma":[0.66680527,0.010173,0.023778891,0.0012881462,0.0010637078,0.00022674324,0.00071769505,0.00038920727,0.29555735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893147,0.00021882803,0.00003465329,0.00012486379,0.00058083364,0.00010930613],"domain_scores_gemma":[0.9989334,0.00044743833,0.00012075996,0.0001189355,0.00026036086,0.000119167344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010718784,0.0006413904,0.00066707603,0.0011550803,0.0005629358,0.0043024756,0.0009975794,0.0009433823,0.011702492],"category_scores_gemma":[0.0056647733,0.0003145243,0.0003383092,0.0013261657,0.0018741774,0.004036168,0.0014866812,0.0025993625,0.0018360217],"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":[0.0000055635815,0.000012600656,0.0001115248,0.000026840336,0.0000034841814,0.000010305779,0.00004532157,0.0044477363,0.00008188702,0.96672654,0.0133010615,0.015227216],"study_design_scores_gemma":[0.0000038100372,0.000025575564,0.0005281087,0.00008592494,0.0000043263476,0.000029563811,0.00009257906,0.013769393,0.00013880648,0.9378139,0.047497828,0.000010349291],"about_ca_topic_score_codex":0.0014890225,"about_ca_topic_score_gemma":0.001387012,"teacher_disagreement_score":0.011702492,"about_ca_system_score_codex":0.0030218184,"about_ca_system_score_gemma":0.0013683835,"threshold_uncertainty_score":0.03914869},"labels":[],"label_agreement":null},{"id":"W3171139534","doi":"10.1080/03461238.2021.1938198","title":"Tail index-linked annuity: A longevity risk sharing retirement plan","year":2021,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Guelph","funders":"","keywords":"Solvency; Life annuity; Longevity risk; Longevity; Actuarial science; Index (typography); Economics; Business; Pension; Finance; Medicine; Gerontology; Market liquidity; Computer science","score_opus":0.03233782542869804,"score_gpt":0.3066045907178497,"score_spread":0.2742667652891516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171139534","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":[0.2279408,0.0016752934,0.7318516,0.0017042177,0.0004211671,0.0003363997,0.00059805985,0.00082532415,0.034647144],"genre_scores_gemma":[0.93372387,0.00043176056,0.055226628,0.00020667615,0.00014113671,0.00010495033,0.00020391015,0.000038574253,0.0099224895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939215,0.00017200575,0.000034119865,0.0001048727,0.00023023321,0.00006656878],"domain_scores_gemma":[0.9991755,0.00017775316,0.00019747611,0.00015055145,0.00015067126,0.00014807169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012913272,0.00037406388,0.00036449364,0.00062132854,0.00044048612,0.0008205049,0.0012422671,0.0009615166,0.003965661],"category_scores_gemma":[0.002415381,0.00017634685,0.00051168376,0.00046489012,0.0005462637,0.0011701399,0.0011154935,0.0008784374,0.00083218963],"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":[0.0010089099,0.0010761771,0.010984924,0.0002576409,0.00015108,0.00056736136,0.00057075435,0.18186198,0.01817908,0.26812643,0.016118946,0.5010967],"study_design_scores_gemma":[0.00027965513,0.0017016422,0.008707544,0.00015984311,0.00018464185,0.0014337683,0.00016179286,0.8297514,0.0059382017,0.08622615,0.06527843,0.00017695384],"about_ca_topic_score_codex":0.0006049555,"about_ca_topic_score_gemma":0.00054556935,"teacher_disagreement_score":0.003965661,"about_ca_system_score_codex":0.00047581102,"about_ca_system_score_gemma":0.00089294143,"threshold_uncertainty_score":0.013266385},"labels":[],"label_agreement":null},{"id":"W3173322372","doi":"10.1007/s42650-021-00044-0","title":"On Mathematical Equalities and Inequalities in the Life Table: Something Old and Something New","year":2021,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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":"Life expectancy; Inequality; Mathematics; Population; Life table; Mathematical economics; Demography; Sociology; Mathematical analysis","score_opus":0.1247641632736648,"score_gpt":0.3792049048720301,"score_spread":0.2544407415983653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173322372","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":[0.087234914,0.077000886,0.5322952,0.08295929,0.0038952199,0.000056806657,0.0016168789,0.00017081009,0.21476991],"genre_scores_gemma":[0.9169482,0.023841841,0.04299466,0.0037969723,0.0035392854,0.00008357271,0.0003092406,0.0001040871,0.008382083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966419,0.0015160709,0.00020290734,0.00040676456,0.0008611403,0.00037123362],"domain_scores_gemma":[0.9826555,0.013625649,0.00092882523,0.0009991637,0.0013409152,0.00044996513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063859634,0.00043394265,0.0006536464,0.0026702846,0.0013431765,0.0046398547,0.0013148912,0.0010442054,0.0060874964],"category_scores_gemma":[0.018661309,0.0002565161,0.00070207403,0.0041812016,0.013089973,0.012881792,0.0025804082,0.0029572363,0.0005066229],"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":[0.0000048535217,0.0000026721611,0.00016697656,0.000027098204,0.0000038628873,0.00001450732,0.00012818015,0.0006436762,0.000018425064,0.99386734,0.0009005563,0.0042219134],"study_design_scores_gemma":[0.0000014632919,0.0000030596434,0.00017135128,0.00003785472,0.0000022459672,0.000016553075,0.000072085364,0.0007670572,0.000020331001,0.9930341,0.00586862,0.0000052986993],"about_ca_topic_score_codex":0.009853322,"about_ca_topic_score_gemma":0.00534447,"teacher_disagreement_score":0.009853322,"about_ca_system_score_codex":0.004275741,"about_ca_system_score_gemma":0.0014290703,"threshold_uncertainty_score":0.033772647},"labels":[],"label_agreement":null},{"id":"W3173430869","doi":"10.1016/j.ejor.2021.05.055","title":"Optimal dynamic longevity hedge with basis risk","year":2021,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Hedge; Stylized fact; Robustness (evolution); Generality; Longevity risk; Mathematical optimization; Computer science; Stochastic control; Discrete time and continuous time; Trading strategy; Econometrics; Mathematics; Economics; Optimal control; Pension; Finance","score_opus":0.05726869876714376,"score_gpt":0.3855672798744237,"score_spread":0.32829858110727994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173430869","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":[0.46649942,0.001338031,0.5107966,0.0027886892,0.00021077602,0.00008664935,0.0004463831,0.000393724,0.017439786],"genre_scores_gemma":[0.98784536,0.00015645454,0.0074494253,0.00004395341,0.000049668277,0.000023567301,0.00007119023,0.00002124919,0.0043390947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999426,0.00021241182,0.000027001422,0.00012355015,0.0000853313,0.00012574065],"domain_scores_gemma":[0.99761534,0.0015813116,0.0002206429,0.0001699224,0.00017197721,0.00024088552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018510048,0.0006394537,0.0015034125,0.00057551,0.00035390983,0.0017865449,0.0008788631,0.0018360986,0.0032845344],"category_scores_gemma":[0.008422036,0.00052596495,0.0004534435,0.00041788383,0.00077410456,0.0019556873,0.0014297297,0.0011394856,0.00024720246],"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":[0.00047166512,0.00014682686,0.0022189475,0.00010484171,0.00009079369,0.0001992031,0.00012546856,0.83652663,0.002357762,0.1171562,0.0032280511,0.037373625],"study_design_scores_gemma":[0.000037725677,0.00009334034,0.0007264852,0.000014105609,0.000026295158,0.00006469025,0.000040534662,0.93286526,0.00027935064,0.06517501,0.00066141336,0.000015724274],"about_ca_topic_score_codex":0.0016057388,"about_ca_topic_score_gemma":0.0007407417,"teacher_disagreement_score":0.0032845344,"about_ca_system_score_codex":0.0010523138,"about_ca_system_score_gemma":0.0010910258,"threshold_uncertainty_score":0.010987878},"labels":[],"label_agreement":null},{"id":"W3174970847","doi":"10.2139/ssrn.3619332","title":"Optimal Asset Allocation for Outperforming a Stochastic Benchmark Target","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Miller Group (Canada); University of Waterloo","funders":"","keywords":"Asset allocation; Benchmark (surveying); Computer science; Portfolio; Robustness (evolution); Mathematical optimization; Stochastic control; Portfolio optimization; Trading strategy; Econometrics; Optimal control; Economics; Finance; Mathematics","score_opus":0.017256172650510413,"score_gpt":0.2884547998398097,"score_spread":0.27119862718929927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174970847","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":[0.51024806,0.00086820853,0.43919054,0.0031372153,0.0001839325,0.00027048655,0.00040088207,0.0007148653,0.044985723],"genre_scores_gemma":[0.9859914,0.00014253326,0.010367727,0.000098110904,0.000057271474,0.00007719791,0.000049962742,0.000028687029,0.0031871013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988244,0.0005123265,0.00004231091,0.00016046803,0.00018330412,0.0002772274],"domain_scores_gemma":[0.99780256,0.0012469408,0.0002241303,0.00018311274,0.00030384026,0.00023948752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002749354,0.00088373234,0.0016319139,0.0010208586,0.0003377211,0.0020780496,0.00093275955,0.0021287268,0.004201364],"category_scores_gemma":[0.0105162775,0.00040253322,0.00040067258,0.00067041535,0.00084086007,0.0018000244,0.0015368579,0.0011106181,0.00055582845],"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":[0.0008149088,0.00022020307,0.0012271481,0.00013431378,0.00009028622,0.00008400014,0.000064927735,0.8827016,0.0041411864,0.07247317,0.0022440085,0.035804186],"study_design_scores_gemma":[0.00006976205,0.00025135052,0.00083847693,0.000020700101,0.00002935721,0.000030848707,0.000027317805,0.9616832,0.0008887992,0.035592645,0.00055436324,0.000013284286],"about_ca_topic_score_codex":0.0011829642,"about_ca_topic_score_gemma":0.00048032537,"teacher_disagreement_score":0.004201364,"about_ca_system_score_codex":0.0012331723,"about_ca_system_score_gemma":0.0017410102,"threshold_uncertainty_score":0.014540136},"labels":[],"label_agreement":null},{"id":"W3176617194","doi":"10.1093/aje/kwab178","title":"“Translating” All-Cause Mortality Rate Ratios or Hazard Ratios to Age-, Longevity-, and Probability-Based Measures","year":2021,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Longevity; Hazard ratio; Statistics; Metric (unit); Demography; Mathematics; Mortality rate; Standardized mortality ratio; Medicine; Odds ratio; Hazard; Confidence interval; Gerontology; Internal medicine; Biology; Operations management; Economics","score_opus":0.13996744662846433,"score_gpt":0.39988324356918276,"score_spread":0.2599157969407184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176617194","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":[0.008258337,0.0026036073,0.9375262,0.009293895,0.006498114,0.00033043115,0.00222129,0.0026107964,0.03065732],"genre_scores_gemma":[0.1280576,0.0060844095,0.84145105,0.006948809,0.00294577,0.0010222971,0.0015555053,0.0020297328,0.009904909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98832244,0.0068960623,0.0017112249,0.0010076484,0.0018885194,0.00017424847],"domain_scores_gemma":[0.9577539,0.027851218,0.0036393614,0.0053156298,0.0051878258,0.0002520478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016990285,0.0017267014,0.0007843396,0.0037112564,0.0005974928,0.0039364016,0.0017334693,0.0014344758,0.01322273],"category_scores_gemma":[0.1473048,0.00067581417,0.0014481132,0.004262343,0.0030615942,0.0052561853,0.0023666024,0.004162072,0.0055616563],"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":[0.00016761025,0.00010199745,0.0039549894,0.0016350187,0.00023452714,0.00052794226,0.0019388,0.007401535,0.0016789895,0.61211354,0.067454666,0.30279043],"study_design_scores_gemma":[0.00011012131,0.00028702014,0.005644713,0.0012599905,0.00022346883,0.0012893379,0.0012763599,0.019182637,0.0067508975,0.7101186,0.25367743,0.000179484],"about_ca_topic_score_codex":0.0018711247,"about_ca_topic_score_gemma":0.001302697,"teacher_disagreement_score":0.016990285,"about_ca_system_score_codex":0.001521875,"about_ca_system_score_gemma":0.0015152083,"threshold_uncertainty_score":0.0898543},"labels":[],"label_agreement":null},{"id":"W3177529707","doi":"10.1007/s12282-021-01271-8","title":"Average lifespan shortened due to breast cancer in Australia, 1990–2015","year":2021,"lang":"en","type":"article","venue":"Breast Cancer","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Calgary; Alberta Cancer Foundation; Alberta Health Services","funders":"","keywords":"Breast cancer; Medicine; Demography; Cancer; Surgical oncology; Mortality rate; Population; Gerontology; Gynecology; Oncology; Internal medicine; Environmental health","score_opus":0.023311373577794257,"score_gpt":0.3366366985087402,"score_spread":0.3133253249309459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177529707","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":[0.9762847,0.0040870323,0.00023807834,0.0007680244,0.00008979975,0.000027356653,0.01622397,0.000040940267,0.0022401144],"genre_scores_gemma":[0.9925563,0.0011981957,0.00017388216,0.00012739921,0.000028676866,0.000027429864,0.0046248585,0.000004273189,0.0012591118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927455,0.000106860556,0.00018383413,0.00013620908,0.00013080137,0.00016774978],"domain_scores_gemma":[0.9979673,0.00009277624,0.0010521687,0.00006967691,0.0004349942,0.00038310973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007251923,0.00024006263,0.00038637233,0.0014694212,0.0004097895,0.0005694974,0.0006881806,0.00056820887,0.0014940063],"category_scores_gemma":[0.003443763,0.00032907585,0.0011761099,0.0018150504,0.00026844878,0.0007539744,0.0014866189,0.00093780056,0.00021757264],"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":[0.0002138854,0.000039757495,0.98196214,0.00040642466,0.0004963414,0.0002434722,0.0007992527,0.00071972545,0.0002769467,0.00025013153,0.0037539492,0.01083794],"study_design_scores_gemma":[0.0000019534089,0.0000249602,0.99845874,0.000034470548,0.00003129236,0.000095155985,0.00014143075,0.00019884159,0.0000149911175,0.000020353047,0.0009735311,0.0000043170385],"about_ca_topic_score_codex":0.23267078,"about_ca_topic_score_gemma":0.28731224,"teacher_disagreement_score":0.23267078,"about_ca_system_score_codex":0.0025341492,"about_ca_system_score_gemma":0.001875889,"threshold_uncertainty_score":0.46263295},"labels":[],"label_agreement":null},{"id":"W3178583316","doi":"10.3390/math9141629","title":"Mortality/Longevity Risk-Minimization with or without Securitization","year":2021,"lang":"en","type":"article","venue":"MDPI (MDPI AG)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Agentschap voor Innovatie door Wetenschap en Technologie","keywords":"Securitization; Longevity risk; Martingale (probability theory); Longevity; Actuarial science; Econometrics; Bond; Mathematics; Economics; Statistics; Finance; Medicine","score_opus":0.025864820143843302,"score_gpt":0.3175541860442148,"score_spread":0.29168936590037153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178583316","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":[0.333768,0.0012787185,0.6492853,0.0012243906,0.00006655173,0.000076872435,0.00011643057,0.000087096334,0.014096551],"genre_scores_gemma":[0.9440543,0.0007337,0.043217603,0.00012178881,0.00011146964,0.000083490806,0.00009834244,0.000060855367,0.011518468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999315,0.00026844212,0.00003323537,0.00015772608,0.00012415675,0.000101424666],"domain_scores_gemma":[0.9986744,0.0005788871,0.0003070488,0.00013427729,0.000107783344,0.00019766642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022495857,0.0010516014,0.00093052397,0.00047791243,0.0003271898,0.0012312235,0.0010113429,0.0012751392,0.0017810109],"category_scores_gemma":[0.0049171294,0.00039108656,0.0009817764,0.00026666824,0.0014556028,0.002305566,0.0016121714,0.0013622612,0.00018691225],"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":[0.00016620109,0.00013642831,0.0019639926,0.00020179707,0.00011091966,0.00028071468,0.00026864957,0.17927907,0.0076246955,0.776745,0.0010485566,0.03217399],"study_design_scores_gemma":[0.000039251096,0.00027930585,0.001590854,0.00007739514,0.00006428569,0.00020639038,0.00009119542,0.59179914,0.0036476532,0.3996778,0.0024885403,0.00003814708],"about_ca_topic_score_codex":0.0006382652,"about_ca_topic_score_gemma":0.00026969917,"teacher_disagreement_score":0.0022495857,"about_ca_system_score_codex":0.0009423399,"about_ca_system_score_gemma":0.0010620301,"threshold_uncertainty_score":0.011897087},"labels":[],"label_agreement":null},{"id":"W3184172994","doi":"10.2139/ssrn.3445761","title":"A Multi-State Model of Functional Disability and Health Status in the Presence of Systematic Trend and Uncertainty","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"State (computer science); Econometrics; Psychology; Medicine; Economics; Computer science","score_opus":0.03758475826733674,"score_gpt":0.3169799237439949,"score_spread":0.27939516547665816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184172994","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":[0.64686054,0.0016816271,0.30799612,0.015874062,0.0003782155,0.00025673647,0.009500686,0.0006684899,0.016783437],"genre_scores_gemma":[0.9711936,0.0006669738,0.007546201,0.00026562056,0.00015556064,0.00022143323,0.0012745132,0.000052618834,0.01862346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974347,0.001153171,0.00013256472,0.0006701889,0.0001866861,0.00042267315],"domain_scores_gemma":[0.983984,0.012933829,0.0012383033,0.00039468976,0.00088274595,0.00056635856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006812495,0.001082158,0.0034066525,0.0021939364,0.0011402294,0.004788095,0.0030251357,0.0046442677,0.007622079],"category_scores_gemma":[0.016761065,0.0018928626,0.002300641,0.0027277765,0.002467423,0.0048843278,0.0023408963,0.0036271717,0.0009469638],"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":[0.00029402168,0.00012651442,0.008359267,0.000064227184,0.00029464817,0.00033673536,0.00030615868,0.93876094,0.00025368837,0.046979412,0.0010929669,0.0031313056],"study_design_scores_gemma":[0.00005781283,0.00006978602,0.0020280546,0.000014899806,0.00008445639,0.000048821374,0.00007129705,0.9850854,0.000027231867,0.012150568,0.00032335607,0.00003825983],"about_ca_topic_score_codex":0.05744112,"about_ca_topic_score_gemma":0.03259247,"teacher_disagreement_score":0.05744112,"about_ca_system_score_codex":0.0031652928,"about_ca_system_score_gemma":0.0030301071,"threshold_uncertainty_score":0.114213526},"labels":[],"label_agreement":null},{"id":"W3185863291","doi":"10.1093/restud/rdad017","title":"Risk Classification in Insurance Markets with Risk and Preference Heterogeneity","year":2023,"lang":"en","type":"article","venue":"The Review of Economic Studies","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"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","funders":"Social Sciences and Humanities Research Council of Canada; University of California, Davis; Hong Kong University of Science and Technology; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Pennsylvania State University; Chinese University of Hong Kong; University of Pennsylvania","keywords":"Stochastic dominance; Economics; Econometrics; Dominance (genetics); Preference; Monotonic function; Monotone polygon; Actuarial science; Population; Distribution (mathematics); Microeconomics; Mathematics","score_opus":0.0812617207541838,"score_gpt":0.35055009089714084,"score_spread":0.26928837014295703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185863291","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":[0.6182123,0.0038592818,0.33586526,0.0069896067,0.0001275346,0.0001849083,0.00043855578,0.000117368756,0.03420518],"genre_scores_gemma":[0.9919348,0.0005005184,0.0046647727,0.00013860031,0.000085040454,0.000034154415,0.00003304086,0.000006214532,0.0026028845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99663997,0.0018187186,0.000099247976,0.0003881898,0.00038633676,0.0006675816],"domain_scores_gemma":[0.991443,0.005851784,0.00150872,0.00036675605,0.00042080038,0.0004090472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043869517,0.00069692574,0.0020139923,0.00087103987,0.0010530249,0.00436336,0.0018428866,0.002990738,0.004310982],"category_scores_gemma":[0.009050896,0.0006593085,0.0014031129,0.0010365034,0.002940909,0.004268427,0.0015677856,0.002170493,0.00032437214],"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":[0.00019730463,0.00019337183,0.0035414367,0.00016972184,0.00014905242,0.0005817415,0.00027696963,0.14609355,0.0013919758,0.8356493,0.0014220078,0.010333534],"study_design_scores_gemma":[0.00017041115,0.00012572439,0.0018297397,0.00005032873,0.000069651316,0.00023580056,0.00021022119,0.5051932,0.00029861234,0.49007112,0.0016889217,0.00005633117],"about_ca_topic_score_codex":0.0053844373,"about_ca_topic_score_gemma":0.0025819761,"teacher_disagreement_score":0.0053844373,"about_ca_system_score_codex":0.0029824332,"about_ca_system_score_gemma":0.0010723574,"threshold_uncertainty_score":0.02320069},"labels":[],"label_agreement":null},{"id":"W3191363507","doi":"10.18267/j.pep.785","title":"Distribution of Expected Time of Old-Age Pension Receipt in Czechia","year":2021,"lang":"en","type":"article","venue":"Prague Economic Papers","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Grantová Agentura České Republiky","keywords":"Receipt; Pension; Demography; Cohort; Quarter (Canadian coin); Retirement age; Czech; Disability pension; Gerontology; Medicine; Economics; Population; Geography; Finance; Sociology","score_opus":0.012349609021898586,"score_gpt":0.26062103180358936,"score_spread":0.2482714227816908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191363507","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":[0.98920923,0.0002496571,0.006272062,0.000062987725,0.000012856364,0.00003422196,0.0019416792,0.00009073727,0.002126575],"genre_scores_gemma":[0.99830294,0.00005975652,0.00038820636,0.0000037624097,0.0000022038644,0.000010030491,0.0008267204,0.000009117306,0.00039714098],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992741,0.000112878486,0.0000742802,0.00021607279,0.00015728327,0.00016535401],"domain_scores_gemma":[0.9969227,0.0011826057,0.0007788253,0.00034505705,0.0005088217,0.0002621311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019110289,0.00014792194,0.00029038114,0.0017677333,0.00018251063,0.0009985984,0.0006657962,0.0003041026,0.0025834783],"category_scores_gemma":[0.0102007985,0.00021067263,0.0005061798,0.0008446154,0.00037771263,0.0003775746,0.0006895909,0.0004134002,0.00038969523],"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":[0.0013936698,0.000061256826,0.8314674,0.00013801924,0.00027823466,0.00080893916,0.0006021986,0.121368505,0.004668561,0.011492219,0.0011014474,0.026619617],"study_design_scores_gemma":[0.00005026861,0.00027389685,0.88276106,0.00009946685,0.00007102687,0.0010993426,0.00065392815,0.10376677,0.0029503058,0.0037232016,0.0044444026,0.00010645408],"about_ca_topic_score_codex":0.0068158805,"about_ca_topic_score_gemma":0.0026591518,"teacher_disagreement_score":0.0068158805,"about_ca_system_score_codex":0.0012512959,"about_ca_system_score_gemma":0.00042565083,"threshold_uncertainty_score":0.013552427},"labels":[],"label_agreement":null},{"id":"W3194943772","doi":"10.3390/risks9090151","title":"Coherent Mortality Forecasting for Less Developed Countries","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Developing country; Convergence (economics); Developed country; China; Development economics; Term (time); Mortality rate; Population; Population projection; Socioeconomic status; Projections of population growth; Economics; Geography; Econometrics; Economic growth; Demography; Population growth","score_opus":0.29029817206745506,"score_gpt":0.4170142000064221,"score_spread":0.12671602793896702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194943772","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":[0.7131358,0.0005072872,0.28096718,0.0012400473,0.00007430827,0.00006710031,0.0009763219,0.00018948158,0.0028425383],"genre_scores_gemma":[0.9778555,0.00025897604,0.020509148,0.00006774892,0.000040753588,0.000029714085,0.000792158,0.000011508877,0.0004344919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947876,0.00022737517,0.000051634495,0.00013847684,0.00005690058,0.00004692465],"domain_scores_gemma":[0.9983304,0.0006531279,0.0004969247,0.00016891748,0.0002426728,0.00010802412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127698,0.00043714372,0.00070869044,0.0015168851,0.00038857773,0.0012436416,0.0007040704,0.00077410747,0.00057209353],"category_scores_gemma":[0.0069885277,0.0003169103,0.00062173285,0.0014043681,0.00024412619,0.0015243632,0.0011396295,0.0010342618,0.00009689414],"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":[0.00004842692,0.000031604694,0.055714384,0.000033464526,0.00009171358,0.00018378937,0.00023223847,0.90331155,0.00047789063,0.009122213,0.0008217123,0.029930979],"study_design_scores_gemma":[0.0000042436823,0.000013818927,0.007709358,0.000009924074,0.000013343226,0.000009403948,0.00005958324,0.9870149,0.00013233714,0.0047516595,0.0002712183,0.000010151784],"about_ca_topic_score_codex":0.015817909,"about_ca_topic_score_gemma":0.014111364,"teacher_disagreement_score":0.015817909,"about_ca_system_score_codex":0.0008629893,"about_ca_system_score_gemma":0.00070699555,"threshold_uncertainty_score":0.031451702},"labels":[],"label_agreement":null},{"id":"W3196455938","doi":"10.1017/s1748499522000033","title":"A multi-parameter-level model for simulating future mortality scenarios with COVID-alike effects","year":2022,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","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":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Range (aeronautics); Econometrics; Gauge (firearms); Actuarial science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Economics; Engineering; Geography; Medicine; Virology","score_opus":0.15336401857685458,"score_gpt":0.4088957933513864,"score_spread":0.2555317747745318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196455938","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":[0.5488301,0.00029142734,0.43810058,0.0023193215,0.00008512662,0.0002452953,0.0021791935,0.00044914676,0.0074998355],"genre_scores_gemma":[0.97720057,0.00010422749,0.018524067,0.0001518756,0.000022820448,0.00019548957,0.0005722255,0.000027254295,0.0032014304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981639,0.0012352978,0.00007254769,0.00023044969,0.00012922396,0.00016853292],"domain_scores_gemma":[0.99196106,0.0060856454,0.00083387294,0.00031050158,0.00053314754,0.0002757442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004967128,0.0005813717,0.00100036,0.0011573568,0.0005180293,0.0018601569,0.0024441923,0.003476489,0.004538044],"category_scores_gemma":[0.012841841,0.00063227577,0.0014330707,0.00093635375,0.0012500861,0.0014938107,0.0013926174,0.002228695,0.000520454],"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":[0.00003240411,0.000020497484,0.0016645683,0.000010447946,0.000017964192,0.000056774956,0.000053384905,0.9922896,0.00014878139,0.00479489,0.00012287729,0.00078783225],"study_design_scores_gemma":[0.000009167416,0.000022953089,0.00053095043,0.000005192934,0.00000732326,0.000012399644,0.000024786747,0.9967422,0.00004538955,0.0024401196,0.00014933318,0.000010176128],"about_ca_topic_score_codex":0.020972269,"about_ca_topic_score_gemma":0.013288278,"teacher_disagreement_score":0.020972269,"about_ca_system_score_codex":0.0019551662,"about_ca_system_score_gemma":0.0008754419,"threshold_uncertainty_score":0.041700423},"labels":[],"label_agreement":null},{"id":"W3201784117","doi":"10.1007/s11146-021-09862-0","title":"Valuation of Reverse Mortgages with Default Risk Models","year":2021,"lang":"en","type":"article","venue":"The Journal of Real Estate Finance and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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":"Shared appreciation mortgage; Mortgage underwriting; Solvency; Mortgage insurance; Business; Loan; Default; Prepayment of loan; Payment; Bridge loan; Actuarial science; Finance; Valuation (finance); Insolvency; Non-performing loan; Insurance policy; Casualty insurance","score_opus":0.028121604444689856,"score_gpt":0.2600261340411253,"score_spread":0.23190452959643543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201784117","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":[0.4871134,0.0029368668,0.48495665,0.0024484522,0.00020556817,0.00014976968,0.00049446267,0.00021117549,0.021483779],"genre_scores_gemma":[0.9852336,0.00046834123,0.007252763,0.000047631598,0.00012197712,0.000029303026,0.00012040886,0.00001780992,0.0067081093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990596,0.00059867866,0.00004251955,0.00011219451,0.00008297799,0.0001039343],"domain_scores_gemma":[0.99592423,0.0030168393,0.00038503768,0.00016205962,0.00021531785,0.00029652094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029203158,0.0011848008,0.0015932127,0.0008352992,0.0003027889,0.0029721837,0.0015252148,0.0027888732,0.0032769828],"category_scores_gemma":[0.009061278,0.0007983099,0.0012223952,0.00068982417,0.0014072628,0.003191462,0.001058269,0.0015503546,0.00022692347],"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":[0.00014305627,0.00007817806,0.0017079738,0.000042656262,0.000051774303,0.00031526483,0.000083905215,0.90114063,0.00071958016,0.09000781,0.0007407986,0.0049683354],"study_design_scores_gemma":[0.00001204908,0.000025912164,0.00020045046,0.000005014825,0.000010283327,0.000039759332,0.000012005689,0.984423,0.000052031606,0.015057281,0.00015580146,0.0000063578964],"about_ca_topic_score_codex":0.0036340472,"about_ca_topic_score_gemma":0.0016717105,"teacher_disagreement_score":0.0036340472,"about_ca_system_score_codex":0.0013448303,"about_ca_system_score_gemma":0.0006706569,"threshold_uncertainty_score":0.015444279},"labels":[],"label_agreement":null},{"id":"W3205759470","doi":"10.1016/j.jvs.2021.08.020","title":"Exploring Estimated Surgical Delay Based on Maximum Acceptable Mortality Risk for Patients With Asymptomatic Abdominal Aortic Aneurysms","year":2021,"lang":"en","type":"article","venue":"Journal of Vascular Surgery","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université Laval; Université de Montréal","funders":"","keywords":"Medicine; Asymptomatic; Abdominal aortic aneurysm; Surgery; Coronary artery disease; Cardiology; Internal medicine; Radiology; Aneurysm","score_opus":0.05714242923421669,"score_gpt":0.296767008882757,"score_spread":0.23962457964854028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205759470","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":[0.99901986,0.00011267589,0.00016655037,0.00009620607,0.0000053537833,0.0000043928117,0.00025762388,0.000002734158,0.0003345207],"genre_scores_gemma":[0.9996656,0.00003093575,0.00011385419,0.000007602874,0.000005896409,0.0000034983477,0.0001317765,6.824938e-7,0.00004032432],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995066,0.00016721908,0.00009332937,0.00008217177,0.00005919237,0.00009145442],"domain_scores_gemma":[0.99662435,0.0015688994,0.001043451,0.00011462839,0.00023392867,0.00041474952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009029008,0.00029172166,0.00027823407,0.0012371418,0.00031678553,0.0006804279,0.00037603322,0.00043332676,0.0014822432],"category_scores_gemma":[0.008139078,0.00018500864,0.0009212032,0.0011243444,0.00017544153,0.0007154561,0.00058876,0.0007759938,0.000120077384],"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":[0.00007697316,0.000023222534,0.9986733,0.000005294126,0.000038258073,0.00002685832,0.00005782821,0.00015690057,0.00001430804,0.000032388172,0.000028617867,0.0008661279],"study_design_scores_gemma":[0.000009042492,0.00020959912,0.99423957,0.000016629117,0.0001404921,0.0002262355,0.0009977514,0.0036817694,0.00004817562,0.00026214088,0.00015600909,0.000012665869],"about_ca_topic_score_codex":0.006524307,"about_ca_topic_score_gemma":0.008812837,"teacher_disagreement_score":0.006524307,"about_ca_system_score_codex":0.00048409056,"about_ca_system_score_gemma":0.0006202314,"threshold_uncertainty_score":0.012972653},"labels":[],"label_agreement":null},{"id":"W3209437238","doi":"10.1186/s40854-021-00287-5","title":"Claim reserving for insurance contracts in line with the International Financial Reporting Standards 17: a new paid-incurred chain approach to risk adjustments","year":2021,"lang":"en","type":"article","venue":"Financial Innovation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Mitacs; China Postdoctoral Science Foundation","keywords":"Actuarial science; Moment (physics); Risk management; Monte Carlo method; Economics; Line (geometry); Distribution (mathematics); Econometrics; Business; Finance; Mathematics; Statistics","score_opus":0.04717496673546776,"score_gpt":0.34037038924585117,"score_spread":0.2931954225103834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209437238","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":[0.15773894,0.0003397044,0.83659464,0.0008162945,0.000040951767,0.0001224196,0.0001527804,0.00013028826,0.0040640254],"genre_scores_gemma":[0.93938726,0.00022167928,0.057381995,0.000048974613,0.000061485945,0.00008073231,0.00011096037,0.000038878385,0.0026680161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995644,0.0020999159,0.00023954238,0.0006374302,0.0010972908,0.00028194804],"domain_scores_gemma":[0.9864028,0.00874523,0.0024158456,0.001143327,0.0009856179,0.00030713007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008306358,0.00048318075,0.0008573642,0.002145788,0.0006334731,0.0021641694,0.0025804476,0.0014368629,0.0030357852],"category_scores_gemma":[0.026167015,0.0005629655,0.0012289205,0.0011629874,0.0019002054,0.0037421251,0.0019537979,0.0018945129,0.00019703447],"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":[0.00006814486,0.000116634226,0.014218639,0.000053260548,0.00009499909,0.00028131335,0.00046258298,0.75452095,0.0010094038,0.19484219,0.0007475104,0.033584394],"study_design_scores_gemma":[0.000004389698,0.000030039799,0.0016409584,0.00001341142,0.000013138355,0.00005580428,0.000041506424,0.96974945,0.00023462037,0.027817791,0.00037784263,0.000021118612],"about_ca_topic_score_codex":0.010371509,"about_ca_topic_score_gemma":0.007075899,"teacher_disagreement_score":0.010371509,"about_ca_system_score_codex":0.002776752,"about_ca_system_score_gemma":0.0020272927,"threshold_uncertainty_score":0.043928742},"labels":[],"label_agreement":null},{"id":"W3210136733","doi":"10.2139/ssrn.3495369","title":"Gompertz Law Revisited: Forecasting Mortality with a Multi-factor Exponential Model","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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; University of Guelph","funders":"","keywords":"Gompertz function; Exponential function; Econometrics; Mathematics; Statistics; Applied mathematics; Mathematical analysis","score_opus":0.03496812347199763,"score_gpt":0.294672564808783,"score_spread":0.2597044413367854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210136733","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":[0.5171756,0.0034941724,0.45933148,0.013476436,0.00054758537,0.000068651054,0.00073769473,0.00034616367,0.004822271],"genre_scores_gemma":[0.98301744,0.0013858819,0.010770545,0.00032185297,0.00032664655,0.000029890803,0.00025194875,0.000035475427,0.0038602434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895597,0.00046657198,0.00005933311,0.00026003152,0.0001364575,0.00012164103],"domain_scores_gemma":[0.9869148,0.010880772,0.00082024233,0.00044747101,0.00063278084,0.00030389283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007265312,0.0008884273,0.0022541916,0.0011370503,0.00054989255,0.0028460613,0.0036175512,0.005069663,0.0018973227],"category_scores_gemma":[0.040186174,0.00076992007,0.0010860432,0.0014717081,0.0015910147,0.0048206635,0.0011298889,0.0040198024,0.00037786606],"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":[0.00011215155,0.00006192392,0.011364912,0.00007449233,0.000088083936,0.00027425442,0.00016180765,0.9214276,0.00022817905,0.050134055,0.0019136397,0.014158896],"study_design_scores_gemma":[0.000008583548,0.000009466319,0.0007642154,0.000010474324,0.000011536534,0.000020075799,0.000019012496,0.9865225,0.0000329992,0.012456006,0.00013485074,0.000010289105],"about_ca_topic_score_codex":0.029223988,"about_ca_topic_score_gemma":0.013402588,"teacher_disagreement_score":0.029223988,"about_ca_system_score_codex":0.001508364,"about_ca_system_score_gemma":0.0011221082,"threshold_uncertainty_score":0.058107734},"labels":[],"label_agreement":null},{"id":"W3211997720","doi":"10.2139/ssrn.3570066","title":"Annuity and Insurance Choice Under Habit Formation","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Bequest; Habit; Economics; Annuity; Portfolio; Life insurance; Life annuity; Consumption (sociology); Microeconomics; Actuarial science; Financial economics; Pension; Finance; Psychology","score_opus":0.018800668518534826,"score_gpt":0.2811626732234167,"score_spread":0.26236200470488186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211997720","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":[0.9952081,0.00016265507,0.000864535,0.0009107258,0.000008329579,0.000010783985,0.00012009113,0.0000063443863,0.0027084902],"genre_scores_gemma":[0.99833053,0.00007578993,0.000060168994,0.000036997877,0.000012167871,0.0000030477086,0.00004227156,0.0000011837242,0.0014379171],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942833,0.00025476058,0.000029362684,0.00006338022,0.0000401503,0.00018403414],"domain_scores_gemma":[0.9870344,0.007321276,0.0025856425,0.000458017,0.0002922598,0.0023084106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027864326,0.00015116065,0.00041034888,0.000708863,0.00051484175,0.0019068359,0.0004464147,0.0013822964,0.009256785],"category_scores_gemma":[0.009755657,0.00021118448,0.00042142844,0.00063907873,0.0013115036,0.0013800267,0.0010814623,0.0011411563,0.00050687796],"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":[0.0025336195,0.0013773215,0.8902035,0.000062654115,0.00027076993,0.0010910349,0.0016107252,0.010868783,0.0011710091,0.068841904,0.0008713064,0.021097444],"study_design_scores_gemma":[0.00026066825,0.0012922426,0.73634756,0.000046844918,0.00026607577,0.0007750194,0.0049202805,0.058626898,0.00077947654,0.19416988,0.0024424542,0.000072626484],"about_ca_topic_score_codex":0.0052508754,"about_ca_topic_score_gemma":0.0063196053,"teacher_disagreement_score":0.009256785,"about_ca_system_score_codex":0.000708342,"about_ca_system_score_gemma":0.0005782818,"threshold_uncertainty_score":0.030966997},"labels":[],"label_agreement":null},{"id":"W3215146792","doi":"10.1093/forestscience/54.2.129","title":"Parameter Estimation of Base-Age Invariant Site Index Models: Which Data Structure to Use?—Reply","year":2008,"lang":"en","type":"article","venue":"Forest Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Statistics; Site index; Mathematics; Invariant (physics); Econometrics; Index (typography); Estimation; Age structure; Base (topology); Geography; Computer science; Forestry; Demography; Economics; Mathematical analysis; Sociology; Population","score_opus":0.08009438476597887,"score_gpt":0.3155314466694495,"score_spread":0.23543706190347063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215146792","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007367428,0.0022537527,0.0031723138,0.98299444,0.010038583,0.000016684744,0.00037184276,0.000055411092,0.0003602936],"genre_scores_gemma":[0.022006229,0.004966331,0.0080633545,0.9111792,0.05058448,0.00013662929,0.00044016272,0.00013593421,0.0024877868],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994833,0.0025486099,0.0009126048,0.00080238184,0.0006619764,0.00024138142],"domain_scores_gemma":[0.8916435,0.08683313,0.0025404482,0.0038706367,0.012995741,0.002116484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01906434,0.0009082712,0.0021153823,0.0010165316,0.0012047808,0.0024633175,0.0041036597,0.01834281,0.005547854],"category_scores_gemma":[0.14693424,0.0011108911,0.0012949167,0.0016615497,0.004007523,0.0056825276,0.002158504,0.03395216,0.0043631275],"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":[0.00021324953,0.0000890183,0.0034686695,0.0003357349,0.00019195212,0.0003666611,0.00038839885,0.0013480124,0.0004383277,0.006635833,0.9397242,0.046799853],"study_design_scores_gemma":[0.0010311846,0.00024025438,0.02199714,0.0020402605,0.000509997,0.0028834667,0.00384599,0.018612769,0.0023452614,0.18116242,0.764538,0.00079321116],"about_ca_topic_score_codex":0.01374148,"about_ca_topic_score_gemma":0.008834436,"teacher_disagreement_score":0.01906434,"about_ca_system_score_codex":0.001691676,"about_ca_system_score_gemma":0.0028325135,"threshold_uncertainty_score":0.100823045},"labels":[],"label_agreement":null},{"id":"W3215841386","doi":"10.1007/s00180-023-01338-4","title":"Spatial correlation in weather forecast accuracy: a functional time series approach","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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":"Natural Sciences and Engineering Research Council of Canada; United States Agency for International Development; David R. Atkinson Center for a Sustainable Future , Cornell University; Xerox; National Science Foundation","keywords":"Heteroscedasticity; Autocorrelation; Autoregressive model; Econometrics; Autoregressive conditional heteroskedasticity; Variance (accounting); Series (stratigraphy); Time series; Spatial correlation; Functional data analysis; Statistics; Conditional variance; Mathematics; Computer science; Meteorology; Geography; Economics; Volatility (finance); Geology","score_opus":0.027845690562298294,"score_gpt":0.28487212612672763,"score_spread":0.25702643556442933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215841386","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":[0.2516706,0.0009253204,0.7417989,0.0018501496,0.000111360685,0.00002665341,0.00029703107,0.00016706763,0.003152977],"genre_scores_gemma":[0.9769205,0.00042974777,0.020501891,0.000109541856,0.00016542754,0.000039892722,0.00019628828,0.000073220435,0.0015636157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820626,0.0010348245,0.000122068304,0.00028917563,0.00022853326,0.00011910444],"domain_scores_gemma":[0.95875096,0.03557478,0.0018449086,0.0018447133,0.0016643582,0.0003202841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008997606,0.0005960012,0.0008208776,0.0030042059,0.00044133267,0.0017259356,0.0016238188,0.0015214897,0.0016114333],"category_scores_gemma":[0.03561099,0.00059250946,0.0013099457,0.0021360128,0.0019963298,0.0033756143,0.001551595,0.0014930648,0.00010953742],"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":[0.000061384366,0.00007625796,0.031088645,0.000093719136,0.0003024336,0.00020569828,0.00021796192,0.74439055,0.00065849506,0.1944534,0.001111103,0.027340285],"study_design_scores_gemma":[0.0000022691884,0.000011120859,0.002427521,0.000006867213,0.000016791308,0.000026645823,0.000023623443,0.97913855,0.00006760008,0.018146157,0.00012520002,0.000007651525],"about_ca_topic_score_codex":0.008917745,"about_ca_topic_score_gemma":0.0053442037,"teacher_disagreement_score":0.008997606,"about_ca_system_score_codex":0.0011119611,"about_ca_system_score_gemma":0.00094057055,"threshold_uncertainty_score":0.047584474},"labels":[],"label_agreement":null},{"id":"W326744236","doi":"10.2139/ssrn.2569680","title":"Robust Longevity Risk Management","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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; University of Manitoba","funders":"","keywords":"Longevity; Longevity risk; Risk management; Risk analysis (engineering); Business; Actuarial science; Medicine; Gerontology","score_opus":0.011291308757295653,"score_gpt":0.2529023241445346,"score_spread":0.24161101538723898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W326744236","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":[0.11008437,0.0021579284,0.8449245,0.00447166,0.0004223969,0.0001020135,0.0008914095,0.0015227441,0.03542299],"genre_scores_gemma":[0.95048815,0.000543926,0.029526222,0.00028923972,0.0002717591,0.000060833718,0.00035782208,0.00010536205,0.0183568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990814,0.0002242909,0.000053873468,0.0002795661,0.00024218392,0.00011884245],"domain_scores_gemma":[0.99759066,0.00076471106,0.0005518197,0.00052939326,0.000407447,0.00015603697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002748904,0.00065701216,0.0008972774,0.00069568877,0.0004007501,0.0021641932,0.0010326864,0.0014462679,0.0053147958],"category_scores_gemma":[0.0119485725,0.00035148198,0.0006299798,0.00045567326,0.00068912766,0.001549869,0.0022479242,0.001423401,0.0011458287],"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":[0.00024026954,0.00020344793,0.0103819845,0.00017055633,0.0004668311,0.00039547583,0.00022772531,0.3881286,0.009142194,0.35065597,0.014090999,0.2258959],"study_design_scores_gemma":[0.000026592981,0.00017754144,0.0047119367,0.00004220616,0.00009593539,0.00018900882,0.00006380128,0.76654774,0.0021928325,0.22013563,0.0057674544,0.00004929814],"about_ca_topic_score_codex":0.0011907853,"about_ca_topic_score_gemma":0.0006138737,"teacher_disagreement_score":0.0053147958,"about_ca_system_score_codex":0.00087149185,"about_ca_system_score_gemma":0.00092900736,"threshold_uncertainty_score":0.017779768},"labels":[],"label_agreement":null},{"id":"W38771261","doi":"10.1084/jem.2022112205142024c","title":"Diagnosis of Stationarity in State Space Models for Longitudinal Data","year":2006,"lang":"en","type":"article","venue":"Far East Journal of Theoretical Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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":"Econometrics; State space; State (computer science); State-space representation; Computer science; Mathematics; Statistics; Algorithm","score_opus":0.047130810082124865,"score_gpt":0.33723794628199,"score_spread":0.2901071361998651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38771261","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":[0.3897584,0.00022627744,0.6079957,0.00053890725,0.00003659034,0.000083464205,0.00037584658,0.00032192637,0.00066287396],"genre_scores_gemma":[0.97574675,0.000116357456,0.022880668,0.000042243402,0.000044998236,0.00006216696,0.0006950126,0.000031345124,0.00038044847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99453485,0.0031477278,0.0004915068,0.0009615673,0.00042326152,0.00044110348],"domain_scores_gemma":[0.765699,0.22027725,0.0053643608,0.0057589514,0.0017439288,0.0011565138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021099195,0.00067161885,0.0014324228,0.002971626,0.0009841603,0.002398202,0.0014258071,0.002250332,0.0024111038],"category_scores_gemma":[0.123228416,0.001155877,0.0018296413,0.0015475103,0.0018277221,0.0031949952,0.0027628844,0.0024447073,0.0002929304],"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":[0.0014903633,0.0004657647,0.27429464,0.00020186421,0.0011447299,0.0011990811,0.0019815352,0.5376877,0.0022371507,0.103803866,0.001887895,0.07360549],"study_design_scores_gemma":[0.000032067175,0.00013613408,0.007648033,0.000018088242,0.000053095384,0.00013243129,0.00016898349,0.96152514,0.00037572306,0.029711623,0.00017690717,0.0000217844],"about_ca_topic_score_codex":0.0037535194,"about_ca_topic_score_gemma":0.002387224,"teacher_disagreement_score":0.021099195,"about_ca_system_score_codex":0.0008457771,"about_ca_system_score_gemma":0.001638357,"threshold_uncertainty_score":0.111584544},"labels":[],"label_agreement":null},{"id":"W4200476446","doi":"10.1007/s10654-021-00821-w","title":"Re: Subramanian and Kumar. Vaccination rates and COVID-19 cases","year":2021,"lang":"en","type":"letter","venue":"European Journal of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Wilfrid Laurier University","funders":"","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Vaccination; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Epidemiology; Public health; Coronavirus Infections; Virology; Family medicine; Outbreak; Internal medicine; Pathology; Disease; Infectious disease (medical specialty)","score_opus":0.12335478584574888,"score_gpt":0.3840531200102668,"score_spread":0.26069833416451793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200476446","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012920776,0.0022980971,0.00006581861,0.97176635,0.016869524,0.000028207416,0.00022398279,0.000036782705,0.0074190814],"genre_scores_gemma":[0.01765318,0.0026788453,0.0002524514,0.8892782,0.068951026,0.00011210179,0.00014469525,0.000054513832,0.020875048],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988464,0.0003285326,0.00019438498,0.00013623993,0.00035776367,0.00013654353],"domain_scores_gemma":[0.99260217,0.004088923,0.00056668924,0.00022404711,0.0015793304,0.0009388446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019450206,0.0006983432,0.0011475519,0.0010815638,0.0022009371,0.0019214355,0.0012134024,0.021274729,0.01614051],"category_scores_gemma":[0.022873688,0.0005346518,0.00064862915,0.00089195924,0.0008286581,0.0026949828,0.0008931226,0.021646414,0.015030094],"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":[0.00007390363,0.000042814423,0.0016971838,0.000039937357,0.000009732036,0.0014409344,0.00008468885,0.000021553096,0.00010373811,0.00078944146,0.9871875,0.008508525],"study_design_scores_gemma":[0.00032798309,0.00012692343,0.016089605,0.0013783696,0.00007099662,0.008205601,0.0013636071,0.0009422362,0.00043175803,0.011376519,0.9595608,0.00012572917],"about_ca_topic_score_codex":0.011312283,"about_ca_topic_score_gemma":0.020223536,"teacher_disagreement_score":0.021274729,"about_ca_system_score_codex":0.0021518245,"about_ca_system_score_gemma":0.002425467,"threshold_uncertainty_score":0.05399543},"labels":[],"label_agreement":null},{"id":"W4200500534","doi":"10.1016/j.insmatheco.2021.11.002","title":"Valuing guaranteed minimum accumulation benefits by a change of numéraire approach","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology of the People's Republic of China","keywords":"Valuation (finance); Monte Carlo method; Computer science; Computation; Econometrics; Mathematical optimization; Actuarial science; Economics; Mathematics; Finance; Algorithm; Statistics","score_opus":0.07156588227444555,"score_gpt":0.2961165948910202,"score_spread":0.22455071261657467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200500534","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":[0.0439389,0.0010845992,0.9376883,0.0041861623,0.00027230373,0.000084344414,0.00011564491,0.00008370864,0.01254602],"genre_scores_gemma":[0.84037805,0.0014585609,0.14194043,0.00039574003,0.00052245456,0.00025544243,0.00017133965,0.00012362924,0.014754486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923315,0.0047524027,0.0003099455,0.00096558605,0.0012086027,0.00043194118],"domain_scores_gemma":[0.97697395,0.018952213,0.00095835014,0.0011740641,0.0010163465,0.00092499296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014995009,0.0018486733,0.003116599,0.0023382274,0.0010378012,0.0069007725,0.0032157002,0.0052754777,0.0040466394],"category_scores_gemma":[0.053655993,0.0013649643,0.0024545733,0.0014680402,0.00575461,0.012278601,0.0043318146,0.0069390717,0.0003161001],"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":[0.00012785092,0.000117972784,0.00097332103,0.00011560133,0.00009842304,0.00014748384,0.00026938776,0.1689415,0.0005601693,0.8129158,0.0014779749,0.014254536],"study_design_scores_gemma":[0.000029027471,0.000066952394,0.00025586964,0.00005242029,0.000041492705,0.000050810395,0.000054725366,0.42880338,0.00017515775,0.5687566,0.001682265,0.000031448828],"about_ca_topic_score_codex":0.002683057,"about_ca_topic_score_gemma":0.0016511565,"teacher_disagreement_score":0.014995009,"about_ca_system_score_codex":0.0051330714,"about_ca_system_score_gemma":0.0021354244,"threshold_uncertainty_score":0.07930213},"labels":[],"label_agreement":null},{"id":"W4205090414","doi":"10.1177/1536867x211063410","title":"Review of Michael N. Mitchell’s Interpreting and Visualizing Regression Models Using Stata, Second Edition","year":2021,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"NOSM University; Lakehead University","funders":"","keywords":"Computer science; Regression; Statistics; Econometrics; Mathematics","score_opus":0.07237149430034216,"score_gpt":0.40096340195799957,"score_spread":0.3285919076576574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205090414","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":[0.00026572368,0.7775678,0.07710835,0.11767177,0.0154691525,0.000083886574,0.00060440623,0.0013136418,0.009915335],"genre_scores_gemma":[0.0031037228,0.88800794,0.055921655,0.028830053,0.015668867,0.0002012684,0.00049222785,0.0012427904,0.0065314155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98046,0.010191237,0.0023301758,0.0010913156,0.0056384075,0.00028891116],"domain_scores_gemma":[0.90145415,0.06723302,0.0042387173,0.0023754067,0.023455888,0.0012427631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028456623,0.00120601,0.0016645193,0.007948651,0.0007352196,0.0037051786,0.0020970819,0.0024786568,0.005236824],"category_scores_gemma":[0.12053401,0.0011803602,0.0015825448,0.010399894,0.002527119,0.0045624245,0.0015119296,0.0061577726,0.006830884],"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":[0.000020360556,0.000018797225,0.00035891437,0.0029926454,0.00007495957,0.00009703266,0.00030915783,0.0010549382,0.00016454965,0.014223869,0.6335667,0.34711802],"study_design_scores_gemma":[0.000009098655,0.000013613509,0.00046863165,0.0030150802,0.00003586911,0.00021097295,0.0000791831,0.00035132896,0.0001521533,0.0067629367,0.988853,0.000048171143],"about_ca_topic_score_codex":0.015940607,"about_ca_topic_score_gemma":0.018420735,"teacher_disagreement_score":0.028456623,"about_ca_system_score_codex":0.0027730048,"about_ca_system_score_gemma":0.008655966,"threshold_uncertainty_score":0.15049481},"labels":[],"label_agreement":null},{"id":"W4210463040","doi":"10.3390/jrfm15020065","title":"Optimal Allocation of Retirement Portfolios","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Economics; Pension; Present value; Actuarial science; Asset allocation; Life expectancy; Asset (computer security); Cash flow; Investment (military); Stochastic control; Real estate; Portfolio; Finance; Computer science","score_opus":0.009612820311514588,"score_gpt":0.25357965589868403,"score_spread":0.24396683558716944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210463040","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":[0.31641528,0.0014715082,0.63843083,0.0011622242,0.00009710066,0.00042943735,0.00042135865,0.0004837475,0.041088637],"genre_scores_gemma":[0.89816654,0.00061400014,0.08506964,0.00012254207,0.000034658122,0.00029623482,0.00020941946,0.000073093564,0.015413751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948776,0.00022968283,0.00002198013,0.000068798676,0.000066007684,0.00012585137],"domain_scores_gemma":[0.99914086,0.0004574925,0.00011183614,0.00005632502,0.000125393,0.00010802812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001209634,0.0006291104,0.0010505164,0.00086559705,0.00030669916,0.0012245866,0.0007697487,0.0013601927,0.007525959],"category_scores_gemma":[0.0038207301,0.0005586551,0.00048673793,0.00047216646,0.00040456862,0.00108933,0.0010865503,0.0005575277,0.0008171277],"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":[0.0002401409,0.000114515606,0.0010672609,0.00009053281,0.000045274934,0.000097089374,0.00010017151,0.9070654,0.0014320192,0.024194943,0.0024684693,0.06308408],"study_design_scores_gemma":[0.000040778636,0.000090396155,0.00066894724,0.00002727678,0.000017054712,0.000026921205,0.000057766592,0.97868514,0.00042165339,0.018117242,0.0018336743,0.000013142262],"about_ca_topic_score_codex":0.002297304,"about_ca_topic_score_gemma":0.0021391565,"teacher_disagreement_score":0.007525959,"about_ca_system_score_codex":0.0012871437,"about_ca_system_score_gemma":0.0013234188,"threshold_uncertainty_score":0.025176823},"labels":[],"label_agreement":null},{"id":"W4213377962","doi":"10.1002/9780470670590.wbeog620","title":"Mortality","year":2014,"lang":"en","type":"other","venue":"The Wiley-Blackwell Encyclopedia of Globalization","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Demographic transition; Fertility; Population growth; Demography; Population; Industrialisation; Birth rate; Developed country; Geography; Historical demography; Sub-replacement fertility; Mortality rate; Total fertility rate; World population; Demographic statistics; Demographic analysis; Research methodology; Economics; Family planning; Sociology","score_opus":0.0114259995451272,"score_gpt":0.28639608033434255,"score_spread":0.27497008078921537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213377962","genre_codex":"dataset","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":[0.03147248,0.01433037,0.007147804,0.0059737884,0.002725075,0.0010156553,0.68002903,0.0023563537,0.2549494],"genre_scores_gemma":[0.17321064,0.016824327,0.005074849,0.0037061048,0.0033562188,0.0018193432,0.56566274,0.00045092576,0.22989488],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99789435,0.00035721328,0.00034461837,0.00031629947,0.0008074873,0.00028007434],"domain_scores_gemma":[0.99711156,0.0003077494,0.0007504118,0.00020990048,0.0012700948,0.0003503558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013093842,0.00087789266,0.00064460735,0.004215474,0.00050416775,0.0013756545,0.00095866394,0.00061466644,0.0851252],"category_scores_gemma":[0.0054631177,0.0001890253,0.001082094,0.0040318863,0.00020445915,0.00061005255,0.0012995531,0.0013882706,0.036496054],"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":[0.00037368317,0.0001422209,0.09823243,0.001998723,0.00037287181,0.00016079233,0.00028896093,0.00085914554,0.0003298489,0.009379999,0.6432593,0.24460213],"study_design_scores_gemma":[0.00010969192,0.0002782925,0.24791223,0.0015257717,0.00021633273,0.00078630523,0.00034709365,0.0007314884,0.0005261375,0.0041461326,0.7433491,0.00007139097],"about_ca_topic_score_codex":0.0084329145,"about_ca_topic_score_gemma":0.00595884,"teacher_disagreement_score":0.0851252,"about_ca_system_score_codex":0.0010191265,"about_ca_system_score_gemma":0.0015959333,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4214504510","doi":"10.31235/osf.io/v3wkj","title":"Kane Tanaka’s 119 birthday and the Supercentenarians’ age estimation. Further remarks on the oldest old record of 122 years by Jeanne Calment","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Centenarian; Demography; Estimation; Population; Series (stratigraphy); Geography; Statistics; Mathematics; Geology; Sociology","score_opus":0.01629419523880919,"score_gpt":0.271014771528016,"score_spread":0.2547205762892068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214504510","genre_codex":"commentary","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":[0.02291099,0.15320769,0.14201187,0.53333765,0.065886624,0.0000824083,0.004538508,0.0004570411,0.07756721],"genre_scores_gemma":[0.5612996,0.0880499,0.098768845,0.073146746,0.05366378,0.0003405769,0.0040598656,0.0012339543,0.11943672],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9981956,0.0009452135,0.00010747061,0.0004335469,0.000245382,0.000072888986],"domain_scores_gemma":[0.9963792,0.0021161677,0.00022407969,0.000394991,0.0007154007,0.00017017162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035626343,0.0005667801,0.00042621276,0.0013034001,0.00077714043,0.0013066223,0.0012676428,0.001292458,0.0038336068],"category_scores_gemma":[0.021277549,0.00036761092,0.0008785605,0.0013789901,0.0014886474,0.0030660683,0.00174075,0.0051896,0.0025024523],"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":[0.00012783062,0.000026637195,0.01584052,0.00030177063,0.00013335375,0.00035445034,0.0019840708,0.0052576205,0.00018738596,0.29356116,0.5508303,0.13139492],"study_design_scores_gemma":[0.000019938654,0.000043365515,0.014237954,0.0007515163,0.000053925934,0.00046484068,0.0007527339,0.010553732,0.00035674067,0.2428937,0.7297224,0.00014918964],"about_ca_topic_score_codex":0.023552675,"about_ca_topic_score_gemma":0.019023797,"teacher_disagreement_score":0.023552675,"about_ca_system_score_codex":0.0012170248,"about_ca_system_score_gemma":0.00076109916,"threshold_uncertainty_score":0.04683119},"labels":[],"label_agreement":null},{"id":"W4214849219","doi":"10.47302/jsr.2018520101","title":"Guidance for practitioners on the choices of software implementation for frailty models: Simulations and an application in determining the birth interval dynamics","year":2018,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Centre for Advancing Health Outcomes; St. Paul's Hospital","funders":"","keywords":"Censoring (clinical trials); Covariate; Computer science; Implementation; Nonparametric statistics; Parametric statistics; Econometrics; Proportional hazards model; Software; Statistics; R package; Hazard; Survival analysis; Mathematics; Machine learning","score_opus":0.182601622132745,"score_gpt":0.5283437798099686,"score_spread":0.3457421576772236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214849219","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":[0.013797428,0.0036288,0.86735916,0.022448331,0.0010656974,0.0018372667,0.0060366048,0.045092866,0.03873375],"genre_scores_gemma":[0.027463071,0.002488234,0.9500412,0.0030657316,0.00017353575,0.0021792192,0.0021843994,0.0058724876,0.0065321927],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9858338,0.009477261,0.0013500609,0.00057629956,0.0023829683,0.0003795401],"domain_scores_gemma":[0.78962857,0.16522633,0.0052495706,0.011334164,0.025705978,0.0028554448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04350861,0.0020305426,0.0015112767,0.0041861623,0.0012807961,0.0038712972,0.005086061,0.0061517353,0.045912486],"category_scores_gemma":[0.25891158,0.001960562,0.001820676,0.003283512,0.001170864,0.0069350903,0.0041069114,0.00469705,0.022791829],"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":[0.0011371009,0.0009054503,0.011677944,0.0025029248,0.00013087588,0.0011412677,0.003246774,0.04746845,0.0042994046,0.06276442,0.4534006,0.41132483],"study_design_scores_gemma":[0.0013309254,0.00061701087,0.0055697924,0.009446788,0.00019679005,0.0015814267,0.0020376649,0.22854927,0.0091354605,0.24049252,0.50031257,0.00072977954],"about_ca_topic_score_codex":0.005804733,"about_ca_topic_score_gemma":0.011907602,"teacher_disagreement_score":0.045912486,"about_ca_system_score_codex":0.0012629463,"about_ca_system_score_gemma":0.005478716,"threshold_uncertainty_score":0.23009825},"labels":[],"label_agreement":null},{"id":"W4220876067","doi":"10.3390/jrfm15030143","title":"Optimal Control Strategies for the Premium Policy of an Insurance Firm with Jump Diffusion Assets and Stochastic Interest Rate","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Stochastic differential equation; Stochastic control; Jump diffusion; Control (management); Jump; Economics; Cash; Cash flow; Set (abstract data type); Actuarial science; Optimal control; Mathematics; Mathematical optimization; Computer science; Applied mathematics; Finance","score_opus":0.010510936031519738,"score_gpt":0.2645968300940252,"score_spread":0.2540858940625054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220876067","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":[0.1291159,0.0011400549,0.8560204,0.00227764,0.00012002186,0.00015242306,0.00015241187,0.0001584334,0.010862702],"genre_scores_gemma":[0.97649854,0.00049969944,0.016637305,0.00012199666,0.00004564333,0.00016591766,0.00005608574,0.000021918713,0.005952857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993679,0.00023931416,0.000023288738,0.00015076192,0.00009315219,0.00012569738],"domain_scores_gemma":[0.99881727,0.0007027673,0.00020997481,0.000030073039,0.0001354438,0.00010445196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016093893,0.00117996,0.0012249809,0.0006200159,0.00051844725,0.0019947023,0.0011593971,0.0024727467,0.0025372414],"category_scores_gemma":[0.0032359632,0.0005503104,0.0008163183,0.00033514304,0.0013720858,0.0011178231,0.0014131798,0.0017777822,0.00021606291],"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":[0.00010749177,0.000088937726,0.00075732474,0.00010687856,0.00007461775,0.00017662466,0.00014062902,0.92175615,0.002817077,0.067421935,0.00061971584,0.0059326296],"study_design_scores_gemma":[0.000027825457,0.000069271526,0.0002178891,0.000011636415,0.000024003863,0.000012855745,0.000023258177,0.989622,0.00022258297,0.009521148,0.00023522947,0.000012290809],"about_ca_topic_score_codex":0.009533123,"about_ca_topic_score_gemma":0.0040739956,"teacher_disagreement_score":0.009533123,"about_ca_system_score_codex":0.0019891227,"about_ca_system_score_gemma":0.0020570995,"threshold_uncertainty_score":0.01895529},"labels":[],"label_agreement":null},{"id":"W4221032302","doi":"10.2139/ssrn.4036480","title":"Collective Longevity Swap: a Novel Longevity Risk Transfer Solution and Its Economic Pricing","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Longevity; Swap (finance); Transfer pricing; Longevity risk; Economics; Financial economics; Finance; Medicine","score_opus":0.013422375719802766,"score_gpt":0.2583646521726177,"score_spread":0.24494227645281497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221032302","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":[0.10138844,0.00068139884,0.8696151,0.0023494286,0.0005906995,0.000119455675,0.000087822584,0.00045592667,0.024711756],"genre_scores_gemma":[0.9181356,0.00037309504,0.05809046,0.00026145068,0.00045547044,0.00011125724,0.00005922017,0.000094353665,0.022419289],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938583,0.00021562513,0.000022620767,0.00010429715,0.00018184264,0.00008984196],"domain_scores_gemma":[0.9990074,0.0003708206,0.00009448538,0.00016904766,0.00017880705,0.00017950346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015363635,0.00061709445,0.0010374938,0.00064808445,0.0008166361,0.0018911986,0.0016339584,0.0028763793,0.008833291],"category_scores_gemma":[0.004594252,0.00024364784,0.0007971745,0.0007790464,0.0011793869,0.002412679,0.0024359955,0.0018784577,0.00071998016],"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":[0.00042426877,0.00041656272,0.0010929389,0.00015883594,0.000101900565,0.0005446472,0.00030268694,0.20272171,0.0080452,0.6182829,0.016262265,0.15164603],"study_design_scores_gemma":[0.000058804475,0.00015858353,0.00024998444,0.000017655328,0.00003465,0.0003070814,0.00004182036,0.830987,0.0006901367,0.16367885,0.0037497506,0.00002569695],"about_ca_topic_score_codex":0.00031669738,"about_ca_topic_score_gemma":0.00025621618,"teacher_disagreement_score":0.008833291,"about_ca_system_score_codex":0.00054828456,"about_ca_system_score_gemma":0.0008465472,"threshold_uncertainty_score":0.029550314},"labels":[],"label_agreement":null},{"id":"W4224063122","doi":"10.31235/osf.io/uqwxj","title":"Using Singular Value Decomposition to Understand Variation Across Mortality Schedules from Multiple Populations","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inequality; Mortality rate; Decomposition; Key (lock); Demography; Econometrics; Mathematics; Biology; Ecology; Sociology","score_opus":0.18471938736079832,"score_gpt":0.4551198232856816,"score_spread":0.2704004359248833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224063122","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":[0.05817369,0.0005068149,0.9381093,0.00056312914,0.000103785744,0.0000776931,0.00075527525,0.00027177596,0.0014385828],"genre_scores_gemma":[0.5024051,0.0014199489,0.4901213,0.00032200426,0.0003272314,0.0003060995,0.0030087277,0.00018188158,0.0019077339],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882585,0.000465011,0.000113875554,0.00027009987,0.000241439,0.00008377174],"domain_scores_gemma":[0.9946425,0.0029336214,0.0009325799,0.0007766098,0.0005537198,0.00016098698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043689567,0.0010101353,0.0007454118,0.0035206454,0.00038776363,0.0013755347,0.0005716695,0.00049862807,0.0011047922],"category_scores_gemma":[0.013501541,0.000274662,0.0013549719,0.003073001,0.00096273195,0.0012820422,0.0011639051,0.0014867067,0.00025649017],"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":[0.00020228475,0.00027563484,0.084727086,0.00059469824,0.0015470416,0.00071304536,0.002315212,0.25056058,0.018734975,0.19686294,0.008374378,0.43509206],"study_design_scores_gemma":[0.000029794952,0.00013052158,0.04540778,0.00011533889,0.00015542867,0.00018144058,0.0005960934,0.6097272,0.002536102,0.33379334,0.0072396877,0.00008734187],"about_ca_topic_score_codex":0.005113375,"about_ca_topic_score_gemma":0.0031207618,"teacher_disagreement_score":0.005113375,"about_ca_system_score_codex":0.0006725493,"about_ca_system_score_gemma":0.0011319758,"threshold_uncertainty_score":0.023105562},"labels":[],"label_agreement":null},{"id":"W4224303238","doi":"10.3390/risks10040078","title":"Unit-Linked Tontine: Utility-Based Design, Pricing and Performance","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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; Actua","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unit (ring theory); Attractiveness; Economics; Stochastic game; Transferable utility; Payment; Unit of account; Microeconomics; Unit price; Actuarial science; Computer science; Finance; Game theory","score_opus":0.09384686988635593,"score_gpt":0.33582738915993054,"score_spread":0.2419805192735746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224303238","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":[0.056318395,0.00048383113,0.93180054,0.00019433764,0.00005249636,0.0001286288,0.000055801567,0.00014557576,0.0108204605],"genre_scores_gemma":[0.8990904,0.000498728,0.09355722,0.00007041027,0.00002685533,0.00015533049,0.000064469044,0.000052368,0.00648413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918634,0.0004513725,0.000028047558,0.00009279102,0.00016941705,0.00007216758],"domain_scores_gemma":[0.99876475,0.0006609131,0.00013101996,0.00013742407,0.00019702537,0.000108883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017947808,0.00060751406,0.00052486546,0.00037022278,0.00024245211,0.0014651171,0.0010941207,0.00080782303,0.006170585],"category_scores_gemma":[0.005550985,0.00028141684,0.00045087957,0.00045619218,0.0007061828,0.0012680357,0.0011589907,0.0010029705,0.0004858331],"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":[0.00032235126,0.00015691953,0.0012406214,0.00016976337,0.00005690022,0.00018661618,0.00013625137,0.7271761,0.0045845774,0.16715743,0.0017728602,0.09703961],"study_design_scores_gemma":[0.000019678031,0.000251135,0.00023126883,0.000018139512,0.000013107283,0.00007012428,0.000020483452,0.9640924,0.00078701955,0.032545496,0.0019374155,0.000013756306],"about_ca_topic_score_codex":0.0006944378,"about_ca_topic_score_gemma":0.0005189288,"teacher_disagreement_score":0.006170585,"about_ca_system_score_codex":0.00068497023,"about_ca_system_score_gemma":0.0004924407,"threshold_uncertainty_score":0.020642638},"labels":[],"label_agreement":null},{"id":"W4226297132","doi":"10.1136/bmjopen-2021-053497","title":"Interrupted time series analyses to assess the impact of alcohol control policy on socioeconomic inequalities in mortality in Lithuania: a study protocol","year":2021,"lang":"en","type":"article","venue":"BMJ Open","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute on Alcohol Abuse and Alcoholism","keywords":"Medicine; Socioeconomic status; Population; Excise; Demography; Record linkage; Poison control; Environmental health; Census; Law","score_opus":0.2682022693003629,"score_gpt":0.5693116402939997,"score_spread":0.3011093709936368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226297132","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071905944,0.00036790268,0.009837854,0.00037786702,0.0002606118,0.9712828,0.009088792,0.000098992474,0.0014946705],"genre_scores_gemma":[0.002268077,0.0001307675,0.004558157,0.00007969732,0.000017353517,0.99180967,0.00078546704,0.000004940783,0.00034587045],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9734847,0.01802589,0.004002216,0.0018471685,0.001524387,0.0011157218],"domain_scores_gemma":[0.9684674,0.0117829805,0.005030736,0.0052143545,0.008302591,0.0012019442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054633133,0.0028064395,0.0035997184,0.003347821,0.0019172599,0.0022047784,0.003250252,0.003828921,0.04555242],"category_scores_gemma":[0.060383372,0.00202173,0.0054998533,0.0046366914,0.0014603108,0.0014784328,0.0022114115,0.0033574975,0.005650944],"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":[0.15082319,0.01867063,0.047070235,0.102816746,0.008714105,0.003323174,0.012502216,0.02646106,0.0040684803,0.06962149,0.22737132,0.32855737],"study_design_scores_gemma":[0.14398581,0.0596772,0.15903556,0.046456028,0.008826699,0.00085767376,0.0062968605,0.035172027,0.005589099,0.04157709,0.4914268,0.0010992314],"about_ca_topic_score_codex":0.0040498385,"about_ca_topic_score_gemma":0.003922529,"teacher_disagreement_score":0.054633133,"about_ca_system_score_codex":0.0034692446,"about_ca_system_score_gemma":0.016551608,"threshold_uncertainty_score":0.288931},"labels":[],"label_agreement":null},{"id":"W4226370262","doi":"10.1017/s1748499521000257","title":"Dynamic importance allocated nested simulation for variable annuity risk measurement","year":2022,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proxy (statistics); Computer science; Measure (data warehouse); Econometrics; Risk measure; Ranking (information retrieval); Tail risk; Mathematical optimization; Mathematics; Economics; Data mining; Machine learning","score_opus":0.08330989263544017,"score_gpt":0.3805908388847172,"score_spread":0.29728094624927703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226370262","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":[0.036461648,0.000035708395,0.96240085,0.0000466155,0.000014443509,0.000052549552,0.000028133167,0.00012641182,0.0008336942],"genre_scores_gemma":[0.7441547,0.00005264561,0.25485545,0.000045792767,0.000016171629,0.0001537362,0.000110860405,0.000052191903,0.0005584775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698955,0.0020870995,0.00009333205,0.00023216271,0.0004327213,0.00016522981],"domain_scores_gemma":[0.981111,0.0147874,0.0010402831,0.0014452765,0.0011432278,0.00047272135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069429637,0.00051913265,0.00081388175,0.0007655096,0.0003478178,0.0007999136,0.001325584,0.00073067186,0.0017992182],"category_scores_gemma":[0.025414916,0.00046632747,0.0007169457,0.0004775563,0.0011789724,0.0010921962,0.0016942533,0.0016356067,0.00019914817],"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":[0.00010690728,0.00004987845,0.0020469276,0.00001864088,0.000029732983,0.00004260881,0.000054272816,0.9660868,0.00088810024,0.022625633,0.00011179484,0.007938636],"study_design_scores_gemma":[0.000003219229,0.000013257562,0.00008661537,0.0000027313868,0.0000015399961,0.0000039811584,0.000002614007,0.9966462,0.00019953928,0.0029775372,0.00005989776,0.000002788052],"about_ca_topic_score_codex":0.0041793017,"about_ca_topic_score_gemma":0.0026851338,"teacher_disagreement_score":0.0069429637,"about_ca_system_score_codex":0.00094510784,"about_ca_system_score_gemma":0.0009949943,"threshold_uncertainty_score":0.03671831},"labels":[],"label_agreement":null},{"id":"W4229739175","doi":"10.1002/9781119971528.ch3","title":"The Life Table","year":2010,"lang":"es","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Table (database); Notation; Terminology; Life expectancy; Sample (material); Computer science; Arithmetic; Mathematics; Linguistics; Data mining; Philosophy; Sociology; Population; Demography","score_opus":0.011566742456238774,"score_gpt":0.2830971458336958,"score_spread":0.271530403377457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229739175","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":[0.008309786,0.010108366,0.24770896,0.012828624,0.0020199916,0.0005553547,0.065063484,0.0020858757,0.6513196],"genre_scores_gemma":[0.19838665,0.023422867,0.3603664,0.0069827097,0.0024544122,0.0018105364,0.08526689,0.0017726346,0.3195369],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988747,0.00042044284,0.00010321018,0.00016655713,0.0003634582,0.00007160644],"domain_scores_gemma":[0.9975932,0.0012251165,0.00015627788,0.00038372033,0.00050363666,0.00013805818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482724,0.00043237218,0.00039610782,0.0026284305,0.0008129082,0.003967677,0.0007381601,0.0008267869,0.07669363],"category_scores_gemma":[0.009371588,0.00031338152,0.00041355987,0.0043583764,0.0008174463,0.0044889003,0.001122999,0.0016195116,0.02250386],"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":[0.00002372802,0.000015014838,0.0011540186,0.00009801657,0.000006544375,0.000035164863,0.00019749936,0.0011680606,0.000106307234,0.7233255,0.15537713,0.118493065],"study_design_scores_gemma":[0.0000056052627,0.000013639982,0.0009980843,0.00017542293,0.0000041958906,0.00013114685,0.00015591388,0.00092700456,0.000107146894,0.20319875,0.7942673,0.000015751262],"about_ca_topic_score_codex":0.0028533672,"about_ca_topic_score_gemma":0.0018612666,"teacher_disagreement_score":0.07669363,"about_ca_system_score_codex":0.0013543707,"about_ca_system_score_gemma":0.0017741547,"threshold_uncertainty_score":0.25656575},"labels":[],"label_agreement":null},{"id":"W4230391408","doi":"10.1017/9781108784184.026","title":"Index","year":2019,"lang":"en","type":"paratext","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Index (typography); Actuarial science; Life insurance; Cash flow; Computer science; Finance; Economics","score_opus":0.021464315559240513,"score_gpt":0.2544993153435353,"score_spread":0.23303499978429482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230391408","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":[0.0005294348,0.0023491178,0.0024214215,0.002982714,0.005793598,0.00022048963,0.016130475,0.0022695533,0.96730316],"genre_scores_gemma":[0.0018137756,0.0016792017,0.0011412251,0.00088715175,0.0010803448,0.000121928526,0.010433806,0.0007449611,0.9820977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876356,0.00010241298,0.0000951325,0.00022137622,0.0007122754,0.00010528419],"domain_scores_gemma":[0.9974956,0.0003108014,0.000116389034,0.00041158422,0.001279106,0.0003864019],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007670727,0.0011542751,0.0010848818,0.0040674345,0.00168501,0.006661746,0.0017516696,0.0015536213,0.7912202],"category_scores_gemma":[0.006295308,0.00038686843,0.0007123513,0.005179268,0.00049198273,0.005059972,0.002821259,0.0017082773,0.7570123],"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":[0.000014826972,0.00002164128,0.00013805418,0.00013879278,0.0000020650637,0.000028576464,0.0000525572,0.000053984633,0.0001397816,0.004328799,0.901268,0.09381298],"study_design_scores_gemma":[0.000002110647,0.000007203789,0.00021968223,0.00007145156,0.0000010712697,0.000040775907,0.000036750065,0.00003069394,0.00004802221,0.0014155129,0.998123,0.0000037581133],"about_ca_topic_score_codex":0.0031167977,"about_ca_topic_score_gemma":0.0036917126,"teacher_disagreement_score":0.20877981,"about_ca_system_score_codex":0.0020876236,"about_ca_system_score_gemma":0.0016609567,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4230546406","doi":"10.4095/301295","title":"Age Structure, 2001 - Oldest Old by Census Division (75 years of age and older)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Age structure; Demography; Gerontology; Sociology; Medicine; Population; Mathematics; Arithmetic","score_opus":0.026183213217900847,"score_gpt":0.3227526012342257,"score_spread":0.2965693880163249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230546406","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":[0.03102648,0.0029975718,0.00024618674,0.0006713759,0.00028988282,0.00021600399,0.94536227,0.0002047733,0.018985426],"genre_scores_gemma":[0.11537309,0.005731484,0.00095566054,0.0009815278,0.00018686966,0.00074958376,0.85621786,0.00010541424,0.019698437],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99908423,0.000037513633,0.00011879542,0.00013067198,0.00036592578,0.00026298672],"domain_scores_gemma":[0.99570554,0.000092428185,0.0005661767,0.00007663896,0.0031488282,0.00041038686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005920533,0.00081304,0.00050818775,0.005382793,0.0011768983,0.0010360448,0.0013396007,0.00047035096,0.012408513],"category_scores_gemma":[0.0047187475,0.00034640566,0.0006303329,0.010273745,0.00026629324,0.00088100537,0.000811119,0.001472142,0.0065664886],"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":[0.00022262032,0.00007604811,0.2473393,0.00092993904,0.000121572535,0.00010528473,0.000523834,0.0005127155,0.00024408146,0.0012073152,0.7258211,0.022896271],"study_design_scores_gemma":[0.0000541053,0.000041764702,0.9130651,0.00037985205,0.000055330933,0.00017314803,0.00069319736,0.00027892395,0.000081745355,0.00019280022,0.08495742,0.000026632692],"about_ca_topic_score_codex":0.78944564,"about_ca_topic_score_gemma":0.77609736,"teacher_disagreement_score":0.78944564,"about_ca_system_score_codex":0.0044527613,"about_ca_system_score_gemma":0.007856157,"threshold_uncertainty_score":0.4235887},"labels":[],"label_agreement":null},{"id":"W4231258378","doi":"10.1002/sim.822.abs","title":"Simultaneous modelling of operative mortality and long‐term survival after coronary artery bypass surgery","year":2001,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"BC Cancer Agency; Simon Fraser University; University of Alberta","funders":"","keywords":"Covariate; Poisson regression; Proportional hazards model; Coronary artery bypass surgery; Medicine; Survival analysis; Accelerated failure time model; Bypass surgery; Term (time); Regression analysis; Poisson distribution; Artery; Surgery; Cardiology; Statistics; Mathematics; Population","score_opus":0.05708445331418451,"score_gpt":0.3571871711612406,"score_spread":0.3001027178470561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231258378","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":[0.64787215,0.0011270801,0.34152633,0.002774926,0.00012601305,0.00016296373,0.0017445447,0.00034581195,0.0043200576],"genre_scores_gemma":[0.9788134,0.000618999,0.012271924,0.000070227215,0.00006729229,0.00030897354,0.00075221295,0.000033994205,0.007062995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976699,0.0013076313,0.00010201314,0.00034282357,0.0002336356,0.00034398847],"domain_scores_gemma":[0.9907656,0.006756375,0.0012752538,0.00041812667,0.00035363386,0.00043110785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045238254,0.0009902229,0.0011542279,0.00087709504,0.00052160124,0.0017337614,0.0020885407,0.0021343802,0.0025601913],"category_scores_gemma":[0.017806783,0.00086059433,0.0019169716,0.0011569121,0.0014511393,0.0015767703,0.002970092,0.0019697149,0.0004828714],"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":[0.0007794752,0.00026325387,0.043585353,0.00019534133,0.00044045414,0.0007512375,0.0013706675,0.87615544,0.0021038298,0.04891614,0.000961933,0.024476852],"study_design_scores_gemma":[0.00007367542,0.00031605328,0.016664354,0.000035095673,0.00014384571,0.0001739881,0.0001557142,0.93893766,0.0005461068,0.041525133,0.0013821943,0.00004615888],"about_ca_topic_score_codex":0.008280212,"about_ca_topic_score_gemma":0.008521244,"teacher_disagreement_score":0.008280212,"about_ca_system_score_codex":0.0013585985,"about_ca_system_score_gemma":0.0022043858,"threshold_uncertainty_score":0.02392459},"labels":[],"label_agreement":null},{"id":"W4231928694","doi":"10.1007/978-1-4842-7147-6_11","title":"Calculations with Dates","year":2021,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Invoice; Quarter (Canadian coin); Period (music); Set (abstract data type); History; Computer science; Art; World Wide Web; Archaeology","score_opus":0.031865743880505455,"score_gpt":0.27991313897045245,"score_spread":0.248047395089947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231928694","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":[0.00091993285,0.005387043,0.17435218,0.0053808894,0.0076425257,0.00031927935,0.003424582,0.003489985,0.7990836],"genre_scores_gemma":[0.020002753,0.014195329,0.25560132,0.0029592868,0.002210573,0.0005935642,0.0066508627,0.005959228,0.6918271],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99670345,0.00086401345,0.00027520803,0.00040779414,0.0016250218,0.00012451368],"domain_scores_gemma":[0.99442333,0.0023393685,0.00027941918,0.0009599577,0.0018382054,0.00015983442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002666735,0.0019260113,0.0007312762,0.0031353647,0.0020198058,0.0072473986,0.0021173118,0.0015224832,0.16593513],"category_scores_gemma":[0.018597312,0.000919873,0.0014259673,0.0037293825,0.0014096831,0.0109966565,0.00301423,0.0044213263,0.10369747],"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":[0.000040510156,0.000035386834,0.0003029959,0.0004848138,0.000019558556,0.000110179586,0.00059783325,0.0024413483,0.00037033999,0.40673995,0.40136477,0.18749219],"study_design_scores_gemma":[0.0000039681627,0.000008272334,0.00008641138,0.0002007782,0.0000056348426,0.0000905119,0.000101586265,0.00048733395,0.00023072062,0.050591532,0.9481787,0.000014548835],"about_ca_topic_score_codex":0.0029837694,"about_ca_topic_score_gemma":0.0033788518,"teacher_disagreement_score":0.16593513,"about_ca_system_score_codex":0.0021140117,"about_ca_system_score_gemma":0.0022293313,"threshold_uncertainty_score":0.5551083},"labels":[],"label_agreement":null},{"id":"W4232036911","doi":"10.1017/cbo9780511755330","title":"Measure Theory and Filtering","year":2004,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":52,"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":"Kalman filter; Measure (data warehouse); Stochastic calculus; Calculus (dental); Computer science; Conditional expectation; Probability theory; Mathematical finance; Conditional probability distribution; Mathematical economics; Mathematics; Artificial intelligence; Econometrics; Data mining; Statistics; Finance; Mathematical analysis; Medicine; Economics","score_opus":0.01826206817968754,"score_gpt":0.22639708940948436,"score_spread":0.20813502122979682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232036911","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":[0.003582097,0.16235434,0.15405658,0.020084858,0.013262145,0.0000958403,0.001169649,0.0009022716,0.64449227],"genre_scores_gemma":[0.08654428,0.1047337,0.045562167,0.0056246263,0.009110453,0.00025766413,0.0014426518,0.0006288361,0.7460956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898714,0.00015751351,0.000051437764,0.00021200573,0.0005322372,0.000059621354],"domain_scores_gemma":[0.9993304,0.0003230403,0.000035047553,0.000071858885,0.00019980228,0.00003993596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007589372,0.0010599631,0.0011862076,0.0016849844,0.0010332435,0.003721341,0.0007316895,0.0016749941,0.026853759],"category_scores_gemma":[0.003238973,0.00040266657,0.00075936515,0.0019263285,0.0023015856,0.0033391502,0.001063411,0.002419187,0.008705033],"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":[0.000013521215,0.000012167625,0.00013225195,0.00022154074,0.000022744649,0.00007744277,0.0002716667,0.0020117224,0.00032530673,0.74727285,0.13623375,0.11340497],"study_design_scores_gemma":[0.0000057625507,0.000021456997,0.00033098744,0.00018474003,0.000008742331,0.00019663955,0.000067956156,0.0024016527,0.00017885155,0.3156293,0.68095785,0.000016100737],"about_ca_topic_score_codex":0.003327529,"about_ca_topic_score_gemma":0.0022531128,"teacher_disagreement_score":0.026853759,"about_ca_system_score_codex":0.0026534887,"about_ca_system_score_gemma":0.0016788201,"threshold_uncertainty_score":0.08983481},"labels":[],"label_agreement":null},{"id":"W4232283691","doi":"10.1111/j.1728-4457.2005.00085.x","title":"D. V. Glass on the Problems of a Declining Population","year":2005,"lang":"en","type":"article","venue":"Population and Development Review","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fertility; Population momentum; Sub-replacement fertility; Population; Total fertility rate; Quarter (Canadian coin); Population projection; Population growth; Projections of population growth; Immigration; Birth rate; Demography; Population decline; Net migration rate; Developed country; Demographic economics; Geography; Economics; Family planning; Sociology; Research methodology","score_opus":0.05924820501958209,"score_gpt":0.34100918913044836,"score_spread":0.28176098411086625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232283691","genre_codex":"commentary","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":[0.0008056294,0.08545001,0.000674369,0.70028025,0.06471877,0.000044605316,0.00021648187,0.00010824119,0.14770159],"genre_scores_gemma":[0.024122851,0.081386685,0.0011944792,0.32463142,0.022661116,0.00009380636,0.00016979416,0.0002357101,0.54550415],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991966,0.00018204885,0.00003216879,0.00019036373,0.00029377342,0.000105135456],"domain_scores_gemma":[0.9988475,0.00040946604,0.000044790402,0.00006812559,0.00036707605,0.0002630192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014806738,0.000630235,0.00043684692,0.0008373066,0.0026141503,0.0025015753,0.001032567,0.0053470233,0.025300885],"category_scores_gemma":[0.0035086314,0.0002705661,0.0003721884,0.0004940039,0.0021514026,0.001942522,0.0017289435,0.008077687,0.008647786],"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":[0.000017425606,0.000014981924,0.00022176263,0.00005105977,0.0000032761172,0.00006899899,0.00006775502,0.0000708508,0.00006444067,0.017875869,0.957548,0.023995513],"study_design_scores_gemma":[0.0000058689307,0.000010947731,0.00036802262,0.00013798171,0.000001495485,0.000086884,0.00006704105,0.000036041394,0.00005387131,0.00403755,0.9951892,0.000005180914],"about_ca_topic_score_codex":0.01587114,"about_ca_topic_score_gemma":0.03067785,"teacher_disagreement_score":0.025300885,"about_ca_system_score_codex":0.0023604622,"about_ca_system_score_gemma":0.0041547995,"threshold_uncertainty_score":0.08463991},"labels":[],"label_agreement":null},{"id":"W4232724021","doi":"10.1002/9780470057339.vnn018","title":"Copula Modeling for Extremes","year":2012,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Copula (linguistics); Econometrics; Statistics; Environmental science; Mathematics","score_opus":0.032415818970931834,"score_gpt":0.29925357751482756,"score_spread":0.26683775854389574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232724021","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":[0.0059748664,0.0015401045,0.98439413,0.00032030494,0.00008148182,0.000026197671,0.00021765089,0.00018178667,0.007263445],"genre_scores_gemma":[0.79102516,0.007274023,0.18032852,0.0005290009,0.0007146149,0.0005189314,0.0013536135,0.00055998884,0.017696163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988575,0.00056011503,0.00004563565,0.000183719,0.0002512955,0.00010176365],"domain_scores_gemma":[0.99711764,0.0018064464,0.00031745312,0.00025811166,0.00039565327,0.00010466756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019929467,0.0011692457,0.0013154804,0.0013467823,0.00051159185,0.0020282432,0.0016130967,0.001117022,0.0061771856],"category_scores_gemma":[0.009778315,0.00040328564,0.00145756,0.001556754,0.0009535849,0.001773075,0.0015686526,0.0025861731,0.0014570659],"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":[0.000027570402,0.00004969061,0.0011372949,0.00017550796,0.00013920057,0.00024014033,0.00015069942,0.39998516,0.0010865342,0.5582462,0.0071321344,0.03162989],"study_design_scores_gemma":[0.0000041301887,0.00001786292,0.00036542027,0.000049599686,0.000017594688,0.00006723488,0.000021041582,0.7965253,0.00022001889,0.19810627,0.0045896303,0.000015786614],"about_ca_topic_score_codex":0.0025299739,"about_ca_topic_score_gemma":0.0010075979,"teacher_disagreement_score":0.0061771856,"about_ca_system_score_codex":0.00066192483,"about_ca_system_score_gemma":0.00066702836,"threshold_uncertainty_score":0.020664752},"labels":[],"label_agreement":null},{"id":"W4233688659","doi":"10.22215/etd/2006-08559","title":"Quantifying site-specific environmental variance for life-history analyses: a novel application of dendrochronology","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Dendrochronology; Forestry; Humanities; Geography; Art; Archaeology","score_opus":0.07489262905150368,"score_gpt":0.3474269717273274,"score_spread":0.2725343426758237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233688659","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":[0.042739894,0.0004932224,0.95413595,0.000094338495,0.00004109534,0.000071069044,0.00068305933,0.00041024678,0.0013312363],"genre_scores_gemma":[0.23634005,0.0005488755,0.7613856,0.00004126865,0.00007349534,0.00015146448,0.00064377656,0.00023450422,0.0005809407],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99815243,0.0008701842,0.00010674331,0.0005367783,0.0002728949,0.00006096842],"domain_scores_gemma":[0.99367565,0.0041422676,0.0004960821,0.0010056096,0.00045745962,0.00022288806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034760695,0.00097465597,0.0010752657,0.0044606314,0.0010943527,0.0025286374,0.00090015755,0.0007269096,0.0019263147],"category_scores_gemma":[0.009553767,0.00047288038,0.0008345632,0.0051525077,0.0008959067,0.0016155929,0.0018607795,0.0012166731,0.0005081514],"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":[0.00021508377,0.00026310867,0.11999087,0.0009487299,0.0018346815,0.0004501772,0.0022999798,0.03857992,0.13292514,0.03829253,0.0020338814,0.66216594],"study_design_scores_gemma":[0.00013360237,0.0005094879,0.2403411,0.00029804392,0.001076233,0.0015469174,0.0018871534,0.54377735,0.03220794,0.14484972,0.032893725,0.00047883007],"about_ca_topic_score_codex":0.0030857376,"about_ca_topic_score_gemma":0.009515715,"teacher_disagreement_score":0.0044606314,"about_ca_system_score_codex":0.00057107507,"about_ca_system_score_gemma":0.00094570377,"threshold_uncertainty_score":0.018383443},"labels":[],"label_agreement":null},{"id":"W4233988076","doi":"10.1017/9781316282229.015","title":"Life Tables","year":2017,"lang":"en","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Computer science","score_opus":0.02817098075998978,"score_gpt":0.32952067200859864,"score_spread":0.30134969124860883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233988076","genre_codex":"dataset","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":[0.0002455061,0.0006948344,0.0012866997,0.00042145886,0.0003488503,0.00015725759,0.96889526,0.0011466425,0.026803453],"genre_scores_gemma":[0.00425217,0.0025209195,0.0035041287,0.0009822455,0.0004958823,0.0008359667,0.93515563,0.0010763013,0.051176783],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976186,0.0005149888,0.00054190797,0.00038337216,0.00076004944,0.00018099991],"domain_scores_gemma":[0.9842047,0.007297931,0.0015155612,0.0016072843,0.0047371774,0.00063740666],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002194236,0.00092201284,0.0009964277,0.008691304,0.0004955322,0.0018338661,0.001208423,0.0008063018,0.7215155],"category_scores_gemma":[0.030407159,0.0005575096,0.0012522336,0.0120033175,0.00017819245,0.0015275795,0.0010076929,0.0015016355,0.37783396],"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":[0.00006153992,0.000014670793,0.0005518165,0.0009896815,0.000040605522,0.00001898042,0.000022552556,0.00015882625,0.0000314325,0.0021113944,0.97578305,0.02021538],"study_design_scores_gemma":[0.00008190331,0.00002237385,0.002147357,0.00066541246,0.000030921172,0.000053361207,0.000034283985,0.00011596231,0.000052190902,0.002666111,0.9941162,0.000014009171],"about_ca_topic_score_codex":0.0049687214,"about_ca_topic_score_gemma":0.0063299416,"teacher_disagreement_score":0.7215155,"about_ca_system_score_codex":0.0010973148,"about_ca_system_score_gemma":0.0019684602,"threshold_uncertainty_score":0.39722437},"labels":[],"label_agreement":null},{"id":"W4234059723","doi":"10.46692/9781447317548.005","title":"A method for collecting lifecourse data: assessing the utility of the lifegrid","year":2015,"lang":"en","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Computer science; Data science","score_opus":0.15395601063905606,"score_gpt":0.47082521439051966,"score_spread":0.3168692037514636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234059723","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":[0.4292525,0.004014844,0.45263463,0.0146837905,0.0007926752,0.027556146,0.005638312,0.0009581113,0.06446891],"genre_scores_gemma":[0.34493408,0.0014367453,0.6076875,0.0013021668,0.00007430503,0.04064306,0.0012058292,0.00020097174,0.0025154513],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.8408642,0.13502611,0.006935928,0.0030426905,0.013122755,0.0010083764],"domain_scores_gemma":[0.5891792,0.35155243,0.0154140815,0.017550858,0.02421846,0.0020849505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.123335645,0.0006237461,0.00069088937,0.0060283197,0.0030779964,0.008763338,0.00301936,0.0021163397,0.007721727],"category_scores_gemma":[0.30012974,0.00091333437,0.0012134654,0.009098639,0.0061580325,0.0076666255,0.0084039485,0.0024067091,0.0008049526],"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":[0.0007420224,0.00045853958,0.11964586,0.010682743,0.0004717323,0.0006000565,0.3231972,0.0026100485,0.0015735046,0.14996739,0.030656511,0.3593945],"study_design_scores_gemma":[0.00042844992,0.0019186501,0.13510278,0.020102976,0.00055168476,0.0014579223,0.37867996,0.014166836,0.005023137,0.11135517,0.33062747,0.00058484473],"about_ca_topic_score_codex":0.002914788,"about_ca_topic_score_gemma":0.0046832557,"teacher_disagreement_score":0.123335645,"about_ca_system_score_codex":0.0055136858,"about_ca_system_score_gemma":0.0062861294,"threshold_uncertainty_score":0.652269},"labels":[],"label_agreement":null},{"id":"W4234091850","doi":"10.1515/iupac.81.0521","title":"Life Table (in Actuarial Science)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Table (database); Relation (database); Computer science; Data science; Ecology; Biology; Data mining; Linguistics; Philosophy","score_opus":0.018114670695945276,"score_gpt":0.4292849115059548,"score_spread":0.4111702408100095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234091850","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":[0.00021254981,0.00017227262,0.00025454268,0.00015293616,0.00004487854,0.000012971956,0.99681515,0.00041865723,0.0019160917],"genre_scores_gemma":[0.0025109588,0.00028685253,0.0006462935,0.00020257784,0.00006117621,0.000104116756,0.99336135,0.00013508176,0.0026915472],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983134,0.00037855236,0.00026927094,0.00043945454,0.000409695,0.00018945937],"domain_scores_gemma":[0.99201715,0.0036094077,0.0010353304,0.001654876,0.0012869149,0.00039635028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001924679,0.0012297508,0.0010308326,0.004120936,0.00045739877,0.002773887,0.00214117,0.0016755905,0.21250743],"category_scores_gemma":[0.020385649,0.000535909,0.0016416649,0.006069586,0.0002866925,0.0019076903,0.0016302515,0.002308548,0.16049945],"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":[0.00005747641,0.000019413324,0.002690076,0.000493088,0.00006388579,0.000016040076,0.000011946751,0.0008544511,0.000015068846,0.0009900815,0.98652226,0.008266187],"study_design_scores_gemma":[0.00030132546,0.000044466622,0.0076819058,0.0006193118,0.00005871386,0.00016954199,0.000060547143,0.0021632556,0.0001303152,0.006511328,0.98221886,0.000040441824],"about_ca_topic_score_codex":0.011675335,"about_ca_topic_score_gemma":0.016419789,"teacher_disagreement_score":0.21250743,"about_ca_system_score_codex":0.0012324526,"about_ca_system_score_gemma":0.0017072335,"threshold_uncertainty_score":0.7109082},"labels":[],"label_agreement":null},{"id":"W4234611348","doi":"10.1002/9781119971528.refs","title":"References","year":2010,"lang":"ca","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Computer science","score_opus":0.0178096623314442,"score_gpt":0.3048423184759571,"score_spread":0.2870326561445129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234611348","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":[0.00040492337,0.007757483,0.0017035527,0.009277358,0.007997479,0.0002882869,0.037941176,0.0012607884,0.933369],"genre_scores_gemma":[0.0017532941,0.0055681695,0.0026780656,0.0035153592,0.0013614984,0.00017515401,0.022161212,0.00063221244,0.9621551],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991548,0.00008971615,0.000042187487,0.000056472967,0.00059495744,0.00006185955],"domain_scores_gemma":[0.9974185,0.0004021308,0.0000847607,0.00013622019,0.0017312831,0.00022697466],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00081051287,0.0006458373,0.0005667431,0.006385319,0.0010223015,0.0015091059,0.0014123599,0.00088674354,0.47378778],"category_scores_gemma":[0.006243821,0.00021005368,0.00055146695,0.0058859633,0.0002755606,0.000955838,0.0009518751,0.0011350617,0.3887555],"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":[0.0000027881713,0.000007938565,0.00007194822,0.000040452895,5.685382e-7,0.000009032641,0.000008709783,0.0000171465,0.000008726228,0.00055443647,0.9778625,0.02141573],"study_design_scores_gemma":[0.000003915302,0.0000020863793,0.00050698034,0.00015170804,0.000002895952,0.000029696292,0.000024384026,0.000025133213,0.000032085074,0.0005961475,0.9986217,0.000003174348],"about_ca_topic_score_codex":0.048545092,"about_ca_topic_score_gemma":0.093910806,"teacher_disagreement_score":0.5262122,"about_ca_system_score_codex":0.0017603817,"about_ca_system_score_gemma":0.0033191594,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4235349699","doi":"10.4095/301294","title":"Age Structure, 2001 - Golden Years by Census Division (65 - 74 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Demography; Genealogy; History; Sociology; Mathematics; Arithmetic; Population","score_opus":0.03239964349884093,"score_gpt":0.3385218034961119,"score_spread":0.30612215999727094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235349699","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":[0.016555473,0.0016905892,0.00021666003,0.00042724883,0.00022292868,0.00018743158,0.96611816,0.00018593272,0.0143955685],"genre_scores_gemma":[0.06912264,0.0036189877,0.00074566243,0.0007113519,0.00012487943,0.00074889156,0.90340513,0.00010080056,0.021421673],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987488,0.000051086205,0.00014531263,0.00019773658,0.00048380735,0.00037329618],"domain_scores_gemma":[0.99594116,0.000081893326,0.00049543154,0.0000773372,0.0030535301,0.0003507364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005779917,0.0009751362,0.0005987467,0.0057494943,0.0011140503,0.0011122461,0.0014599306,0.00051034766,0.01794603],"category_scores_gemma":[0.004555201,0.00040156196,0.00074854033,0.011397033,0.00030672696,0.0009754044,0.0009112027,0.0015197014,0.01031469],"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":[0.00023039273,0.0000726832,0.15248308,0.001164304,0.00013336906,0.000087679364,0.0005390016,0.0004986481,0.00029987012,0.0014901775,0.81752676,0.025474088],"study_design_scores_gemma":[0.00006958019,0.00005199168,0.83624864,0.0005278438,0.00006106833,0.00016988293,0.0008473211,0.00034587306,0.00012170488,0.00027160058,0.16125321,0.000031360934],"about_ca_topic_score_codex":0.7942645,"about_ca_topic_score_gemma":0.7791671,"teacher_disagreement_score":0.7942645,"about_ca_system_score_codex":0.0052766167,"about_ca_system_score_gemma":0.0093576135,"threshold_uncertainty_score":0.41389424},"labels":[],"label_agreement":null},{"id":"W4235979659","doi":"10.21203/rs.2.13887/v5","title":"A Probabilistic Approach for Economic Evaluation of Occupational Health and Safety Interventions: A Case Study of Silica Exposure Reduction Interventions in the Construction Sector","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cancer Care Ontario; Institute for Work & Health","funders":"Workplace Safety and Insurance Board; Cancer Care Ontario","keywords":"Psychological intervention; Cost–benefit analysis; Economic evaluation; Intervention (counseling); Environmental health; Medicine","score_opus":0.3907534566646841,"score_gpt":0.5296479972743491,"score_spread":0.138894540609665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235979659","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":[0.30436,0.003060237,0.663346,0.0046720887,0.00019108596,0.0020582585,0.0017520383,0.00018285861,0.020377459],"genre_scores_gemma":[0.9055384,0.0012140551,0.08779034,0.00015269748,0.000090182715,0.0014827115,0.00033556705,0.000051389026,0.0033446653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97886825,0.01861133,0.00039819253,0.00067776634,0.00083586964,0.0006086569],"domain_scores_gemma":[0.8997557,0.096760385,0.0013865909,0.00075210416,0.00089074526,0.00045449182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02915025,0.0021610907,0.002803316,0.0038603111,0.0012879764,0.0037336638,0.003030666,0.0039126803,0.009488922],"category_scores_gemma":[0.06595338,0.0018520092,0.0029710366,0.0030232554,0.0026993568,0.0042826612,0.0028197623,0.0029992969,0.0002166188],"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":[0.00074443023,0.00065208366,0.0053229057,0.00043183536,0.00057068246,0.0005116802,0.0003213331,0.8707785,0.0003419363,0.09756635,0.0010395775,0.021718778],"study_design_scores_gemma":[0.00019443556,0.00030998228,0.0019711757,0.000058855476,0.00023001264,0.00013189447,0.00027195917,0.9366769,0.00010972207,0.059294607,0.0006968831,0.000053469215],"about_ca_topic_score_codex":0.018194405,"about_ca_topic_score_gemma":0.013097649,"teacher_disagreement_score":0.02915025,"about_ca_system_score_codex":0.005122295,"about_ca_system_score_gemma":0.004783512,"threshold_uncertainty_score":0.15416306},"labels":[],"label_agreement":null},{"id":"W4236015924","doi":"10.31899/pgy3.1005","title":"Fertility transitions in developing countries: Progress or stagnation?","year":2008,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":65,"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":"Fertility; Pace; Developing country; Sub-replacement fertility; Quarter (Canadian coin); Demographic transition; Total fertility rate; Development economics; Developed country; Population; Population momentum; Economic stagnation; Demographic economics; Economics; Geography; Economic growth; Birth rate; Demography; Political science; Family planning; Research methodology; Sociology","score_opus":0.07255825242873798,"score_gpt":0.382362623181882,"score_spread":0.30980437075314404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236015924","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":[0.9200543,0.02758772,0.0005676098,0.022159798,0.0001665955,0.00009134777,0.007877735,0.000118728225,0.021376152],"genre_scores_gemma":[0.9806793,0.014928102,0.00061118853,0.0006811071,0.00016329638,0.000034550187,0.0019601267,0.00001147608,0.0009308222],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990901,0.00028965675,0.00011244398,0.00008040831,0.00013026476,0.00029717572],"domain_scores_gemma":[0.99615425,0.0007813927,0.0014958272,0.00017469931,0.00073486543,0.00065900537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031234452,0.00021462994,0.0004438499,0.0021866227,0.0008526914,0.0019005772,0.00041306284,0.00047874014,0.0034934527],"category_scores_gemma":[0.0073302304,0.00018299489,0.00034812387,0.0037403635,0.00082619267,0.0022795321,0.0017345556,0.00083368697,0.0004364769],"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":[0.0001876786,0.00007040781,0.8571103,0.00069953094,0.000072166345,0.00041664712,0.0095584765,0.00053973624,0.00020969413,0.00475437,0.00916803,0.11721302],"study_design_scores_gemma":[0.0000139169115,0.00015043931,0.9609753,0.00052653375,0.000038122143,0.0003490844,0.013874904,0.00024389713,0.00015122254,0.0012644965,0.022392105,0.000019974106],"about_ca_topic_score_codex":0.023460899,"about_ca_topic_score_gemma":0.023572022,"teacher_disagreement_score":0.023460899,"about_ca_system_score_codex":0.0013794235,"about_ca_system_score_gemma":0.002812589,"threshold_uncertainty_score":0.04664868},"labels":[],"label_agreement":null},{"id":"W4237867265","doi":"10.4095/301300","title":"Age Structure, 2001 - Oldest Old by Census Subdivision (75 years of age and older)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Age structure; Geography; Demography; Gerontology; Archaeology; Medicine; Sociology; Population","score_opus":0.02665374542107387,"score_gpt":0.32249382544932886,"score_spread":0.295840080028255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237867265","genre_codex":"dataset","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":[0.044904727,0.0027872368,0.00030223775,0.0007145506,0.00024520702,0.00024249822,0.93138206,0.00023868105,0.019182792],"genre_scores_gemma":[0.13731472,0.0042831963,0.0010187308,0.0007749999,0.00014386143,0.0006388249,0.83729464,0.00010354343,0.018427482],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99902546,0.000042841275,0.0001228344,0.00013911993,0.00037391935,0.00029584984],"domain_scores_gemma":[0.99508506,0.00010046278,0.00060660177,0.00009040671,0.003666221,0.00045129575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006934366,0.0007949463,0.00049693533,0.0055267825,0.001277278,0.0010925339,0.0014986792,0.0004883003,0.010987088],"category_scores_gemma":[0.005075373,0.0003569043,0.00066878326,0.009446721,0.00028693807,0.000885049,0.00090962916,0.0014583076,0.005683252],"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":[0.0002776542,0.00008778596,0.3632483,0.00085848756,0.00013453988,0.00012357098,0.00072968437,0.00067396695,0.00028857597,0.0014321278,0.6075598,0.024585446],"study_design_scores_gemma":[0.000043211658,0.00003902668,0.93968475,0.00028260672,0.0000461536,0.00015259132,0.0006438381,0.00030618388,0.00007760205,0.00016441129,0.058536246,0.000023280705],"about_ca_topic_score_codex":0.8478784,"about_ca_topic_score_gemma":0.833308,"teacher_disagreement_score":0.1521216,"about_ca_system_score_codex":0.0053188405,"about_ca_system_score_gemma":0.009032432,"threshold_uncertainty_score":0.30603492},"labels":[],"label_agreement":null},{"id":"W4238181502","doi":"10.4095/301310","title":"Age Structure, 2006 - Oldest Old by Census Subdivision (80 years of age and older)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Age structure; Geography; Demography; Gerontology; Genealogy; History; Archaeology; Medicine; Sociology; Population","score_opus":0.019944888450226517,"score_gpt":0.30991328363128906,"score_spread":0.28996839518106254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238181502","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":[0.020115454,0.001140516,0.00026187336,0.00045802756,0.00018199602,0.0002582618,0.96471167,0.00021545718,0.012656742],"genre_scores_gemma":[0.0783124,0.0025018894,0.0017757658,0.00059794256,0.00011842859,0.0006006985,0.8999921,0.000120350946,0.015980398],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99897766,0.000039313054,0.00011995425,0.00013127404,0.0004767132,0.0002550949],"domain_scores_gemma":[0.99433064,0.000067784815,0.0005398263,0.00009600815,0.0045090904,0.0004566602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071111077,0.0009379328,0.0005882899,0.004791193,0.0018204423,0.0012709335,0.0019308783,0.0005178005,0.011891624],"category_scores_gemma":[0.005521685,0.00037993488,0.0008005293,0.009146521,0.00029001758,0.000971175,0.0012044362,0.0015543286,0.0058725746],"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":[0.00019059774,0.00007769693,0.24392062,0.0007852334,0.00010700704,0.00007930345,0.0005605544,0.0005378718,0.00022469663,0.0011522304,0.7299454,0.02241882],"study_design_scores_gemma":[0.00005079878,0.00003740384,0.9090613,0.0003100537,0.000046000005,0.00016196094,0.0007960529,0.00044103252,0.00009407029,0.0002302127,0.08873943,0.000031693526],"about_ca_topic_score_codex":0.940257,"about_ca_topic_score_gemma":0.94753546,"teacher_disagreement_score":0.059742987,"about_ca_system_score_codex":0.008344249,"about_ca_system_score_gemma":0.016727464,"threshold_uncertainty_score":0.12018961},"labels":[],"label_agreement":null},{"id":"W4239455399","doi":"10.4095/301293","title":"Age Structure, 2001 - Later Working Years by Census Division (35 - 64 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Demography; Genealogy; Gerontology; History; Sociology; Medicine; Population; Arithmetic; Mathematics","score_opus":0.040679343786923845,"score_gpt":0.33443264908691306,"score_spread":0.2937533052999892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239455399","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":[0.022713149,0.0020840848,0.00025963533,0.00047753335,0.0002469261,0.00023322507,0.9552973,0.00019205092,0.01849614],"genre_scores_gemma":[0.09600186,0.004946757,0.0009672436,0.000850393,0.00017229593,0.0011105818,0.86121,0.00012653338,0.034614403],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988568,0.000050857656,0.0001376781,0.0001748028,0.0004135899,0.00036634872],"domain_scores_gemma":[0.99642605,0.000080790895,0.00046791436,0.00008569681,0.002614347,0.00032526735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059309957,0.00089671335,0.0005362158,0.005499793,0.0011928031,0.0011820524,0.0014311833,0.0004911148,0.021766322],"category_scores_gemma":[0.004409889,0.00036621108,0.00074815593,0.010426016,0.00026697046,0.00096340146,0.0008852308,0.0014218445,0.0127965],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020270802,0.00008371712,0.19922648,0.0011618079,0.00013678212,0.00010300112,0.0009360012,0.00046811794,0.00037841115,0.0017099765,0.7622227,0.03337029],"study_design_scores_gemma":[0.000048994832,0.000044390556,0.84994125,0.00045419266,0.000045228368,0.0001571739,0.00096088735,0.00023846485,0.00010809876,0.00023209376,0.14774126,0.000027929567],"about_ca_topic_score_codex":0.7382178,"about_ca_topic_score_gemma":0.70937645,"teacher_disagreement_score":0.7382178,"about_ca_system_score_codex":0.003919916,"about_ca_system_score_gemma":0.007226429,"threshold_uncertainty_score":0.5266478},"labels":[],"label_agreement":null},{"id":"W4239781768","doi":"10.4095/301471","title":"Population Distribution, 2006","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Distribution (mathematics); Geography; Mathematics; Mathematical analysis","score_opus":0.029621204973196653,"score_gpt":0.35503048015602245,"score_spread":0.3254092751828258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239781768","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":[0.009519142,0.0015645514,0.00033336235,0.00054931344,0.00017968498,0.00024612946,0.9217709,0.00029233415,0.065544546],"genre_scores_gemma":[0.040569663,0.005109186,0.0023619353,0.0004541496,0.000055864606,0.0003422305,0.8421119,0.00011756827,0.10887754],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987618,0.000025908954,0.00007596467,0.00012390046,0.00075954286,0.00025298144],"domain_scores_gemma":[0.99774617,0.000029565914,0.000069088775,0.00003785572,0.0019579306,0.00015938205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005248387,0.0008159547,0.00043415968,0.0049766847,0.0017293091,0.0017790571,0.001115519,0.00029264763,0.01875788],"category_scores_gemma":[0.002213421,0.00026746292,0.00038733947,0.01137921,0.00025643688,0.0005766288,0.0006291572,0.00097401976,0.010612618],"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":[0.00012436857,0.000052122352,0.037408564,0.00054560014,0.000039434883,0.000107174594,0.0005652771,0.000564189,0.00033208757,0.002351106,0.8819206,0.075989455],"study_design_scores_gemma":[0.000021649745,0.00002222731,0.2858962,0.0002470276,0.000015994121,0.00011240104,0.00061117037,0.00036473578,0.00020807167,0.0001879258,0.7122821,0.000030416415],"about_ca_topic_score_codex":0.98328453,"about_ca_topic_score_gemma":0.98763895,"teacher_disagreement_score":0.98328453,"about_ca_system_score_codex":0.022856094,"about_ca_system_score_gemma":0.044242676,"threshold_uncertainty_score":0.16583335},"labels":[],"label_agreement":null},{"id":"W4240927721","doi":"10.1002/9780470391341.ch21","title":"Simulation","year":2008,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Computer science","score_opus":0.02420287239603292,"score_gpt":0.29938403001275826,"score_spread":0.27518115761672535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240927721","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":[0.003930597,0.001914223,0.5346428,0.002559566,0.0014652194,0.0005393241,0.0068635806,0.011109186,0.4369755],"genre_scores_gemma":[0.15190488,0.0064178337,0.44212905,0.002657192,0.0010864481,0.0019062301,0.023088453,0.0054391664,0.36537078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876815,0.00040485262,0.000086041226,0.00014695156,0.0005112285,0.000082694554],"domain_scores_gemma":[0.9975212,0.0011501204,0.0000842169,0.000549683,0.0005870791,0.00010772308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014290287,0.00068809406,0.00068656786,0.0011160935,0.00057623786,0.0025682282,0.0019489013,0.0013153858,0.12561522],"category_scores_gemma":[0.0077915587,0.00038950692,0.00090365467,0.0011850682,0.00044713618,0.0021815319,0.001780432,0.0011922352,0.045025803],"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":[0.000112862646,0.00014562174,0.0012832955,0.00036431776,0.00007333139,0.000105613915,0.00014239209,0.14215457,0.0010984755,0.36238793,0.25964993,0.2324816],"study_design_scores_gemma":[0.00006843388,0.000041771356,0.00035726686,0.00019145536,0.000026015276,0.00017600459,0.000052837542,0.18682437,0.001384174,0.14915264,0.66169,0.000035099212],"about_ca_topic_score_codex":0.0019540966,"about_ca_topic_score_gemma":0.0016512359,"teacher_disagreement_score":0.12561522,"about_ca_system_score_codex":0.00078700914,"about_ca_system_score_gemma":0.0012697394,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4241177657","doi":"10.4095/301468","title":"Population Change, 2001-2006 (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Geography; Population; Demography; Sociology; Archaeology","score_opus":0.08457004021724505,"score_gpt":0.3806512247427294,"score_spread":0.2960811845254843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241177657","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":[0.034623645,0.0009084555,0.00035942395,0.0005028051,0.00024074657,0.0002873533,0.9402148,0.00031806505,0.02254463],"genre_scores_gemma":[0.06478555,0.0027101156,0.0013505288,0.00034537137,0.00010047344,0.00055165193,0.8967489,0.000067512854,0.033340048],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987424,0.000053459327,0.00010208425,0.00012515487,0.00078607065,0.0001909028],"domain_scores_gemma":[0.998095,0.00004865977,0.00024766987,0.000051931507,0.0014154186,0.00014134175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051003543,0.00084165815,0.00034831112,0.0035385739,0.00088830845,0.0011737931,0.0010429517,0.00047513185,0.008576748],"category_scores_gemma":[0.002959602,0.00034610956,0.0005646785,0.0075153955,0.0002059807,0.00089111016,0.00086106634,0.0010850416,0.008584195],"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":[0.00018937403,0.00014161416,0.21762337,0.0009305454,0.00013058518,0.00012878042,0.000818659,0.001091115,0.00047808548,0.0009245463,0.71980023,0.05774314],"study_design_scores_gemma":[0.00003920773,0.000045320176,0.7600249,0.00016968288,0.00003922613,0.00023685914,0.0005186773,0.00064880034,0.00039308536,0.00006343804,0.23779844,0.000022357113],"about_ca_topic_score_codex":0.730098,"about_ca_topic_score_gemma":0.7522846,"teacher_disagreement_score":0.730098,"about_ca_system_score_codex":0.005053715,"about_ca_system_score_gemma":0.006430058,"threshold_uncertainty_score":0.54298294},"labels":[],"label_agreement":null},{"id":"W4242173535","doi":"10.4095/294938","title":"Canada's Population Density","year":2005,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.029719776711484323,"score_gpt":0.3239631262370046,"score_spread":0.2942433495255202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242173535","genre_codex":"dataset","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":[0.012077017,0.0035913647,0.00079972675,0.0022399188,0.00049894006,0.00024799138,0.800106,0.0009589104,0.17948008],"genre_scores_gemma":[0.14448948,0.010864132,0.0067878654,0.0014727,0.00013521532,0.00047392087,0.57375675,0.0003820728,0.26163784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988166,0.00003317365,0.00004968503,0.00010792522,0.0007173074,0.00027537206],"domain_scores_gemma":[0.9976199,0.00004413051,0.00006607304,0.00003560306,0.001969997,0.0002642125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054250495,0.00074279803,0.0005122982,0.005761481,0.0028013133,0.0022138793,0.00090055476,0.00035389478,0.03901728],"category_scores_gemma":[0.001717101,0.00034838513,0.00059844885,0.011826091,0.00031864658,0.000579586,0.00071798085,0.0008579975,0.007912144],"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":[0.000025924322,0.000014724806,0.012436824,0.00027496327,0.00002375907,0.000037004673,0.00037859517,0.00022993397,0.00008476362,0.0025834956,0.9548818,0.029028205],"study_design_scores_gemma":[0.000022630447,0.000018149181,0.22076902,0.0003434455,0.00004215958,0.00010340772,0.0011396029,0.0005090314,0.00018621415,0.00033369698,0.7764754,0.000057156045],"about_ca_topic_score_codex":0.99702257,"about_ca_topic_score_gemma":0.9979469,"teacher_disagreement_score":0.03901728,"about_ca_system_score_codex":0.033288054,"about_ca_system_score_gemma":0.08000132,"threshold_uncertainty_score":0.24152285},"labels":[],"label_agreement":null},{"id":"W4242223694","doi":"10.1017/s0021900200006732","title":"Random effect bivariate survival models and stochastic comparisons","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 New Brunswick","funders":"","keywords":"Bivariate analysis; Mathematics; Multiplicative function; Statistics; Stochastic ordering; Econometrics; Random effects model; Hazard; Stochastic modelling; Survival function; Applied mathematics; Survival analysis","score_opus":0.022974522008671557,"score_gpt":0.2917908127697958,"score_spread":0.26881629076112423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242223694","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":[0.018211305,0.0005073166,0.97610736,0.0006514391,0.0000837812,0.00003817048,0.0002957009,0.00009428285,0.004010645],"genre_scores_gemma":[0.73729056,0.0022239785,0.24661589,0.0004645527,0.0003360981,0.0005424344,0.0012757466,0.0001181087,0.011132748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99580944,0.0028880145,0.00012300567,0.000469728,0.0005103152,0.00019948205],"domain_scores_gemma":[0.9936035,0.0044252793,0.0007828689,0.0004850906,0.0005052847,0.00019796206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009863044,0.0008228759,0.0009786264,0.0013875676,0.00036005827,0.0012545197,0.0013446081,0.0009366929,0.005319308],"category_scores_gemma":[0.01783429,0.0003384358,0.0013060425,0.0015341325,0.0014886113,0.0023260412,0.001767622,0.0017473442,0.000528356],"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":[0.000029718534,0.000022006414,0.0016215142,0.000054153665,0.000063700994,0.00007646751,0.00008376324,0.09163863,0.00016491044,0.89325196,0.000740696,0.0122524975],"study_design_scores_gemma":[0.000027938566,0.00007108446,0.0010754239,0.000031758635,0.00004925394,0.00008881627,0.000052628373,0.22900854,0.00015479334,0.76542234,0.0039876997,0.000029733474],"about_ca_topic_score_codex":0.002396642,"about_ca_topic_score_gemma":0.0017354583,"teacher_disagreement_score":0.009863044,"about_ca_system_score_codex":0.0011941543,"about_ca_system_score_gemma":0.0009202005,"threshold_uncertainty_score":0.052161396},"labels":[],"label_agreement":null},{"id":"W4242980021","doi":"10.21203/rs.2.13887/v1","title":"A Probabilistic Approach for Economic Evaluation of Occupational Health and Safety Interventions: A Case Study of Silica Exposure Reduction Interventions in the Construction Sector","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cancer Care Ontario; Institute for Work & Health","funders":"Workplace Safety and Insurance Board; Cancer Care Ontario","keywords":"Psychological intervention; Cost–benefit analysis; Economic evaluation; Intervention (counseling); Environmental health; Medicine","score_opus":0.3292203198947608,"score_gpt":0.5232236359368521,"score_spread":0.19400331604209126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242980021","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":[0.47476202,0.005879585,0.4654015,0.005758662,0.0002713063,0.0046190717,0.0031372576,0.000172449,0.039998226],"genre_scores_gemma":[0.9188091,0.0019000102,0.07160402,0.00021495683,0.00009604227,0.0024372635,0.00040518842,0.000035122714,0.004498186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98840094,0.009509052,0.00026597257,0.00051846565,0.0006278594,0.00067775225],"domain_scores_gemma":[0.95417494,0.042559456,0.0013189738,0.00049896917,0.001011027,0.00043660877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015772477,0.0023479853,0.002441529,0.0036855761,0.001323935,0.0031171679,0.0027425338,0.0045628506,0.009257454],"category_scores_gemma":[0.029736612,0.0014721242,0.0046789325,0.0026616973,0.0015944586,0.0020166815,0.0023753357,0.003743647,0.0002833223],"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":[0.00040382522,0.0005218516,0.004288368,0.00045096083,0.00040277865,0.0011207631,0.00014505952,0.9456468,0.00035614023,0.03638524,0.0008268463,0.009451301],"study_design_scores_gemma":[0.00018446775,0.00037210845,0.0016835367,0.00011233997,0.00027031402,0.00023538902,0.00022905772,0.97976273,0.00016926018,0.015857372,0.0010697731,0.000053614614],"about_ca_topic_score_codex":0.02314645,"about_ca_topic_score_gemma":0.01293608,"teacher_disagreement_score":0.02314645,"about_ca_system_score_codex":0.0062533314,"about_ca_system_score_gemma":0.0052590384,"threshold_uncertainty_score":0.08341384},"labels":[],"label_agreement":null},{"id":"W4243158513","doi":"10.1002/9781119971528.ch9","title":"Select Mortality","year":2010,"lang":"en","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Annuity; Computer science; Life annuity; Economics; Pension; Finance","score_opus":0.02047061864393841,"score_gpt":0.33550256764379505,"score_spread":0.31503194899985665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243158513","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":[0.001651067,0.0005915626,0.004728736,0.0019224797,0.0010327057,0.00020716073,0.26204062,0.005057575,0.72276807],"genre_scores_gemma":[0.010939254,0.0013678822,0.0061246785,0.0012517958,0.0009884777,0.00042117928,0.33215958,0.0030811476,0.64366615],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992079,0.00009901543,0.00004424934,0.000096814394,0.00048476958,0.00006724454],"domain_scores_gemma":[0.9984267,0.0004170685,0.00011020231,0.00021073609,0.0006427042,0.00019260155],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010718426,0.0006247536,0.0004498224,0.002480892,0.000515112,0.0019641593,0.0008663739,0.00052559684,0.4780635],"category_scores_gemma":[0.0050080833,0.00028920636,0.0005907349,0.002781122,0.00013856239,0.0012096629,0.0010830212,0.0011124526,0.31372258],"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":[0.00003452475,0.000016166709,0.00036051328,0.000052978576,0.0000022456438,0.000007401913,0.0000139154845,0.00015226817,0.000056143937,0.0033902328,0.9509292,0.04498431],"study_design_scores_gemma":[0.000026081241,0.00001585399,0.0033999747,0.000089295754,0.0000036025438,0.000022919994,0.000028218328,0.00037029132,0.00024357952,0.0032770473,0.99251175,0.000011437751],"about_ca_topic_score_codex":0.0044055246,"about_ca_topic_score_gemma":0.0062745474,"teacher_disagreement_score":0.4780635,"about_ca_system_score_codex":0.00077524094,"about_ca_system_score_gemma":0.0010488444,"threshold_uncertainty_score":0.74447906},"labels":[],"label_agreement":null},{"id":"W4243444271","doi":"10.1002/9780470012505.tam018","title":"Mean Residual Lifetime","year":2004,"lang":"en","type":"other","venue":"Encyclopedia of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Residual; Statistics; Reliability (semiconductor); Residual risk; Mathematics; Econometrics; Reliability engineering; Engineering; Algorithm; Physics; Thermodynamics","score_opus":0.009662587112825931,"score_gpt":0.28865682591465514,"score_spread":0.2789942388018292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243444271","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":[0.0888007,0.011965684,0.82698613,0.002057681,0.00047444427,0.00006484826,0.0014156343,0.0009313847,0.0673034],"genre_scores_gemma":[0.9324785,0.004905346,0.040909737,0.000326134,0.00048757083,0.000112235975,0.0009210566,0.00043924793,0.019420234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984364,0.00038469458,0.00008287911,0.0003379625,0.00059411244,0.00016395096],"domain_scores_gemma":[0.98744303,0.0061818296,0.0016394767,0.0016198154,0.0027874042,0.00032839668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031269896,0.00039468738,0.0007676654,0.0015423519,0.0005143297,0.0022523263,0.001139833,0.0007109637,0.009767785],"category_scores_gemma":[0.022408212,0.00019610111,0.0006644736,0.0014405752,0.0012989066,0.003939855,0.0010468499,0.0013239805,0.0015734364],"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":[0.00007387139,0.000025307805,0.0042504035,0.0002533774,0.000047816462,0.00014930588,0.00020900676,0.047242448,0.0020427038,0.846478,0.0069435514,0.09228412],"study_design_scores_gemma":[0.000015151159,0.00014521695,0.005504891,0.00024415704,0.00005358669,0.0014646195,0.00018112417,0.16822234,0.003737862,0.7769081,0.04344735,0.00007563221],"about_ca_topic_score_codex":0.0010819399,"about_ca_topic_score_gemma":0.0005029857,"teacher_disagreement_score":0.009767785,"about_ca_system_score_codex":0.0013752347,"about_ca_system_score_gemma":0.0009877274,"threshold_uncertainty_score":0.03267646},"labels":[],"label_agreement":null},{"id":"W4243796514","doi":"10.4095/301462","title":"Population Change, 1996 to 2001 (by census division)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Population; Demography; Sociology; Mathematics; Arithmetic","score_opus":0.09409683978780119,"score_gpt":0.39607211984645424,"score_spread":0.30197528005865304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243796514","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":[0.022262767,0.001119583,0.00035939962,0.0005930202,0.0006131703,0.0003386555,0.9464199,0.00025329413,0.028040228],"genre_scores_gemma":[0.043083627,0.0035178456,0.0013340481,0.0005394714,0.00016679974,0.0013285563,0.90384585,0.00006894534,0.04611493],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9990212,0.000057965866,0.00013126976,0.00013115862,0.0005281586,0.0001302564],"domain_scores_gemma":[0.9986946,0.000057342153,0.00023485832,0.000048530877,0.0008687998,0.00009574591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005837103,0.00097091316,0.00031592176,0.003185736,0.0005726044,0.0010140578,0.0009013033,0.00059817685,0.010206366],"category_scores_gemma":[0.0030980073,0.00036595788,0.00049060734,0.0073975534,0.00018485048,0.0010787135,0.0008879458,0.0010989236,0.012695963],"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":[0.0002454859,0.00013315688,0.08614455,0.0011719296,0.000099431694,0.0001444189,0.00035575696,0.00089746295,0.00043400913,0.0007937239,0.85398424,0.055595804],"study_design_scores_gemma":[0.00008561251,0.00008284004,0.5167404,0.00031424293,0.00005291241,0.00034674307,0.00050638046,0.00059424766,0.00058905355,0.000118212425,0.48054373,0.000025685467],"about_ca_topic_score_codex":0.2580273,"about_ca_topic_score_gemma":0.2793441,"teacher_disagreement_score":0.7419727,"about_ca_system_score_codex":0.002956699,"about_ca_system_score_gemma":0.003050888,"threshold_uncertainty_score":0.5130508},"labels":[],"label_agreement":null},{"id":"W4243824495","doi":"10.2307/3088317","title":"Evaluating the Performance of the Lee-Carter Method for Forecasting Mortality","year":2001,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":65,"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":"Demography; Econometrics; Computer science; Statistics; Economics; Sociology; Mathematics","score_opus":0.13723372836556766,"score_gpt":0.41700879304303023,"score_spread":0.2797750646774626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243824495","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":[0.7056903,0.0040189005,0.2762422,0.0010768627,0.00044306542,0.00044711868,0.0013701515,0.00067012594,0.010041212],"genre_scores_gemma":[0.91514957,0.00084492477,0.081599586,0.00010991914,0.00010930072,0.00018615514,0.00077161886,0.000049275706,0.0011794874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964143,0.0023584121,0.00017908704,0.00022571054,0.00064185483,0.0001805674],"domain_scores_gemma":[0.9602596,0.033844262,0.00087115716,0.00064626144,0.003952298,0.00042647865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014081205,0.0006894946,0.0007615322,0.0025371017,0.00066100067,0.0008267412,0.00068329135,0.001009499,0.0011340864],"category_scores_gemma":[0.040491346,0.0002789422,0.00055004016,0.0018225161,0.0003964221,0.0010804614,0.0006876781,0.0007893363,0.00018555684],"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":[0.0018915806,0.00023635649,0.10257837,0.00027707923,0.00042194393,0.0001322234,0.0004080894,0.63807017,0.0018105437,0.008248414,0.004103786,0.24182147],"study_design_scores_gemma":[0.00007905217,0.00039205834,0.0097145,0.000043989927,0.000045264253,0.00004470782,0.00016416037,0.98508596,0.0012285192,0.0017482024,0.0013916467,0.00006194506],"about_ca_topic_score_codex":0.03578237,"about_ca_topic_score_gemma":0.022471135,"teacher_disagreement_score":0.03578237,"about_ca_system_score_codex":0.0012418034,"about_ca_system_score_gemma":0.0014300783,"threshold_uncertainty_score":0.07446945},"labels":[],"label_agreement":null},{"id":"W4243873461","doi":"10.5539/ijsp.v10n3p154","title":"Effect of Education on Attitude Towards Domestic Violence in Nigeria: An Exploration Using Propensity Score Methodology","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"UNICEF","keywords":"Demography; Logistic regression; Socioeconomic status; Propensity score matching; Medicine; Statistics; Multinomial logistic regression; Selection bias; Marital status; Mathematics; Population; Sociology","score_opus":0.11899288585327933,"score_gpt":0.4248557868348231,"score_spread":0.3058629009815438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243873461","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":[0.97728294,0.004107982,0.015158685,0.00087941444,0.00008964485,0.00029685744,0.00051414943,0.000021840633,0.0016485758],"genre_scores_gemma":[0.99710673,0.0005179958,0.0017694728,0.00007128256,0.00002101826,0.00010310014,0.00015321786,0.0000048096754,0.00025251237],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9926685,0.0044342345,0.0008184806,0.0008608745,0.0008517713,0.00036606408],"domain_scores_gemma":[0.9905606,0.0054722386,0.002529739,0.00078808726,0.0004453876,0.00020390758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010327738,0.00033117758,0.00068534666,0.0020053955,0.0006400177,0.001166819,0.0004832609,0.0006006065,0.0025552937],"category_scores_gemma":[0.020830404,0.0003418918,0.002432582,0.0033480183,0.0007149953,0.00094353565,0.0011010855,0.0008000207,0.00013312061],"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":[0.00023009561,0.00010800729,0.97725016,0.00023787715,0.0009997161,0.00014175766,0.00062042533,0.00046811404,0.00007120739,0.0016710559,0.00031157865,0.01788988],"study_design_scores_gemma":[0.000048803162,0.00056770147,0.9775097,0.00047274682,0.001991806,0.0005344577,0.0017453624,0.009254998,0.00024673188,0.0045582205,0.0030388946,0.000030518637],"about_ca_topic_score_codex":0.0034457173,"about_ca_topic_score_gemma":0.0034125939,"teacher_disagreement_score":0.010327738,"about_ca_system_score_codex":0.00049879873,"about_ca_system_score_gemma":0.0013666657,"threshold_uncertainty_score":0.054618955},"labels":[],"label_agreement":null},{"id":"W4244112255","doi":"10.4095/301464","title":"Population Density, 2001 (by census division)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Population; Genealogy; Demography; History; Mathematics; Sociology; Arithmetic","score_opus":0.04373860786619777,"score_gpt":0.36597321242942826,"score_spread":0.3222346045632305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244112255","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":[0.007203858,0.0010747919,0.00028445208,0.00035062392,0.00024058852,0.00039696164,0.95773935,0.00024064795,0.0324688],"genre_scores_gemma":[0.026743399,0.003794328,0.0014164387,0.00043052505,0.00010785968,0.0009568624,0.9177912,0.00006929383,0.04869018],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9992163,0.000028162878,0.000059994174,0.00007891585,0.0004974121,0.00011912188],"domain_scores_gemma":[0.9986583,0.000043328277,0.000094367366,0.0000314799,0.0010719255,0.00010058957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004746002,0.0012356979,0.000551169,0.0044604116,0.0008491291,0.0011648245,0.0012772274,0.00044413476,0.020082416],"category_scores_gemma":[0.002044458,0.0003523741,0.0004346734,0.009177784,0.0002315877,0.0008207369,0.0006116448,0.0012483388,0.02017016],"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":[0.000048763122,0.0000606393,0.027747586,0.0005063225,0.000041211904,0.000046307825,0.00019766967,0.00035649032,0.00017216375,0.0006719676,0.9450844,0.025066392],"study_design_scores_gemma":[0.00006175466,0.000048678674,0.4808585,0.00038443806,0.000052125673,0.00019497299,0.0005945583,0.0008323946,0.00029053466,0.00023541995,0.5164049,0.000041779796],"about_ca_topic_score_codex":0.80905885,"about_ca_topic_score_gemma":0.79929185,"teacher_disagreement_score":0.80905885,"about_ca_system_score_codex":0.0066549494,"about_ca_system_score_gemma":0.00870152,"threshold_uncertainty_score":0.38413125},"labels":[],"label_agreement":null},{"id":"W4244422941","doi":"10.1017/s0515036100001665","title":"Actuarial Vacancy","year":2001,"lang":"ca","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Action (physics); Content (measure theory); Vacancy defect; Computer science; Business; Mathematics; Physics; Mathematical analysis; Nuclear magnetic resonance","score_opus":0.01825893804013504,"score_gpt":0.2836850318330115,"score_spread":0.26542609379287646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244422941","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":[0.0066667967,0.0061753155,0.0033022258,0.052741572,0.029398432,0.00032834907,0.017315723,0.0023404353,0.8817311],"genre_scores_gemma":[0.024136744,0.0024773825,0.0008321627,0.0049062427,0.0040982286,0.00016281499,0.007305524,0.00046211047,0.9556188],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9960912,0.0004287422,0.00022815018,0.00035369463,0.0021383278,0.00075991335],"domain_scores_gemma":[0.98771614,0.0012991449,0.0006099097,0.0009814854,0.004694773,0.0046985843],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003594926,0.0006148624,0.0010279587,0.002706313,0.0033405088,0.003902189,0.0010859697,0.0018830955,0.34963185],"category_scores_gemma":[0.018257884,0.00039463807,0.0005144216,0.0016309081,0.00053365953,0.0015464047,0.0028641229,0.0032447204,0.22861989],"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":[0.00003885676,0.00004240876,0.0007161555,0.000027188367,0.00000357422,0.000029832428,0.00003323075,0.000040459196,0.00010988336,0.0071638916,0.9530987,0.03869578],"study_design_scores_gemma":[0.00000973757,0.0000424204,0.002635909,0.000035146524,0.0000026471719,0.00007136042,0.000051354586,0.00014912385,0.00006956809,0.0013658213,0.9955596,0.000007211684],"about_ca_topic_score_codex":0.004901813,"about_ca_topic_score_gemma":0.010280336,"teacher_disagreement_score":0.34963185,"about_ca_system_score_codex":0.0018602543,"about_ca_system_score_gemma":0.0056338436,"threshold_uncertainty_score":0.92767125},"labels":[],"label_agreement":null},{"id":"W4244485049","doi":"10.4095/301309","title":"Age Structure, 2006 - Golden Years by Census Subdivision (65 - 79 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Geography; Age structure; Demography; Genealogy; Archaeology; History; Population; Sociology","score_opus":0.024458965321374045,"score_gpt":0.32708167983346265,"score_spread":0.3026227145120886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244485049","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":[0.008227291,0.0006844773,0.00018253396,0.0002509665,0.00013092373,0.00021515306,0.9804232,0.00015192012,0.009733594],"genre_scores_gemma":[0.03527094,0.0016049453,0.0011668539,0.000373262,0.0000778813,0.0005182294,0.94599277,0.00009206815,0.014902975],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99877924,0.00004219384,0.00012985553,0.0001597555,0.00059048773,0.00029846345],"domain_scores_gemma":[0.99503714,0.00005590239,0.00040684306,0.00008243028,0.004058283,0.0003594324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068642455,0.0010712495,0.00063809095,0.0054104524,0.001658722,0.0013754077,0.0018827674,0.00048320036,0.018058158],"category_scores_gemma":[0.0045132404,0.00040693334,0.0009233658,0.010564964,0.00028708417,0.00096594356,0.001100884,0.001453696,0.009203598],"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":[0.0001772123,0.00006590885,0.13209297,0.000850473,0.00010255028,0.000064285385,0.0004404452,0.00044865484,0.00022329447,0.0012074859,0.84031117,0.024015522],"study_design_scores_gemma":[0.00006206847,0.00003920987,0.81479245,0.00036483997,0.000048036956,0.00013698191,0.0007073997,0.00043950306,0.000118683936,0.00027212268,0.18298627,0.000032349424],"about_ca_topic_score_codex":0.94080156,"about_ca_topic_score_gemma":0.94895315,"teacher_disagreement_score":0.05919844,"about_ca_system_score_codex":0.009529982,"about_ca_system_score_gemma":0.018584356,"threshold_uncertainty_score":0.119094074},"labels":[],"label_agreement":null},{"id":"W4244750983","doi":"10.1111/j.1728-4457.2009.00278.x","title":"Introduction","year":2009,"lang":"en","type":"article","venue":"Population and Development Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institute of Aging","funders":"","keywords":"Population ageing; Context (archaeology); Pace; Affect (linguistics); Population; Gerontology; Fertility; Demographic economics; Sociology; Psychology; Demography; Geography; Economics; Medicine","score_opus":0.023441164255473657,"score_gpt":0.32688168073888046,"score_spread":0.3034405164834068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244750983","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011275779,0.024435261,0.0046691853,0.13935938,0.17134765,0.00042601992,0.0076564737,0.0012871744,0.6496912],"genre_scores_gemma":[0.0061104405,0.0112537015,0.0025106177,0.050514936,0.017999794,0.00028383266,0.005305715,0.00047852958,0.90554243],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980622,0.0003301169,0.000116850526,0.00032294804,0.0009657279,0.00020210187],"domain_scores_gemma":[0.99587363,0.0007733266,0.00014479154,0.00030579141,0.0024704735,0.0004319071],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019905544,0.0007431712,0.0006102148,0.0011905159,0.001986889,0.0033063642,0.0024913894,0.0039493907,0.41724455],"category_scores_gemma":[0.010782197,0.0002446647,0.00085855403,0.0012616315,0.0007629225,0.003760648,0.0027222524,0.0029206949,0.2474542],"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":[0.000012265073,0.000008335943,0.00008447741,0.00010419726,0.000001632001,0.000050460654,0.000110815585,0.000014815427,0.00006059437,0.0056755194,0.9653211,0.028555783],"study_design_scores_gemma":[0.0000015483396,0.0000049602986,0.00010429846,0.00007086187,8.1947513e-7,0.00004721399,0.000053400756,0.000005872394,0.000017785109,0.00055945345,0.9991316,0.0000021544347],"about_ca_topic_score_codex":0.0057886927,"about_ca_topic_score_gemma":0.007127068,"teacher_disagreement_score":0.41724455,"about_ca_system_score_codex":0.001997955,"about_ca_system_score_gemma":0.0030261858,"threshold_uncertainty_score":0.8312299},"labels":[],"label_agreement":null},{"id":"W4245845322","doi":"10.4095/301298","title":"Age Structure, 2001 - Later Working Years by Census Subdivision (35 - 64 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Age structure; Geography; Demography; Gerontology; Medicine; Archaeology; Sociology; Population","score_opus":0.04143344764867962,"score_gpt":0.3341770785782298,"score_spread":0.2927436309295502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245845322","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":[0.034806482,0.0019244022,0.00031659083,0.0005156221,0.00020590352,0.00025935675,0.942742,0.00022057405,0.019009097],"genre_scores_gemma":[0.12253004,0.0036674761,0.0010258321,0.0006747516,0.00012764671,0.0008985056,0.83588487,0.00012254411,0.035068233],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9988643,0.000050617797,0.00012325188,0.0001706029,0.00039995508,0.00039131954],"domain_scores_gemma":[0.99621576,0.000074577954,0.00043956618,0.000089719826,0.0028536515,0.00032672312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006345196,0.0008090964,0.00048667946,0.0053836047,0.0012392546,0.0012131111,0.001466383,0.00047803792,0.019055128],"category_scores_gemma":[0.004349319,0.00034521733,0.00074570236,0.009294399,0.00027066516,0.0008993277,0.0009355601,0.0012938977,0.010404509],"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":[0.0002596247,0.000093453215,0.2994876,0.0010450854,0.0001473763,0.00012194828,0.0012903205,0.0005911372,0.00046904795,0.0020517292,0.6555858,0.03885681],"study_design_scores_gemma":[0.00003644646,0.000038118564,0.8945635,0.00031218937,0.00003586475,0.00012832592,0.00088824966,0.00025021503,0.0001036415,0.00019044382,0.10342983,0.000023196204],"about_ca_topic_score_codex":0.82397246,"about_ca_topic_score_gemma":0.80383927,"teacher_disagreement_score":0.17602754,"about_ca_system_score_codex":0.004706775,"about_ca_system_score_gemma":0.008499605,"threshold_uncertainty_score":0.35412836},"labels":[],"label_agreement":null},{"id":"W4245936232","doi":"10.4095/301304","title":"Age Structure, 2006 - Golden Years by Census Division (65 - 79 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Genealogy; Demography; History; Sociology; Mathematics; Population; Arithmetic","score_opus":0.024015707255079267,"score_gpt":0.3273484472472588,"score_spread":0.3033327399921795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245936232","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":[0.00421858,0.00077165046,0.00012611975,0.00023449537,0.00015283312,0.00016353966,0.9854146,0.0001256356,0.008792428],"genre_scores_gemma":[0.022133654,0.0022575294,0.0008828134,0.00045349178,0.00010262524,0.00048635286,0.9584135,0.000091058195,0.015178966],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9986558,0.00004198933,0.00014851967,0.00016747478,0.00067807816,0.000308025],"domain_scores_gemma":[0.99479043,0.000065933215,0.00043152785,0.000079770514,0.004266104,0.00036619938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063763803,0.0011836265,0.000730941,0.006227761,0.0015588854,0.0014405203,0.0017709984,0.00052335573,0.024101654],"category_scores_gemma":[0.00476734,0.00043793954,0.000994107,0.013516398,0.0002934632,0.0010896294,0.0010234899,0.0016312351,0.012854153],"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":[0.00012214822,0.00004691192,0.06632786,0.0008570135,0.00007951672,0.000042494907,0.00024467404,0.00029050303,0.00014769538,0.0008446932,0.9114901,0.0195065],"study_design_scores_gemma":[0.000083367064,0.000040862316,0.7215969,0.0005685422,0.00006374716,0.00014879486,0.00068286934,0.0003983254,0.00012669466,0.00031802867,0.27593118,0.00004066603],"about_ca_topic_score_codex":0.9258529,"about_ca_topic_score_gemma":0.9331795,"teacher_disagreement_score":0.9258529,"about_ca_system_score_codex":0.009174264,"about_ca_system_score_gemma":0.018346077,"threshold_uncertainty_score":0.14916748},"labels":[],"label_agreement":null},{"id":"W4247369394","doi":"10.18356/1768c00d-en","title":"Acknowledgements","year":2016,"lang":"en","type":"book-chapter","venue":"Statistical papers. Series M","topic":"Insurance, Mortality, Demography, Risk Management","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":"Economic statistics; Commission; Official statistics; European commission; Work (physics); Business cycle; Statistical analysis; Summary statistics; Order (exchange); Statistics; Political science; Economics; Geography; Finance; Economic policy; Engineering; Macroeconomics; European union; Mathematics","score_opus":0.018640222809030636,"score_gpt":0.28927280311459164,"score_spread":0.270632580305561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247369394","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":[0.0046058847,0.011185471,0.020013254,0.119343825,0.1251294,0.0012975498,0.04271015,0.0037246665,0.6719898],"genre_scores_gemma":[0.016038086,0.0036337534,0.011595648,0.014601905,0.007037222,0.0008929392,0.015957873,0.0029784304,0.9272641],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954804,0.0009185223,0.00034165895,0.00064563804,0.0023481832,0.00026558037],"domain_scores_gemma":[0.9787169,0.0027525006,0.000575578,0.0014271317,0.0137864705,0.002741495],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0047015217,0.00082328415,0.0010215106,0.0024139003,0.0020044032,0.0030503266,0.0022889236,0.0012686991,0.4027008],"category_scores_gemma":[0.033154447,0.0002864062,0.000500872,0.0019735713,0.0007947001,0.0028180853,0.004047922,0.0027581102,0.2623593],"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":[0.000032873028,0.000013107475,0.00023271164,0.00017039859,0.000003358963,0.00014417365,0.0006366863,0.00005796005,0.00023195386,0.013254416,0.9429854,0.042236846],"study_design_scores_gemma":[0.000003103966,0.000003887106,0.00016288307,0.00009676584,0.0000018287535,0.00011074776,0.00026508464,0.00002590429,0.000050658873,0.0016740531,0.99760157,0.0000036036283],"about_ca_topic_score_codex":0.003655931,"about_ca_topic_score_gemma":0.0053712027,"teacher_disagreement_score":0.5972992,"about_ca_system_score_codex":0.0025317734,"about_ca_system_score_gemma":0.004431055,"threshold_uncertainty_score":0.8519748},"labels":[],"label_agreement":null},{"id":"W4247579112","doi":"10.1017/9781108784184.009","title":"Multiple state models","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actuarial science; Life insurance; Cash flow; Computer science; State (computer science); Finance; Economics; Algorithm","score_opus":0.033014170625033194,"score_gpt":0.22557554231019136,"score_spread":0.19256137168515816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247579112","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":[0.019056622,0.00918298,0.65991694,0.00906717,0.001611899,0.00012625019,0.0030241732,0.00092494657,0.29708904],"genre_scores_gemma":[0.60517836,0.008922469,0.06065974,0.0012675477,0.0011278896,0.00041577485,0.0026346887,0.00041000047,0.31938362],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99936,0.00021663487,0.000030295198,0.00016675492,0.00014625044,0.000080073994],"domain_scores_gemma":[0.9989489,0.0005457717,0.00012346444,0.00013333364,0.00016821006,0.00008025508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000968846,0.0010591456,0.0011955457,0.0008417472,0.0009159654,0.003107033,0.002021775,0.0021853268,0.044680923],"category_scores_gemma":[0.004064681,0.00046091247,0.001667699,0.0011770675,0.0011443316,0.0030678676,0.0019189443,0.0025890798,0.006715622],"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":[0.000020773401,0.000034601162,0.0005713542,0.000089587134,0.000047728794,0.00011768259,0.00011774479,0.07156693,0.0001532659,0.895494,0.016093101,0.015693326],"study_design_scores_gemma":[0.0000207917,0.000026038177,0.00038349789,0.000067544206,0.000034891513,0.00013994645,0.00006270921,0.3849715,0.00010738224,0.57224625,0.04190996,0.000029428544],"about_ca_topic_score_codex":0.00567747,"about_ca_topic_score_gemma":0.0044426373,"teacher_disagreement_score":0.044680923,"about_ca_system_score_codex":0.0015905731,"about_ca_system_score_gemma":0.0013027746,"threshold_uncertainty_score":0.1494726},"labels":[],"label_agreement":null},{"id":"W4247740186","doi":"10.4095/301299","title":"Age Structure, 2001 - Golden Years by Census Subdivision (65 - 74 years)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Geography; Age structure; Genealogy; Demography; Cartography; Archaeology; History; Population; Sociology","score_opus":0.03300431832390803,"score_gpt":0.3382680578960898,"score_spread":0.3052637395721818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247740186","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":[0.028993696,0.0016926214,0.00027081164,0.0004851111,0.00020376791,0.00021875244,0.95234716,0.00022390734,0.015564283],"genre_scores_gemma":[0.098857306,0.002939636,0.0008160132,0.00058596296,0.00010147911,0.0006465898,0.87384754,0.00010232712,0.022103176],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873954,0.00005102774,0.00013680589,0.00019341947,0.00047287397,0.0004063759],"domain_scores_gemma":[0.9957353,0.00007699568,0.0004888222,0.00008054916,0.0032588392,0.00035946202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063439173,0.0009015902,0.0005618401,0.00572614,0.0011780345,0.0011380017,0.0015250724,0.00048624678,0.015537505],"category_scores_gemma":[0.0045316936,0.00037850457,0.0007511807,0.009942142,0.0003163274,0.0009043516,0.0009819391,0.0014194687,0.008334087],"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":[0.00030976764,0.000089659145,0.25978965,0.001104864,0.00015623315,0.00011144649,0.00082140707,0.00067367195,0.00038320755,0.0018749533,0.70356643,0.031118713],"study_design_scores_gemma":[0.000050944258,0.000046971145,0.89185584,0.00036214315,0.00004879226,0.0001432515,0.0008152986,0.00035367315,0.000112056594,0.00021707102,0.1059684,0.000025567302],"about_ca_topic_score_codex":0.85356194,"about_ca_topic_score_gemma":0.8444074,"teacher_disagreement_score":0.14643806,"about_ca_system_score_codex":0.006063551,"about_ca_system_score_gemma":0.010574793,"threshold_uncertainty_score":0.2946009},"labels":[],"label_agreement":null},{"id":"W4247913804","doi":"10.1515/iupac.81.0522","title":"Life-Table Response Experiment (LTRE)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Table (database); Relation (database); Computer science; Ecology; Biology; Data mining; Linguistics; Philosophy","score_opus":0.020424263304318683,"score_gpt":0.4326818456253851,"score_spread":0.4122575823210664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247913804","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":[0.00060134236,0.00014723906,0.00028418773,0.00010974013,0.00004742053,0.00007066359,0.9975435,0.00036833333,0.00082747603],"genre_scores_gemma":[0.0020255325,0.00012430921,0.0012132408,0.00020384736,0.000017681547,0.00053610647,0.9943914,0.000074622345,0.0014132643],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971342,0.0009056429,0.00038931143,0.0008714397,0.00046317448,0.0002362361],"domain_scores_gemma":[0.9922212,0.0032982798,0.0009856864,0.0021952975,0.0009342435,0.00036545395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003591665,0.0021220865,0.0017026908,0.0019898317,0.0008031176,0.0018467832,0.0045798994,0.0026781834,0.06903526],"category_scores_gemma":[0.017534938,0.0006079235,0.0028567757,0.002894249,0.00060108985,0.0013502152,0.0021271068,0.0025292966,0.05626409],"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":[0.00090570754,0.00023750353,0.00818863,0.0033951516,0.00035881772,0.00008089734,0.000049982966,0.0018581528,0.0002779551,0.0015086877,0.97384495,0.009293436],"study_design_scores_gemma":[0.0019377919,0.000325847,0.01752928,0.0010296812,0.00039767963,0.00027431376,0.00012930075,0.0028495556,0.0010864241,0.004940215,0.9693708,0.00012917542],"about_ca_topic_score_codex":0.012402483,"about_ca_topic_score_gemma":0.032056194,"teacher_disagreement_score":0.06903526,"about_ca_system_score_codex":0.0017753865,"about_ca_system_score_gemma":0.0022541229,"threshold_uncertainty_score":0.23094594},"labels":[],"label_agreement":null},{"id":"W4248168597","doi":"10.1037/amp0000862","title":"Increasing population densities predict decreasing fertility rates over time: A 174-nation investigation.","year":2021,"lang":"en","type":"article","venue":"American Psychologist","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Fertility; Demography; Total fertility rate; Population; Socioeconomic status; Population density; Population growth; Religiosity; Demographic transition; Geography; Psychology; Sociology; Family planning; Social psychology","score_opus":0.027732570886156546,"score_gpt":0.3418420656114966,"score_spread":0.31410949472534005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248168597","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":[0.9967642,0.00040912515,0.00015239474,0.00045512142,0.00001617478,0.000013964024,0.0013553359,0.000004513331,0.0008291626],"genre_scores_gemma":[0.99811184,0.00026360768,0.00016652393,0.000084001666,0.00001932652,0.000022520082,0.0010717314,0.0000025914858,0.0002578197],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996927,0.0001360465,0.000031288157,0.000052440442,0.00004087405,0.00004667825],"domain_scores_gemma":[0.99699247,0.00070641906,0.0015964104,0.00015953055,0.00024085844,0.000304235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009299461,0.00013101456,0.00015561008,0.0010518122,0.00054582494,0.0004319645,0.000524131,0.00039756217,0.002957464],"category_scores_gemma":[0.0053737583,0.00020648292,0.00043277565,0.0016470875,0.00034491517,0.00065945525,0.0006829938,0.00078657124,0.0002970443],"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":[0.000010921688,0.000015343216,0.9977442,0.0000064702754,0.000038021466,0.000031379244,0.00026437573,0.000023787485,0.00001554224,0.000063676394,0.00044799078,0.0013382706],"study_design_scores_gemma":[0.0000019156575,0.00002292883,0.99843305,0.0000074771565,0.000022693017,0.000069899754,0.00078394724,0.0001713221,0.0000106226435,0.000048584145,0.00042488283,0.0000026300156],"about_ca_topic_score_codex":0.025276154,"about_ca_topic_score_gemma":0.039574973,"teacher_disagreement_score":0.025276154,"about_ca_system_score_codex":0.0004621586,"about_ca_system_score_gemma":0.00037747054,"threshold_uncertainty_score":0.05025804},"labels":[],"label_agreement":null},{"id":"W4248202516","doi":"10.4095/301467","title":"Population Change, 2001-2006 (by census division)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Population; Genealogy; Demography; History; Sociology; Mathematics; Arithmetic","score_opus":0.08294071297939618,"score_gpt":0.38086926503056645,"score_spread":0.2979285520511703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248202516","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":[0.014213498,0.0009579363,0.00029075224,0.00041937595,0.0003258874,0.00026496808,0.96363485,0.00024956884,0.019643115],"genre_scores_gemma":[0.0370898,0.0033646934,0.0013908221,0.00042374452,0.0001317523,0.000709,0.9252102,0.00006781451,0.03161217],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987306,0.00004921381,0.00011123212,0.00012543454,0.0008170928,0.00016643149],"domain_scores_gemma":[0.9981036,0.000051629246,0.00023109556,0.000046402176,0.0014443425,0.0001228665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050937885,0.0009771902,0.0003989584,0.003798737,0.0008375615,0.0012630888,0.0009878878,0.00050477864,0.010496765],"category_scores_gemma":[0.0028381774,0.000379733,0.000571815,0.008655198,0.00021531373,0.0009771223,0.0007566683,0.0012524059,0.010859348],"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":[0.00013297553,0.00009668485,0.088037334,0.0010103543,0.000097463046,0.00007970056,0.00041069338,0.00068719324,0.00033539257,0.0007561101,0.8658069,0.042549223],"study_design_scores_gemma":[0.000056361696,0.000048513386,0.5903334,0.00025137313,0.000049973143,0.00026652,0.00044357663,0.0006094818,0.0004310276,0.000083616585,0.40739974,0.00002639449],"about_ca_topic_score_codex":0.6340459,"about_ca_topic_score_gemma":0.65917754,"teacher_disagreement_score":0.6340459,"about_ca_system_score_codex":0.005037239,"about_ca_system_score_gemma":0.0060130134,"threshold_uncertainty_score":0.7362186},"labels":[],"label_agreement":null},{"id":"W4248427798","doi":"10.4095/301305","title":"Age Structure, 2006 - Oldest Old by Census Division (80 years of age and older)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Age structure; Gerontology; Geography; Demography; Medicine; Sociology; Population; Mathematics; Arithmetic","score_opus":0.019596604562484153,"score_gpt":0.31018158800139795,"score_spread":0.2905849834389138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248427798","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":[0.0114891,0.0012893677,0.00019034692,0.00044644618,0.00021985424,0.00020219224,0.9737896,0.00018095982,0.012192049],"genre_scores_gemma":[0.055595987,0.0036517417,0.0014718168,0.00075725507,0.0001651917,0.00060303864,0.92056054,0.00012373539,0.017070616],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9989278,0.000035713983,0.00012914972,0.00013111498,0.00052869885,0.00024748206],"domain_scores_gemma":[0.99453884,0.00006877913,0.0005428808,0.0000826007,0.004338977,0.00042793606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006372149,0.00097127666,0.0006246578,0.0053784293,0.0016637492,0.001278906,0.001657422,0.00050997175,0.015074328],"category_scores_gemma":[0.005533569,0.0003647451,0.0007875339,0.010936027,0.00028295734,0.0010098726,0.0010717221,0.0016064728,0.007484214],"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":[0.00013149175,0.000054535427,0.13898717,0.00080412993,0.000084020554,0.000056311983,0.00033088092,0.00035512898,0.00016126646,0.0008987284,0.83770627,0.020430095],"study_design_scores_gemma":[0.00006042506,0.000036398716,0.8588166,0.00045614332,0.000058548096,0.00017697843,0.0007815956,0.00038330324,0.00010162638,0.0002685472,0.13882199,0.00003776211],"about_ca_topic_score_codex":0.920881,"about_ca_topic_score_gemma":0.93208444,"teacher_disagreement_score":0.920881,"about_ca_system_score_codex":0.007616934,"about_ca_system_score_gemma":0.015865518,"threshold_uncertainty_score":0.15916991},"labels":[],"label_agreement":null},{"id":"W4248821185","doi":"10.21203/rs.2.13887/v3","title":"A Probabilistic Approach for Economic Evaluation of Occupational Health and Safety Interventions: A Case Study of Silica Exposure Reduction Interventions in the Construction Sector","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cancer Care Ontario; Institute for Work & Health","funders":"","keywords":"Psychological intervention; Cost–benefit analysis; Economic evaluation; Environmental health; Intervention (counseling); Medicine","score_opus":0.3907534566646841,"score_gpt":0.5296479972743491,"score_spread":0.138894540609665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248821185","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":[0.30436,0.003060237,0.663346,0.0046720887,0.00019108596,0.0020582585,0.0017520383,0.00018285861,0.020377459],"genre_scores_gemma":[0.9055384,0.0012140551,0.08779034,0.00015269748,0.000090182715,0.0014827115,0.00033556705,0.000051389026,0.0033446653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97886825,0.01861133,0.00039819253,0.00067776634,0.00083586964,0.0006086569],"domain_scores_gemma":[0.8997557,0.096760385,0.0013865909,0.00075210416,0.00089074526,0.00045449182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02915025,0.0021610907,0.002803316,0.0038603111,0.0012879764,0.0037336638,0.003030666,0.0039126803,0.009488922],"category_scores_gemma":[0.06595338,0.0018520092,0.0029710366,0.0030232554,0.0026993568,0.0042826612,0.0028197623,0.0029992969,0.0002166188],"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":[0.00074443023,0.00065208366,0.0053229057,0.00043183536,0.00057068246,0.0005116802,0.0003213331,0.8707785,0.0003419363,0.09756635,0.0010395775,0.021718778],"study_design_scores_gemma":[0.00019443556,0.00030998228,0.0019711757,0.000058855476,0.00023001264,0.00013189447,0.00027195917,0.9366769,0.00010972207,0.059294607,0.0006968831,0.000053469215],"about_ca_topic_score_codex":0.018194405,"about_ca_topic_score_gemma":0.013097649,"teacher_disagreement_score":0.02915025,"about_ca_system_score_codex":0.005122295,"about_ca_system_score_gemma":0.004783512,"threshold_uncertainty_score":0.15416306},"labels":[],"label_agreement":null},{"id":"W4248944335","doi":"10.25336/p6mk6q","title":"On teaching demography: some non-traditional guidelines","year":2004,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Demography; Geography; Demographic economics; Sociology; Economics","score_opus":0.12729100891788223,"score_gpt":0.39702393736559466,"score_spread":0.26973292844771246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248944335","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023141617,0.048704386,0.023720367,0.86762017,0.013666356,0.0006723118,0.0008925898,0.00043809283,0.04197157],"genre_scores_gemma":[0.07914798,0.08928563,0.20106657,0.560365,0.029304544,0.0024610797,0.0014311016,0.0011453864,0.03579282],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9633896,0.012212542,0.0056958543,0.0018820893,0.014162812,0.0026571548],"domain_scores_gemma":[0.80470234,0.06252879,0.004891056,0.007281344,0.10590905,0.014687464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.062038165,0.001895551,0.0026299623,0.017009946,0.010128586,0.009274911,0.01245035,0.015154551,0.010897675],"category_scores_gemma":[0.14473638,0.0011905102,0.0024593908,0.019520348,0.023447143,0.00652409,0.007800953,0.021158565,0.0038031335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015219874,0.00035348494,0.006499045,0.0025118603,0.00014199938,0.0004609933,0.0034346245,0.0005863198,0.0003583356,0.06363536,0.6518876,0.26997817],"study_design_scores_gemma":[0.0005568094,0.00018298476,0.027665844,0.019573504,0.0005577014,0.0010518904,0.006730032,0.0018741229,0.0006219515,0.123067625,0.81764793,0.00046968562],"about_ca_topic_score_codex":0.66215426,"about_ca_topic_score_gemma":0.8129279,"teacher_disagreement_score":0.66215426,"about_ca_system_score_codex":0.034069758,"about_ca_system_score_gemma":0.08132924,"threshold_uncertainty_score":0.67967075},"labels":[],"label_agreement":null},{"id":"W4249558037","doi":"10.1002/9781405165518.wbeos1478","title":"Population Aging","year":2019,"lang":"en","type":"other","venue":"The Blackwell Encyclopedia of Sociology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Fertility; Population ageing; Demography; Gerontology; Population; Total fertility rate; Psychology; Medicine; Research methodology; Sociology; Family planning","score_opus":0.011381617407004512,"score_gpt":0.29179073763485797,"score_spread":0.28040912022785347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249558037","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":[0.0032909198,0.071284264,0.002526879,0.036022577,0.014727514,0.00018567214,0.0068120286,0.0009051074,0.86424506],"genre_scores_gemma":[0.04073148,0.15803824,0.0041255816,0.021039132,0.009263395,0.0003186876,0.009056337,0.00036133087,0.75706583],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918026,0.00016440767,0.0000648633,0.00012948763,0.0003349764,0.00012599636],"domain_scores_gemma":[0.99873847,0.00014887884,0.00013817199,0.00012533007,0.0005898406,0.00025926557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012304421,0.000600371,0.00033758383,0.0014950109,0.0012333879,0.0030712849,0.0006303957,0.0016132571,0.14793466],"category_scores_gemma":[0.004779788,0.00014324879,0.00046404317,0.0019121851,0.00053780334,0.0029670494,0.0026407635,0.0013502307,0.062446237],"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":[0.000015889482,0.00001689698,0.0015205827,0.0003845053,0.000008126465,0.00006746293,0.0004390659,0.000044428383,0.00011247473,0.033457614,0.6590243,0.30490878],"study_design_scores_gemma":[0.000001480944,0.000010059822,0.001336365,0.000264816,0.0000030213848,0.00014159096,0.00013375617,0.000012572415,0.000024919176,0.0027251982,0.9953434,0.0000027908247],"about_ca_topic_score_codex":0.003617209,"about_ca_topic_score_gemma":0.00590514,"teacher_disagreement_score":0.14793466,"about_ca_system_score_codex":0.0010557999,"about_ca_system_score_gemma":0.0025983453,"threshold_uncertainty_score":0.49489075},"labels":[],"label_agreement":null},{"id":"W4249738160","doi":"10.17846/gi.2014.18.1.5-18","title":"Population of Slovakia After a Quarter of Century of Transformation","year":2014,"lang":"sk","type":"article","venue":"Geografické informácie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Transformation (genetics); Geography; Population; Ancient history; History; Archaeology; Demography; Sociology; Biology","score_opus":0.00452697376520033,"score_gpt":0.23769505695977383,"score_spread":0.2331680831945735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249738160","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":[0.9874334,0.0020245195,0.00006885694,0.0010477903,0.00008324732,0.000010204438,0.0049868436,0.000016314423,0.004328862],"genre_scores_gemma":[0.9935782,0.0011483668,0.000065937515,0.00012593322,0.000025445845,0.000011358747,0.002169461,0.0000062879717,0.0028689369],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975044,0.000028866709,0.000026326234,0.00004830338,0.000030091664,0.00011600657],"domain_scores_gemma":[0.9997962,0.000014997162,0.00006014698,0.00001682238,0.000057635196,0.00005406163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030474956,0.00017889128,0.00034490545,0.0010482849,0.00063899194,0.001167206,0.00036920334,0.00042448746,0.0029549284],"category_scores_gemma":[0.00060017244,0.00017404926,0.00056979543,0.0014365647,0.00048768646,0.0005060686,0.001036143,0.0005535268,0.00047353664],"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":[0.0009799779,0.00011684518,0.9005637,0.00076832616,0.00061795476,0.003236962,0.008738972,0.0026571935,0.0027169716,0.008874882,0.018926363,0.051801816],"study_design_scores_gemma":[0.000009084557,0.000032303033,0.9849368,0.000049321912,0.000029983916,0.0002995649,0.0017457777,0.00015030194,0.00008555898,0.00007875961,0.012572579,0.000010033378],"about_ca_topic_score_codex":0.17138216,"about_ca_topic_score_gemma":0.13704373,"teacher_disagreement_score":0.17138216,"about_ca_system_score_codex":0.0034751066,"about_ca_system_score_gemma":0.0031540932,"threshold_uncertainty_score":0.34076923},"labels":[],"label_agreement":null},{"id":"W4249976298","doi":"10.4095/301469","title":"Population Density, 2006 (by census division)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Population; Mathematics; Demography; Sociology; Arithmetic","score_opus":0.029646174340214344,"score_gpt":0.3492518522979175,"score_spread":0.31960567795770317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249976298","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":[0.0072964993,0.00093908823,0.0004113189,0.00027682516,0.00030701747,0.00033629753,0.961543,0.00023602496,0.028653907],"genre_scores_gemma":[0.02477952,0.003074089,0.0022567906,0.0004555989,0.00015097465,0.000950839,0.936769,0.00007171244,0.031491403],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99916565,0.000049134025,0.00009212471,0.00012022549,0.0004691481,0.00010371283],"domain_scores_gemma":[0.99890625,0.000049747618,0.00014289524,0.000034782035,0.00078182825,0.000084397885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049646,0.0011983059,0.0005807182,0.004029568,0.00064380065,0.00090872345,0.0011720263,0.0004504712,0.019895393],"category_scores_gemma":[0.0022530314,0.00040902104,0.0005605016,0.008655098,0.0001808542,0.00089220155,0.0006332509,0.0013758995,0.022291804],"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":[0.000075257136,0.000094666495,0.032547075,0.0005916436,0.000053611635,0.000037056518,0.000121135265,0.00047215619,0.00018478832,0.000715128,0.9466272,0.018480407],"study_design_scores_gemma":[0.000108640364,0.00008922503,0.48783195,0.00046569866,0.000065782704,0.00029545513,0.00038364236,0.0010019273,0.00030327876,0.00029164963,0.50912994,0.00003282584],"about_ca_topic_score_codex":0.2728556,"about_ca_topic_score_gemma":0.3041313,"teacher_disagreement_score":0.72714436,"about_ca_system_score_codex":0.002167368,"about_ca_system_score_gemma":0.0033065763,"threshold_uncertainty_score":0.5425348},"labels":[],"label_agreement":null},{"id":"W4250067824","doi":"10.4095/301470","title":"Population Density, 2006 (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Geography; Population; Demography; Archaeology; Sociology","score_opus":0.030250613604586786,"score_gpt":0.3490213200052711,"score_spread":0.3187707064006843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250067824","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":[0.01307154,0.00082646706,0.0004865832,0.0002853006,0.00026788196,0.0003513054,0.95503706,0.00029966127,0.029374184],"genre_scores_gemma":[0.035353627,0.0023806926,0.0023688276,0.00034194268,0.00011946023,0.0007931034,0.9297153,0.000071288145,0.028855786],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991953,0.000054749125,0.00008783688,0.00011482451,0.0004383714,0.00010897752],"domain_scores_gemma":[0.9989492,0.000046745914,0.0001503432,0.000036964255,0.0007297707,0.00008700832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004905758,0.0010773635,0.000533115,0.0038419177,0.00066037476,0.0007844824,0.0012249553,0.00041414655,0.015155974],"category_scores_gemma":[0.0022782334,0.00037569492,0.00054600684,0.0077614216,0.00016323233,0.00077062426,0.00069959677,0.0012241954,0.016745104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008989067,0.000115678,0.06388853,0.0005804628,0.00006672124,0.000047082925,0.00019851125,0.0006557281,0.00022998847,0.0008368882,0.911036,0.022254465],"study_design_scores_gemma":[0.00008314655,0.00008730943,0.6224534,0.00035223222,0.0000587891,0.0002764191,0.000441052,0.001158766,0.00030570076,0.00025651467,0.3744988,0.000027865828],"about_ca_topic_score_codex":0.31094933,"about_ca_topic_score_gemma":0.34464997,"teacher_disagreement_score":0.6890507,"about_ca_system_score_codex":0.001946847,"about_ca_system_score_gemma":0.0030486332,"threshold_uncertainty_score":0.6182788},"labels":[],"label_agreement":null},{"id":"W4251634634","doi":"10.1017/9781108784184.011","title":"Joint life and last survivor benefits","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actuarial science; Life insurance; Cash flow; Computer science; Finance; Economics","score_opus":0.033424292946347946,"score_gpt":0.21715131920684724,"score_spread":0.1837270262604993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251634634","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":[0.0052267644,0.036409605,0.0033943853,0.005725396,0.0028132698,0.000022346523,0.0010905298,0.0001896987,0.94512796],"genre_scores_gemma":[0.059211433,0.017411862,0.001237237,0.00062830164,0.0020652916,0.00002709808,0.0011154122,0.00009611227,0.91820735],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997905,0.000027167878,0.000007321185,0.000029722316,0.000116089686,0.000029225259],"domain_scores_gemma":[0.99973637,0.00008295616,0.00002519791,0.000030775765,0.00006320584,0.00006160094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040444292,0.00034307572,0.00025030557,0.0010008195,0.0004717116,0.0016003196,0.00034450783,0.00064783863,0.09580915],"category_scores_gemma":[0.0013880269,0.000121615274,0.000270866,0.00091226643,0.00033803066,0.0010308537,0.00074758654,0.0011805355,0.0149807595],"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":[0.0000520203,0.00004838238,0.00066774274,0.00015991551,0.000012454962,0.000102675476,0.0003382389,0.0007806771,0.0002614514,0.2042224,0.47699204,0.31636214],"study_design_scores_gemma":[0.0000032013133,0.000021465858,0.0023967929,0.00012871141,0.000006111687,0.00028982753,0.000074603355,0.00031009558,0.00011753602,0.02175997,0.9748858,0.0000058595806],"about_ca_topic_score_codex":0.0018144826,"about_ca_topic_score_gemma":0.0035938672,"teacher_disagreement_score":0.09580915,"about_ca_system_score_codex":0.00070228585,"about_ca_system_score_gemma":0.00085821585,"threshold_uncertainty_score":0.32051355},"labels":[],"label_agreement":null},{"id":"W4251952192","doi":"10.4095/301463","title":"Population Change, 1996 to 2001 (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Subdivision; Geography; Population; Demography; Archaeology; Sociology","score_opus":0.0959873103046204,"score_gpt":0.3958723586630828,"score_spread":0.2998850483584624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251952192","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":[0.057677604,0.0010960373,0.00046824012,0.0007149314,0.0004969828,0.00038903634,0.90647846,0.0003233867,0.032355297],"genre_scores_gemma":[0.07249273,0.0027713147,0.0012375951,0.00043334102,0.00013150676,0.0010691164,0.87450236,0.000068415844,0.04729361],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9990055,0.00006774141,0.00012819789,0.00013870014,0.0005056437,0.00015422821],"domain_scores_gemma":[0.9986714,0.000058876667,0.00026599073,0.0000568209,0.00083829544,0.000108768836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006291044,0.0008532107,0.00029026432,0.0029598582,0.0005954678,0.00097218866,0.0010128291,0.0005762428,0.008099041],"category_scores_gemma":[0.003335924,0.00035075043,0.0004913445,0.006379903,0.00018557931,0.0010202291,0.0010423855,0.0010282359,0.010138175],"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":[0.00037554614,0.0002075265,0.21512327,0.0011692792,0.00014507593,0.00024457075,0.00077488844,0.0014407955,0.0006741512,0.0010151628,0.7039985,0.074831255],"study_design_scores_gemma":[0.00006594194,0.00008750046,0.7057699,0.00022882255,0.000049039467,0.00033985838,0.0006304962,0.00068231166,0.00058387854,0.00009577922,0.29144287,0.000023656112],"about_ca_topic_score_codex":0.31137785,"about_ca_topic_score_gemma":0.3306165,"teacher_disagreement_score":0.6886221,"about_ca_system_score_codex":0.0029694368,"about_ca_system_score_gemma":0.0031223288,"threshold_uncertainty_score":0.61913085},"labels":[],"label_agreement":null},{"id":"W4252412751","doi":"10.1017/9781108784184.006","title":"Annuities","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actuarial science; Life insurance; Cash flow; Computer science; Finance; Economics","score_opus":0.025154581684322917,"score_gpt":0.22848463960725438,"score_spread":0.20333005792293146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252412751","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":[0.00082258787,0.006512812,0.0022590393,0.0016397239,0.0019288692,0.000042457887,0.0013403627,0.0002930008,0.9851612],"genre_scores_gemma":[0.010939208,0.0052451687,0.0019837741,0.00071321335,0.00091749564,0.000058884954,0.0018287615,0.00018691679,0.9781266],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994197,0.00007576142,0.000035786405,0.00012529659,0.00028456587,0.00005899235],"domain_scores_gemma":[0.9994382,0.000066810666,0.000044543205,0.00015997622,0.0002054284,0.00008497164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006792131,0.00068303326,0.00044297962,0.0018037896,0.0013050566,0.0036798392,0.0010695134,0.001181269,0.30826616],"category_scores_gemma":[0.0021369073,0.00028889993,0.0005183156,0.0019450012,0.0005680835,0.0033402166,0.002084942,0.0019854598,0.17791463],"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":[0.000057660658,0.000035823832,0.0003241937,0.00024892722,0.000011806982,0.00010701041,0.00024169829,0.00040218575,0.00043533518,0.26514637,0.4782098,0.2547792],"study_design_scores_gemma":[0.0000016114038,0.000006246822,0.00015166089,0.000044518794,0.0000013652116,0.00007683169,0.000021912738,0.000042320564,0.00005695599,0.0077478467,0.991846,0.000002713761],"about_ca_topic_score_codex":0.0012157335,"about_ca_topic_score_gemma":0.001843089,"teacher_disagreement_score":0.30826616,"about_ca_system_score_codex":0.0011223803,"about_ca_system_score_gemma":0.0009266736,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4253537181","doi":"10.4095/301288","title":"Age, 1996 - The Oldest Old (75 years and over) by Census Division","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Division (mathematics); Geography; Demography; Gerontology; Genealogy; History; Medicine; Sociology; Arithmetic; Mathematics; Population","score_opus":0.027784193779835546,"score_gpt":0.332042917014897,"score_spread":0.3042587232350615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253537181","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":[0.07738287,0.00093610724,0.00025013203,0.00055167056,0.00038500215,0.00038273004,0.8799549,0.00012904826,0.040027514],"genre_scores_gemma":[0.15618108,0.0048034913,0.0009446412,0.00046962994,0.00021563075,0.0013046693,0.767691,0.000054666965,0.06833517],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9997161,0.000025776053,0.000051245184,0.00003739511,0.00010603575,0.00006340337],"domain_scores_gemma":[0.9990269,0.00006608429,0.00020979246,0.00004021793,0.0005057066,0.00015134928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003996188,0.0008079411,0.00028152738,0.0022798674,0.00041689092,0.0005834925,0.0008766393,0.0003405704,0.010390859],"category_scores_gemma":[0.0020858035,0.00025698903,0.00027663723,0.0039640544,0.0001022003,0.00081035955,0.0007274297,0.0005607105,0.009533236],"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":[0.0003651792,0.00018804584,0.4159448,0.0008236664,0.00011177255,0.0003331252,0.0006390879,0.0008513866,0.0005417422,0.00082660676,0.5468644,0.032510247],"study_design_scores_gemma":[0.000057660884,0.000070647126,0.8931879,0.00015074751,0.000032028696,0.00021058836,0.00086259004,0.0002585022,0.00023927845,0.000119931625,0.104798496,0.000011570326],"about_ca_topic_score_codex":0.10765498,"about_ca_topic_score_gemma":0.12200086,"teacher_disagreement_score":0.10765498,"about_ca_system_score_codex":0.0007346779,"about_ca_system_score_gemma":0.0013014124,"threshold_uncertainty_score":0.21405673},"labels":[],"label_agreement":null},{"id":"W4253729612","doi":"10.1002/9780470012505.tao008","title":"Options and Guarantees in Life Insurance","year":2004,"lang":"en","type":"other","venue":"Encyclopedia of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Life insurance; Actuarial science; Context (archaeology); Payment; Insurance policy; Cover (algebra); Business; Variable (mathematics); Finance; Mathematics; Engineering; Geography","score_opus":0.01032997680229546,"score_gpt":0.2860385354895342,"score_spread":0.2757085586872387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253729612","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":[0.037942894,0.055259008,0.24062836,0.04144459,0.0019145653,0.000083567764,0.0006117976,0.00017952148,0.6219358],"genre_scores_gemma":[0.86999315,0.024257628,0.039514508,0.00228702,0.0034731184,0.00022087689,0.0003385873,0.000106663734,0.05980839],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974819,0.0011742748,0.00015820103,0.00027720537,0.000749293,0.00015924577],"domain_scores_gemma":[0.9945209,0.003949607,0.00043700816,0.0002797223,0.0004916715,0.00032107194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033600752,0.0007622583,0.00076722034,0.0023451252,0.0018041657,0.0051776,0.0010007942,0.0035923582,0.014553383],"category_scores_gemma":[0.0074941064,0.00037178292,0.0008220707,0.003125854,0.0101310285,0.0077384557,0.0026557269,0.006279765,0.0011405151],"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":[0.0000038953717,0.00000269674,0.00003247687,0.000012006693,0.0000015561252,0.000022825327,0.00004134832,0.0005914672,0.000014475462,0.99658453,0.0008708373,0.0018218386],"study_design_scores_gemma":[0.0000033604026,0.0000030157523,0.000035522844,0.000024395107,0.000001248388,0.000017796036,0.000022043516,0.0013993009,0.000013804836,0.9917385,0.006737874,0.0000029932767],"about_ca_topic_score_codex":0.002839353,"about_ca_topic_score_gemma":0.0014139843,"teacher_disagreement_score":0.014553383,"about_ca_system_score_codex":0.0035959152,"about_ca_system_score_gemma":0.0012865309,"threshold_uncertainty_score":0.048685968},"labels":[],"label_agreement":null},{"id":"W4253793614","doi":"10.19124/ima.2021.01.2","title":"A Novel Pattern- driven Stochastic Process for End-of-Life Forecasting","year":2021,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Process (computing); Computer science; Stochastic process; Artificial intelligence; Statistics; Mathematics; Programming language","score_opus":0.08597512596955864,"score_gpt":0.3447538203400974,"score_spread":0.25877869437053874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253793614","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":[0.019028187,0.00038742676,0.97874063,0.00022538105,0.00008517241,0.000031617834,0.00014440628,0.0002542519,0.0011028654],"genre_scores_gemma":[0.88475347,0.00089447206,0.1085071,0.00019516665,0.00019408602,0.00020895994,0.000728713,0.000066669825,0.0044512404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949956,0.000121626006,0.000033468372,0.00015404145,0.00013082246,0.000060457343],"domain_scores_gemma":[0.9987935,0.0007542182,0.00012969179,0.000046633726,0.00023073914,0.000045187884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014788588,0.00079355476,0.0012079536,0.000720405,0.00038595006,0.0009797983,0.0017195424,0.0012594776,0.0016714813],"category_scores_gemma":[0.0036536132,0.00047924515,0.001070514,0.0010606778,0.0004957055,0.00095163396,0.000797121,0.0014673447,0.00035567692],"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":[0.000035778292,0.000027294958,0.001582644,0.000045271365,0.000036894908,0.00007601125,0.000033388464,0.9648255,0.00063816144,0.0074398457,0.00052663434,0.024732575],"study_design_scores_gemma":[0.0000011440364,0.0000027943129,0.00006638531,0.0000012912303,0.0000018145403,0.0000047511744,9.1588925e-7,0.99918073,0.000036866066,0.0006313239,0.00007022936,0.0000016512126],"about_ca_topic_score_codex":0.013406321,"about_ca_topic_score_gemma":0.00714334,"teacher_disagreement_score":0.013406321,"about_ca_system_score_codex":0.00068523566,"about_ca_system_score_gemma":0.0009485503,"threshold_uncertainty_score":0.026656628},"labels":[],"label_agreement":null},{"id":"W4254863923","doi":"10.18356/c0947823-en","title":"Low fertility","year":2014,"lang":"en","type":"book-chapter","venue":"Statistical papers - United Nations. Series A, Population and vital statistics report","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fertility; Sub-replacement fertility; Latin Americans; Geography; China; Total fertility rate; Socioeconomics; Demography; Political science; Population; Birth rate; Family planning; Economics; Research methodology; Sociology; Archaeology","score_opus":0.017403488113496453,"score_gpt":0.29665301309803604,"score_spread":0.2792495249845396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254863923","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":[0.0014698275,0.058613762,0.0018545213,0.009550442,0.0040291063,0.00009100588,0.0018098629,0.000417866,0.92216355],"genre_scores_gemma":[0.010589131,0.06027434,0.002166878,0.0043239305,0.0017488129,0.00008031637,0.0018653566,0.00014043605,0.91881084],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9997812,0.000035585246,0.000010567564,0.00003961459,0.00010464063,0.000028365486],"domain_scores_gemma":[0.99984026,0.000051245,0.000013240317,0.00002095271,0.000048074064,0.00002628184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037305782,0.00037661957,0.00031639315,0.00087955006,0.0006588302,0.0013134786,0.00049555674,0.0006755286,0.07827242],"category_scores_gemma":[0.00088565255,0.000174829,0.00023961601,0.00087935064,0.00053689856,0.0010666664,0.0008321849,0.0011555011,0.0444829],"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":[0.000008264554,0.000017398324,0.00025194077,0.00016916293,0.0000022159436,0.000059775408,0.00038760592,0.00008341254,0.00012215483,0.0422215,0.72051233,0.23616421],"study_design_scores_gemma":[0.0000010165793,0.000007941314,0.00031643783,0.00012520308,8.128904e-7,0.00016845779,0.000042288593,0.000011649988,0.00004520022,0.0026363812,0.99664235,0.0000022539582],"about_ca_topic_score_codex":0.0022911525,"about_ca_topic_score_gemma":0.004921023,"teacher_disagreement_score":0.07827242,"about_ca_system_score_codex":0.0008299822,"about_ca_system_score_gemma":0.0009272467,"threshold_uncertainty_score":0.26184732},"labels":[],"label_agreement":null},{"id":"W4254941186","doi":"10.1111/j.1728-4457.2003.00493.x","title":"Kenneth Boulding on Possible Consequences of Increased Life Expectancy","year":2003,"lang":"en","type":"article","venue":"Population and Development Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Longevity; Casual; Population; Consumption (sociology); Life span; Demography; Sociology; Political science; Gerontology; Law; Social science; Medicine","score_opus":0.04938819650980255,"score_gpt":0.33547684526331034,"score_spread":0.2860886487535078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254941186","genre_codex":"commentary","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":[0.0033652845,0.07709655,0.00036605683,0.8190392,0.018232796,0.000015555715,0.00018870704,0.000018113966,0.08167767],"genre_scores_gemma":[0.1520176,0.18479271,0.0011352736,0.50989515,0.024527501,0.00011234634,0.00017220993,0.000089242385,0.12725809],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990049,0.00040515757,0.0000399343,0.00016080575,0.00030873803,0.00008058899],"domain_scores_gemma":[0.99719787,0.001792131,0.00015256928,0.00012539477,0.00048845634,0.00024362672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00150385,0.00027250592,0.00028511017,0.00055059086,0.002344341,0.0021240897,0.00072690065,0.0041184146,0.00922265],"category_scores_gemma":[0.009596781,0.00014968474,0.00027855445,0.00051671907,0.0028491835,0.0025942912,0.0013329935,0.0044572917,0.0017910703],"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":[0.0000456661,0.000026191045,0.0014953909,0.00008752506,0.000010049503,0.0004442512,0.0005344543,0.00025658892,0.00012181471,0.082798384,0.8781186,0.036061063],"study_design_scores_gemma":[0.000019524168,0.00004032651,0.0052106497,0.0009311653,0.000008680594,0.0004941438,0.0013535132,0.00017483393,0.0002637593,0.09428722,0.89718133,0.000034909175],"about_ca_topic_score_codex":0.012236294,"about_ca_topic_score_gemma":0.016540855,"teacher_disagreement_score":0.012236294,"about_ca_system_score_codex":0.0014041775,"about_ca_system_score_gemma":0.0013466008,"threshold_uncertainty_score":0.030852854},"labels":[],"label_agreement":null},{"id":"W4254977789","doi":"10.1007/978-94-017-1506-5_6","title":"Demographic Models","year":2003,"lang":"en","type":"book-chapter","venue":"Mathematical modelling: theory and applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Fibonacci number; Age structure; Reproduction; Population; Census; Sequence (biology); Statistics; Demography; Population structure; Geography; Mathematics; Econometrics; Genealogy; Biology; Combinatorics; History; Ecology; Sociology; Genetics","score_opus":0.04302541267176178,"score_gpt":0.28059154262421887,"score_spread":0.2375661299524571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254977789","genre_codex":"other","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":[0.025142781,0.0061182026,0.3040965,0.008437207,0.0013947904,0.00034514643,0.014709767,0.001169389,0.6385863],"genre_scores_gemma":[0.42146266,0.009939693,0.045063816,0.0028646127,0.0010090647,0.0010120422,0.010887238,0.0003833553,0.50737756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996105,0.00012300118,0.000016967033,0.000106316875,0.00007781455,0.000065459455],"domain_scores_gemma":[0.9993674,0.00024259728,0.0000626659,0.00011573531,0.00014730416,0.00006431928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067844003,0.0007606702,0.0007755188,0.0011397299,0.0007541167,0.002013398,0.0015372246,0.0015812351,0.046543334],"category_scores_gemma":[0.0036088852,0.0002975613,0.000783282,0.0016478592,0.00068934547,0.0018139422,0.0009894543,0.0014393027,0.016663063],"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":[0.0000133074245,0.000053489683,0.0014679709,0.00007997631,0.000025369185,0.00008163276,0.00022201346,0.03132153,0.00012642854,0.88200784,0.05001046,0.03458999],"study_design_scores_gemma":[0.000032365842,0.000026126107,0.0015170297,0.00008493728,0.000034028526,0.00027452572,0.00022147803,0.085631035,0.00010404818,0.68806887,0.22397716,0.000028390035],"about_ca_topic_score_codex":0.008935274,"about_ca_topic_score_gemma":0.0069751614,"teacher_disagreement_score":0.046543334,"about_ca_system_score_codex":0.0013338183,"about_ca_system_score_gemma":0.0010178263,"threshold_uncertainty_score":0.15570295},"labels":[],"label_agreement":null},{"id":"W4255170324","doi":"10.22215/etd/2016-11618","title":"Robust Instrumental Variables and Accelerated Life Regressions","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Instrumental variable; Endogeneity; Statistic; Censoring (clinical trials); Econometrics; Causal inference; Statistics; Inference; Regression; Population; Linear regression; Medicine; Mathematics; Computer science","score_opus":0.06201366699431244,"score_gpt":0.3277740439863022,"score_spread":0.2657603769919898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255170324","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":[0.015330142,0.001060097,0.97901785,0.0010975834,0.00012012414,0.0000523435,0.00026298905,0.0001890339,0.0028698482],"genre_scores_gemma":[0.6671828,0.0058665453,0.2933724,0.00080427574,0.0009100258,0.000791978,0.001518629,0.00044095202,0.029112484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9901787,0.007007242,0.00029859907,0.0008903808,0.0010663397,0.0005586855],"domain_scores_gemma":[0.9414093,0.04628665,0.0055875033,0.0036816888,0.002533685,0.00050115795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015388871,0.0013858607,0.002107704,0.0019358892,0.0005182304,0.0023016273,0.0028119176,0.0016501741,0.0050767125],"category_scores_gemma":[0.08539834,0.00088783837,0.0019634566,0.0022790495,0.002474036,0.0026698972,0.003240488,0.0038439513,0.0008538447],"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":[0.00005971291,0.000068134614,0.0046515665,0.00014421223,0.00024218188,0.00023238087,0.00019009726,0.24209115,0.0002569127,0.72318685,0.0020766847,0.026800087],"study_design_scores_gemma":[0.000045886252,0.000051163846,0.0010261437,0.000073796684,0.000047869755,0.000051467166,0.000045216664,0.5887374,0.00030008153,0.40569046,0.0038857425,0.00004473729],"about_ca_topic_score_codex":0.004952686,"about_ca_topic_score_gemma":0.0027000383,"teacher_disagreement_score":0.015388871,"about_ca_system_score_codex":0.0015629029,"about_ca_system_score_gemma":0.0025473721,"threshold_uncertainty_score":0.081385136},"labels":[],"label_agreement":null},{"id":"W4255746467","doi":"10.1007/978-94-007-0753-5_2210","title":"Population Growth","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Medicine","score_opus":0.01844379264229289,"score_gpt":0.2712507450402454,"score_spread":0.2528069523979525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255746467","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":[0.0009079107,0.004723298,0.0024522908,0.0031086097,0.0011864018,0.000023426883,0.00031755527,0.00008523831,0.98719525],"genre_scores_gemma":[0.023018327,0.014408094,0.0023352422,0.0010433878,0.0008701187,0.000068674264,0.0006194675,0.0001119654,0.9575247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9998505,0.00002321891,0.0000051578413,0.00003832948,0.000066499284,0.000016308852],"domain_scores_gemma":[0.99993515,0.00001287119,0.000003819535,0.0000120815885,0.000026988911,0.0000091154525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026602347,0.00052491593,0.00022198776,0.00081727986,0.0005629239,0.0016268371,0.00047423615,0.0005686041,0.06539584],"category_scores_gemma":[0.00068270817,0.00013640588,0.0002309467,0.0010085995,0.0008487874,0.001815223,0.0010829696,0.0012992672,0.024047766],"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":[0.000008083351,0.0000146044285,0.00030440523,0.00009258503,0.000004137815,0.000043763448,0.0003251665,0.0004938933,0.00019300023,0.50411075,0.26024684,0.2341628],"study_design_scores_gemma":[0.000001413647,0.000004800658,0.00032983162,0.000062120715,0.000001533069,0.000048930342,0.0000892826,0.00015846312,0.00006262095,0.04138504,0.9578536,0.0000023631592],"about_ca_topic_score_codex":0.005365133,"about_ca_topic_score_gemma":0.006404531,"teacher_disagreement_score":0.06539584,"about_ca_system_score_codex":0.001225355,"about_ca_system_score_gemma":0.000981335,"threshold_uncertainty_score":0.21877086},"labels":[],"label_agreement":null},{"id":"W4256051109","doi":"10.2143/ast.33.2.503687","title":"Guaranteed Annuity Options","year":2003,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":64,"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":"Life annuity; Annuity; Actuarial science; Solvency; Interest rate; Economics; Life insurance; Business; Embedded option; Pension; Finance","score_opus":0.01850134186072535,"score_gpt":0.289132554037539,"score_spread":0.2706312121768137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256051109","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":[0.035440512,0.007815465,0.31776237,0.0057979324,0.0011818198,0.00026864343,0.0029782818,0.003521252,0.6252337],"genre_scores_gemma":[0.5760047,0.004605269,0.092008084,0.0009639939,0.0007353534,0.00033966952,0.0030303285,0.00062192563,0.32169068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863225,0.00030202474,0.00009113397,0.00010412293,0.00073453033,0.00013583],"domain_scores_gemma":[0.9976739,0.00064527424,0.0002528204,0.0005906093,0.0005560264,0.00028134199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017807913,0.00038094746,0.0003174988,0.00096250133,0.0008499897,0.0025776692,0.0016699774,0.001646153,0.062241524],"category_scores_gemma":[0.008160115,0.00026664155,0.0004935001,0.0010061896,0.0006271444,0.0029287322,0.0018942254,0.0014637545,0.011260704],"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":[0.00027866813,0.0001021653,0.0009692629,0.00013993964,0.000022980495,0.00035195387,0.0002570592,0.007324779,0.0015044872,0.73788613,0.05645462,0.19470799],"study_design_scores_gemma":[0.00011497584,0.0001405414,0.0010854537,0.00017411886,0.000024346178,0.001163388,0.00009720313,0.022005437,0.0020376556,0.25230968,0.7207945,0.000052644893],"about_ca_topic_score_codex":0.0005842207,"about_ca_topic_score_gemma":0.0007142731,"teacher_disagreement_score":0.062241524,"about_ca_system_score_codex":0.00065109535,"about_ca_system_score_gemma":0.00077723304,"threshold_uncertainty_score":0.20821863},"labels":[],"label_agreement":null},{"id":"W4280625434","doi":"10.1017/s0269964822000122","title":"On approximation of the analytic fixed finite time large <i>t</i> probability distributions in an extreme renewal process with no-mean inter-renewals","year":2022,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Brock University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Renewal theory; Limiting; Probability density function; Pareto principle; Applied mathematics; Computation; Statistical physics; Statistics; Physics; Algorithm","score_opus":0.019710406095187715,"score_gpt":0.2533820935514902,"score_spread":0.2336716874563025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280625434","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":[0.12611276,0.00038431716,0.859305,0.0003764567,0.00005690984,0.000041435425,0.000057679557,0.00022509934,0.01344025],"genre_scores_gemma":[0.9565052,0.00041944897,0.038525555,0.00012853886,0.000035079425,0.00006260661,0.00008879883,0.00007475914,0.0041599153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99954,0.00013013401,0.000019520336,0.00006364571,0.00015448197,0.00009229283],"domain_scores_gemma":[0.9980076,0.0010517417,0.00028192016,0.00014935501,0.00039315748,0.00011616505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002006011,0.00045015741,0.00054752047,0.0012619927,0.00047386924,0.0011409498,0.0015761799,0.0007905987,0.001996157],"category_scores_gemma":[0.00807954,0.00024451705,0.00076087215,0.00061387103,0.0023054378,0.0015914366,0.000999895,0.0014541293,0.00030871338],"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":[0.00004367473,0.00004333394,0.002048501,0.000074373325,0.000019366373,0.00054725946,0.00030238568,0.5097628,0.003252044,0.47413883,0.0007205506,0.009046824],"study_design_scores_gemma":[0.0000035164485,0.000010699477,0.0003572155,0.000021191534,0.0000050572285,0.00009893423,0.00006869698,0.9521929,0.00066293654,0.045886055,0.0006795968,0.000013218922],"about_ca_topic_score_codex":0.004238855,"about_ca_topic_score_gemma":0.0014735023,"teacher_disagreement_score":0.004238855,"about_ca_system_score_codex":0.0015420545,"about_ca_system_score_gemma":0.0007294077,"threshold_uncertainty_score":0.011188507},"labels":[],"label_agreement":null},{"id":"W4281668024","doi":"10.3390/jrfm15060258","title":"The Pricing Model of Pension Benefit Guaranty Corporation Insurance with Regime-Switching Processes","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Akaike information criterion; Economics; Econometrics; Actuarial science; Solvency; Pension; Bayesian information criterion; Surety; Mathematics; Finance; Statistics; Market liquidity","score_opus":0.011779707060229709,"score_gpt":0.23203599953456538,"score_spread":0.22025629247433567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281668024","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":[0.48860425,0.0011125337,0.49553514,0.0027326522,0.00015833501,0.00011581596,0.00043230425,0.0002381629,0.011070863],"genre_scores_gemma":[0.9867154,0.0004000784,0.005656394,0.000071452094,0.00006283791,0.00006000137,0.00012858053,0.000018503335,0.0068868976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891603,0.00040000593,0.000046097317,0.00023532499,0.00019141988,0.00021118933],"domain_scores_gemma":[0.9969336,0.0018198245,0.00057805557,0.00013159156,0.00032548825,0.00021144083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033453973,0.0008703226,0.0013002297,0.0009226601,0.0005437471,0.0022431647,0.002129682,0.002760573,0.003371177],"category_scores_gemma":[0.008714687,0.0005588985,0.0013664477,0.00078480545,0.0016350588,0.0026620037,0.0010079531,0.0022885099,0.00029024828],"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":[0.00012733635,0.000119208264,0.007986494,0.00009247577,0.00012487033,0.0007190141,0.00032339824,0.78624624,0.0021635136,0.19042039,0.0017018989,0.009975172],"study_design_scores_gemma":[0.000009582183,0.000018268072,0.0007863957,0.000004904847,0.000014377992,0.000035032357,0.000018879913,0.98667043,0.00006160256,0.012224546,0.0001431804,0.000012813638],"about_ca_topic_score_codex":0.009459619,"about_ca_topic_score_gemma":0.0035473038,"teacher_disagreement_score":0.009459619,"about_ca_system_score_codex":0.0015301883,"about_ca_system_score_gemma":0.0010087505,"threshold_uncertainty_score":0.01880908},"labels":[],"label_agreement":null},{"id":"W4281907378","doi":"10.1186/s12939-022-01683-8","title":"The gap in life expectancy and lifespan inequality between Iran and neighbour countries: the contributions of avoidable causes of death","year":2022,"lang":"en","type":"article","venue":"International Journal for Equity in Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"Lunds Universitet","keywords":"Life expectancy; Public health; Demography; Inequality; Operationalization; Population; Infant mortality; Health care; Mortality rate; Health policy; Population health; Medicine; Economic growth; Economics; Sociology","score_opus":0.12000359743438584,"score_gpt":0.46518097866638486,"score_spread":0.34517738123199904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281907378","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":[0.99285513,0.0018423853,0.0005093149,0.0006612276,0.000038073867,0.000012137127,0.0012054378,0.000007802842,0.0028685406],"genre_scores_gemma":[0.9991872,0.00021743416,0.00011770313,0.000024721778,0.000014297297,0.0000047968383,0.00036626114,8.957687e-7,0.000066668516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994863,0.00010979365,0.00004092587,0.00008990954,0.00011862809,0.00015443361],"domain_scores_gemma":[0.99912137,0.00018025855,0.00036095933,0.000057188092,0.00015632185,0.00012392948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007891934,0.00022697606,0.00024941273,0.0010600257,0.00033262806,0.0005769721,0.0003535751,0.0002598307,0.001284201],"category_scores_gemma":[0.002471285,0.00007596536,0.00044447402,0.0012674708,0.0004014183,0.0006069026,0.0012201163,0.0005240686,0.00009913694],"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":[0.00008453067,0.000032602005,0.9762744,0.000086488726,0.00015098164,0.00016208025,0.0009086798,0.0010725317,0.00012430584,0.002179999,0.00067747576,0.018245919],"study_design_scores_gemma":[0.0000028507532,0.000036832727,0.9946964,0.000060225597,0.000040570132,0.00013685501,0.001404746,0.0011499542,0.000077266996,0.0011016533,0.0012855494,0.0000071424206],"about_ca_topic_score_codex":0.015560176,"about_ca_topic_score_gemma":0.015239825,"teacher_disagreement_score":0.015560176,"about_ca_system_score_codex":0.0007753053,"about_ca_system_score_gemma":0.0009531131,"threshold_uncertainty_score":0.030939221},"labels":[],"label_agreement":null},{"id":"W4283075033","doi":"10.1111/insr.12510","title":"Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records","year":2022,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Estimator; Survival analysis; Survival function; Statistics; Cohort; Meta-analysis; Computer science; Parametric statistics; Parametric model; Health records; Econometrics; Cohort study; Data mining; Range (aeronautics); Medicine; Mathematics; Engineering; Internal medicine","score_opus":0.2857925271715103,"score_gpt":0.49032943313485144,"score_spread":0.20453690596334112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283075033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005048582,0.34915483,0.62379473,0.015808113,0.0018769336,0.00046218245,0.0013406051,0.0003669473,0.0021471365],"genre_scores_gemma":[0.1825441,0.3272304,0.4599927,0.013261041,0.0076411976,0.004238956,0.002029841,0.0004398992,0.0026219566],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8860681,0.10107368,0.0050838855,0.0028832443,0.00453865,0.00035254477],"domain_scores_gemma":[0.6516391,0.32383102,0.008672716,0.011492293,0.004073581,0.00029125038],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09726788,0.0013986869,0.0058364654,0.0056825867,0.00044837626,0.004220962,0.002720922,0.002986947,0.0022381297],"category_scores_gemma":[0.27247095,0.0007976479,0.012086013,0.007945192,0.0015313013,0.0048138513,0.0028948504,0.0053057764,0.00031842428],"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":[0.00088098284,0.00012633266,0.018610518,0.070356175,0.10120584,0.00062905846,0.0014600224,0.028438266,0.00095693785,0.24188599,0.020423807,0.51502603],"study_design_scores_gemma":[0.0005873982,0.0008105298,0.011895721,0.03378596,0.033129767,0.0009656135,0.000539774,0.039987724,0.0012426067,0.7824578,0.09421627,0.00038086058],"about_ca_topic_score_codex":0.0023055014,"about_ca_topic_score_gemma":0.0026741645,"teacher_disagreement_score":0.90273213,"about_ca_system_score_codex":0.0016739912,"about_ca_system_score_gemma":0.0031456472,"threshold_uncertainty_score":0.5144079},"labels":[],"label_agreement":null},{"id":"W4283585289","doi":"10.1080/03461238.2022.2090272","title":"Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation","year":2022,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fonds Wetenschappelijk Onderzoek; AXA Research Fund","keywords":"Actuarial science; Valuation (finance); Actuarial Analysis; Consistency (knowledge bases); Economics; Finance; Mathematics; Medicine","score_opus":0.037360600539319194,"score_gpt":0.33028221402869085,"score_spread":0.2929216134893716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283585289","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":[0.014979468,0.00034514072,0.9765429,0.00091386674,0.00008320557,0.00003994193,0.000053253134,0.000052016752,0.006990213],"genre_scores_gemma":[0.70703477,0.0007164869,0.28642854,0.00045854418,0.0003930914,0.00016513275,0.00012480277,0.00014145716,0.0045371642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995021,0.0025403793,0.00023240804,0.0005081286,0.0014402876,0.00025791797],"domain_scores_gemma":[0.9899822,0.0055316673,0.0009805572,0.0017935365,0.0013423586,0.0003697254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009789607,0.000752698,0.00077804225,0.0014171043,0.0007610056,0.0036653015,0.0018202637,0.001443391,0.0029522744],"category_scores_gemma":[0.021529613,0.00051526766,0.0016734535,0.0012377357,0.0048508276,0.008970982,0.0028500236,0.0046544205,0.00043195646],"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":[0.000012227232,0.000014228003,0.00022418133,0.000016934082,0.000015394971,0.000028630615,0.000114473536,0.0056771943,0.00037527594,0.98509467,0.0003052395,0.008121548],"study_design_scores_gemma":[0.000009421815,0.000031562693,0.00019529932,0.000022975712,0.000007735951,0.000044778848,0.000032286418,0.04472174,0.00037457724,0.95243233,0.0021075266,0.00001971353],"about_ca_topic_score_codex":0.0004803432,"about_ca_topic_score_gemma":0.00027211235,"teacher_disagreement_score":0.009789607,"about_ca_system_score_codex":0.0014676739,"about_ca_system_score_gemma":0.0012639174,"threshold_uncertainty_score":0.051772952},"labels":[],"label_agreement":null},{"id":"W4285080460","doi":"10.5220/0010601000002993","title":"Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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; Toronto Metropolitan University","funders":"","keywords":"Generalized linear model; Econometrics; Computer science; Applied mathematics; Mathematics; Statistics","score_opus":0.0578758002898316,"score_gpt":0.3319912071757398,"score_spread":0.27411540688590824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285080460","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":[0.90237993,0.0002924971,0.09419359,0.0003795664,0.00005757566,0.0001408885,0.0009863505,0.00030417758,0.0012653415],"genre_scores_gemma":[0.97013265,0.00006599622,0.02771143,0.000035916473,0.000026643223,0.00016831346,0.0009477619,0.000038390983,0.00087288657],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98748976,0.008607585,0.0004394744,0.0025165067,0.00044281964,0.0005038387],"domain_scores_gemma":[0.9402469,0.049083237,0.005037917,0.0036508974,0.0014339614,0.0005471217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018174643,0.00089968037,0.0014977383,0.0023756926,0.0011145152,0.002562723,0.0026268773,0.0017156524,0.0024980654],"category_scores_gemma":[0.04417162,0.00094587763,0.004153217,0.0024655869,0.0011045466,0.001609374,0.0025456098,0.0020419562,0.0004546918],"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":[0.00090726523,0.00051355193,0.6866599,0.00015720076,0.0042685266,0.00030193586,0.0010525616,0.23504516,0.00067957863,0.01495314,0.0018728067,0.05358836],"study_design_scores_gemma":[0.00006240469,0.00036746837,0.11301395,0.00005396567,0.0009046357,0.00012148449,0.00080118544,0.87280077,0.00047080583,0.009958114,0.0013697918,0.00007536932],"about_ca_topic_score_codex":0.049506396,"about_ca_topic_score_gemma":0.03797229,"teacher_disagreement_score":0.049506396,"about_ca_system_score_codex":0.0022113977,"about_ca_system_score_gemma":0.0021000756,"threshold_uncertainty_score":0.098436475},"labels":[],"label_agreement":null},{"id":"W4286685933","doi":"10.1080/23737484.2022.2093294","title":"Evaluation of the forecasting accuracy of stochastic mortality models: An analysis of developed and developing countries","year":2022,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Insurance, Mortality, Demography, Risk Management","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":"Mortality rate; Demography; Cohort; Age groups; Developing country; Statistics; Geography; Econometrics; Medicine; Mathematics; Economics; Economic growth; Sociology","score_opus":0.40445728650811863,"score_gpt":0.4905363894244721,"score_spread":0.08607910291635346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286685933","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":[0.9848963,0.0011481503,0.009903878,0.00034795402,0.000035015153,0.000030310372,0.0010903105,0.00009042587,0.0024577617],"genre_scores_gemma":[0.99618465,0.00041985014,0.0019927518,0.000019996192,0.000011406295,0.000012450554,0.0012100467,0.000009320624,0.00013948648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998654,0.00066185155,0.00014262908,0.00018302779,0.00022226958,0.00013621773],"domain_scores_gemma":[0.9919693,0.004574245,0.0010522628,0.00084519124,0.0013547842,0.00020430994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006893159,0.0006186447,0.0007503112,0.0022606247,0.00033779454,0.0010173226,0.0006321923,0.00056777115,0.00034069314],"category_scores_gemma":[0.0149399815,0.00021728966,0.0009975638,0.0023286496,0.00043553006,0.0008961184,0.00083929725,0.00069412385,0.00009995976],"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":[0.00024080623,0.00008671223,0.23780948,0.00009687739,0.000442834,0.0003441405,0.00017711948,0.7361096,0.00042711792,0.0027155576,0.0009753303,0.020574493],"study_design_scores_gemma":[0.000034829372,0.00027857826,0.10885409,0.000111611625,0.00016498324,0.00019962706,0.000588801,0.88392174,0.0014867565,0.002438293,0.0018708765,0.000049798033],"about_ca_topic_score_codex":0.02369091,"about_ca_topic_score_gemma":0.007427891,"teacher_disagreement_score":0.02369091,"about_ca_system_score_codex":0.00088430423,"about_ca_system_score_gemma":0.0008025767,"threshold_uncertainty_score":0.047106028},"labels":[],"label_agreement":null},{"id":"W4286707414","doi":"10.56573/gcistem.v1i.5","title":"Implications of Unisex Assumptions in the Analysis of Longevity for Insurance Portfolios in Indonesia","year":2022,"lang":"en","type":"article","venue":"GCISTEM Proceeding","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Longevity risk; Longevity; Actuarial science; Portfolio; Life insurance; Economics; Actuary; Population; Demography; Financial economics; Medicine; Sociology; Gerontology","score_opus":0.05253888639318946,"score_gpt":0.35454126823630844,"score_spread":0.302002381843119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286707414","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":[0.8593644,0.0012607411,0.11563486,0.0025026342,0.00018223998,0.00012385305,0.0006909125,0.00007667311,0.02016365],"genre_scores_gemma":[0.98649293,0.0003278361,0.011518089,0.00010850623,0.000022944158,0.000041180905,0.00018009996,0.000012519333,0.0012960001],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961898,0.0019685812,0.00032178836,0.000480848,0.0007910397,0.0002479479],"domain_scores_gemma":[0.9932059,0.00399969,0.001165885,0.0007566114,0.00070165127,0.00017030966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008201145,0.00035569657,0.00043218053,0.0009760653,0.00068823766,0.0021763057,0.0008179324,0.00051267556,0.0019006542],"category_scores_gemma":[0.017672114,0.00023293654,0.0012455573,0.0008562971,0.0010521014,0.002106865,0.0016936219,0.0013283734,0.00022451075],"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":[0.0005474315,0.00027319693,0.50698847,0.00029059482,0.0003690965,0.002448836,0.004763678,0.11873696,0.0018983909,0.21080583,0.0040501324,0.1488274],"study_design_scores_gemma":[0.000038392787,0.00041973987,0.26611897,0.00057813677,0.00023246896,0.0018341765,0.008157768,0.4927965,0.0038360124,0.21124955,0.014597182,0.00014108322],"about_ca_topic_score_codex":0.008884043,"about_ca_topic_score_gemma":0.006446972,"teacher_disagreement_score":0.008884043,"about_ca_system_score_codex":0.0015783244,"about_ca_system_score_gemma":0.0011425599,"threshold_uncertainty_score":0.043372273},"labels":[],"label_agreement":null},{"id":"W4289242702","doi":"10.48550/arxiv.1811.11326","title":"Swimming with Wealthy Sharks: Longevity, Volatility and the Value of\\n Risk Pooling","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Longevity; Longevity risk; Life expectancy; Annuity; Life annuity; Pooling; Economics; Volatility (finance); Actuarial science; Centenarian; Value (mathematics); Demographic economics; Pension; Financial economics; Demography; Finance; Gerontology; Medicine; Sociology","score_opus":0.03586288698253704,"score_gpt":0.20379501234120845,"score_spread":0.1679321253586714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289242702","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":[0.97711474,0.0013080894,0.0032238541,0.0069660884,0.000058591966,0.000007204883,0.00008858005,0.00001512296,0.011217758],"genre_scores_gemma":[0.99702877,0.00044419532,0.00022180249,0.00020805639,0.00006516412,0.0000034689901,0.000024155306,0.0000038501003,0.0020006993],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998116,0.00008054292,0.0000066359066,0.000034875335,0.000023603685,0.000042702493],"domain_scores_gemma":[0.995994,0.0018828203,0.0012938594,0.00024398208,0.00013189262,0.00045352883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014378177,0.00017127309,0.0002545648,0.00039011674,0.00061878975,0.0018995915,0.00035303266,0.0009908907,0.006004296],"category_scores_gemma":[0.0097553395,0.00015753703,0.00024289147,0.0003668427,0.0015660073,0.0017615469,0.0015550584,0.00082873116,0.00034484218],"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":[0.001006515,0.0006397002,0.5972264,0.0001558709,0.0003779631,0.0014387141,0.007090983,0.020070028,0.0020823102,0.22901776,0.014212522,0.12668133],"study_design_scores_gemma":[0.000073135496,0.00042391827,0.42469302,0.00023513693,0.00023554607,0.00059986796,0.010270937,0.047892433,0.000511175,0.506691,0.008268619,0.000105206585],"about_ca_topic_score_codex":0.0030382974,"about_ca_topic_score_gemma":0.003858665,"teacher_disagreement_score":0.006004296,"about_ca_system_score_codex":0.00061536185,"about_ca_system_score_gemma":0.0002854322,"threshold_uncertainty_score":0.020086408},"labels":[],"label_agreement":null},{"id":"W4289517609","doi":"10.1007/s10815-022-02586-x","title":"Correction to: A synopsis of global frontiers in fertility preservation","year":2022,"lang":"en","type":"erratum","venue":"Journal of Assisted Reproduction and Genetics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Reproductive medicine; Fertility; Human genetics; Fertility preservation; Data science; Biology; Computational biology; Medicine; Computer science; Pregnancy; Genetics; Environmental health","score_opus":0.021702841823049122,"score_gpt":0.3087044948229629,"score_spread":0.2870016529999138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289517609","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":[0.00005929348,0.0013421597,0.00024517215,0.06275035,0.93209374,0.000021239777,0.0008818638,0.000096545635,0.0025097188],"genre_scores_gemma":[0.004783927,0.007983682,0.002271998,0.19437157,0.63584703,0.00041271368,0.0017471354,0.0009444592,0.15163742],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959674,0.0007788916,0.00070030784,0.0004856943,0.0016107534,0.00045699877],"domain_scores_gemma":[0.9722968,0.008280027,0.0014784418,0.0013562792,0.014886731,0.0017018531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004951337,0.0023105247,0.0021123688,0.0036336067,0.003352545,0.004322227,0.0031204505,0.008262747,0.044496495],"category_scores_gemma":[0.052939992,0.0009425572,0.0014468011,0.0025811065,0.0026698555,0.0026522598,0.002656337,0.015527216,0.02637713],"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":[0.0000104859,0.0000016383398,0.00002394841,0.000048275913,0.0000027696453,0.00005632938,0.000010452941,0.0000119646675,0.000008003686,0.00026422017,0.9981534,0.0014085125],"study_design_scores_gemma":[0.000024371528,0.000007179573,0.0005503421,0.00033572543,0.000013185826,0.00016142668,0.00007042014,0.00008062321,0.00006476978,0.0006932563,0.9979804,0.000018351875],"about_ca_topic_score_codex":0.03833325,"about_ca_topic_score_gemma":0.044153426,"teacher_disagreement_score":0.044496495,"about_ca_system_score_codex":0.004919144,"about_ca_system_score_gemma":0.00785703,"threshold_uncertainty_score":0.14885563},"labels":[],"label_agreement":null},{"id":"W4291151210","doi":"10.1016/j.eeh.2022.101472","title":"The mortality risk of being overweight in the twentieth century: Evidence from two cohorts of New Zealand men","year":2022,"lang":"en","type":"article","venue":"Explorations in Economic History","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Guelph","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Victoria University; Health Research Council of New Zealand; Minnesota Population Center, University of Minnesota; Marsden Fund; Victoria University of Wellington","keywords":"Overweight; Life expectancy; Demography; Socioeconomic status; Obesity; Gerontology; Indigenous; Population; Medicine; Sociology","score_opus":0.03650449653405344,"score_gpt":0.29149587159403656,"score_spread":0.25499137505998315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291151210","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":[0.9978689,0.0007748106,0.000029255118,0.00017264907,0.000008850593,0.000009260458,0.00034416176,6.4769586e-7,0.0007915725],"genre_scores_gemma":[0.99681467,0.0016346064,0.000056444173,0.00008753657,0.000013286689,0.000015298372,0.0005094664,0.0000020111315,0.0008666894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999551,0.00006602999,0.000051846717,0.00008968447,0.00009427884,0.00014719035],"domain_scores_gemma":[0.99822336,0.00021421653,0.000636818,0.00012972162,0.00036721406,0.00042861595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012030602,0.00034679275,0.00042944643,0.0022039718,0.0015339756,0.0015486733,0.00066300126,0.0008114738,0.001780326],"category_scores_gemma":[0.0035881326,0.0005285907,0.0007033699,0.0034591851,0.001287292,0.0012608214,0.0016001473,0.0011115612,0.00023700083],"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":[0.00021736854,0.000056085748,0.9844757,0.00004729208,0.0001342377,0.00029017165,0.010366321,0.000024391797,0.00026921777,0.00013627233,0.00021059102,0.003772432],"study_design_scores_gemma":[0.00000585682,0.000031428215,0.99699783,0.000024554645,0.000028439343,0.000058827853,0.0023814982,0.000019607385,0.000013475112,0.000022119684,0.0004107708,0.00000558803],"about_ca_topic_score_codex":0.5961445,"about_ca_topic_score_gemma":0.62766933,"teacher_disagreement_score":0.5961445,"about_ca_system_score_codex":0.0020044928,"about_ca_system_score_gemma":0.0017990266,"threshold_uncertainty_score":0.81246775},"labels":[],"label_agreement":null},{"id":"W4292003614","doi":"","title":"Life expectancy.","year":2005,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Statistics Canada","funders":"","keywords":"Life expectancy; Demography; Psychology; Expectancy theory; Gerontology; Medicine; Social psychology; Sociology; Population","score_opus":0.029885668795629386,"score_gpt":0.2643514454605594,"score_spread":0.23446577666493001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292003614","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":[0.016284171,0.059047416,0.001213994,0.006082087,0.0026199962,0.00037011737,0.5359143,0.0010494146,0.3774185],"genre_scores_gemma":[0.15614393,0.047623497,0.003441121,0.0031826713,0.0027138873,0.0015324808,0.5328646,0.00027319684,0.25222468],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9997317,0.0000472383,0.000052149087,0.000037299,0.00009784428,0.000033640325],"domain_scores_gemma":[0.9991819,0.00016913429,0.0001262819,0.000048958344,0.0003200142,0.00015363576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056323124,0.0003620069,0.00047047445,0.0037039712,0.0002666858,0.0005254491,0.00044181128,0.0003962419,0.09251116],"category_scores_gemma":[0.0034196232,0.00011433129,0.00041620637,0.004327829,0.00010782961,0.00065302843,0.00048622364,0.0006397896,0.036131356],"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":[0.00024711573,0.00007391622,0.023836592,0.0012450907,0.00007703051,0.000074183445,0.000122022044,0.00014300435,0.00013205333,0.0029418068,0.7303614,0.24074583],"study_design_scores_gemma":[0.00009633963,0.00018228426,0.21811396,0.0011527683,0.00010124973,0.0005846981,0.00016422935,0.00029176706,0.00014500905,0.0021242818,0.777019,0.000024349321],"about_ca_topic_score_codex":0.0078044487,"about_ca_topic_score_gemma":0.009935593,"teacher_disagreement_score":0.09251116,"about_ca_system_score_codex":0.00042233692,"about_ca_system_score_gemma":0.00063402153,"threshold_uncertainty_score":0.30948067},"labels":[],"label_agreement":null},{"id":"W4292616212","doi":"10.31235/osf.io/87acb","title":"From bust to boom? Birth and fertility responses to the COVID-19 pandemic","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":23,"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":"Max-Planck-Institut für demografische Forschung","keywords":"Fertility; Pandemic; Bust; Birth rate; Demography; Baby boom; Sub-replacement fertility; Total fertility rate; Geography; Recession; Coronavirus disease 2019 (COVID-19); Economics; Boom; Population; Medicine; Family planning; Research methodology; Sociology","score_opus":0.09775948917808458,"score_gpt":0.3890364455560522,"score_spread":0.29127695637796763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292616212","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":[0.99460727,0.0004460645,0.00016275821,0.0008570329,0.000026161593,0.0000071143445,0.0021626,0.000010896501,0.0017202167],"genre_scores_gemma":[0.99786395,0.0003073927,0.000102831276,0.00013011084,0.000024053126,0.000005106901,0.0011895272,0.0000048281636,0.00037234303],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997811,0.00007459745,0.000009863136,0.00003796275,0.000022865652,0.00007364733],"domain_scores_gemma":[0.99927753,0.00020083343,0.00027306078,0.000043766326,0.00008115843,0.00012367255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008440273,0.000104297535,0.00023639486,0.00048585027,0.0002538745,0.0008143178,0.00020035589,0.0004240232,0.0019338874],"category_scores_gemma":[0.0030142071,0.00012440032,0.00030376244,0.0005835068,0.00034606236,0.00032245403,0.0006146948,0.0006114044,0.00019976815],"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":[0.00034398338,0.00006538202,0.9692095,0.00007555239,0.00013240169,0.00033896076,0.000954697,0.0045525506,0.00076944864,0.0023322473,0.004326191,0.016898982],"study_design_scores_gemma":[0.0000041057538,0.000055674125,0.9952988,0.000023619095,0.000015094295,0.00006170115,0.0012886499,0.0012969814,0.00014035919,0.00029762596,0.0015089064,0.0000085776],"about_ca_topic_score_codex":0.018872723,"about_ca_topic_score_gemma":0.017569177,"teacher_disagreement_score":0.018872723,"about_ca_system_score_codex":0.00046864935,"about_ca_system_score_gemma":0.00031687738,"threshold_uncertainty_score":0.037525773},"labels":[],"label_agreement":null},{"id":"W4293032710","doi":"10.1016/j.insmatheco.2022.08.006","title":"Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns","year":2022,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"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":"Point (geometry); High resolution; Process (computing); Computer science; Tipping point (physics); Environmental science; Point process; Meteorology; Remote sensing; Geography; Engineering; Mathematics; Statistics","score_opus":0.01857413027724988,"score_gpt":0.257165657320781,"score_spread":0.23859152704353112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293032710","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":[0.73772866,0.00043083986,0.2590413,0.0006720654,0.00007891503,0.00007157255,0.0005838524,0.0002445246,0.0011482523],"genre_scores_gemma":[0.98847544,0.000119427474,0.010425977,0.00002272264,0.000036565114,0.000023434523,0.00024292996,0.000013188168,0.0006403389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995351,0.00012279197,0.000031751384,0.00018134312,0.000072368275,0.000056757235],"domain_scores_gemma":[0.9957029,0.0024398,0.00058164686,0.00058904244,0.0004528348,0.0002337748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023046308,0.0004625621,0.00091909815,0.0014062077,0.0005353028,0.0017211674,0.0021937964,0.0018330729,0.0014308445],"category_scores_gemma":[0.009889002,0.00059524283,0.0013990186,0.0014952815,0.0010366051,0.002475099,0.0015946742,0.0013916608,0.00028395077],"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":[0.00016058919,0.0001607651,0.05693387,0.00004954816,0.00021906609,0.00032643988,0.00018559072,0.9030905,0.0016844881,0.0122489715,0.00053181837,0.024408335],"study_design_scores_gemma":[0.00000441052,0.0000110233095,0.002485807,0.000003109516,0.000008085472,0.000012996624,0.000014213781,0.9939912,0.00007266439,0.0033384732,0.000051706145,0.00000623372],"about_ca_topic_score_codex":0.014174726,"about_ca_topic_score_gemma":0.010840427,"teacher_disagreement_score":0.014174726,"about_ca_system_score_codex":0.000730971,"about_ca_system_score_gemma":0.00066418864,"threshold_uncertainty_score":0.028184474},"labels":[],"label_agreement":null},{"id":"W4294723379","doi":"10.1007/978-3-319-69909-7_1647-2","title":"Life Quality Index","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Index (typography); Quality (philosophy); Environmental science; Computer science; Philosophy; World Wide Web; Epistemology","score_opus":0.05623337174234894,"score_gpt":0.3367528463469508,"score_spread":0.28051947460460186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294723379","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":[0.012422421,0.01281777,0.004403055,0.003487333,0.0013016263,0.00048063064,0.15186346,0.0010168405,0.8122069],"genre_scores_gemma":[0.12725164,0.014773688,0.01517563,0.0038555532,0.0012732145,0.0016087334,0.23307309,0.0005727203,0.6024157],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930835,0.00008519994,0.000053495023,0.000060406594,0.00044316403,0.00004942426],"domain_scores_gemma":[0.999071,0.00016300527,0.00012346554,0.00004032298,0.00046541443,0.00013671549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065401004,0.00046670946,0.00059452583,0.0029597064,0.0003167647,0.0009686097,0.00056195253,0.00029308963,0.0904791],"category_scores_gemma":[0.0031655966,0.00007779659,0.0005221536,0.0028012309,0.00013383233,0.00077594613,0.00073091075,0.00093893154,0.035643186],"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":[0.00017686583,0.00011805254,0.014514103,0.00032001597,0.00006331759,0.000035621273,0.000076247794,0.0003322658,0.00017022848,0.0072921687,0.64280736,0.3340938],"study_design_scores_gemma":[0.000050981704,0.00013911734,0.096989214,0.00036279092,0.000060471986,0.0003385383,0.00010519581,0.00061693246,0.00019546528,0.007123032,0.8939927,0.000025517009],"about_ca_topic_score_codex":0.0040704627,"about_ca_topic_score_gemma":0.0050091455,"teacher_disagreement_score":0.0904791,"about_ca_system_score_codex":0.0010400119,"about_ca_system_score_gemma":0.0005742758,"threshold_uncertainty_score":0.30268276},"labels":[],"label_agreement":null},{"id":"W4295073998","doi":"10.1215/00703370-10216406","title":"A Bayesian Cohort Component Projection Model to Estimate Women of Reproductive Age at the Subnational Level in Data-Sparse Settings","year":2022,"lang":"en","type":"article","venue":"Demography","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"","keywords":"Population projection; Projections of population growth; Census; Population; Estimation; Geography; Demography; Demographic analysis; Cohort; Population statistics; Econometrics; Population growth; Statistics; Economics; Mathematics","score_opus":0.059702004572411795,"score_gpt":0.3430871258115872,"score_spread":0.28338512123917536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295073998","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":[0.04443824,0.0008370351,0.94300646,0.0020876664,0.00015410142,0.000510107,0.0040175966,0.0004697518,0.004479098],"genre_scores_gemma":[0.59757227,0.002787295,0.3617546,0.000960997,0.0002859669,0.003532724,0.009442842,0.00022929642,0.023434088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980925,0.0011973013,0.00005810184,0.0003521315,0.00015067682,0.00014935006],"domain_scores_gemma":[0.99600804,0.0027953451,0.00028327687,0.00018007014,0.00055919326,0.00017393644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006295197,0.0010555006,0.0014789503,0.0013262036,0.0007064399,0.0014690197,0.002925317,0.001311127,0.0064149625],"category_scores_gemma":[0.011882113,0.0010098496,0.0013905287,0.0021890313,0.0011088797,0.0015472339,0.0022719663,0.0024964358,0.0010772203],"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":[0.00035744757,0.00012415997,0.024641668,0.0001620164,0.0004736143,0.0002994225,0.00046333272,0.80105644,0.00032700165,0.10161078,0.012099645,0.058384396],"study_design_scores_gemma":[0.00006305401,0.000050936866,0.002127394,0.000048350896,0.00007861781,0.00006226386,0.00007266821,0.96388453,0.00007788053,0.029956903,0.0035484103,0.000028928527],"about_ca_topic_score_codex":0.062305447,"about_ca_topic_score_gemma":0.044055287,"teacher_disagreement_score":0.062305447,"about_ca_system_score_codex":0.0015703053,"about_ca_system_score_gemma":0.004025607,"threshold_uncertainty_score":0.12388557},"labels":[],"label_agreement":null},{"id":"W4295722048","doi":"10.5281/zenodo.7078560","title":"R code for Demographic consequences of changing environmental periodicity","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Seventh Framework Programme; Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Code (set theory); Geography; Computer science; Programming language","score_opus":0.03609661189114434,"score_gpt":0.2666703678245051,"score_spread":0.23057375593336077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295722048","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00527043,0.0010027194,0.1634437,0.001273177,0.0010457822,0.00086722494,0.50774544,0.3067338,0.012617716],"genre_scores_gemma":[0.041217465,0.0011786591,0.36025998,0.002794025,0.0005019741,0.010166697,0.29183775,0.27163818,0.020405347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966293,0.0008806354,0.00037423795,0.0009452192,0.0008470426,0.000323437],"domain_scores_gemma":[0.9818185,0.012626097,0.0011693728,0.0019323508,0.0018930992,0.00056059414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006960416,0.0040761284,0.0034622652,0.0031871765,0.0011114808,0.0040110685,0.004601043,0.001629198,0.28171334],"category_scores_gemma":[0.034838766,0.00222855,0.0035856566,0.0026572794,0.0010840823,0.003423553,0.0031365724,0.004352446,0.14694224],"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":[0.00043377027,0.000088503184,0.007164285,0.0033438741,0.0013320326,0.00037543123,0.00032199567,0.0066639725,0.0022050606,0.0102358805,0.93810225,0.02973298],"study_design_scores_gemma":[0.0016711822,0.00028414256,0.019227702,0.0016861153,0.0013830217,0.0012812146,0.00022031549,0.045345724,0.0056035304,0.08215011,0.8406339,0.0005130081],"about_ca_topic_score_codex":0.005971014,"about_ca_topic_score_gemma":0.0067789364,"teacher_disagreement_score":0.28171334,"about_ca_system_score_codex":0.0011154196,"about_ca_system_score_gemma":0.0041467277,"threshold_uncertainty_score":0.942425},"labels":[],"label_agreement":null},{"id":"W4295736450","doi":"10.1016/j.cam.2022.114816","title":"Robust optimal reinsurance in minimizing the penalized expected time to reach a goal","year":2022,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Reinsurance; Mathematics; Ambiguity; Bellman equation; Mathematical optimization; Exponential utility; Stochastic control; Optimal control; Exponential function; Function (biology); Computer science; Economics; Actuarial science","score_opus":0.02205859181439924,"score_gpt":0.26414843013147726,"score_spread":0.24208983831707803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295736450","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":[0.12766433,0.0018135773,0.8609598,0.0034412334,0.00022554616,0.00017350116,0.00042161168,0.0005880276,0.004712328],"genre_scores_gemma":[0.92615736,0.00064254244,0.06335268,0.00040343852,0.00023575715,0.00027562855,0.0003925602,0.00029734333,0.008242686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964479,0.0019417757,0.00015242778,0.00059481635,0.00035387094,0.00050923595],"domain_scores_gemma":[0.976552,0.018936848,0.0015199179,0.0007455636,0.0011808804,0.0010647371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01105337,0.0020286534,0.004451728,0.0014227736,0.00052179914,0.0026957146,0.003036328,0.0037016335,0.004542591],"category_scores_gemma":[0.036262643,0.0015580514,0.0011519948,0.0009731144,0.002417849,0.002748257,0.002469916,0.0034162286,0.0003833844],"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":[0.00025526638,0.00008410546,0.0010356979,0.00013018485,0.00012812192,0.00008124857,0.00005564914,0.96709675,0.00043119176,0.023926599,0.0010614714,0.005713728],"study_design_scores_gemma":[0.000024657566,0.00006214272,0.00022213218,0.000022480537,0.000023840224,0.000022630076,0.000013885698,0.99097085,0.00016053733,0.008338712,0.00012674514,0.000011386694],"about_ca_topic_score_codex":0.010173797,"about_ca_topic_score_gemma":0.0047211,"teacher_disagreement_score":0.01105337,"about_ca_system_score_codex":0.003288755,"about_ca_system_score_gemma":0.0054426063,"threshold_uncertainty_score":0.05845648},"labels":[],"label_agreement":null},{"id":"W4299123894","doi":"","title":"A generalization of Kaplan-Meier estimator for analyzing bivariate mortality under right-censoring and left-truncation with applications to model-checking for survival copula models","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Bivariate analysis; Censoring (clinical trials); Estimator; Copula (linguistics); Econometrics; Statistics; Truncation (statistics); Generalization; Mathematics; Survival analysis","score_opus":0.04843558092521611,"score_gpt":0.3114648034651791,"score_spread":0.26302922253996297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299123894","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":[0.0059517515,0.000163762,0.9932367,0.000057231457,0.000015833164,0.000026670516,0.00009165162,0.00017711674,0.0002792748],"genre_scores_gemma":[0.25506577,0.0005149351,0.74196047,0.00013416792,0.000114062685,0.0002431806,0.0007580036,0.00019537874,0.0010140477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971812,0.0015059303,0.00018513713,0.00043107683,0.0005668157,0.00012993281],"domain_scores_gemma":[0.9827271,0.011054218,0.0017162493,0.0028593421,0.0014420272,0.00020105642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0111192595,0.0005270555,0.0009722483,0.0024528706,0.00045247973,0.0008375623,0.0014065753,0.00094206375,0.0027098865],"category_scores_gemma":[0.04985619,0.00032433256,0.0010753373,0.0015838818,0.0008133003,0.0022871024,0.0012390616,0.0019046684,0.00044532664],"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":[0.00016190035,0.00020496847,0.05103033,0.00042491805,0.00051252753,0.00041793793,0.00087261974,0.15070277,0.0075562564,0.29871273,0.0048275115,0.4845755],"study_design_scores_gemma":[0.000047600486,0.00018538124,0.01629824,0.00014143306,0.00012960186,0.0008153058,0.00018484809,0.7740558,0.004724698,0.19102673,0.012236388,0.00015404406],"about_ca_topic_score_codex":0.0030716748,"about_ca_topic_score_gemma":0.0026299085,"teacher_disagreement_score":0.0111192595,"about_ca_system_score_codex":0.00048274483,"about_ca_system_score_gemma":0.0014685126,"threshold_uncertainty_score":0.05880499},"labels":[],"label_agreement":null},{"id":"W4299566812","doi":"10.1007/s10985-022-09577-1","title":"A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model","year":2022,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Algorithm; Mathematics; Computer science; Distribution (mathematics); Phase (matter); Type (biology); Exponential function; Maximum likelihood; Matrix (chemical analysis); Estimation; Mathematical optimization; Statistics; Artificial intelligence","score_opus":0.049259366889573705,"score_gpt":0.37627718876549626,"score_spread":0.32701782187592254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299566812","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":[0.0016090695,0.000056585475,0.9977502,0.000045448458,0.000015256128,0.00004461797,0.000043216936,0.0003394753,0.00009619227],"genre_scores_gemma":[0.073592424,0.00013157046,0.92295665,0.00019876704,0.00008933443,0.0005032863,0.001003426,0.00027590152,0.0012486273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969958,0.0015256165,0.00022304578,0.00069367496,0.00037609218,0.00018574958],"domain_scores_gemma":[0.9807916,0.015590229,0.00047989123,0.0013046899,0.0015522386,0.00028140788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009734863,0.0010644703,0.0021071455,0.001579239,0.00091739296,0.0015724808,0.004178568,0.0023394667,0.0047514914],"category_scores_gemma":[0.039129138,0.0013376736,0.0017773837,0.0017797736,0.0012745151,0.001987727,0.0035341661,0.003531539,0.0017001535],"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":[0.0007574595,0.00021459365,0.0040898365,0.00026599903,0.00033774538,0.00020351836,0.00028273516,0.55448407,0.003655752,0.048949935,0.0052740085,0.38148436],"study_design_scores_gemma":[0.000053934786,0.000032424618,0.00020184781,0.000016673373,0.000018848288,0.00004062694,0.000010673747,0.98013663,0.0005615684,0.01823868,0.00067522045,0.000012871325],"about_ca_topic_score_codex":0.009304459,"about_ca_topic_score_gemma":0.009624925,"teacher_disagreement_score":0.009734863,"about_ca_system_score_codex":0.0010744792,"about_ca_system_score_gemma":0.0031211355,"threshold_uncertainty_score":0.051483512},"labels":[],"label_agreement":null},{"id":"W4300014084","doi":"10.48550/arxiv.1706.05510","title":"Statistical foundations for assessing the difference between the\\n classical and weighted-Gini betas","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capital asset pricing model; Econometrics; Economics; Statistical inference; BETA (programming language); Inference; Actuarial science; Mathematics; Financial economics; Statistics; Computer science","score_opus":0.14425817516120434,"score_gpt":0.3057732427039248,"score_spread":0.16151506754272046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300014084","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":[0.016162083,0.0012476445,0.976137,0.001369103,0.00015196124,0.000060006852,0.00034286283,0.00021730876,0.004312055],"genre_scores_gemma":[0.63153607,0.0028118875,0.3581964,0.0014981421,0.0017492723,0.0010429565,0.0012348336,0.0003728072,0.0015576758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98089427,0.012403412,0.0007917026,0.0024870047,0.0029280835,0.00049541635],"domain_scores_gemma":[0.7541478,0.21147515,0.012041757,0.015423549,0.005298375,0.001613414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040784895,0.001202469,0.0018295174,0.005842608,0.001178122,0.0046945163,0.0033234702,0.0030030864,0.004077948],"category_scores_gemma":[0.24447693,0.0008839753,0.0017132253,0.004669874,0.011009857,0.0070037725,0.0055496353,0.0072434233,0.00094245706],"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":[0.000072479255,0.000057911497,0.011578546,0.00020760713,0.00025240463,0.00019580897,0.00045728593,0.030109849,0.0008590727,0.91144794,0.00267106,0.042090032],"study_design_scores_gemma":[0.000017370474,0.000075415985,0.0041506044,0.00009648455,0.00003352848,0.00017526365,0.00009613882,0.1065208,0.0004373833,0.8845499,0.0038074753,0.000039570266],"about_ca_topic_score_codex":0.0016085751,"about_ca_topic_score_gemma":0.00071661366,"teacher_disagreement_score":0.040784895,"about_ca_system_score_codex":0.0022302074,"about_ca_system_score_gemma":0.0019165111,"threshold_uncertainty_score":0.21569371},"labels":[],"label_agreement":null},{"id":"W4300407696","doi":"10.48550/arxiv.1305.0113","title":"Divergence in age-patterns of mortality change drives international\\n divergence in lifespan inequality","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Life expectancy; Inequality; Demography; Divergence (linguistics); Longevity; Demographic economics; Economics; Gerontology; Population; Medicine; Sociology","score_opus":0.15212752371272784,"score_gpt":0.26701660455409265,"score_spread":0.11488908084136482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300407696","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":[0.99384165,0.000119256765,0.0013215264,0.00013601067,0.0000050173267,0.000005342801,0.0005507534,0.000009898067,0.00401063],"genre_scores_gemma":[0.9989574,0.000048987502,0.00024915492,0.000016954136,0.0000015639564,0.0000027596063,0.0003557721,0.0000026124535,0.00036469803],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997392,0.00004461521,0.000013755716,0.00009708349,0.000033663233,0.000071778406],"domain_scores_gemma":[0.9993668,0.000118895456,0.00018169756,0.000101128375,0.00013689169,0.00009456247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005513295,0.00012502549,0.00018552711,0.0006265231,0.0005126235,0.0007929142,0.00027900547,0.00019099683,0.0021846592],"category_scores_gemma":[0.002647942,0.0000786629,0.00020203598,0.0008639523,0.0005132393,0.00050836726,0.00073641556,0.00042092256,0.00020729998],"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":[0.00005706047,0.000021337379,0.9732612,0.00002178964,0.000060108472,0.000108137574,0.002356979,0.0017638872,0.0009680978,0.006411964,0.0007823673,0.01418719],"study_design_scores_gemma":[0.0000018194689,0.00000941925,0.99338526,0.000012278658,0.000013101676,0.000039521965,0.0010883386,0.0024727476,0.0002228871,0.0015160153,0.0012314714,0.0000072849243],"about_ca_topic_score_codex":0.09521352,"about_ca_topic_score_gemma":0.15302669,"teacher_disagreement_score":0.09521352,"about_ca_system_score_codex":0.0010002995,"about_ca_system_score_gemma":0.0006678671,"threshold_uncertainty_score":0.18931866},"labels":[],"label_agreement":null},{"id":"W4306391365","doi":"10.3390/jrfm15100463","title":"Pricing Cat Bonds for Cloud Service Failures","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Reinsurance; Cloud computing; Solvency; Context (archaeology); Bond; Business; Service provider; Poisson distribution; Pareto principle; Actuarial science; Service (business); Computer science; Finance; Economics; Market liquidity; Operations management; Marketing; Mathematics; Statistics","score_opus":0.010378788123076493,"score_gpt":0.2561998394377915,"score_spread":0.24582105131471502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306391365","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":[0.18025516,0.0013120731,0.7973918,0.0018525702,0.0004870591,0.0001711652,0.0001857263,0.00033965323,0.018004749],"genre_scores_gemma":[0.9647657,0.0005963447,0.027627673,0.00010711007,0.00013055593,0.000087704786,0.000103496335,0.00010208991,0.006479252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985549,0.0005657451,0.00007163691,0.00018720633,0.00046079818,0.00015965108],"domain_scores_gemma":[0.99617296,0.0020281062,0.00061307783,0.0003772475,0.00057215453,0.0002364134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037353951,0.0006482077,0.0007108936,0.00095436245,0.0006997325,0.002821958,0.0020181225,0.0019555972,0.0039651203],"category_scores_gemma":[0.021900859,0.00045610828,0.0008755979,0.00096194807,0.001396822,0.003913723,0.0013997749,0.003257047,0.0004173641],"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":[0.00010648085,0.000080367536,0.0027472135,0.00007451633,0.000034892877,0.0006623484,0.00024047423,0.2747081,0.0018420541,0.69222385,0.0033920663,0.023887677],"study_design_scores_gemma":[0.000010675367,0.000044225428,0.0005549151,0.000020152364,0.000011253659,0.00020181875,0.000061005307,0.92795175,0.00035457808,0.068999425,0.0017633341,0.000026874928],"about_ca_topic_score_codex":0.0034542063,"about_ca_topic_score_gemma":0.0023723904,"teacher_disagreement_score":0.0039651203,"about_ca_system_score_codex":0.002388762,"about_ca_system_score_gemma":0.0013981782,"threshold_uncertainty_score":0.019754887},"labels":[],"label_agreement":null},{"id":"W4307248954","doi":"10.5539/ijsp.v11n6p28","title":"Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Copula (linguistics); Bivariate analysis; Count data; Mathematics; Poisson distribution; Econometrics; Markov chain; Overdispersion; Inference; Bivariate data; Statistics; Computer science; Artificial intelligence","score_opus":0.034085776557639565,"score_gpt":0.34684257084594206,"score_spread":0.3127567942883025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307248954","genre_codex":"review","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":[0.0019049675,0.7789027,0.20530097,0.0022256784,0.0010205708,0.00005551509,0.00036298012,0.00026582496,0.00996072],"genre_scores_gemma":[0.048465546,0.8761536,0.066739365,0.0010393785,0.0036206949,0.00014600572,0.0007144149,0.00024439165,0.0028766524],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99733174,0.0010060221,0.0002906978,0.0005435533,0.0007270665,0.00010097877],"domain_scores_gemma":[0.9912253,0.006386856,0.00047116788,0.00047896715,0.0013300913,0.00010770272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004409475,0.001399373,0.0018118125,0.0039646723,0.0005384509,0.0025293794,0.0022259334,0.0014629664,0.003334285],"category_scores_gemma":[0.01670223,0.0008330703,0.0019664406,0.007239293,0.0012138815,0.0038124216,0.00093100464,0.0022220311,0.0017084342],"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":[0.00006669075,0.000094827345,0.0028148515,0.008895714,0.0003140339,0.0005662392,0.00039531657,0.02589122,0.00077419606,0.24016234,0.05010381,0.66992086],"study_design_scores_gemma":[0.000020684802,0.0001755509,0.008403219,0.006135688,0.00034646923,0.0037555923,0.00036900505,0.10390136,0.0011018679,0.22966012,0.6458953,0.00023524722],"about_ca_topic_score_codex":0.0045345337,"about_ca_topic_score_gemma":0.0015228862,"teacher_disagreement_score":0.0045345337,"about_ca_system_score_codex":0.0016008439,"about_ca_system_score_gemma":0.002087856,"threshold_uncertainty_score":0.02331984},"labels":[],"label_agreement":null},{"id":"W4307290173","doi":"10.1016/j.insmatheco.2022.10.001","title":"Editorial to the virtual special issue on emerging risks and insurance technology","year":2022,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Business; Risk analysis (engineering); Actuarial science","score_opus":0.018864605912248398,"score_gpt":0.2769897901453943,"score_spread":0.2581251842331459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307290173","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000063757776,0.0028320267,0.00012214675,0.0321846,0.96358734,0.000012487661,0.00006385672,0.000031874737,0.0011019508],"genre_scores_gemma":[0.00040638587,0.0011063402,0.00004171465,0.00865335,0.9855651,0.000011681587,0.000022861186,0.000018745117,0.0041738134],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99708146,0.0005382485,0.00035244107,0.00055040774,0.0011634832,0.00031391688],"domain_scores_gemma":[0.9821571,0.010133078,0.0011881883,0.00054601155,0.003952644,0.0020229255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004682407,0.004360906,0.0041027223,0.0039968463,0.0023225283,0.007760624,0.0034990085,0.015003617,0.021727273],"category_scores_gemma":[0.022765514,0.0011821521,0.0039430107,0.0012241703,0.0021659073,0.004296405,0.001944555,0.015556669,0.0067093205],"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":[0.000059455553,0.000022265554,0.00005198968,0.0001016149,0.000022997416,0.00010083779,0.000008232616,0.000043336135,0.000039968796,0.00044366406,0.9966738,0.002431874],"study_design_scores_gemma":[0.00021141191,0.00008524179,0.0012753174,0.00055777765,0.0001638503,0.00035409795,0.00007220233,0.0008784916,0.00016819933,0.004800605,0.99139154,0.000041347208],"about_ca_topic_score_codex":0.0017581006,"about_ca_topic_score_gemma":0.003163805,"teacher_disagreement_score":0.021727273,"about_ca_system_score_codex":0.0025048542,"about_ca_system_score_gemma":0.0018170885,"threshold_uncertainty_score":0.07268494},"labels":[],"label_agreement":null},{"id":"W4308766298","doi":"10.1111/1365-2656.13842","title":"Life span, growth, senescence and island syndrome: Accounting for imperfect detection and continuous growth","year":2022,"lang":"en","type":"review","venue":"Journal of Animal Ecology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":19,"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","funders":"","keywords":"Life span; Senescence; Biology; Imperfect; Span (engineering); Ecology; Evolutionary biology; Genetics; Engineering","score_opus":0.02600129712350699,"score_gpt":0.31122697474283617,"score_spread":0.28522567761932915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308766298","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":[0.72444177,0.23281433,0.037555877,0.0006074138,0.00015924415,0.00012488099,0.002499178,0.00017998442,0.0016173653],"genre_scores_gemma":[0.9822334,0.008903088,0.007675144,0.00006311263,0.00006484665,0.00008324872,0.00069674314,0.000020438989,0.00025992916],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684405,0.0016810563,0.0005096984,0.0006123665,0.00028043884,0.000072482726],"domain_scores_gemma":[0.96206075,0.02847726,0.0060816132,0.0018129802,0.0013650986,0.00020227974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085119065,0.0008138058,0.0012000535,0.0030291046,0.0002567894,0.0009177261,0.001154422,0.0005841057,0.00083075126],"category_scores_gemma":[0.022794887,0.0004934928,0.0025560048,0.002535678,0.00052639405,0.0014538538,0.0009378564,0.0005298932,0.000119136566],"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":[0.00037035238,0.000052785424,0.85803103,0.011877249,0.015008236,0.00030560556,0.00036247371,0.018840786,0.0013819664,0.0009148393,0.00058891036,0.092265666],"study_design_scores_gemma":[0.00007338857,0.0006362281,0.8898754,0.00486124,0.01725742,0.0007525352,0.0004732137,0.07310343,0.0014021528,0.005706167,0.0057704267,0.00008849228],"about_ca_topic_score_codex":0.0069192206,"about_ca_topic_score_gemma":0.0090441285,"teacher_disagreement_score":0.0085119065,"about_ca_system_score_codex":0.00050268136,"about_ca_system_score_gemma":0.00075861835,"threshold_uncertainty_score":0.045015752},"labels":[],"label_agreement":null},{"id":"W4310060702","doi":"10.1017/asb.2022.24","title":"Modelling mortality: A bayesian factor-augmented var (favar) approach","year":2022,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Université Paris 13","keywords":"Autoregressive model; Econometrics; Vector autoregression; Life expectancy; Bayesian probability; Sample (material); Longevity risk; Factor analysis; Computer science; Estimation; Statistics; Artificial intelligence; Mathematics; Economics; Pension; Demography; Finance; Sociology","score_opus":0.040581975834103425,"score_gpt":0.2797584431863985,"score_spread":0.23917646735229506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310060702","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":[0.010337663,0.00042709673,0.98799497,0.00019580421,0.000041015403,0.000016438713,0.00009279776,0.00017654263,0.0007176582],"genre_scores_gemma":[0.69924873,0.0017261528,0.2908089,0.0002922073,0.00032412083,0.00019416737,0.0008541418,0.00020181453,0.0063498192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985475,0.0008774844,0.000057149686,0.00024131787,0.00017180514,0.00010472013],"domain_scores_gemma":[0.9968233,0.0023696956,0.00027910853,0.000120108256,0.00034172184,0.00006596539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034558005,0.0007741997,0.0013480944,0.0012701626,0.0003910683,0.0013505676,0.0019461345,0.0014276638,0.0019530868],"category_scores_gemma":[0.00829745,0.00079776236,0.0013847814,0.0011362907,0.00062679936,0.0008592919,0.0010135209,0.0014679136,0.00038612523],"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":[0.000038180846,0.000033695567,0.0023751655,0.00006170859,0.0001418619,0.00009143234,0.000064413005,0.9292492,0.0004671874,0.03855149,0.0010977632,0.027827948],"study_design_scores_gemma":[0.0000040494456,0.000015436957,0.00032283858,0.000011594761,0.000018932993,0.000022114591,0.000007902267,0.98776615,0.000060359504,0.011237241,0.00052118767,0.000012173034],"about_ca_topic_score_codex":0.01563038,"about_ca_topic_score_gemma":0.010371431,"teacher_disagreement_score":0.01563038,"about_ca_system_score_codex":0.0005987196,"about_ca_system_score_gemma":0.0011387011,"threshold_uncertainty_score":0.031078756},"labels":[],"label_agreement":null},{"id":"W4310213237","doi":"10.14428/rqj2021.09.01.03","title":"Trajectoire des taux de mortalité aux âges extrêmes de la vie","year":2022,"lang":"fr","type":"article","venue":"Revue Quetelet + Quetelet journal/Revue Quetelet + Quetelet Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal","funders":"","keywords":"Humanities; Political science; Gompertz function; Art; Mathematics","score_opus":0.026703326084547375,"score_gpt":0.31369303337237975,"score_spread":0.28698970728783235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310213237","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":[0.82224554,0.0041457308,0.14772476,0.0015726418,0.00018183983,0.00011520652,0.008644252,0.00057255675,0.014797503],"genre_scores_gemma":[0.95814866,0.0016834025,0.020905953,0.00018148473,0.000029042098,0.00019950264,0.0040775617,0.00013424769,0.014640147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99938047,0.00018583977,0.000020844838,0.00025542834,0.00007300934,0.0000843458],"domain_scores_gemma":[0.9975579,0.001522244,0.0003198834,0.0001997618,0.00030260748,0.00009759708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018384727,0.00071963377,0.00072376936,0.0010237616,0.0005138992,0.0012200658,0.00089120696,0.0009988433,0.009528046],"category_scores_gemma":[0.008157606,0.00048878946,0.002433487,0.00070165395,0.00075739104,0.0009487583,0.0008281375,0.0016143526,0.0016565771],"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":[0.0008741862,0.00012934946,0.22834665,0.0010017401,0.00078293006,0.00050802814,0.0024041634,0.6659084,0.010098848,0.02498959,0.0037370743,0.061219],"study_design_scores_gemma":[0.00012534138,0.0013491877,0.41716862,0.00085622177,0.00076504867,0.0011094824,0.0027013377,0.4865484,0.006507772,0.036278594,0.046230324,0.00035970053],"about_ca_topic_score_codex":0.049519524,"about_ca_topic_score_gemma":0.041042086,"teacher_disagreement_score":0.049519524,"about_ca_system_score_codex":0.0011730018,"about_ca_system_score_gemma":0.0016442023,"threshold_uncertainty_score":0.09846258},"labels":[],"label_agreement":null},{"id":"W4311495920","doi":"10.1002/fut.22390","title":"A new option for mortality–interest rates","year":2022,"lang":"en","type":"article","venue":"Journal of Futures Markets","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Hedge; Interest rate; Annuity; Life annuity; Actuarial science; Mortality rate; Longevity risk; Interest rate risk; Life insurance; Economics; Econometrics; Finance; Medicine; Internal medicine; Pension","score_opus":0.04567452775456005,"score_gpt":0.35933686810162824,"score_spread":0.3136623403470682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311495920","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":[0.033454984,0.0004989967,0.94962764,0.0014964956,0.00026378315,0.000052641633,0.00009546835,0.0001010265,0.014409022],"genre_scores_gemma":[0.80193067,0.00053079217,0.1696707,0.00053500745,0.0006333667,0.00024860626,0.00013558951,0.00006129914,0.02625401],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99790907,0.0010427206,0.00007257976,0.00031892268,0.00053234975,0.00012444654],"domain_scores_gemma":[0.99675107,0.0018602614,0.00038656447,0.00032432415,0.00043151123,0.00024631876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037138227,0.000601536,0.0005508009,0.0007646479,0.00041490875,0.0017304,0.0017736101,0.0024487274,0.008279852],"category_scores_gemma":[0.007367743,0.00033958413,0.0009140021,0.00055707217,0.001724744,0.003261338,0.0014776243,0.0026598284,0.0007299351],"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":[0.00009519065,0.00007216972,0.0008598484,0.000051735457,0.000026593672,0.0002630565,0.000094133706,0.063008964,0.0045976443,0.9089137,0.0014850445,0.020531919],"study_design_scores_gemma":[0.00007775374,0.00012694541,0.00060981,0.000036393478,0.00002282318,0.00039408586,0.00003842693,0.6406472,0.0016515604,0.34475654,0.0115681,0.00007032696],"about_ca_topic_score_codex":0.00034002456,"about_ca_topic_score_gemma":0.0002962634,"teacher_disagreement_score":0.008279852,"about_ca_system_score_codex":0.000993893,"about_ca_system_score_gemma":0.0005007331,"threshold_uncertainty_score":0.027698874},"labels":[],"label_agreement":null},{"id":"W4311724399","doi":"10.1177/00207640221141785","title":"Age, period and cohort effects in depression prevalence among Canadians 65+, 1994 to 2018: A multi-level analysis","year":2022,"lang":"en","type":"article","venue":"International Journal of Social Psychiatry","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"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 Saskatchewan","funders":"","keywords":"Demography; Cohort effect; Cohort; Depression (economics); Medicine; Population; Cohort study; Population health; Multilevel model; Gerontology","score_opus":0.013729264772845338,"score_gpt":0.3186501366835366,"score_spread":0.3049208719106912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311724399","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":[0.97741413,0.002078797,0.00063845783,0.0003190289,0.00004046586,0.00009496224,0.018171526,0.000044335527,0.0011982032],"genre_scores_gemma":[0.9925553,0.0005172596,0.0006256535,0.00006288347,0.000012610237,0.000059863727,0.0054932814,0.000012674668,0.0006604321],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99819845,0.00022630476,0.00015245721,0.00039591218,0.0005088522,0.0005180958],"domain_scores_gemma":[0.99708444,0.00043826943,0.00065061415,0.00028720338,0.0010915399,0.0004479666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029406687,0.0006101712,0.000851962,0.0023430977,0.002063372,0.0012137371,0.001598448,0.000663163,0.0022567464],"category_scores_gemma":[0.004461996,0.0005068876,0.002878054,0.0058089695,0.0005042746,0.00044042736,0.0011482314,0.0009403774,0.00020043345],"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":[0.00013380726,0.000010566109,0.99633247,0.00005234284,0.0006501693,0.000029114484,0.00017847597,0.00018996946,0.00005853997,0.00007157659,0.0006394829,0.0016534949],"study_design_scores_gemma":[0.000006733922,0.000021442444,0.99800795,0.000026177593,0.0003015729,0.000022107133,0.00026847635,0.0007169149,0.000022543738,0.000023479366,0.00057371514,0.000008896918],"about_ca_topic_score_codex":0.97866565,"about_ca_topic_score_gemma":0.9763214,"teacher_disagreement_score":0.02133435,"about_ca_system_score_codex":0.010978562,"about_ca_system_score_gemma":0.019127704,"threshold_uncertainty_score":0.07965541},"labels":[],"label_agreement":null},{"id":"W4312173564","doi":"10.3390/jrfm16010006","title":"The Declining Effect of Insurance on Life Expectancy","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Liberian dollar; Per capita; Economics; Life insurance; Actuarial science; Demographic economics; Demography; Business; Finance; Sociology; Population","score_opus":0.007480643726935646,"score_gpt":0.26123911113805737,"score_spread":0.25375846741112174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312173564","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":[0.9845404,0.00061521324,0.010677935,0.0005758789,0.000020915164,0.000013345681,0.0006476442,0.0000735907,0.0028350484],"genre_scores_gemma":[0.9980725,0.000086423584,0.0010143651,0.000039088838,0.000007916961,0.000004019911,0.00028147319,0.000007540425,0.00048663342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99758327,0.0016354652,0.00007718523,0.00032569756,0.00023243859,0.00014584445],"domain_scores_gemma":[0.98270345,0.014245113,0.0011596949,0.0010317463,0.0006459502,0.00021417021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004584251,0.00026350247,0.0004128522,0.00050850393,0.00014598558,0.00057949073,0.00043049024,0.000351695,0.0027713333],"category_scores_gemma":[0.024276434,0.0001616959,0.0012495142,0.0004792632,0.0005127354,0.0008001533,0.000819465,0.0010430639,0.00035474516],"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":[0.0012223175,0.000104512816,0.7722368,0.00017768973,0.0012705643,0.00058403413,0.0008939058,0.12486001,0.0023446581,0.006456674,0.0011863252,0.08866247],"study_design_scores_gemma":[0.000043403652,0.0011149319,0.84399617,0.000051636445,0.00037191116,0.0003454318,0.0005644244,0.1401879,0.002301105,0.007016695,0.0039614267,0.000044911794],"about_ca_topic_score_codex":0.00894625,"about_ca_topic_score_gemma":0.0051283226,"teacher_disagreement_score":0.00894625,"about_ca_system_score_codex":0.0004383056,"about_ca_system_score_gemma":0.00028710687,"threshold_uncertainty_score":0.02424413},"labels":[],"label_agreement":null},{"id":"W4312378887","doi":"10.2139/ssrn.4310566","title":"Two-Phase Selection of Representative Contracts for Valuation of Large Variable Annuity Portfolios","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Annuity; Economics; Actuarial science; Econometrics; Valuation (finance); Financial economics; Mathematics; Life annuity; Finance; Pension","score_opus":0.022812089570165436,"score_gpt":0.3678568265794956,"score_spread":0.34504473700933014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312378887","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":[0.5808162,0.00024921732,0.40557557,0.00056969695,0.00008009668,0.0036639092,0.00066538114,0.000316641,0.0080632875],"genre_scores_gemma":[0.83498454,0.000106950545,0.1573554,0.00015933532,0.000065601576,0.001828576,0.0011866372,0.00005683774,0.0042561083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99403316,0.004018557,0.00020834524,0.000341831,0.0009702717,0.0004278933],"domain_scores_gemma":[0.9711079,0.021638678,0.001216943,0.002140264,0.0027416286,0.0011545499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01874586,0.00046213812,0.0010524106,0.0012993829,0.00054528285,0.0014229382,0.0015764987,0.0014697249,0.007650144],"category_scores_gemma":[0.04891359,0.0005451839,0.0006016013,0.0009589996,0.00038604758,0.0012863831,0.0012221681,0.0010550874,0.0012567084],"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":[0.020773297,0.0053629866,0.07780044,0.0006819963,0.00035720025,0.0010304193,0.0015487081,0.13399988,0.030647365,0.0831369,0.016337655,0.62832314],"study_design_scores_gemma":[0.003223935,0.0048293904,0.031089725,0.000115186966,0.00015202578,0.0005927985,0.0008067435,0.8731831,0.02045176,0.055497292,0.009919397,0.00013872724],"about_ca_topic_score_codex":0.00043562744,"about_ca_topic_score_gemma":0.0005735262,"teacher_disagreement_score":0.01874586,"about_ca_system_score_codex":0.000517668,"about_ca_system_score_gemma":0.0019910433,"threshold_uncertainty_score":0.09913874},"labels":[],"label_agreement":null},{"id":"W4313132432","doi":"10.2139/ssrn.4277936","title":"Two-Phase Selection of Representative Contracts for Valuation of Large Variable Annuity Portfolios","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Annuity; Actuarial science; Valuation (finance); Business; Economics; Econometrics; Financial economics; Life annuity; Finance; Pension","score_opus":0.022812089570165436,"score_gpt":0.3678568265794956,"score_spread":0.34504473700933014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313132432","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":[0.5808157,0.00024921732,0.40557608,0.0005696973,0.000080096834,0.0036639161,0.00066538237,0.00031664176,0.008063295],"genre_scores_gemma":[0.8349841,0.00010695069,0.15735577,0.00015933554,0.00006560174,0.0018285819,0.0011866401,0.000056837875,0.004256124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99403316,0.0040185507,0.00020834502,0.00034183115,0.0009702717,0.00042789313],"domain_scores_gemma":[0.9711079,0.02163871,0.001216943,0.002140265,0.0027416286,0.0011545499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018745868,0.00046213812,0.0010524116,0.0012993842,0.00054528343,0.0014229395,0.0015764979,0.0014697249,0.0076501663],"category_scores_gemma":[0.04891359,0.00054518436,0.0006016022,0.0009590006,0.00038604814,0.0012863838,0.0012221687,0.0010550874,0.0012567107],"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":[0.020773314,0.005362981,0.07780032,0.0006819962,0.0003572,0.0010304186,0.001548708,0.13399965,0.030647326,0.083137035,0.016337674,0.6283234],"study_design_scores_gemma":[0.003223939,0.004829385,0.031089727,0.000115187155,0.00015202603,0.0005927984,0.00080674415,0.8731829,0.020451756,0.055497397,0.00991941,0.00013872734],"about_ca_topic_score_codex":0.000435627,"about_ca_topic_score_gemma":0.00057352585,"teacher_disagreement_score":0.018745868,"about_ca_system_score_codex":0.00051766826,"about_ca_system_score_gemma":0.0019910433,"threshold_uncertainty_score":0.0991388},"labels":[],"label_agreement":null},{"id":"W4313216565","doi":"10.1016/j.ehb.2022.101216","title":"Surviving the Deluge: British servicemen in World War I","year":2022,"lang":"en","type":"article","venue":"Economics & Human Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Infantry; Officer; Demography; Action (physics); Socioeconomic status; First world war; Military service; History; Political science; Sociology; Ancient history; Law; Population","score_opus":0.02189712352117871,"score_gpt":0.2905423348839861,"score_spread":0.2686452113628074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313216565","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":[0.99835026,0.00015490538,0.00004458247,0.00009640914,0.000004190447,0.000007296165,0.0007890985,0.0000023293828,0.00055091607],"genre_scores_gemma":[0.99386406,0.0004831215,0.0001400636,0.00007963258,0.0000126417435,0.00002449353,0.002596083,0.0000033420226,0.0027965875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997223,0.000059137656,0.000017814657,0.000033631153,0.000061727806,0.00010542758],"domain_scores_gemma":[0.9990409,0.00012822944,0.00039929934,0.00004724934,0.00013608765,0.00024818297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042618474,0.00030258254,0.00030954805,0.0014887721,0.0010469162,0.0007873463,0.00055794336,0.00043472953,0.003240847],"category_scores_gemma":[0.0018247692,0.00028792047,0.00024713224,0.00232891,0.00039107222,0.00043858352,0.00086605403,0.0008513672,0.0010965201],"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":[0.00002954614,0.00003380985,0.993131,0.000011972231,0.000020387195,0.000116731164,0.0017450821,0.00010524893,0.000055982484,0.0000842348,0.0013355302,0.003330454],"study_design_scores_gemma":[0.0000034273826,0.000044310655,0.99328965,0.000014592036,0.0000068880386,0.000079476566,0.0046562552,0.00038715522,0.00001805781,0.0000379547,0.0014568003,0.0000054762004],"about_ca_topic_score_codex":0.5750838,"about_ca_topic_score_gemma":0.7142915,"teacher_disagreement_score":0.5750838,"about_ca_system_score_codex":0.0017265527,"about_ca_system_score_gemma":0.0007879182,"threshold_uncertainty_score":0.85483724},"labels":[],"label_agreement":null},{"id":"W4313530601","doi":"10.1016/j.econmod.2022.106179","title":"Optimal longevity risk transfer under asymmetric information","year":2023,"lang":"en","type":"article","venue":"Economic Modelling","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Longevity risk; Longevity; Information asymmetry; Economics; Life expectancy; Reinsurance; Capital market; Actuarial science; Financial economics; Pension; Business; Finance","score_opus":0.030962341944117174,"score_gpt":0.27227659669682325,"score_spread":0.24131425475270607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313530601","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":[0.47728384,0.0013829158,0.48681962,0.007808543,0.00020858059,0.00011137322,0.00074301625,0.00019732481,0.025444716],"genre_scores_gemma":[0.9794946,0.0005033393,0.006721497,0.00013178212,0.000097134376,0.00005240576,0.00009042244,0.000038853243,0.012869976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988261,0.00060579804,0.00004651386,0.00016631032,0.0001196905,0.0002355528],"domain_scores_gemma":[0.9916447,0.0061838804,0.0010137613,0.00032266692,0.00038303505,0.00045189884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037104012,0.00078294246,0.0018612038,0.0012317009,0.0005914583,0.002472803,0.0015525069,0.0030414837,0.004802151],"category_scores_gemma":[0.018528653,0.0008451226,0.00096999767,0.00093433075,0.002175851,0.00440778,0.0018846572,0.0017838225,0.0004211642],"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":[0.00013573159,0.000105178835,0.0012770274,0.00006183658,0.00005109705,0.00019830586,0.00016970187,0.76478976,0.0004891035,0.22522627,0.0014020682,0.006093859],"study_design_scores_gemma":[0.000030061405,0.0000318317,0.00057878793,0.000015677384,0.000018811572,0.000046633828,0.00004575458,0.88285327,0.00009763983,0.11594637,0.00031362876,0.000021541855],"about_ca_topic_score_codex":0.006079591,"about_ca_topic_score_gemma":0.0025378296,"teacher_disagreement_score":0.006079591,"about_ca_system_score_codex":0.003087682,"about_ca_system_score_gemma":0.0016005855,"threshold_uncertainty_score":0.022402823},"labels":[],"label_agreement":null},{"id":"W4313889301","doi":"10.1017/s0269964822000468","title":"Rotation in age patterns of mortality decline: statistical evidence and modeling","year":2023,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"National Research Foundation of Korea","keywords":"Rotation (mathematics); Context (archaeology); Econometrics; Term (time); Statistics; Computer science; Mathematics; Demography; Geography; Artificial intelligence; Physics; Sociology","score_opus":0.08316429674792503,"score_gpt":0.3485930552124357,"score_spread":0.2654287584645107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313889301","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":[0.7933998,0.005998961,0.1839067,0.006628647,0.0002268683,0.0002243715,0.0024135534,0.0004935009,0.0067075673],"genre_scores_gemma":[0.98779315,0.0012801968,0.008841512,0.00020941494,0.00015158317,0.000048651647,0.0012262199,0.000034927027,0.00041434588],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99319106,0.004381788,0.00038184895,0.0012069709,0.000507543,0.00033081428],"domain_scores_gemma":[0.9021584,0.07412616,0.012831049,0.007915232,0.0023361729,0.0006328916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029001582,0.00079394545,0.0013575682,0.003067414,0.0006370042,0.0022599914,0.0022901967,0.0015265835,0.0026246097],"category_scores_gemma":[0.08176562,0.00044587778,0.0024415106,0.003762873,0.0027217062,0.0027088316,0.0017642179,0.0018001323,0.00065464637],"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":[0.00063379086,0.00022208839,0.538785,0.0003812226,0.0012573065,0.0006915071,0.0013965777,0.29185745,0.00050951785,0.08151673,0.0068193874,0.07592959],"study_design_scores_gemma":[0.00010162433,0.00015707764,0.07673706,0.00013254148,0.0002559618,0.00027316483,0.00049324566,0.82924,0.00034371365,0.087907724,0.0042569754,0.000100875804],"about_ca_topic_score_codex":0.01435193,"about_ca_topic_score_gemma":0.0068801376,"teacher_disagreement_score":0.029001582,"about_ca_system_score_codex":0.0012345444,"about_ca_system_score_gemma":0.0009715081,"threshold_uncertainty_score":0.15337682},"labels":[],"label_agreement":null},{"id":"W4315485867","doi":"10.3390/curroncol30010071","title":"Secular Trends of Liver Cancer Mortality and Years of Life Lost in Wuhan, China 2010–2019","year":2023,"lang":"en","type":"article","venue":"Current Oncology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"National Natural Science Foundation of China","keywords":"Years of potential life lost; Medicine; Demography; Mortality rate; Population; China; Liver cancer; Cause of death; Cancer; Census; Gerontology; Life expectancy; Environmental health; Geography; Surgery; Internal medicine; Disease","score_opus":0.11584949369246221,"score_gpt":0.42947258982350517,"score_spread":0.31362309613104294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315485867","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":[0.98263127,0.0024506438,0.00063596666,0.0009754171,0.00005427577,0.000041984924,0.010926581,0.00007517191,0.0022087295],"genre_scores_gemma":[0.9872213,0.0007362095,0.00036861838,0.00014443985,0.00002982732,0.00007556189,0.010078648,0.000010882079,0.0013345464],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995598,0.000045559336,0.00006928378,0.00011366693,0.00010797916,0.00010369538],"domain_scores_gemma":[0.99914575,0.000057023815,0.00026318355,0.000044972185,0.00036542484,0.00012355251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010322164,0.00041836675,0.00030733552,0.0020115133,0.00030404664,0.00044683492,0.0005655198,0.0003503544,0.0014771593],"category_scores_gemma":[0.0017998264,0.00021857566,0.0007855065,0.0021163288,0.0002272504,0.0006017688,0.00066734216,0.0004580052,0.00030031556],"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":[0.000096039366,0.00002452443,0.979478,0.00020588194,0.00036001115,0.00023506334,0.00041834236,0.0014049123,0.001167164,0.0005569521,0.003457091,0.012595962],"study_design_scores_gemma":[0.0000050979806,0.00002915314,0.99553204,0.000022762308,0.00006401271,0.00009509247,0.00016101329,0.0014124925,0.00013228483,0.00007116577,0.0024623137,0.000012486784],"about_ca_topic_score_codex":0.06614962,"about_ca_topic_score_gemma":0.07559687,"teacher_disagreement_score":0.06614962,"about_ca_system_score_codex":0.001784653,"about_ca_system_score_gemma":0.0024236469,"threshold_uncertainty_score":0.13152915},"labels":[],"label_agreement":null},{"id":"W4317824266","doi":"10.55365/1923.x2022.20.94","title":"Forensic Demography: An Overlooked Area of Practice among Applied Demographers","year":2022,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Damages; Forensic science; Value (mathematics); Personal injury; Sociology; Law; Criminology; Actuarial science; Political science; History; Economics; Computer science; Archaeology","score_opus":0.01650517939104779,"score_gpt":0.2623162683576714,"score_spread":0.24581108896662363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317824266","genre_codex":"commentary","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":[0.019003417,0.34458,0.09089593,0.4575461,0.0028741218,0.00018557034,0.00021424206,0.00016361868,0.084537014],"genre_scores_gemma":[0.4122709,0.46513143,0.06567958,0.037258547,0.008642912,0.00039035903,0.00017142373,0.00016693419,0.010288009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9851306,0.010129207,0.0008260889,0.0011354027,0.0023753115,0.00040348803],"domain_scores_gemma":[0.91806895,0.065895356,0.0034418553,0.0040664393,0.0072027068,0.0013246927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03241983,0.0006362655,0.0011816409,0.00878455,0.0030245548,0.006603774,0.003073331,0.004197575,0.004032497],"category_scores_gemma":[0.06291917,0.00048418817,0.00050501106,0.008432296,0.028441815,0.011414895,0.0060355957,0.008164732,0.00087711803],"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":[0.000020765865,0.000050793642,0.0045101987,0.0018352476,0.000039131282,0.00050651404,0.012834308,0.0014906463,0.00015753493,0.66438615,0.028672451,0.28549632],"study_design_scores_gemma":[0.000011241371,0.00003564207,0.003431735,0.0058045,0.000010463421,0.00086378824,0.017791685,0.0012547787,0.00023295339,0.6390739,0.33143374,0.000055671197],"about_ca_topic_score_codex":0.005405905,"about_ca_topic_score_gemma":0.0050756643,"teacher_disagreement_score":0.03241983,"about_ca_system_score_codex":0.00711483,"about_ca_system_score_gemma":0.006964765,"threshold_uncertainty_score":0.17145449},"labels":[],"label_agreement":null},{"id":"W4317853057","doi":"10.1186/s40621-023-00417-w","title":"The contributions of injury deaths to the gender gap in life expectancy and life disparity in Eastern Mediterranean Region","year":2023,"lang":"en","type":"article","venue":"Injury Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"Lunds Universitet","keywords":"Life expectancy; Biostatistics; Poison control; Injury prevention; Occupational safety and health; Suicide prevention; Public health; Demography; Medicine; Quality of Life Research; Human factors and ergonomics; Mediterranean climate; Epidemiology; Years of potential life lost; Gerontology; Environmental health; Medical emergency; Geography; Population; Sociology","score_opus":0.10531139766493965,"score_gpt":0.4008418344468984,"score_spread":0.2955304367819588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317853057","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":[0.9967577,0.0011108478,0.00016340376,0.00028180296,0.000016124435,0.000009196183,0.0007887189,0.0000048634515,0.00086726865],"genre_scores_gemma":[0.99923694,0.00021809024,0.00008719921,0.00003730465,0.000014758828,0.0000062697322,0.0003247695,0.00000117442,0.00007352145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995365,0.00017746282,0.000044756456,0.00006633451,0.00006248809,0.000112469446],"domain_scores_gemma":[0.99894994,0.0001732581,0.0004977777,0.00006247215,0.00019266005,0.0001239354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012214496,0.00026558986,0.00025812845,0.000940063,0.00019745331,0.00043632084,0.0003459306,0.00025572052,0.00090236287],"category_scores_gemma":[0.0028954893,0.00009704126,0.00060523005,0.00086401735,0.0002189866,0.0003957589,0.0007726954,0.0003320685,0.00011956026],"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":[0.000045753004,0.000011741405,0.99211794,0.00003415186,0.00009299769,0.00009163573,0.0002708498,0.0002436025,0.00004925608,0.000120191275,0.00030537322,0.006616503],"study_design_scores_gemma":[0.0000019584754,0.00003090321,0.9979449,0.000036309335,0.000029314822,0.00010826246,0.00059454754,0.00040770965,0.00003185844,0.0001309525,0.0006797922,0.0000035146181],"about_ca_topic_score_codex":0.010290331,"about_ca_topic_score_gemma":0.009361752,"teacher_disagreement_score":0.010290331,"about_ca_system_score_codex":0.0005109529,"about_ca_system_score_gemma":0.00046921283,"threshold_uncertainty_score":0.020460844},"labels":[],"label_agreement":null},{"id":"W4318323502","doi":"10.1016/j.annepidem.2023.01.012","title":"Average lifespan shortened due to cancer in selected countries of North America, Europe, Asia and Oceania, 2006 and 2016","year":2023,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Calgary; University of Alberta; Alberta Health; Alberta Cancer Foundation; Alberta Health Services","funders":"World Health Organization","keywords":"Medicine; Demography; Cancer; Population; Gerontology; Environmental health; Internal medicine","score_opus":0.05448780621934501,"score_gpt":0.3675182610049849,"score_spread":0.3130304547856399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318323502","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":[0.9525295,0.004302648,0.00012597584,0.00026684502,0.00011140338,0.000010443519,0.04116539,0.000039719675,0.0014480798],"genre_scores_gemma":[0.9839804,0.0015974477,0.000088615205,0.00006269465,0.000038882354,0.000015766911,0.013432599,0.000006037844,0.00077755185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995715,0.00006709256,0.000097256874,0.00009597138,0.00005836506,0.00010978751],"domain_scores_gemma":[0.99835145,0.00011467459,0.0009659963,0.00006841567,0.00023789515,0.00026151104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005518537,0.0003769872,0.0003845299,0.0016198115,0.00031185674,0.00070346677,0.000481358,0.00049598276,0.0012956593],"category_scores_gemma":[0.0019460383,0.00026802835,0.0011416788,0.0027884394,0.00027314684,0.000713406,0.0009149135,0.0006460232,0.00021610431],"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":[0.00018395143,0.000010590862,0.99468875,0.00010517623,0.00028495817,0.00008565482,0.0001770777,0.00030605358,0.000062613246,0.000044731587,0.001829521,0.0022209038],"study_design_scores_gemma":[0.000003487382,0.000025310235,0.9987795,0.00002261088,0.000042827414,0.00009679024,0.00023683111,0.00008257475,0.000013335141,0.000012898917,0.00067903614,0.0000048270344],"about_ca_topic_score_codex":0.073078446,"about_ca_topic_score_gemma":0.11564471,"teacher_disagreement_score":0.92692155,"about_ca_system_score_codex":0.0009496816,"about_ca_system_score_gemma":0.0008314177,"threshold_uncertainty_score":0.14530617},"labels":[],"label_agreement":null},{"id":"W4318711953","doi":"10.2139/ssrn.4325813","title":"Adult Income Prediction Using various ML Algorithms","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lambton College","funders":"","keywords":"Algorithm; Computer science; Econometrics; Mathematics","score_opus":0.015540853791033317,"score_gpt":0.2991394890255707,"score_spread":0.2835986352345374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318711953","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":[0.38022843,0.0031374656,0.6002879,0.0020122482,0.00046628749,0.00022760629,0.0026352177,0.0053780493,0.005626781],"genre_scores_gemma":[0.8401135,0.000701305,0.1516855,0.00039095903,0.00035711064,0.00017398408,0.0034014275,0.000146932,0.0030291912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988691,0.00057008443,0.000113673894,0.00023117049,0.00011009312,0.00010577036],"domain_scores_gemma":[0.99167174,0.006676299,0.00022931925,0.00042344438,0.0008345939,0.00016458642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066562826,0.0012027875,0.0014094972,0.0019696935,0.0006933844,0.0016283155,0.0014980971,0.0013003078,0.0042519695],"category_scores_gemma":[0.015145246,0.00039507114,0.0012814538,0.0013245076,0.00033420275,0.0017156466,0.0010343706,0.0015917608,0.001634752],"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":[0.0018233629,0.0009022152,0.06225992,0.00016706184,0.0005996947,0.00015806768,0.00011731086,0.5325272,0.00075818883,0.0037463033,0.0069662062,0.38997436],"study_design_scores_gemma":[0.000035939276,0.00006001249,0.0028314851,0.000015595426,0.000054489734,0.000019969712,0.000019826366,0.9948644,0.00040695674,0.0014557131,0.000225981,0.000009713655],"about_ca_topic_score_codex":0.005977643,"about_ca_topic_score_gemma":0.003946668,"teacher_disagreement_score":0.0066562826,"about_ca_system_score_codex":0.0005242887,"about_ca_system_score_gemma":0.0011408324,"threshold_uncertainty_score":0.035202265},"labels":[],"label_agreement":null},{"id":"W4320163187","doi":"10.1002/9781405165518.wbeosd027.pub2","title":"Demography: Historical","year":2020,"lang":"en","type":"other","venue":"The Blackwell Encyclopedia of Sociology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Demographic transition; Historical demography; Industrial Revolution; Population; Population growth; Industrialisation; Agricultural revolution; Extant taxon; Domestication; Demographic history; Demographic change; Fertility; Geography; History; Secular variation; Demography; Agriculture; Sociology; Political science; Ecology; Biology; Archaeology; Developed country; Evolutionary biology; Law","score_opus":0.016696961140150755,"score_gpt":0.2740319150321552,"score_spread":0.25733495389200445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320163187","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":[0.042355977,0.10504765,0.013590791,0.033639554,0.0019502094,0.0001876022,0.021278474,0.00024194251,0.7817077],"genre_scores_gemma":[0.63841456,0.18113711,0.009765669,0.0029662312,0.0028956567,0.00023622676,0.012905634,0.00021927935,0.15145962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936277,0.00032620452,0.000053841177,0.00012502367,0.000087104454,0.00004513028],"domain_scores_gemma":[0.9991014,0.00031197674,0.00015087747,0.00013419703,0.00022091871,0.00008066283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009269587,0.000476248,0.0002881775,0.005096442,0.0011264248,0.0023983081,0.0004477811,0.00061071286,0.033529762],"category_scores_gemma":[0.0027262387,0.00017884183,0.00016173106,0.00930756,0.0027690546,0.0025277357,0.0017529874,0.0011940212,0.0052558016],"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":[0.000042570882,0.000045050736,0.012547606,0.0011512547,0.000029366303,0.000372646,0.00864485,0.0024858057,0.00024524124,0.6053753,0.12816417,0.24089609],"study_design_scores_gemma":[0.0000024548967,0.000013153788,0.015622149,0.00082763925,0.0000049359596,0.0004483348,0.0021329043,0.00034478016,0.00010311218,0.032868285,0.9476159,0.000016450644],"about_ca_topic_score_codex":0.01206605,"about_ca_topic_score_gemma":0.014935316,"teacher_disagreement_score":0.033529762,"about_ca_system_score_codex":0.0031544755,"about_ca_system_score_gemma":0.0011685942,"threshold_uncertainty_score":0.11216819},"labels":[],"label_agreement":null},{"id":"W4322486474","doi":"10.4054/demres.2023.48.11","title":"The question of the human mortality plateau: Contrasting insights by longevity pioneers","year":2023,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"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","funders":"","keywords":"Longevity; Plateau (mathematics); Life span; Life expectancy; Demography; Geography; Sociology; History; Gerontology; Population; Medicine","score_opus":0.08484871685774485,"score_gpt":0.4365096792669717,"score_spread":0.35166096240922684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322486474","genre_codex":"commentary","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":[0.33428043,0.11747286,0.021212254,0.39870602,0.0010584564,0.000027418866,0.00054621464,0.00007353199,0.12662287],"genre_scores_gemma":[0.95297533,0.027928758,0.0015536438,0.011418919,0.0042646136,0.000028840808,0.00015363813,0.000055657416,0.001620767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.997526,0.0014169258,0.00007965919,0.00033010103,0.0004418982,0.00020538234],"domain_scores_gemma":[0.9652788,0.028934281,0.001676675,0.0013643862,0.0014566601,0.0012891191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014194148,0.00027043972,0.0010345412,0.002849453,0.0014914534,0.0049415478,0.0013628204,0.0022553403,0.0025235862],"category_scores_gemma":[0.031947013,0.00017059375,0.00055494014,0.0023785343,0.018249752,0.012034818,0.0043665427,0.0052596526,0.0003282934],"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":[0.00025016812,0.000040734267,0.0085684825,0.00020469227,0.000047908525,0.0001108019,0.014414026,0.0005608515,0.00021430566,0.9323742,0.00698599,0.03622784],"study_design_scores_gemma":[0.000036873287,0.00007072926,0.013569978,0.00050147355,0.000027419255,0.00009799064,0.009877991,0.000819814,0.00013637348,0.95367074,0.021162875,0.000027675293],"about_ca_topic_score_codex":0.0022089805,"about_ca_topic_score_gemma":0.0021292374,"teacher_disagreement_score":0.014194148,"about_ca_system_score_codex":0.0015266009,"about_ca_system_score_gemma":0.0013375526,"threshold_uncertainty_score":0.075066686},"labels":[],"label_agreement":null},{"id":"W4322487278","doi":"10.1007/s00180-023-01329-5","title":"Examining the identifiability and estimability of the phase-type ageing model","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identifiability; Markov chain; Computer science; Markov process; State (computer science); Mathematics; Applied mathematics; Phase (matter); Steady state (chemistry); Algorithm; Econometrics; Statistics; Chemistry","score_opus":0.08354960392470571,"score_gpt":0.3749713143170593,"score_spread":0.2914217103923536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322487278","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":[0.37586972,0.00032475902,0.61737156,0.0029343413,0.00006507888,0.00009518345,0.00040587236,0.00016312297,0.002770492],"genre_scores_gemma":[0.9703912,0.00025619558,0.027248403,0.00019489774,0.000082400875,0.0000864733,0.00041484274,0.00003480727,0.0012907065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99154705,0.0064689326,0.00028516448,0.0008646822,0.00052405027,0.0003100294],"domain_scores_gemma":[0.45999914,0.5144983,0.010196873,0.011059893,0.003589643,0.0006561432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03273936,0.000562589,0.0011342425,0.0014982161,0.0004895777,0.002217363,0.0019925106,0.0021129781,0.003285777],"category_scores_gemma":[0.29486918,0.00059638877,0.0012736293,0.0011665034,0.0029127684,0.0036622118,0.002241205,0.0023822756,0.00022072355],"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":[0.0006192241,0.00032277734,0.06709991,0.0003132817,0.0006794837,0.00043731206,0.0011809238,0.47914487,0.0009028434,0.39906713,0.0021997548,0.048032396],"study_design_scores_gemma":[0.00008837274,0.00011560082,0.00454365,0.000048298727,0.000077663804,0.0001208078,0.00016737956,0.7910872,0.00036128133,0.20299059,0.00037296957,0.000026169162],"about_ca_topic_score_codex":0.0045163096,"about_ca_topic_score_gemma":0.0017303037,"teacher_disagreement_score":0.03273936,"about_ca_system_score_codex":0.00081375235,"about_ca_system_score_gemma":0.0027825374,"threshold_uncertainty_score":0.17314434},"labels":[],"label_agreement":null},{"id":"W4323530314","doi":"10.2139/ssrn.4377349","title":"Evaluation of Participating Endowment Life Insurance Policies in a Stochastic Environment","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Concordia University","funders":"","keywords":"Endowment policy; Endowment; Life insurance; Actuarial science; Business; Economics; Public economics; Political science","score_opus":0.04871765293219604,"score_gpt":0.3431206500371309,"score_spread":0.29440299710493484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323530314","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":[0.9885832,0.00010336102,0.008872035,0.00025484062,0.000017820554,0.00009938312,0.0001942021,0.000042862197,0.0018322631],"genre_scores_gemma":[0.9980147,0.000031899704,0.0014189854,0.0000108374825,0.00000438074,0.000020582625,0.00008892987,0.000004214741,0.0004055117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627835,0.0023885584,0.00014002822,0.00027969253,0.00038290987,0.0005304706],"domain_scores_gemma":[0.9633518,0.030754192,0.0018963515,0.000774578,0.0014874074,0.0017357295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010083459,0.0009229203,0.0014652865,0.00095975056,0.0004957382,0.0022320622,0.0012625584,0.0017060944,0.0031017235],"category_scores_gemma":[0.025794575,0.00040323986,0.0007654881,0.00073616893,0.0012109304,0.0015843757,0.0014238817,0.0011206245,0.00012650245],"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":[0.00255207,0.00068263395,0.009486879,0.0000643365,0.00009472857,0.0001269785,0.00004937788,0.9755457,0.00071242236,0.0055761663,0.00022604848,0.004882556],"study_design_scores_gemma":[0.00018742256,0.0014231635,0.0034397836,0.000010451278,0.00007786455,0.000020769136,0.00013962934,0.9913686,0.00078079104,0.002402594,0.0001324763,0.000016470825],"about_ca_topic_score_codex":0.009928677,"about_ca_topic_score_gemma":0.004674247,"teacher_disagreement_score":0.010083459,"about_ca_system_score_codex":0.0027902075,"about_ca_system_score_gemma":0.003158321,"threshold_uncertainty_score":0.053327024},"labels":[],"label_agreement":null},{"id":"W4323656340","doi":"10.1080/10920277.2023.2167832","title":"Enhancing Mortality Forecasting through Bivariate Model–Based Ensemble","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Bivariate analysis; Population; Computer science; Econometrics; Base (topology); Cascade; Completeness (order theory); Statistics; Bivariate data; Mathematics; Machine learning; Demography; Engineering","score_opus":0.06941962073033156,"score_gpt":0.340336549670819,"score_spread":0.27091692894048747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323656340","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":[0.04896102,0.00052752125,0.9484193,0.0002852826,0.000077946854,0.000028512359,0.00014140706,0.0003824736,0.0011765567],"genre_scores_gemma":[0.804331,0.0008261544,0.192068,0.00022737675,0.00023835603,0.0000989995,0.0007360773,0.00006550236,0.001408559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939203,0.00022500174,0.000036032532,0.00013100651,0.00015324526,0.00006279218],"domain_scores_gemma":[0.9984206,0.0007035019,0.00011876895,0.00022210533,0.0004524312,0.000082665065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026514865,0.0010084406,0.0014202347,0.0011784672,0.00036976463,0.0008587914,0.001085391,0.00072998734,0.0007768191],"category_scores_gemma":[0.0059586675,0.0003608232,0.0010184399,0.001066413,0.00021841818,0.0017469657,0.0015196551,0.0012078558,0.0003041716],"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":[0.00008967835,0.000119932534,0.010253038,0.00005050121,0.00025318508,0.00007778516,0.000111861074,0.78189033,0.0036660195,0.003436446,0.0019526965,0.1980984],"study_design_scores_gemma":[0.000002692918,0.00002137719,0.0005490926,0.000004293584,0.000023007262,0.000012745732,0.000007988438,0.9972013,0.00046570893,0.0013802784,0.00032324635,0.0000083343075],"about_ca_topic_score_codex":0.004604969,"about_ca_topic_score_gemma":0.004493968,"teacher_disagreement_score":0.004604969,"about_ca_system_score_codex":0.00030929846,"about_ca_system_score_gemma":0.0007747629,"threshold_uncertainty_score":0.014022529},"labels":[],"label_agreement":null},{"id":"W4324052882","doi":"10.1017/asb.2023.7","title":"Risk allocation through shapley decompositions, with applications to variable annuities","year":2023,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Laval; Actua; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decomposition; Equity (law); Variable (mathematics); Embedding; Maturity (psychological); Life insurance; Computer science; Mathematical optimization; Shapley value; Actuarial science; Economics; Econometrics; Mathematical economics; Mathematics; Game theory","score_opus":0.01724053700836865,"score_gpt":0.2947823895234641,"score_spread":0.27754185251509544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324052882","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":[0.012844307,0.00021434389,0.9802801,0.00018675305,0.00004784771,0.000052097188,0.00003414236,0.000044856522,0.0062955627],"genre_scores_gemma":[0.55935633,0.0006778572,0.43240854,0.00015232134,0.00013108474,0.00029080702,0.00008977982,0.00010266396,0.0067905067],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983481,0.0010701562,0.000052452633,0.00014465122,0.00028480837,0.00009982736],"domain_scores_gemma":[0.99748355,0.0015786573,0.00019666777,0.00030432333,0.00021639439,0.00022041882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005525624,0.00088246004,0.0010290042,0.0010897964,0.0007272779,0.0020266676,0.0011147106,0.00093914144,0.00559001],"category_scores_gemma":[0.008256318,0.0005372654,0.0013967655,0.0012787465,0.0023724188,0.0024185597,0.002365056,0.0025327057,0.00041396832],"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":[0.000034524975,0.00006564854,0.00026134378,0.000048654132,0.000032149343,0.000034198132,0.00011717268,0.16917446,0.0007249362,0.8067406,0.0008983645,0.021867981],"study_design_scores_gemma":[0.00001493806,0.000025758203,0.0000794311,0.000017290782,0.0000074515387,0.000014795459,0.000024841946,0.5301663,0.00019054167,0.46825644,0.0011918534,0.0000103338025],"about_ca_topic_score_codex":0.001197466,"about_ca_topic_score_gemma":0.0010554031,"teacher_disagreement_score":0.00559001,"about_ca_system_score_codex":0.0018645765,"about_ca_system_score_gemma":0.0011276406,"threshold_uncertainty_score":0.029222608},"labels":[],"label_agreement":null},{"id":"W4327811076","doi":"10.18154/rwth-2024-05454","title":"Euclid: Validation of the MontePython forecasting tools","year":2023,"lang":"en","type":"preprint","venue":"University of Groningen research database (University of Groningen / Centre for Information Technology)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Perimeter Institute; University of Waterloo","funders":"Agencia Estatal de Investigación; Dipartimenti di Eccellenza; Fundação para a Ciência e a Tecnologia; National Astronomical Observatory of Japan; Norsk Romsenter; Agenția Spațială Română; Ministerio de Ciencia e Innovación; European Space Agency; Agenzia Spaziale Italiana; European Commission; National Aeronautics and Space Administration; Staatssekretariat für Bildung, Forschung und Innovation; Deutsche Forschungsgemeinschaft","keywords":"Weak gravitational lensing; Physics; Statistical physics; Cluster analysis; Galaxy; Dark energy; Theoretical physics; Cosmology; Astrophysics; Computer science; Machine learning; Redshift","score_opus":0.12387531116582975,"score_gpt":0.3125358119368552,"score_spread":0.18866050077102545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327811076","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":[0.36135575,0.0017360976,0.32433158,0.0030795613,0.001193524,0.00042615805,0.061255403,0.19299427,0.053627647],"genre_scores_gemma":[0.5908681,0.0005152652,0.3168144,0.00062784634,0.0001625773,0.0004202992,0.06706065,0.017480444,0.006050554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997529,0.00083069433,0.0001987796,0.0004610268,0.00076843554,0.00021212107],"domain_scores_gemma":[0.99069047,0.00448128,0.00041922068,0.0021412885,0.0017394203,0.0005283498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054662847,0.0011949433,0.0006374995,0.0013688698,0.00064333447,0.0019397932,0.0024928285,0.0013189091,0.008064654],"category_scores_gemma":[0.02252251,0.0006439154,0.0008490446,0.0010967709,0.00073818373,0.0023378504,0.0017877333,0.0020065943,0.0032083672],"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":[0.0012345664,0.00032581756,0.030488102,0.0005504202,0.00030086335,0.00033406267,0.00068695133,0.7049943,0.004456272,0.043781444,0.11651206,0.09633513],"study_design_scores_gemma":[0.00010061954,0.00005144792,0.0024896201,0.000051263574,0.000015619024,0.000047977606,0.000049847444,0.9686035,0.0043226783,0.0067390227,0.01746988,0.000058413887],"about_ca_topic_score_codex":0.039350804,"about_ca_topic_score_gemma":0.02860275,"teacher_disagreement_score":0.039350804,"about_ca_system_score_codex":0.0015723925,"about_ca_system_score_gemma":0.0031264992,"threshold_uncertainty_score":0.078243494},"labels":[],"label_agreement":null},{"id":"W4360612650","doi":"10.1553/p-g5fe-hafz","title":"How much would reduced emigration mitigate ageing in Norway?","year":2023,"lang":"en","type":"article","venue":"Vienna Yearbook of Population Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Emigration; Ageing; Fertility; Population ageing; Immigration; Population; Dependency ratio; Demography; Quarter (Canadian coin); Demographic economics; Geography; Economics; Medicine; Sociology","score_opus":0.12367631808330679,"score_gpt":0.428753951096496,"score_spread":0.30507763301318924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360612650","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":[0.9566237,0.0043200143,0.00619671,0.012982192,0.0009541448,0.000093120194,0.0035689932,0.00010245186,0.015158676],"genre_scores_gemma":[0.9932475,0.0012220466,0.0025386605,0.0009743001,0.00007992971,0.000048817477,0.0006208752,0.000014006678,0.0012539099],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990339,0.00061024906,0.000032143867,0.00009583482,0.000063812666,0.00016404485],"domain_scores_gemma":[0.9987973,0.00055436813,0.0002348762,0.00008130562,0.00015948346,0.00017262061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003931697,0.0002714402,0.0003857509,0.00023295432,0.00029717205,0.00070691673,0.0006167238,0.00051233283,0.002714425],"category_scores_gemma":[0.009079608,0.000107042695,0.00079194084,0.00024462666,0.0004041082,0.00071643345,0.0008457792,0.0004311216,0.00028652334],"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":[0.0049202135,0.0009992855,0.4718104,0.0030437855,0.0015304683,0.0010039627,0.0024114933,0.13483459,0.0033696615,0.04588032,0.031695396,0.29850036],"study_design_scores_gemma":[0.0008612032,0.0060318033,0.8079178,0.0020879733,0.001535301,0.00038839228,0.006044754,0.06570914,0.0034178016,0.026498755,0.07935525,0.00015185021],"about_ca_topic_score_codex":0.053302962,"about_ca_topic_score_gemma":0.06815844,"teacher_disagreement_score":0.053302962,"about_ca_system_score_codex":0.0013202551,"about_ca_system_score_gemma":0.002920964,"threshold_uncertainty_score":0.1059854},"labels":[],"label_agreement":null},{"id":"W4361286552","doi":"10.1177/00811750231151949","title":"The Anatomy of Cohort Analysis: Decomposing Comparative Cohort Careers","year":2023,"lang":"en","type":"article","venue":"Sociological Methodology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Cohort; Computer science; Econometrics; Data science; Psychology; Statistics; Mathematics","score_opus":0.2088264576884719,"score_gpt":0.4864860578802014,"score_spread":0.27765960019172947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361286552","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":[0.030528637,0.004397296,0.94385505,0.004210185,0.00046473808,0.00031866803,0.00064458983,0.00025175777,0.015329075],"genre_scores_gemma":[0.4735392,0.004396468,0.5136984,0.0014670262,0.00080253923,0.0011712142,0.00096477434,0.00022704048,0.0037333288],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98177344,0.013693261,0.00047706583,0.0020751292,0.0014152866,0.0005658455],"domain_scores_gemma":[0.9367938,0.049089417,0.003377314,0.007230925,0.0025733,0.00093530683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03034693,0.001077731,0.0009935166,0.008884916,0.0016046842,0.0046679997,0.00195323,0.0017433114,0.005311908],"category_scores_gemma":[0.0742571,0.00058249984,0.0027484728,0.008079662,0.006871909,0.008226612,0.004697556,0.0038285186,0.00046016526],"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":[0.000052698666,0.0000521349,0.012278846,0.00020145862,0.00022567203,0.000099097546,0.0031958893,0.0060687214,0.00020598447,0.8936861,0.003059465,0.08087399],"study_design_scores_gemma":[0.000014592256,0.0000801822,0.008147947,0.00020374481,0.0000658694,0.00011067054,0.0012842924,0.03076612,0.00019852213,0.9396449,0.01943165,0.000051554758],"about_ca_topic_score_codex":0.008040376,"about_ca_topic_score_gemma":0.0050381497,"teacher_disagreement_score":0.03034693,"about_ca_system_score_codex":0.0034410625,"about_ca_system_score_gemma":0.0035132375,"threshold_uncertainty_score":0.16049182},"labels":[],"label_agreement":null},{"id":"W4362601288","doi":"","title":"Novelty, and its Assessment: A Multidisciplinary and Complex Systems Approach.","year":2023,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Novelty; Surprise; Computer science; Context (archaeology); Multidisciplinary approach; Data science; Process (computing); USable; Novelty detection; Discipline; Artificial intelligence; Management science; Psychology; Sociology; Engineering; Social psychology; World Wide Web; Social science","score_opus":0.08949734545503797,"score_gpt":0.32608906459894915,"score_spread":0.2365917191439112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362601288","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":[0.1023148,0.1153456,0.583923,0.053998854,0.002892881,0.0015319292,0.0015529479,0.0005187555,0.13792121],"genre_scores_gemma":[0.8408007,0.022191523,0.12669684,0.0015750276,0.0012683577,0.0009019162,0.00041497653,0.00006564712,0.0060850005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904032,0.0043852217,0.00095780863,0.0010259683,0.0030026068,0.00022507945],"domain_scores_gemma":[0.9690424,0.020418152,0.0040408173,0.0015612263,0.0035007566,0.0014365686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014800231,0.0009168556,0.0013942695,0.012604939,0.0021529703,0.009638694,0.001581967,0.0020138638,0.0030785277],"category_scores_gemma":[0.03830478,0.00042227175,0.0012821503,0.00782522,0.010995511,0.009986322,0.00688443,0.0024995178,0.00032144116],"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":[0.00010538594,0.00010232801,0.03539228,0.0033570551,0.00046205072,0.0007036947,0.013633972,0.00607754,0.0012592985,0.60095,0.010350022,0.3276064],"study_design_scores_gemma":[0.000024448129,0.0003842079,0.03776725,0.0026088168,0.00023742867,0.0015276863,0.016704597,0.015873803,0.00072513695,0.8375415,0.08639938,0.00020577133],"about_ca_topic_score_codex":0.0017622891,"about_ca_topic_score_gemma":0.002397594,"teacher_disagreement_score":0.014800231,"about_ca_system_score_codex":0.005058234,"about_ca_system_score_gemma":0.0041297968,"threshold_uncertainty_score":0.078272045},"labels":[],"label_agreement":null},{"id":"W4366283192","doi":"10.11113/mjfas.v19n2.2848","title":"Transition Intensities for Critical Illness: A Study on Canadian Health Data","year":2023,"lang":"en","type":"article","venue":"Malaysian Journal of Fundamental and Applied Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gompertz function; Transition (genetics); Critical illness; Mathematics; Health care; Econometrics; Statistics; Statistical physics; Medicine; Critically ill; Intensive care medicine; Physics; Economics; Chemistry","score_opus":0.10602936321221314,"score_gpt":0.3915011784678439,"score_spread":0.28547181525563076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366283192","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":[0.97658676,0.0010828746,0.004926212,0.0012836137,0.00002530319,0.00018323018,0.011175653,0.00013171157,0.0046045985],"genre_scores_gemma":[0.9875733,0.00042011103,0.0026445293,0.000100165395,0.0000076239494,0.000047413298,0.008416038,0.000023752033,0.00076695945],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964258,0.0008412831,0.00017107294,0.00070577045,0.0011898112,0.00066618767],"domain_scores_gemma":[0.9805906,0.010687336,0.0015257417,0.0018650282,0.004522171,0.0008091769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006575319,0.00037152477,0.0006661285,0.0026168695,0.0018496987,0.001340085,0.0020355037,0.00076728984,0.002299931],"category_scores_gemma":[0.03813438,0.00028244118,0.0011489635,0.0069047636,0.0010284828,0.0010575963,0.0008422727,0.0016171825,0.0001500709],"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":[0.0005693986,0.0002976553,0.8681684,0.00048751474,0.00047322194,0.0007302477,0.0027611703,0.054399066,0.0004479168,0.017802265,0.012509601,0.04135344],"study_design_scores_gemma":[0.000066359,0.000077551646,0.83300513,0.00012007071,0.00020642014,0.00033909368,0.0027249905,0.14981024,0.00041484152,0.0028292555,0.010269924,0.00013615412],"about_ca_topic_score_codex":0.98105925,"about_ca_topic_score_gemma":0.9633147,"teacher_disagreement_score":0.021283936,"about_ca_system_score_codex":0.021283936,"about_ca_system_score_gemma":0.015179337,"threshold_uncertainty_score":0.15442652},"labels":[],"label_agreement":null},{"id":"W4366602754","doi":"10.1371/journal.pone.0283879","title":"The burden of premature mortality from cardiovascular diseases: A systematic review of years of life lost","year":2023,"lang":"en","type":"review","venue":"PLoS ONE","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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":"Medicine; Intensive care medicine; Gerontology; Pediatrics","score_opus":0.0789677089470856,"score_gpt":0.3191231601544803,"score_spread":0.24015545120739468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366602754","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":[0.0008730472,0.9978509,0.000101821104,0.00019981152,0.00007746709,0.00017884209,0.0005422386,0.000006144851,0.00016984182],"genre_scores_gemma":[0.01938754,0.97813696,0.0006414823,0.00046832347,0.000096258285,0.00078982534,0.00038421733,0.000005990654,0.000089334615],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98550504,0.0059612193,0.004740689,0.00101765,0.0024383795,0.00033710338],"domain_scores_gemma":[0.95756954,0.03061213,0.008159266,0.0005405159,0.0027115494,0.00040699623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01317784,0.0014826462,0.009608813,0.012754563,0.00066345924,0.0033015967,0.002087266,0.0017245526,0.0040835915],"category_scores_gemma":[0.05739027,0.0011146256,0.010295679,0.012360868,0.0009528533,0.0030335574,0.001976215,0.0017904625,0.0002846596],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017336778,0.000009469916,0.0010134964,0.9646023,0.013187919,0.000059962258,0.00018573909,0.00007816086,0.00004905196,0.00015783479,0.0010513959,0.0194313],"study_design_scores_gemma":[0.0002375667,0.00019813469,0.0066636093,0.90132165,0.07776727,0.000319691,0.00031686932,0.00008562684,0.000075557415,0.00036106605,0.012611322,0.000041598156],"about_ca_topic_score_codex":0.0070969863,"about_ca_topic_score_gemma":0.020051312,"teacher_disagreement_score":0.01317784,"about_ca_system_score_codex":0.004464453,"about_ca_system_score_gemma":0.009849852,"threshold_uncertainty_score":0.0696919},"labels":[],"label_agreement":null},{"id":"W4377107745","doi":"10.3390/jrfm16050276","title":"A Stochastic Markov Chain for Estimating New Entrants into Professional Pension Funds","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fondazione di Sardegna","keywords":"Pension; Markov chain; Order (exchange); Economics; Actuarial science; Business; Econometrics; Finance; Statistics; Mathematics","score_opus":0.015467852681959491,"score_gpt":0.3008222519506735,"score_spread":0.28535439926871403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377107745","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":[0.095312744,0.00033296747,0.9000079,0.0004669234,0.000056987115,0.00014917039,0.0012474373,0.00031795498,0.0021079828],"genre_scores_gemma":[0.8840989,0.0009291542,0.10466662,0.00013340749,0.00011893989,0.000606134,0.002921028,0.000060741833,0.0064651337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988199,0.00047434634,0.00005575335,0.00028706552,0.00016004198,0.00020294005],"domain_scores_gemma":[0.9919286,0.0064706258,0.0006515901,0.00020846161,0.00053170865,0.0002089883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004216739,0.0007780441,0.0012868334,0.0016005863,0.00057957467,0.0014021858,0.0015273875,0.0013213533,0.0045176307],"category_scores_gemma":[0.00999485,0.00073276734,0.0012039819,0.0010793591,0.0010033973,0.0014730771,0.00097187515,0.0018377816,0.0005964357],"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":[0.000054257955,0.000027588683,0.005157303,0.000022440416,0.000030880125,0.000061966304,0.000037449074,0.9737612,0.00021611457,0.014822064,0.0003686486,0.005440088],"study_design_scores_gemma":[0.000007884891,0.00001582413,0.00055007474,0.000009356692,0.000010142442,0.000011835358,0.0000099137305,0.99497116,0.00010555322,0.00408882,0.0002079524,0.000011477087],"about_ca_topic_score_codex":0.04013488,"about_ca_topic_score_gemma":0.024042087,"teacher_disagreement_score":0.04013488,"about_ca_system_score_codex":0.0018319181,"about_ca_system_score_gemma":0.003127217,"threshold_uncertainty_score":0.07980251},"labels":[],"label_agreement":null},{"id":"W4377262767","doi":"10.1515/9780773574618-005","title":"The Expression of Age: Age (Mis)reporting in the Canadian and US Censuses","year":2008,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Expression (computer science); Demography; Geography; History; Computer science; Sociology","score_opus":0.030887193302188833,"score_gpt":0.2472293782704688,"score_spread":0.21634218496827998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377262767","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":[0.20969191,0.1338994,0.012184784,0.13272654,0.0068068323,0.00022861826,0.039530784,0.00044785126,0.4644833],"genre_scores_gemma":[0.81004685,0.07478883,0.009119173,0.005007098,0.0012350318,0.00009165901,0.005764121,0.000181185,0.093765944],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99467695,0.0010376717,0.00029896633,0.00023969526,0.0030455734,0.0007011482],"domain_scores_gemma":[0.9898391,0.0027221167,0.0008973289,0.00038002164,0.005709381,0.00045212536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005410178,0.00037280362,0.0003311495,0.0047643525,0.0025266104,0.0036626263,0.0014827162,0.00078811875,0.003596684],"category_scores_gemma":[0.03143309,0.00034107565,0.00033536452,0.0137519725,0.0025878425,0.0020479877,0.0011335738,0.0010942057,0.00050018256],"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":[0.00007110116,0.000022842869,0.13351946,0.00033845368,0.00004162811,0.00017736902,0.03303529,0.0022039402,0.00025181865,0.122417204,0.25482908,0.45309192],"study_design_scores_gemma":[0.0000048510087,0.000020776199,0.47743335,0.0014304052,0.00006591539,0.0002934238,0.02807067,0.0029752897,0.0005682108,0.017118715,0.47184393,0.00017453144],"about_ca_topic_score_codex":0.99133635,"about_ca_topic_score_gemma":0.9951881,"teacher_disagreement_score":0.035607975,"about_ca_system_score_codex":0.035607975,"about_ca_system_score_gemma":0.057283353,"threshold_uncertainty_score":0.2583552},"labels":[],"label_agreement":null},{"id":"W4377262835","doi":"10.1515/9780773574618-001","title":"Tables and Figures","year":2008,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"Geology","score_opus":0.01938786836228229,"score_gpt":0.22664757018174203,"score_spread":0.20725970181945974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377262835","genre_codex":"dataset","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":[0.00038972613,0.0015319532,0.0006181775,0.00057450874,0.0018608103,0.0002888113,0.8173204,0.0010893566,0.17632619],"genre_scores_gemma":[0.0034822768,0.0044911657,0.0026803901,0.0008463135,0.0007952905,0.00053561886,0.7069365,0.00075943366,0.279473],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99864393,0.00008963658,0.00014953024,0.00023239499,0.0007259196,0.00015858228],"domain_scores_gemma":[0.9932807,0.0011302289,0.0004277146,0.00037553246,0.0044111884,0.00037472555],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00061325525,0.0010979876,0.0011435014,0.0072128503,0.0011076417,0.0025942482,0.0016675949,0.00053974293,0.7265109],"category_scores_gemma":[0.010181579,0.00049638667,0.00067322387,0.020566178,0.0003200965,0.0011156354,0.000853538,0.0010233597,0.49790892],"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":[0.0000151802,0.000010535132,0.00027526263,0.0003268033,0.000004723417,0.000012976761,0.0000127565345,0.0001492922,0.000023411718,0.0009553657,0.98080003,0.017413767],"study_design_scores_gemma":[0.000024661234,0.000011461191,0.0036618742,0.0002474948,0.000005966778,0.000034277185,0.00004267773,0.000065339125,0.00002958479,0.0008853369,0.9949822,0.000009097869],"about_ca_topic_score_codex":0.13071269,"about_ca_topic_score_gemma":0.1259095,"teacher_disagreement_score":0.27348912,"about_ca_system_score_codex":0.0032124997,"about_ca_system_score_gemma":0.005264804,"threshold_uncertainty_score":0.390099},"labels":[],"label_agreement":null},{"id":"W4378443658","doi":"10.1515/9780773599949-044","title":"The Bank of Montreal Centenary Medal","year":2016,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medal; History; Art history","score_opus":0.01442886517610413,"score_gpt":0.22323158124083967,"score_spread":0.20880271606473555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378443658","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":[0.001608644,0.01905659,0.0002252288,0.054649398,0.011917808,0.00006038997,0.002354869,0.00027656576,0.9098506],"genre_scores_gemma":[0.004219827,0.0014699062,0.00006452801,0.0018033385,0.00046245,0.000012049378,0.00016145692,0.000051375806,0.991755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991042,0.0000638672,0.0000128032225,0.00011219032,0.00046694325,0.00023996006],"domain_scores_gemma":[0.99914193,0.00007635318,0.0000363975,0.000059619048,0.00038624872,0.00029932163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090796826,0.00084418425,0.00052542303,0.0017737462,0.0040079285,0.0066762418,0.0013414953,0.003689421,0.20473592],"category_scores_gemma":[0.0031932965,0.0005439692,0.0003825854,0.0016857032,0.0012904013,0.0014860719,0.0016277884,0.0032978738,0.034735058],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012792007,0.0000050355884,0.00014151058,0.000009743468,0.0000017236648,0.000026216998,0.000040081395,0.000023452092,0.00001777926,0.011727769,0.9779324,0.0100615015],"study_design_scores_gemma":[0.000004840568,0.0000024246099,0.0008906268,0.00002392986,0.0000012492849,0.0000120093255,0.0000495903,0.000025195815,0.000017706316,0.0006151596,0.9983518,0.000005498485],"about_ca_topic_score_codex":0.5829921,"about_ca_topic_score_gemma":0.77912456,"teacher_disagreement_score":0.5829921,"about_ca_system_score_codex":0.016001292,"about_ca_system_score_gemma":0.016679838,"threshold_uncertainty_score":0.8389275},"labels":[],"label_agreement":null},{"id":"W4379383149","doi":"10.32920/23296250.v1","title":"Toward the Automation of Search and Rescue Operations: An Algorithm for Finding Missing Lost Persons Living With Dementia Using Drones","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Search and rescue; Automation; Drone; Obstacle; Process (computing); Computer science; Field (mathematics); Data collection; Work (physics); Artificial intelligence; Machine learning; Data science; Operations research; Engineering; Mathematics; Statistics; Geography; Robot","score_opus":0.12031775296020629,"score_gpt":0.3769439065965874,"score_spread":0.2566261536363811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379383149","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":[0.108381405,0.00060862314,0.87770516,0.0007664106,0.00015830512,0.00065955194,0.0010751056,0.00819079,0.0024547135],"genre_scores_gemma":[0.26898676,0.00026172883,0.72431284,0.0002653202,0.00006001328,0.00047728975,0.0029118208,0.00013015179,0.0025940973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994234,0.00007462874,0.00006590587,0.00022876194,0.00013503998,0.00007234238],"domain_scores_gemma":[0.99927217,0.00027228674,0.000093157665,0.00007843316,0.00021694205,0.00006702157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008606108,0.0010544528,0.0009978347,0.0019866205,0.00069324626,0.0011200107,0.001910685,0.001291569,0.0014388106],"category_scores_gemma":[0.0037308321,0.0003613335,0.0008393868,0.0009889234,0.0003288729,0.0011052692,0.001492585,0.0010700393,0.0014449209],"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":[0.00066406006,0.00050767005,0.03481934,0.00022398565,0.00018266312,0.0006331591,0.0007219514,0.067738585,0.009953722,0.0018494269,0.011550391,0.871155],"study_design_scores_gemma":[0.00011702779,0.00021257527,0.007021704,0.000056223045,0.00006755541,0.00052408816,0.00074542523,0.9767769,0.0056470907,0.0029211086,0.0058754594,0.000034836085],"about_ca_topic_score_codex":0.010985564,"about_ca_topic_score_gemma":0.011618322,"teacher_disagreement_score":0.010985564,"about_ca_system_score_codex":0.00047594888,"about_ca_system_score_gemma":0.0012357469,"threshold_uncertainty_score":0.021843195},"labels":[],"label_agreement":null},{"id":"W4379390365","doi":"10.32920/23296250","title":"Toward the Automation of Search and Rescue Operations: An Algorithm for Finding Missing Lost Persons Living With Dementia Using Drones","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Automation; Obstacle; Search and rescue; Drone; Process (computing); Field (mathematics); Computer science; Data collection; Work (physics); Software; Artificial intelligence; Machine learning; Operations research; Data science; Engineering; Mathematics; Statistics; Geography; Robot","score_opus":0.12031775296020629,"score_gpt":0.3769439065965874,"score_spread":0.2566261536363811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379390365","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":[0.108381405,0.00060862314,0.87770516,0.0007664106,0.00015830512,0.00065955194,0.0010751056,0.00819079,0.0024547135],"genre_scores_gemma":[0.26898676,0.00026172883,0.72431284,0.0002653202,0.00006001328,0.00047728975,0.0029118208,0.00013015179,0.0025940973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994234,0.00007462874,0.00006590587,0.00022876194,0.00013503998,0.00007234238],"domain_scores_gemma":[0.99927217,0.00027228674,0.000093157665,0.00007843316,0.00021694205,0.00006702157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008606108,0.0010544528,0.0009978347,0.0019866205,0.00069324626,0.0011200107,0.001910685,0.001291569,0.0014388106],"category_scores_gemma":[0.0037308321,0.0003613335,0.0008393868,0.0009889234,0.0003288729,0.0011052692,0.001492585,0.0010700393,0.0014449209],"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":[0.00066406006,0.00050767005,0.03481934,0.00022398565,0.00018266312,0.0006331591,0.0007219514,0.067738585,0.009953722,0.0018494269,0.011550391,0.871155],"study_design_scores_gemma":[0.00011702779,0.00021257527,0.007021704,0.000056223045,0.00006755541,0.00052408816,0.00074542523,0.9767769,0.0056470907,0.0029211086,0.0058754594,0.000034836085],"about_ca_topic_score_codex":0.010985564,"about_ca_topic_score_gemma":0.011618322,"teacher_disagreement_score":0.010985564,"about_ca_system_score_codex":0.00047594888,"about_ca_system_score_gemma":0.0012357469,"threshold_uncertainty_score":0.021843195},"labels":[],"label_agreement":null},{"id":"W4379532900","doi":"10.1353/hub.2009.a362943","title":"Demography and Archaeology","year":2009,"lang":"en","type":"article","venue":"Human Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Arthur B. McDonald-Canadian Astroparticle Physics Research Institute","funders":"","keywords":"Population; Agricultural revolution; Prehistory; Settlement (finance); History; Archaeology; Prehistoric archaeology; Archaeological theory; Historical demography; Population growth; Historical archaeology; Geography; Sociology; Anthropology; Ethnology; Demography; Agriculture; Research methodology","score_opus":0.0236036725602622,"score_gpt":0.3505405364390966,"score_spread":0.3269368638788344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379532900","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":[0.0076028532,0.39142296,0.018720493,0.17809449,0.004449838,0.000040794766,0.00052126945,0.000119861674,0.3990274],"genre_scores_gemma":[0.49571815,0.3210373,0.022060478,0.024026234,0.008188894,0.0001661367,0.0006105379,0.00022731183,0.12796493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99785584,0.0013619248,0.00009972392,0.00035954642,0.0002563381,0.000066679386],"domain_scores_gemma":[0.9977397,0.0014002868,0.00020034962,0.00032641288,0.00022570901,0.00010756227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018569919,0.0005464171,0.00059163844,0.0026922352,0.0013611899,0.004171504,0.0005588346,0.0021962759,0.009595512],"category_scores_gemma":[0.0049423357,0.00033502132,0.00020638882,0.0028071122,0.017199963,0.007483892,0.003099265,0.0030510402,0.0015537669],"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":[0.0000066632065,0.0000065864187,0.00060597336,0.00013709792,0.000009299311,0.000041991603,0.0018728493,0.00056200515,0.0000513367,0.90518343,0.03752544,0.05399743],"study_design_scores_gemma":[0.0000038466956,0.000006687727,0.0010113376,0.00056776195,0.0000028994875,0.00015876189,0.0014002774,0.00022953663,0.000037441467,0.46932104,0.52724856,0.000011837138],"about_ca_topic_score_codex":0.006343759,"about_ca_topic_score_gemma":0.006804324,"teacher_disagreement_score":0.009595512,"about_ca_system_score_codex":0.0031333566,"about_ca_system_score_gemma":0.0014659704,"threshold_uncertainty_score":0.0321002},"labels":[],"label_agreement":null},{"id":"W4380740934","doi":"10.1080/10920277.2023.2202707","title":"GAMLSS for Longitudinal Multivariate Claim Count Models","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Nonparametric statistics; Covariate; Econometrics; Multivariate statistics; Predictive power; Univariate; Parametric statistics; Generalized additive model; Semiparametric model; Predictive modelling; Computer science; Statistics; Mathematics","score_opus":0.062083629312055404,"score_gpt":0.35404260597667037,"score_spread":0.29195897666461496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380740934","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":[0.0059758835,0.0005130979,0.98960215,0.00037166796,0.00007889238,0.00008983436,0.0007650571,0.00058707537,0.0020163693],"genre_scores_gemma":[0.36328202,0.0031656455,0.59819406,0.001120668,0.00064293295,0.0020858392,0.005679456,0.0010000448,0.024829406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99620193,0.002186843,0.000224529,0.0005514105,0.0005912957,0.00024400486],"domain_scores_gemma":[0.99022746,0.006738241,0.0010249682,0.0009918022,0.00075303967,0.00026454256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009991288,0.0019409078,0.0016504712,0.0032044873,0.00075288594,0.0027722993,0.00396893,0.002153327,0.014605311],"category_scores_gemma":[0.021827146,0.0009113815,0.003936073,0.002925135,0.0017052549,0.003584442,0.0031906762,0.0051094033,0.0031738381],"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":[0.00007823756,0.000097346674,0.0039457246,0.00026360655,0.00017815254,0.00035277937,0.00038087022,0.23607112,0.00067173113,0.6985463,0.006721118,0.052692965],"study_design_scores_gemma":[0.00001196246,0.000029346784,0.0005101073,0.00006254107,0.000028889488,0.00008510866,0.00004453402,0.7827408,0.0001280792,0.21096303,0.0053665494,0.000029067742],"about_ca_topic_score_codex":0.0050520753,"about_ca_topic_score_gemma":0.0053605903,"teacher_disagreement_score":0.014605311,"about_ca_system_score_codex":0.001433352,"about_ca_system_score_gemma":0.001452434,"threshold_uncertainty_score":0.052839637},"labels":[],"label_agreement":null},{"id":"W4381562444","doi":"10.48550/arxiv.2306.10582","title":"Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamilton–Jacobi–Bellman equation; Mathematical optimization; Stochastic control; Optimal control; Bellman equation; Computer science; Viscosity solution; Artificial neural network; Partial differential equation; Mathematics; Applied mathematics; Artificial intelligence","score_opus":0.20533898227186428,"score_gpt":0.2987020907294419,"score_spread":0.09336310845757764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381562444","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":[0.119436555,0.0063240966,0.8500205,0.0043548713,0.00027904657,0.00007469415,0.00019538753,0.00016646876,0.01914829],"genre_scores_gemma":[0.9242485,0.002406432,0.06684663,0.00032416347,0.00023329878,0.00012084893,0.00017318323,0.00006919878,0.0055778073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994585,0.0002596069,0.000026843267,0.000075078235,0.00012882114,0.000051261402],"domain_scores_gemma":[0.99643457,0.0026373605,0.00025102132,0.00015827248,0.00040553394,0.00011330019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028558401,0.0005882964,0.0011464439,0.00047278818,0.00042059447,0.0011068534,0.0011770788,0.0015014239,0.0026228763],"category_scores_gemma":[0.0091443,0.00029000212,0.00062340434,0.00064282335,0.0010675637,0.0016899478,0.0010158854,0.001670985,0.00021978503],"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":[0.000025295223,0.000025274461,0.0006660071,0.000065433924,0.000022516648,0.000027513355,0.00002425365,0.9286158,0.00015962287,0.06104741,0.00056616863,0.008754616],"study_design_scores_gemma":[0.000003114662,0.0000074012833,0.00008478111,0.0000051662737,0.0000018851352,0.000003481788,0.0000022005609,0.9916996,0.00003793071,0.00799961,0.0001524947,0.00000245341],"about_ca_topic_score_codex":0.007995729,"about_ca_topic_score_gemma":0.004131821,"teacher_disagreement_score":0.007995729,"about_ca_system_score_codex":0.0016286527,"about_ca_system_score_gemma":0.001642514,"threshold_uncertainty_score":0.015898347},"labels":[],"label_agreement":null},{"id":"W4383219933","doi":"10.2139/ssrn.4496740","title":"An Insurance Risk Process With a Generalized Income Process: A Solvency Analysis","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; University of Waterloo","funders":"","keywords":"Solvency; Process (computing); Actuarial science; Business; Risk analysis (engineering); Econometrics; Economics; Computer science; Finance","score_opus":0.007992649002824183,"score_gpt":0.30912664323213185,"score_spread":0.30113399422930764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383219933","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":[0.35950428,0.001519254,0.60984105,0.0071323677,0.0002156053,0.00036450446,0.00041852702,0.0003171881,0.020687222],"genre_scores_gemma":[0.93737936,0.0015857152,0.029611792,0.0004063554,0.00041995718,0.00021769915,0.0002301722,0.0001128717,0.030036042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974215,0.0011528182,0.00009866945,0.00041629947,0.00038184595,0.0005288802],"domain_scores_gemma":[0.98283666,0.012499739,0.0017396108,0.00066836225,0.0010351129,0.0012204535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076628104,0.0016898545,0.0034072078,0.0027504012,0.0016415297,0.0043589952,0.003249452,0.003916458,0.009561427],"category_scores_gemma":[0.028064141,0.0010929967,0.0034368543,0.002098791,0.0061365273,0.007871175,0.004141118,0.005066869,0.00045817072],"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":[0.000077482066,0.0001293534,0.002586106,0.00010087012,0.00012488602,0.00051552156,0.00047459154,0.1482379,0.00045398678,0.8388372,0.0020151192,0.006447013],"study_design_scores_gemma":[0.00008333314,0.000098482946,0.0014011347,0.000034849752,0.00009959519,0.00014817748,0.00028473514,0.6705293,0.00012873663,0.32613885,0.0009963647,0.000056459743],"about_ca_topic_score_codex":0.015792422,"about_ca_topic_score_gemma":0.0074306233,"teacher_disagreement_score":0.015792422,"about_ca_system_score_codex":0.0031755855,"about_ca_system_score_gemma":0.003825605,"threshold_uncertainty_score":0.040525317},"labels":[],"label_agreement":null},{"id":"W4383533281","doi":"10.1080/15326349.2023.2222463","title":"Optimizing Erlangization-based approximations for finite discrete distributions and discrete phase-type distributions","year":2023,"lang":"en","type":"article","venue":"Stochastic Models","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Mathematics; Approximations of π; Applied mathematics; Phase-type distribution; Erlang distribution; Markov chain; Type (biology); Probability distribution; Statistics; Gamma distribution","score_opus":0.048855041494866115,"score_gpt":0.3469684541068084,"score_spread":0.29811341261194224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383533281","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":[0.0061605447,0.00011432605,0.99254453,0.00008197464,0.00001712481,0.00002162136,0.000026614947,0.00008573264,0.00094755],"genre_scores_gemma":[0.54833007,0.0006430303,0.44499537,0.00025077802,0.00007525946,0.00023076464,0.0003154869,0.0003132487,0.0048460565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845874,0.00073796295,0.00005918916,0.00016281405,0.00035397685,0.00022740495],"domain_scores_gemma":[0.9924775,0.0060736337,0.000330173,0.00035416786,0.00063069095,0.00013384364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038912373,0.0011510109,0.00091186183,0.0009713952,0.00054533535,0.0014321057,0.0016098775,0.00091729074,0.002220914],"category_scores_gemma":[0.016005158,0.0006726579,0.0011416047,0.0010502973,0.0011746924,0.0020678549,0.0015378124,0.002375758,0.0005344139],"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":[0.00012861047,0.000056420453,0.0010607835,0.000076877266,0.000033733882,0.000048806505,0.00009921863,0.9014425,0.0009038203,0.07054205,0.0011803958,0.024426669],"study_design_scores_gemma":[0.000004484219,0.00000816347,0.000038968545,0.000004415014,0.000003022086,0.00000792142,0.000008806098,0.98966837,0.00021954843,0.009807442,0.00022593513,0.0000029149085],"about_ca_topic_score_codex":0.006953295,"about_ca_topic_score_gemma":0.00501674,"teacher_disagreement_score":0.006953295,"about_ca_system_score_codex":0.0023207057,"about_ca_system_score_gemma":0.001713028,"threshold_uncertainty_score":0.0205791},"labels":[],"label_agreement":null},{"id":"W4385291841","doi":"10.48550/arxiv.2307.13081","title":"Fairness Under Demographic Scarce Regime","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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; Canadian Institute for Advanced Research","keywords":"Computer science; Classifier (UML); Proxy (statistics); Scarcity; Fairness measure; Data mining; Machine learning; Artificial intelligence; Microeconomics; Economics","score_opus":0.13119696288579005,"score_gpt":0.24066938031412177,"score_spread":0.10947241742833172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385291841","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":[0.15326573,0.001167508,0.83161986,0.0031239023,0.00028764768,0.00018718262,0.0012543049,0.00060555333,0.00848827],"genre_scores_gemma":[0.9581612,0.00034742078,0.03727117,0.0005897654,0.00037574387,0.00016636985,0.0004833888,0.000097671895,0.0025071425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9893405,0.004332198,0.0005089359,0.0033019534,0.001516997,0.0009993861],"domain_scores_gemma":[0.9581515,0.02911428,0.0028776303,0.0059428127,0.0023848463,0.0015288839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018850477,0.0012595172,0.002372114,0.0011151916,0.0020285337,0.0033082615,0.0027742605,0.0021641345,0.0026771533],"category_scores_gemma":[0.0645736,0.00058225286,0.0011889028,0.0010083489,0.0023858936,0.0069047967,0.00302658,0.0030125037,0.00046796972],"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":[0.0010210571,0.00029418338,0.032481678,0.00027416565,0.00024857954,0.00046075525,0.0009965472,0.56918454,0.0023686376,0.28311265,0.009240406,0.10031684],"study_design_scores_gemma":[0.000034253077,0.0000801737,0.0025512204,0.000045655244,0.00004080479,0.00016446746,0.000090466034,0.74831384,0.0010159808,0.24499811,0.0026267506,0.00003832868],"about_ca_topic_score_codex":0.0039149257,"about_ca_topic_score_gemma":0.0021210536,"teacher_disagreement_score":0.018850477,"about_ca_system_score_codex":0.002768754,"about_ca_system_score_gemma":0.0027021358,"threshold_uncertainty_score":0.09969205},"labels":[],"label_agreement":null},{"id":"W4385293498","doi":"10.28924/2291-8639-21-2023-79","title":"Expectile-Based Capital Allocation","year":2023,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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":"Capital allocation line; Mathematics; Capital (architecture); Optimal allocation; Econometrics; Mathematical economics; Economics; Microeconomics; Mathematical optimization","score_opus":0.01421204606575922,"score_gpt":0.33461886299046467,"score_spread":0.32040681692470546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385293498","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":[0.025567312,0.00037927792,0.9555744,0.00061478105,0.00009596949,0.00006080699,0.00014288588,0.00015072827,0.017413955],"genre_scores_gemma":[0.8747735,0.0010772581,0.09652364,0.00029687924,0.00018901592,0.0002709646,0.0002897328,0.00014724577,0.026431756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988354,0.00052205415,0.00004220297,0.00020013821,0.00026496613,0.00013533494],"domain_scores_gemma":[0.99840647,0.00072724395,0.00023050305,0.00023937208,0.0002620035,0.00013437818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003169133,0.0008395371,0.0007824204,0.00083724153,0.00040943397,0.0019487239,0.0015656062,0.0009183659,0.008867818],"category_scores_gemma":[0.009079081,0.00031589405,0.0007059998,0.0005632578,0.0010332065,0.0030163154,0.0019643146,0.0014110988,0.0009935185],"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":[0.00005748169,0.00004556075,0.0013707178,0.000068501184,0.000036381018,0.000103166625,0.00009541262,0.27784228,0.0007475055,0.68274754,0.0022138502,0.034671523],"study_design_scores_gemma":[0.000009258523,0.00007552994,0.0010390059,0.00006025644,0.000018435156,0.00013046234,0.00004275634,0.5742313,0.00080835406,0.41786078,0.0056979186,0.000025938918],"about_ca_topic_score_codex":0.0005367686,"about_ca_topic_score_gemma":0.00048296596,"teacher_disagreement_score":0.008867818,"about_ca_system_score_codex":0.0011807092,"about_ca_system_score_gemma":0.00090223254,"threshold_uncertainty_score":0.029665828},"labels":[],"label_agreement":null},{"id":"W4385348074","doi":"10.1017/asb.2023.26","title":"A hybrid data mining framework for variable annuity portfolio valuation","year":2023,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valuation (finance); Annuity; Computer science; Portfolio; Econometrics; Monte Carlo method; Actuarial science; Variable (mathematics); Regression; Economics; Finance; Life annuity; Mathematics; Statistics","score_opus":0.10296066557058521,"score_gpt":0.37257203467861905,"score_spread":0.26961136910803385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385348074","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":[0.0070163724,0.00023325071,0.991831,0.00018204848,0.000013702917,0.000035835918,0.00011009318,0.0002562635,0.00032142492],"genre_scores_gemma":[0.23828036,0.0002360427,0.7596955,0.00011435962,0.00006847114,0.00017483885,0.0004935431,0.00005685158,0.0008800317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983909,0.0006858335,0.00010030365,0.00027552887,0.00043747269,0.00010996342],"domain_scores_gemma":[0.99676776,0.001932524,0.00024660636,0.00025791753,0.0006335565,0.00016173057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047813924,0.00074923545,0.0015526717,0.0025872434,0.00060325646,0.0016782986,0.0025419912,0.0011751401,0.0014376055],"category_scores_gemma":[0.0058522406,0.000575101,0.0015567829,0.002117052,0.00062789716,0.0016511063,0.0017232365,0.0013373806,0.00031057588],"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":[0.00008525326,0.00019860217,0.0036591676,0.00009744867,0.00015068782,0.00013550778,0.00009728534,0.842848,0.0011950169,0.044713072,0.0015961893,0.105223775],"study_design_scores_gemma":[0.000002626384,0.0000058156684,0.00005700521,0.0000030684696,0.0000030990877,0.0000075008184,0.0000035715755,0.9945093,0.000076867655,0.0051416396,0.00018706627,0.000002442411],"about_ca_topic_score_codex":0.00843108,"about_ca_topic_score_gemma":0.009825977,"teacher_disagreement_score":0.00843108,"about_ca_system_score_codex":0.0010706644,"about_ca_system_score_gemma":0.0015535739,"threshold_uncertainty_score":0.025286734},"labels":[],"label_agreement":null},{"id":"W4385687998","doi":"10.1017/dem.2023.8","title":"The impact of long memory in mortality differentials on index-based longevity hedges","year":2023,"lang":"en","type":"article","venue":"Journal of Demographic Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Longevity; Econometrics; Index (typography); Autoregressive model; Autoregressive–moving-average model; Statistics; Variance (accounting); Population; Demography; Mathematics; Economics; Medicine; Computer science; Gerontology","score_opus":0.03511338145024247,"score_gpt":0.34577890656110166,"score_spread":0.3106655251108592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385687998","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":[0.9402823,0.00053405046,0.055739727,0.00039507812,0.00004302196,0.00002234953,0.00013332837,0.00013344428,0.0027166791],"genre_scores_gemma":[0.9983084,0.000039242528,0.0014290788,0.000020498894,0.000008937872,0.000002706361,0.000023043087,0.000005173495,0.00016288442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870193,0.0004769181,0.00012938786,0.000290404,0.0002762845,0.00012504385],"domain_scores_gemma":[0.96779877,0.02220582,0.0055566514,0.0025689544,0.0012804099,0.00058949174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009110012,0.0003975207,0.00067593786,0.0010286086,0.00038942284,0.0017322297,0.0007976332,0.0013888467,0.0013281937],"category_scores_gemma":[0.040094037,0.00031019293,0.00049891573,0.00056561764,0.0010821244,0.0018798906,0.0012026397,0.0009488464,0.00007914038],"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":[0.0008979457,0.0003045164,0.20036414,0.00023049544,0.000784253,0.00071103976,0.0006223698,0.5872393,0.006661067,0.0853028,0.0014633846,0.11541862],"study_design_scores_gemma":[0.000045231536,0.00067207665,0.08623176,0.0001017962,0.0002082064,0.00029951282,0.00022890234,0.8415582,0.0040244577,0.06563678,0.0008803533,0.000112600246],"about_ca_topic_score_codex":0.0019680227,"about_ca_topic_score_gemma":0.0011389045,"teacher_disagreement_score":0.009110012,"about_ca_system_score_codex":0.00081104937,"about_ca_system_score_gemma":0.00035890186,"threshold_uncertainty_score":0.04817897},"labels":[],"label_agreement":null},{"id":"W4385832586","doi":"10.1007/978-3-031-39821-6_31","title":"Toward Healthy Aging: Temporal Regression for Disability Prediction and Warning Decision-Making","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Sherbrooke","funders":"","keywords":"Warning system; Computer science; Population ageing; Regression; Regression analysis; Population; Healthy aging; Artificial intelligence; Machine learning; Gerontology; Demography; Medicine; Statistics; Mathematics; Telecommunications","score_opus":0.04238820405075316,"score_gpt":0.3434833588677106,"score_spread":0.3010951548169574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385832586","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":[0.007614569,0.0029520763,0.98370475,0.0013272517,0.00020253916,0.000017695515,0.000263319,0.0005388429,0.0033790073],"genre_scores_gemma":[0.30823383,0.008105645,0.65859425,0.0006670234,0.0010275379,0.00016228762,0.0013683718,0.00044826893,0.021392835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995017,0.00025714343,0.0000246351,0.00010309075,0.000085090076,0.000028364886],"domain_scores_gemma":[0.9968816,0.0025774315,0.00012785217,0.000118190816,0.00024183812,0.00005312585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031609146,0.0008413711,0.0008174867,0.0006027073,0.00017748128,0.0012067127,0.0009665235,0.0006900971,0.003933615],"category_scores_gemma":[0.00906028,0.00043432545,0.000694019,0.0009637443,0.00036962295,0.0015377839,0.0006810007,0.0018293704,0.0013436595],"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":[0.00018097121,0.00016672087,0.0047780504,0.00024216542,0.00024556203,0.000107454776,0.00016586513,0.35358438,0.0014675753,0.07203472,0.025750127,0.54127645],"study_design_scores_gemma":[0.000005291916,0.000022857708,0.0004829554,0.000023155904,0.000025386205,0.000020619007,0.000014531558,0.9637463,0.0002296183,0.03278696,0.0026332622,0.000008984896],"about_ca_topic_score_codex":0.0062979003,"about_ca_topic_score_gemma":0.006484266,"teacher_disagreement_score":0.0062979003,"about_ca_system_score_codex":0.00038841343,"about_ca_system_score_gemma":0.00088207755,"threshold_uncertainty_score":0.01671666},"labels":[],"label_agreement":null},{"id":"W4385870327","doi":"10.59962/9780774857192-001","title":"Figures and Tables","year":2007,"lang":"en","type":"book-chapter","venue":"University of British Columbia Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","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":"History","score_opus":0.024703821370559893,"score_gpt":0.22384232394398676,"score_spread":0.19913850257342686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385870327","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":[0.00043149162,0.0030371144,0.0017544178,0.0027056024,0.0041721133,0.00030041955,0.48020947,0.0025305524,0.50485885],"genre_scores_gemma":[0.0034116094,0.0052324566,0.003631319,0.0015801669,0.0010966052,0.00046880986,0.4112276,0.0014809199,0.57187057],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988997,0.00012357735,0.00010028961,0.00021237526,0.0005463049,0.00011769082],"domain_scores_gemma":[0.9969087,0.00043367376,0.00016797446,0.00029654388,0.0019960525,0.00019711889],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008051444,0.0009825943,0.0009971586,0.004155401,0.001194077,0.0038125936,0.0019112598,0.0011036904,0.7603201],"category_scores_gemma":[0.007825105,0.00045922145,0.000733856,0.010737072,0.00031710888,0.0023551276,0.0011647247,0.0014407515,0.61629844],"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":[0.000009885242,0.0000083047025,0.00015816267,0.00012654498,0.000002476409,0.000012091941,0.0000126474215,0.000102023856,0.000021576801,0.0025246127,0.98054415,0.016477637],"study_design_scores_gemma":[0.0000057671114,0.0000039112356,0.0005166276,0.000100369834,0.0000017553122,0.000017799463,0.000026582175,0.00003344272,0.000015590176,0.0008664836,0.9984078,0.0000038884664],"about_ca_topic_score_codex":0.02913014,"about_ca_topic_score_gemma":0.023049904,"teacher_disagreement_score":0.23967987,"about_ca_system_score_codex":0.0024047901,"about_ca_system_score_gemma":0.0033338715,"threshold_uncertainty_score":0.34187424},"labels":[],"label_agreement":null},{"id":"W4386002334","doi":"10.1016/j.insmatheco.2023.08.001","title":"Hedging longevity risk under non-Gaussian state-space stochastic mortality models: A mean-variance-skewness-kurtosis approach","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Kurtosis; Longevity risk; Skewness; Econometrics; Hedge; Portfolio; Basis risk; Gaussian; Variance (accounting); State space; Modern portfolio theory; Mathematics; Actuarial science; Economics; Statistics; Pension; Finance","score_opus":0.04497496071633781,"score_gpt":0.2801897044357109,"score_spread":0.2352147437193731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386002334","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":[0.1653273,0.0015388591,0.82695097,0.0019594769,0.00017934357,0.00006479728,0.00022537539,0.00021250121,0.003541417],"genre_scores_gemma":[0.97291756,0.0012427662,0.01704932,0.00021366545,0.00019865579,0.00007851434,0.00017177215,0.00007969822,0.008048031],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846554,0.00077010057,0.000084239335,0.00023429179,0.00022992762,0.0002158152],"domain_scores_gemma":[0.99121827,0.00637038,0.0010228793,0.0003768486,0.0006480404,0.000363568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072913175,0.0016865154,0.002673155,0.0014727854,0.00053986773,0.003362486,0.0023569614,0.0032982219,0.001807179],"category_scores_gemma":[0.02038464,0.0013501876,0.0022597364,0.0010660046,0.0024553172,0.0044536158,0.0028768252,0.0023817716,0.00018622719],"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":[0.000060820432,0.000069831585,0.0021309927,0.00005942445,0.0001969312,0.0001854265,0.00012130242,0.8894911,0.000645129,0.1019663,0.0005649343,0.00450774],"study_design_scores_gemma":[0.000005937379,0.000017057428,0.00034339543,0.000008739292,0.000023958486,0.000022103168,0.000016117936,0.97463244,0.00005697778,0.024776004,0.000081549755,0.000015612255],"about_ca_topic_score_codex":0.006333239,"about_ca_topic_score_gemma":0.004784284,"teacher_disagreement_score":0.0072913175,"about_ca_system_score_codex":0.00206085,"about_ca_system_score_gemma":0.0018634542,"threshold_uncertainty_score":0.03856063},"labels":[],"label_agreement":null},{"id":"W4386186558","doi":"10.2139/ssrn.4544565","title":"A Bayesian Generalized Additive Model Approach for Forecasting Mortality Improvement with External Information","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Bayesian probability; Econometrics; Bayesian inference; Statistics; Computer science; Mathematics","score_opus":0.02588836351000383,"score_gpt":0.2853758138325987,"score_spread":0.25948745032259485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386186558","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":[0.08000854,0.0007999203,0.9146698,0.00078464334,0.0001657626,0.000075873766,0.0006482955,0.00052671,0.0023203716],"genre_scores_gemma":[0.82338315,0.0008894627,0.16835183,0.0002184999,0.00026878153,0.0002314224,0.0012132154,0.000083874475,0.0053597055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852544,0.00076465204,0.00009209589,0.00029020428,0.00021002218,0.000117596464],"domain_scores_gemma":[0.99441814,0.0043949257,0.00034647877,0.000174868,0.00055008754,0.00011555489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004567965,0.0011737696,0.0025246367,0.0020355037,0.0006807739,0.0019023998,0.0022629835,0.0024798587,0.002798576],"category_scores_gemma":[0.013993394,0.0009274535,0.0014600076,0.0022268624,0.00059840863,0.002074395,0.0012275513,0.002193741,0.0005416222],"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":[0.000095294214,0.00009191007,0.0026620892,0.000045859433,0.00014823329,0.000048539045,0.00004744006,0.95640206,0.00024159487,0.009341382,0.0008790623,0.029996535],"study_design_scores_gemma":[0.000007525714,0.000020016809,0.00032758477,0.0000043636524,0.000016879136,0.00000614892,0.0000039459173,0.99534374,0.000036003603,0.0041180113,0.00010695263,0.000008711127],"about_ca_topic_score_codex":0.02964919,"about_ca_topic_score_gemma":0.02584914,"teacher_disagreement_score":0.02964919,"about_ca_system_score_codex":0.0012491726,"about_ca_system_score_gemma":0.0014994299,"threshold_uncertainty_score":0.058953226},"labels":[],"label_agreement":null},{"id":"W4386216420","doi":"10.32920/24043044","title":"Valuation of Precipitation Contracts","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Valuation (finance); Martingale (probability theory); Monte Carlo method; Econometrics; Fast Fourier transform; Fourier transform; Mathematics; Precipitation; Statistics; Economics; Algorithm; Finance; Meteorology; Geography; Mathematical analysis","score_opus":0.1322033190955691,"score_gpt":0.38411841445094913,"score_spread":0.25191509535538004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386216420","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":[0.058881335,0.00039141194,0.9293571,0.0005246446,0.00009497324,0.000099695935,0.00034678137,0.00019851916,0.010105513],"genre_scores_gemma":[0.8098197,0.0006856875,0.17864546,0.00005037451,0.00011881495,0.00011583061,0.00048165827,0.00010452366,0.009977965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99880016,0.00047869957,0.00005538975,0.00013486379,0.00045540102,0.00007543322],"domain_scores_gemma":[0.9984824,0.0007234347,0.00018024517,0.000188165,0.00033185945,0.00009390093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020392926,0.0003736168,0.00040944194,0.0008603788,0.00036873162,0.0020185872,0.0009309652,0.0007142237,0.0038123892],"category_scores_gemma":[0.0099515915,0.00023374014,0.0004905657,0.000990733,0.0008234909,0.0023707163,0.00078249647,0.0010304414,0.0003428841],"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":[0.000112693495,0.000057408382,0.009788922,0.00012397327,0.00004881199,0.00032618805,0.000349103,0.34140015,0.0055749887,0.48292765,0.0039306073,0.15535946],"study_design_scores_gemma":[0.000015066217,0.000043257645,0.0028120358,0.000021690097,0.0000068710947,0.00013075578,0.000092046714,0.89676404,0.001877932,0.093071386,0.0051412713,0.0000236022],"about_ca_topic_score_codex":0.003962882,"about_ca_topic_score_gemma":0.0022938333,"teacher_disagreement_score":0.003962882,"about_ca_system_score_codex":0.0015531789,"about_ca_system_score_gemma":0.0010474694,"threshold_uncertainty_score":0.012753785},"labels":[],"label_agreement":null},{"id":"W4386225081","doi":"10.32920/24043044.v1","title":"Valuation of Precipitation Contracts","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Martingale (probability theory); Valuation (finance); Monte Carlo method; Econometrics; Fast Fourier transform; Mathematics; Fourier transform; Precipitation; Seasonality; Statistics; Applied mathematics; Economics; Algorithm; Finance; Meteorology; Geography; Mathematical analysis","score_opus":0.1322033190955691,"score_gpt":0.38411841445094913,"score_spread":0.25191509535538004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386225081","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":[0.058881335,0.00039141194,0.9293571,0.0005246446,0.00009497324,0.000099695935,0.00034678137,0.00019851916,0.010105513],"genre_scores_gemma":[0.8098197,0.0006856875,0.17864546,0.00005037451,0.00011881495,0.00011583061,0.00048165827,0.00010452366,0.009977965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99880016,0.00047869957,0.00005538975,0.00013486379,0.00045540102,0.00007543322],"domain_scores_gemma":[0.9984824,0.0007234347,0.00018024517,0.000188165,0.00033185945,0.00009390093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020392926,0.0003736168,0.00040944194,0.0008603788,0.00036873162,0.0020185872,0.0009309652,0.0007142237,0.0038123892],"category_scores_gemma":[0.0099515915,0.00023374014,0.0004905657,0.000990733,0.0008234909,0.0023707163,0.00078249647,0.0010304414,0.0003428841],"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":[0.000112693495,0.000057408382,0.009788922,0.00012397327,0.00004881199,0.00032618805,0.000349103,0.34140015,0.0055749887,0.48292765,0.0039306073,0.15535946],"study_design_scores_gemma":[0.000015066217,0.000043257645,0.0028120358,0.000021690097,0.0000068710947,0.00013075578,0.000092046714,0.89676404,0.001877932,0.093071386,0.0051412713,0.0000236022],"about_ca_topic_score_codex":0.003962882,"about_ca_topic_score_gemma":0.0022938333,"teacher_disagreement_score":0.003962882,"about_ca_system_score_codex":0.0015531789,"about_ca_system_score_gemma":0.0010474694,"threshold_uncertainty_score":0.012753785},"labels":[],"label_agreement":null},{"id":"W4386272999","doi":"10.5281/zenodo.8299692","title":"Density-dependent selection during range expansion affects expansion load in life-history traits","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Selection (genetic algorithm); Range (aeronautics); Life history theory; Biology; Evolutionary biology; Life history; Econometrics; Ecology; Mathematics; Computer science; Materials science; Artificial intelligence","score_opus":0.03792132124264352,"score_gpt":0.26178965744506283,"score_spread":0.2238683362024193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386272999","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":[0.9971424,0.000026679856,0.002152364,0.000028276754,0.0000011730542,0.0000026991297,0.0000127578205,0.000011778762,0.0006219213],"genre_scores_gemma":[0.99943906,0.000016950347,0.00044418086,0.0000063557377,8.071279e-7,0.0000024459425,0.000011527845,0.0000023224952,0.000076235076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980086,0.000110102636,0.000009190497,0.000034094057,0.0000186653,0.000027112279],"domain_scores_gemma":[0.9990386,0.0006030744,0.00016850873,0.00007048526,0.000039908766,0.00007950456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058976,0.00020869536,0.00025746974,0.00029151872,0.00021622695,0.00047822573,0.00031487894,0.00037693995,0.0008577834],"category_scores_gemma":[0.0023491548,0.00014564813,0.00026972644,0.00019674312,0.00055226963,0.00047650613,0.00051903416,0.00027447456,0.000084634914],"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":[0.0006302858,0.00028428994,0.33516175,0.00016545932,0.00037734886,0.0008599788,0.0007562254,0.4444025,0.17745444,0.011897051,0.00041425446,0.02759639],"study_design_scores_gemma":[0.00007142648,0.00034826316,0.3269632,0.000017744665,0.00012008124,0.00033764803,0.0002941326,0.65749365,0.0064490344,0.007400938,0.0004456835,0.00005815378],"about_ca_topic_score_codex":0.0016356005,"about_ca_topic_score_gemma":0.0025685544,"teacher_disagreement_score":0.0016356005,"about_ca_system_score_codex":0.00035226025,"about_ca_system_score_gemma":0.00018505963,"threshold_uncertainty_score":0.0032522082},"labels":[],"label_agreement":null},{"id":"W4386445636","doi":"10.3390/books978-3-0365-8390-7","title":"Actuarial Mathematics and Risk Management","year":2023,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund","keywords":"Risk management; Calculus (dental); Mathematics; Mathematics education; Computer science; Medicine; Economics; Management","score_opus":0.020756611973592166,"score_gpt":0.28580192291342543,"score_spread":0.26504531093983325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386445636","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":[0.0044254484,0.10488043,0.23621193,0.033831485,0.005116478,0.0000697351,0.00058656064,0.0006211209,0.61425686],"genre_scores_gemma":[0.32234254,0.121348806,0.118063256,0.0086006215,0.017207006,0.0003693876,0.00075257284,0.00074086926,0.410575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998637,0.00045894075,0.00008166415,0.00014666654,0.0006077681,0.00006795125],"domain_scores_gemma":[0.997512,0.0016441533,0.00017335685,0.00029249856,0.00031127786,0.00006664496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024289745,0.0010189513,0.0006471413,0.0017436186,0.00089071505,0.0036364738,0.0006333161,0.0013378533,0.014759591],"category_scores_gemma":[0.006378071,0.0004609709,0.00061750313,0.0019297583,0.0035921978,0.003854829,0.0013758091,0.0039641787,0.0068967245],"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":[0.0000028570528,0.0000061898068,0.00007540685,0.000057229787,0.0000067297638,0.000017746723,0.00007874734,0.0020923198,0.000057925103,0.95229256,0.0235886,0.021723714],"study_design_scores_gemma":[0.0000027012125,0.000010012312,0.00017720764,0.00007807147,0.0000037556485,0.00005385069,0.000041238232,0.003328901,0.0000475471,0.85109216,0.14515644,0.000007986045],"about_ca_topic_score_codex":0.0015091684,"about_ca_topic_score_gemma":0.0009288356,"teacher_disagreement_score":0.014759591,"about_ca_system_score_codex":0.002233628,"about_ca_system_score_gemma":0.0012633837,"threshold_uncertainty_score":0.049375772},"labels":[],"label_agreement":null},{"id":"W4386461466","doi":"10.3390/math11183808","title":"Forecasting Canadian Age-Specific Mortality Rates: Application of Functional Time Series Analysis","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Mortality rate; Series (stratigraphy); Demography; Time series; Population; Regression analysis; Statistics; Functional principal component analysis; Descriptive statistics; Econometrics; Principal component analysis; Gerontology; Medicine; Mathematics; Biology","score_opus":0.0696950545567446,"score_gpt":0.30520061092294115,"score_spread":0.23550555636619655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386461466","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":[0.56587934,0.0038675899,0.39189887,0.0044256775,0.0004371821,0.00038101024,0.012852644,0.0016091561,0.018648483],"genre_scores_gemma":[0.93409705,0.0016164632,0.057665557,0.00010137591,0.000067726985,0.0000814437,0.0035252415,0.000056732726,0.002788391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.00018244357,0.000037642858,0.00011210518,0.00020635428,0.00008578737],"domain_scores_gemma":[0.9983552,0.00047989708,0.00013500555,0.000071023074,0.0008882505,0.00007069648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024168543,0.0008964229,0.0005908033,0.0028341543,0.0007947239,0.0011936926,0.0011452379,0.0005315586,0.0014086376],"category_scores_gemma":[0.0092195915,0.00023430822,0.00089124864,0.003108902,0.00029240974,0.00056621176,0.00056271796,0.0006956712,0.0002280244],"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":[0.0001492774,0.000084524916,0.08715406,0.00026365253,0.00029225426,0.00025547744,0.00033308577,0.7248179,0.0008384423,0.01856618,0.008310936,0.15893424],"study_design_scores_gemma":[0.000005295998,0.000013759902,0.01661111,0.000019605915,0.000026310156,0.00001958336,0.00011238118,0.9789068,0.00018039842,0.002147099,0.0019349486,0.000022611788],"about_ca_topic_score_codex":0.9339309,"about_ca_topic_score_gemma":0.8741937,"teacher_disagreement_score":0.066069126,"about_ca_system_score_codex":0.007868025,"about_ca_system_score_gemma":0.010363808,"threshold_uncertainty_score":0.13291639},"labels":[],"label_agreement":null},{"id":"W4386778427","doi":"10.2979/vic.2022.a901299","title":"Irish Famines before and after the Great Hunger ed. by Christine Kinealy and Gerard Moran (review)","year":2022,"lang":"en","type":"article","venue":"Victorian Studies","topic":"Insurance, Mortality, Demography, Risk Management","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":"Famine; Irish; Subsistence agriculture; Theme (computing); History; Economic history; Sociology; Philosophy; Archaeology; Agriculture","score_opus":0.014481183576065586,"score_gpt":0.30095909631094553,"score_spread":0.28647791273487994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386778427","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":[0.00007879735,0.9916278,0.000023689418,0.0018588427,0.0036489458,0.000017362583,0.000119245466,0.000009652533,0.0026157072],"genre_scores_gemma":[0.0006054301,0.9880427,0.0000794988,0.002950448,0.0021326654,0.000040350747,0.00033339008,0.000007981631,0.0058075082],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993636,0.00009455053,0.000077126926,0.00010126084,0.00028086413,0.00008258571],"domain_scores_gemma":[0.9989189,0.00025070886,0.00017972068,0.000024354,0.000468563,0.0001577541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012173193,0.0014207081,0.002236898,0.0020195693,0.00048152875,0.0026399875,0.0012230445,0.0016020882,0.020952508],"category_scores_gemma":[0.0027769464,0.0005392521,0.00092332537,0.004741024,0.0006703179,0.002394421,0.0014559549,0.0029486527,0.010448552],"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":[0.00010216183,0.000030045065,0.000236944,0.021177111,0.0000990448,0.00015190657,0.000205153,0.000090237496,0.00024593627,0.0005892271,0.74195296,0.23511927],"study_design_scores_gemma":[0.000022840313,0.000032165583,0.0018935817,0.011436549,0.000059643204,0.00028621886,0.00020305075,0.000011147839,0.000039238,0.00010792493,0.98588854,0.00001908627],"about_ca_topic_score_codex":0.014748555,"about_ca_topic_score_gemma":0.019076688,"teacher_disagreement_score":0.020952508,"about_ca_system_score_codex":0.002788275,"about_ca_system_score_gemma":0.006454774,"threshold_uncertainty_score":0.070093095},"labels":[],"label_agreement":null},{"id":"W4386817813","doi":"10.1097/cm9.0000000000002823","title":"Epidemiological characteristics of centenarian deaths in China during 2013–2020: A trend and subnational analysis","year":2023,"lang":"en","type":"article","venue":"Chinese Medical Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"CAE (Canada)","funders":"National Natural Science Foundation of China","keywords":"Centenarian; Medicine; Epidemiology; Demography; Cause of death; China; Gerontology; Confidence interval; Disease; Internal medicine; Longevity; Geography","score_opus":0.013181923818285972,"score_gpt":0.3207572077265685,"score_spread":0.30757528390828254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386817813","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":[0.99319106,0.0013302732,0.0002428351,0.00016609674,0.000016975619,0.00003568313,0.004111001,0.000021266058,0.00088475086],"genre_scores_gemma":[0.9944606,0.00080097956,0.00021464739,0.000043461372,0.000016950955,0.000049171646,0.0041913893,0.0000039455686,0.00021894858],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951255,0.000057256984,0.00011747732,0.00010807965,0.000103386556,0.000101195124],"domain_scores_gemma":[0.9987147,0.000093108,0.00049556955,0.00008894983,0.00042040052,0.0001873356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013548648,0.00040906196,0.0004289369,0.0033948321,0.00041332873,0.0005818174,0.00049264025,0.00025574418,0.00089238473],"category_scores_gemma":[0.0016190838,0.00024024489,0.0009667045,0.0038281276,0.00030973222,0.00074496266,0.0007576784,0.0004223746,0.00013749197],"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":[0.000044742705,0.000007939226,0.9960706,0.000049288657,0.00007204986,0.000043149124,0.0001787392,0.00014115545,0.00009875191,0.000061719555,0.00031281248,0.002919144],"study_design_scores_gemma":[0.0000019610166,0.000019492149,0.99873275,0.000014647002,0.000040779305,0.00006271086,0.00025301895,0.00041674322,0.000028768076,0.000027753433,0.00039502332,0.000006284032],"about_ca_topic_score_codex":0.043073688,"about_ca_topic_score_gemma":0.04777244,"teacher_disagreement_score":0.043073688,"about_ca_system_score_codex":0.0013416972,"about_ca_system_score_gemma":0.0015977997,"threshold_uncertainty_score":0.085645914},"labels":[],"label_agreement":null},{"id":"W4386890818","doi":"10.1007/s10676-023-09720-y","title":"Melting contestation: insurance fairness and machine learning","year":2023,"lang":"en","type":"article","venue":"Ethics and Information Technology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"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 du Québec à Montréal","funders":"","keywords":"Typology; Context (archaeology); Social insurance; Perception; Positive economics; Big data; Actuarial science; Economics; Social psychology; Sociology; Psychology; Political science; Computer science; Law; History; Data mining","score_opus":0.03273019741945431,"score_gpt":0.3131924827714198,"score_spread":0.2804622853519655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386890818","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":[0.34363002,0.01067101,0.4662671,0.109056234,0.0012654868,0.000103378006,0.0002881748,0.00015698222,0.06856158],"genre_scores_gemma":[0.9856124,0.0005879517,0.008973267,0.0012859693,0.00070306216,0.00004345879,0.000029614393,0.00002797611,0.0027362902],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98809206,0.0092311865,0.00030007726,0.0010499941,0.00072784995,0.0005988435],"domain_scores_gemma":[0.8585046,0.12697312,0.004883584,0.0052624866,0.002793596,0.0015826043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023689767,0.00044833764,0.001842384,0.0014459133,0.0028016195,0.0062320707,0.0016379897,0.004211277,0.00855558],"category_scores_gemma":[0.10735428,0.00037152035,0.0007502961,0.0014287838,0.016314732,0.010015139,0.0033845827,0.005145759,0.00027726198],"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":[0.00007773978,0.0000623165,0.0029363544,0.00006017947,0.000047457284,0.00006353928,0.00049471256,0.010151607,0.00007740251,0.9708397,0.0019368067,0.013252274],"study_design_scores_gemma":[0.0000092937335,0.000005176097,0.00035263013,0.000012900869,0.0000036033948,0.000010267466,0.00008005562,0.012714646,0.00002731369,0.98617667,0.00060045015,0.0000071034024],"about_ca_topic_score_codex":0.004425837,"about_ca_topic_score_gemma":0.0032320851,"teacher_disagreement_score":0.023689767,"about_ca_system_score_codex":0.0030439745,"about_ca_system_score_gemma":0.0024553556,"threshold_uncertainty_score":0.12528491},"labels":[],"label_agreement":null},{"id":"W4386927742","doi":"10.7202/1091846ar","title":"Mortalité prospective en cas de petits échantillons : modélisation à partir d’informations externes en utilisant l’approche de Bongaarts","year":2012,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humanities; Art","score_opus":0.036950727279512416,"score_gpt":0.32850011125766165,"score_spread":0.2915493839781492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386927742","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":[0.7139522,0.002548609,0.25394177,0.0053642415,0.00047192702,0.0005483465,0.0042783376,0.00071491476,0.018179687],"genre_scores_gemma":[0.9557881,0.00072676426,0.035133995,0.00019404617,0.00007818803,0.0004209449,0.0012284088,0.00008128649,0.0063482523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979863,0.0010376439,0.000119304634,0.00037182213,0.00025252864,0.00023235826],"domain_scores_gemma":[0.98618186,0.011033788,0.00082277285,0.00045614908,0.0011339753,0.00037133956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057780165,0.0012215885,0.0017123048,0.00087840646,0.00077107165,0.0030943323,0.0022077984,0.0026635814,0.0055066287],"category_scores_gemma":[0.015104049,0.0009740323,0.0030155743,0.0009068992,0.00085370673,0.0021621534,0.0016719254,0.0033823266,0.00053010817],"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":[0.00021329777,0.00011787737,0.0077760504,0.0000885554,0.00015461256,0.000100213205,0.00023445644,0.978973,0.00022218094,0.0035435765,0.00066967163,0.007906453],"study_design_scores_gemma":[0.000037231508,0.00014012899,0.003432226,0.000050193965,0.00010169432,0.00003678578,0.00013906661,0.99035805,0.00023068953,0.0041749203,0.0012525297,0.00004646612],"about_ca_topic_score_codex":0.07782542,"about_ca_topic_score_gemma":0.048150145,"teacher_disagreement_score":0.07782542,"about_ca_system_score_codex":0.0028299233,"about_ca_system_score_gemma":0.0037946159,"threshold_uncertainty_score":0.15474486},"labels":[],"label_agreement":null},{"id":"W4386927745","doi":"10.7202/1091845ar","title":"Essays on Boundaries Effects and Practical Considerations for Univariate Graduation of Mortality by Local Likelihood Models","year":2012,"lang":"en","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mathematics; Estimator; Weighting; Univariate; Smoothing; Context (archaeology); Parametric statistics; Boundary (topology); Polynomial regression; Statistic; Statistics; Polynomial; Mathematical optimization; Regression analysis; Econometrics; Multivariate statistics","score_opus":0.07213286750922067,"score_gpt":0.3644577093645003,"score_spread":0.2923248418552796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386927745","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":[0.009319586,0.010061932,0.8460067,0.08840492,0.0039462415,0.000101830105,0.00033068127,0.00039787972,0.041430306],"genre_scores_gemma":[0.469703,0.009932498,0.41605276,0.017078098,0.01799303,0.0007597234,0.0003571153,0.00090909644,0.06721465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9798917,0.015252199,0.0008196507,0.0012427593,0.0024239046,0.00036981373],"domain_scores_gemma":[0.832916,0.15245204,0.0028147462,0.0058599715,0.004924011,0.0010331889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040466666,0.000960962,0.0015993424,0.0012199212,0.001259591,0.0029195189,0.0036571955,0.0029328384,0.010601133],"category_scores_gemma":[0.13428812,0.0008103997,0.002905566,0.0013188481,0.0083583435,0.010212591,0.0045445086,0.0094947135,0.0013867677],"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":[0.000059243197,0.000059173297,0.0013609898,0.00010486332,0.00006139901,0.00015740948,0.00047995144,0.013370814,0.000084879124,0.9236665,0.026316106,0.034278657],"study_design_scores_gemma":[0.000023090824,0.000033904595,0.00088263175,0.00011718831,0.000033423712,0.00009069646,0.000099739955,0.032263443,0.000121198274,0.94833153,0.017968277,0.000034860954],"about_ca_topic_score_codex":0.0028047787,"about_ca_topic_score_gemma":0.001991665,"teacher_disagreement_score":0.040466666,"about_ca_system_score_codex":0.0035414684,"about_ca_system_score_gemma":0.0016885994,"threshold_uncertainty_score":0.21401072},"labels":[],"label_agreement":null},{"id":"W4387195915","doi":"10.2139/ssrn.4580817","title":"On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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; University of Toronto","funders":"","keywords":"Longevity; Longevity risk; Actuarial science; Risk analysis (engineering); Econometrics; Reliability engineering; Economics; Business; Computer science; Engineering; Medicine; Gerontology","score_opus":0.026075542831184153,"score_gpt":0.3190548444085338,"score_spread":0.2929793015773497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387195915","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":[0.46482566,0.0015617876,0.48251644,0.00589143,0.00027625958,0.00025795645,0.0011788232,0.0005678244,0.04292374],"genre_scores_gemma":[0.98228997,0.00043913897,0.009848886,0.00022425347,0.00010083123,0.00013808187,0.00021381768,0.000043181466,0.006701817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989317,0.00064725976,0.000035890644,0.00012981393,0.00009414426,0.00016122358],"domain_scores_gemma":[0.98868585,0.009661104,0.0006739039,0.00018212237,0.00049383537,0.00030315906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003365992,0.0012467187,0.002234977,0.0017945478,0.0010616088,0.0025326733,0.0026536249,0.004316852,0.005470701],"category_scores_gemma":[0.013185793,0.0011997472,0.0015542264,0.0014890175,0.0020478705,0.0019486434,0.0019188933,0.0025911985,0.00034822544],"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":[0.0000118643175,0.000015098115,0.00038909042,0.000005785091,0.000009598282,0.00002000845,0.00001710485,0.99569315,0.000016861584,0.003300768,0.0000926179,0.00042819485],"study_design_scores_gemma":[0.0000058028263,0.000005983426,0.00008859944,0.000003148693,0.00000578861,0.000003924236,0.000010320681,0.9979867,0.000012308095,0.0018282892,0.000044896417,0.000004187954],"about_ca_topic_score_codex":0.07370118,"about_ca_topic_score_gemma":0.02319222,"teacher_disagreement_score":0.07370118,"about_ca_system_score_codex":0.0023876817,"about_ca_system_score_gemma":0.0028298858,"threshold_uncertainty_score":0.1465444},"labels":[],"label_agreement":null},{"id":"W4387498563","doi":"10.1080/03461238.2023.2264555","title":"Two hybrid models for dependent death times of couple: a common shock approach","year":2023,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Life insurance; Econometrics; Actuarial science; Shock (circulatory); Bivariate analysis; Function (biology); Wife; Event (particle physics); Survival function; Economics; Mathematics; Computer science; Statistics; Law; Survival analysis; Physics; Political science","score_opus":0.045061631720932346,"score_gpt":0.3345193185115907,"score_spread":0.28945768679065836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387498563","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":[0.13865373,0.0010558773,0.84919286,0.0015462397,0.0001625174,0.0001336958,0.00051521737,0.00018766837,0.008552333],"genre_scores_gemma":[0.9396764,0.0008013035,0.039162252,0.00030881073,0.00026257287,0.00022546218,0.00043700193,0.00009018147,0.019036073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988444,0.00053602125,0.000057678044,0.00021904532,0.00016592776,0.00017696084],"domain_scores_gemma":[0.99617815,0.001900541,0.00071037206,0.0003941279,0.00036206053,0.00045471868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036751833,0.0011458953,0.0016140192,0.0019636275,0.0005951401,0.0021604318,0.003622701,0.003171231,0.0068268934],"category_scores_gemma":[0.0067462735,0.000811093,0.0025022812,0.0013879766,0.0017408793,0.0030124667,0.0029972969,0.0029858835,0.0007698976],"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":[0.00010929004,0.0001281766,0.005988515,0.00007566301,0.00021833935,0.00062327884,0.0004706436,0.6932392,0.0008755151,0.28170407,0.0019407109,0.014626707],"study_design_scores_gemma":[0.000024537014,0.000035748195,0.00066061236,0.00001155971,0.00003989745,0.000081550075,0.000047029927,0.9559975,0.00008495222,0.042179115,0.0008023804,0.00003499543],"about_ca_topic_score_codex":0.0067120823,"about_ca_topic_score_gemma":0.0040334337,"teacher_disagreement_score":0.0068268934,"about_ca_system_score_codex":0.0012427996,"about_ca_system_score_gemma":0.000780373,"threshold_uncertainty_score":0.022838295},"labels":[],"label_agreement":null},{"id":"W4387568023","doi":"10.1080/10920277.2023.2231996","title":"Modeling and Forecasting Subnational Mortality in the Presence of Aggregated Data","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Computer science; Econometrics; Pension; Estimation; Socioeconomic status; Bayesian probability; Aggregate (composite); Aggregate data; Actuarial science; Geography; Operations research; Statistics; Population; Demography; Economics; Sociology; Artificial intelligence; Mathematics; Finance","score_opus":0.11831762928690649,"score_gpt":0.35702741347042116,"score_spread":0.23870978418351468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387568023","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":[0.6358964,0.00047984478,0.3586111,0.0011557153,0.000072095114,0.00005825631,0.0016809328,0.00024776458,0.0017978802],"genre_scores_gemma":[0.96445304,0.0003591999,0.03292069,0.00006268182,0.000045475965,0.000051364248,0.0011418518,0.000018272893,0.00094734185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995103,0.0001937233,0.000032375374,0.00013024786,0.00006647133,0.00006684098],"domain_scores_gemma":[0.9983095,0.00094663934,0.00030234517,0.00015200782,0.0002100711,0.00007936008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019527011,0.00044956908,0.0006360169,0.000824787,0.00028053363,0.00092230004,0.00081050355,0.00065276056,0.0004531209],"category_scores_gemma":[0.0050222646,0.0003271795,0.0006048448,0.000944436,0.00034888767,0.0011157539,0.0009578924,0.00095102855,0.00008089687],"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":[0.0000295138,0.00003346917,0.0322095,0.0000211914,0.000065275286,0.00007351034,0.00011449212,0.946151,0.0003966143,0.0070911627,0.00046151283,0.013352807],"study_design_scores_gemma":[0.00000195638,0.000008463051,0.0035876876,0.0000039331103,0.0000082476245,0.000007149272,0.000044851116,0.99298686,0.00007227639,0.002994525,0.00027890728,0.0000052050664],"about_ca_topic_score_codex":0.0798008,"about_ca_topic_score_gemma":0.08226698,"teacher_disagreement_score":0.0798008,"about_ca_system_score_codex":0.0012222317,"about_ca_system_score_gemma":0.0012690804,"threshold_uncertainty_score":0.15867257},"labels":[],"label_agreement":null},{"id":"W4387581600","doi":"10.1080/10920277.2023.2242910","title":"Longevity Risk Modeling with the Consumer Price Index","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","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":"Gross domestic product; Index (typography); Econometrics; Price index; Consumer price index (South Africa); Sample (material); Economics; Per capita; Product (mathematics); Estimation; Statistics; Mathematics; Macroeconomics; Demography; Computer science; Monetary policy; Sociology","score_opus":0.017903680346475836,"score_gpt":0.2820476340571259,"score_spread":0.2641439537106501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387581600","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":[0.47940868,0.001982312,0.4901203,0.0030001034,0.00032539768,0.00013309564,0.0022997446,0.00052067474,0.022209678],"genre_scores_gemma":[0.9760389,0.0006515086,0.013038577,0.00007769994,0.00012776502,0.00007522172,0.00057872763,0.00003193781,0.009379654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996561,0.00013929793,0.000016851538,0.00007684491,0.00006541707,0.000045559293],"domain_scores_gemma":[0.9988763,0.0006447436,0.00018242595,0.000061281986,0.00018115187,0.000054169013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011817979,0.0005273507,0.0005118057,0.00062170974,0.00030164068,0.000993894,0.0010305218,0.0011054771,0.003059147],"category_scores_gemma":[0.003866042,0.00032370794,0.00078955817,0.0008899833,0.00034874768,0.0011499044,0.0006076918,0.0010713817,0.00041267005],"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":[0.000052401174,0.00007718614,0.014506244,0.00003741516,0.0000982552,0.00022244889,0.00013209974,0.9314051,0.00028851337,0.032577664,0.0023320003,0.018270725],"study_design_scores_gemma":[0.0000053982717,0.000021657474,0.0016245778,0.000005355021,0.000015336096,0.000030058878,0.000014310485,0.9918264,0.000037504396,0.005713728,0.00069720764,0.000008319131],"about_ca_topic_score_codex":0.023747848,"about_ca_topic_score_gemma":0.014371667,"teacher_disagreement_score":0.023747848,"about_ca_system_score_codex":0.000720746,"about_ca_system_score_gemma":0.0006728984,"threshold_uncertainty_score":0.047219276},"labels":[],"label_agreement":null},{"id":"W4387626007","doi":"10.2979/victorianstudies.65.1.21","title":"Irish Famines before and after the Great Hunger ed. by Christine Kinealy and Gerard Moran (review)","year":2022,"lang":"en","type":"article","venue":"Victorian Studies","topic":"Insurance, Mortality, Demography, Risk Management","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":"Famine; Irish; Subsistence agriculture; Theme (computing); History; Economic history; Sociology; Philosophy; Archaeology; Agriculture","score_opus":0.014481183576065586,"score_gpt":0.30095909631094553,"score_spread":0.28647791273487994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387626007","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":[0.00007879735,0.9916278,0.000023689418,0.0018588427,0.0036489458,0.000017362583,0.000119245466,0.000009652533,0.0026157072],"genre_scores_gemma":[0.0006054301,0.9880427,0.0000794988,0.002950448,0.0021326654,0.000040350747,0.00033339008,0.000007981631,0.0058075082],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993636,0.00009455053,0.000077126926,0.00010126084,0.00028086413,0.00008258571],"domain_scores_gemma":[0.9989189,0.00025070886,0.00017972068,0.000024354,0.000468563,0.0001577541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012173193,0.0014207081,0.002236898,0.0020195693,0.00048152875,0.0026399875,0.0012230445,0.0016020882,0.020952508],"category_scores_gemma":[0.0027769464,0.0005392521,0.00092332537,0.004741024,0.0006703179,0.002394421,0.0014559549,0.0029486527,0.010448552],"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":[0.00010216183,0.000030045065,0.000236944,0.021177111,0.0000990448,0.00015190657,0.000205153,0.000090237496,0.00024593627,0.0005892271,0.74195296,0.23511927],"study_design_scores_gemma":[0.000022840313,0.000032165583,0.0018935817,0.011436549,0.000059643204,0.00028621886,0.00020305075,0.000011147839,0.000039238,0.00010792493,0.98588854,0.00001908627],"about_ca_topic_score_codex":0.014748555,"about_ca_topic_score_gemma":0.019076688,"teacher_disagreement_score":0.020952508,"about_ca_system_score_codex":0.002788275,"about_ca_system_score_gemma":0.006454774,"threshold_uncertainty_score":0.070093095},"labels":[],"label_agreement":null},{"id":"W4387867020","doi":"10.1017/s1748499523000222","title":"A changing climate for actuarial science","year":2023,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo; Université du Québec à Montréal","funders":"","keywords":"Content (measure theory); Action (physics); Actuarial science; Content analysis; Computer science; Business; Mathematics; Sociology; Social science","score_opus":0.10433017889938276,"score_gpt":0.4335007270194109,"score_spread":0.3291705481200281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387867020","genre_codex":"commentary","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":[0.0015834492,0.017945206,0.0018660428,0.9508959,0.01098441,0.0000065158533,0.0002558019,0.00007993428,0.01638273],"genre_scores_gemma":[0.2879846,0.081780985,0.01310707,0.48106745,0.10111759,0.00013559614,0.0007099481,0.00038425226,0.0337125],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913368,0.00434694,0.0003845989,0.0008855587,0.001994433,0.0010516264],"domain_scores_gemma":[0.9712237,0.012098167,0.0011456985,0.0021900192,0.006909196,0.0064330995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026475558,0.00064561144,0.0011426868,0.0016386401,0.0034536528,0.011454496,0.0012868298,0.011598308,0.028151961],"category_scores_gemma":[0.03512737,0.00034356292,0.0009311577,0.0014774301,0.008399519,0.013868187,0.00484543,0.015778488,0.005984053],"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":[0.000108535816,0.00009561067,0.0013389812,0.00021822787,0.000033826316,0.00011281397,0.0005088192,0.0009251769,0.0004466656,0.44786426,0.49151108,0.056835942],"study_design_scores_gemma":[0.000019958483,0.00004940691,0.0032268504,0.0003641729,0.000009551786,0.00013882686,0.00080110214,0.00083814905,0.00015185721,0.30107558,0.6932721,0.00005235527],"about_ca_topic_score_codex":0.0035006977,"about_ca_topic_score_gemma":0.0039827465,"teacher_disagreement_score":0.028151961,"about_ca_system_score_codex":0.00870642,"about_ca_system_score_gemma":0.0072634555,"threshold_uncertainty_score":0.14001781},"labels":[],"label_agreement":null},{"id":"W4387975017","doi":"10.3390/risks11110187","title":"Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Solvency; Multivariate statistics; Econometrics; Line of business; Flexibility (engineering); Diversification (marketing strategy); Inference; Rank (graph theory); Computer science; Operational risk; Capital (architecture); Estimation; Actuarial science; Economics; Risk management; Statistics; Business; Mathematics; Finance; Market liquidity; Business model; Artificial intelligence","score_opus":0.19774733076733125,"score_gpt":0.42998656699577337,"score_spread":0.23223923622844211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387975017","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":[0.017309653,0.00018275934,0.98139316,0.00016910344,0.000015360018,0.000042285006,0.00012924179,0.00015271628,0.00060572504],"genre_scores_gemma":[0.74536675,0.00088388746,0.24489498,0.00025035525,0.00020867035,0.00034779604,0.0010224449,0.00016156244,0.0068634963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99703133,0.0018376409,0.00011884247,0.00038852045,0.0003937442,0.00022984273],"domain_scores_gemma":[0.9762028,0.01894138,0.0022159568,0.001226642,0.001080004,0.0003332432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009279597,0.0010768268,0.0012917209,0.0017432292,0.0005354423,0.0012833856,0.0022781356,0.0013680983,0.003818622],"category_scores_gemma":[0.026468312,0.0008126756,0.0014641567,0.0017103683,0.001712834,0.002742834,0.0015237769,0.0025997544,0.00065236434],"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":[0.00012345276,0.00009587655,0.008424956,0.00010309033,0.0001593021,0.00017405413,0.00016536828,0.80116355,0.00090162776,0.14092754,0.0012228196,0.04653839],"study_design_scores_gemma":[0.000005463525,0.000017983106,0.00044778374,0.0000061726037,0.0000073454735,0.000012732355,0.000008284944,0.98362005,0.00016893397,0.015460676,0.00023565184,0.000008929748],"about_ca_topic_score_codex":0.009169302,"about_ca_topic_score_gemma":0.008370703,"teacher_disagreement_score":0.009279597,"about_ca_system_score_codex":0.001071982,"about_ca_system_score_gemma":0.0015634336,"threshold_uncertainty_score":0.049075782},"labels":[],"label_agreement":null},{"id":"W4388301516","doi":"10.32388/2bp5yb","title":"Review of: \"Towards a model-based approach: applications to historical demography and palaeodemography\"","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Memorial University of Newfoundland","funders":"","keywords":"Historical demography; Demography; Genealogy; Geography; History; Computer science; Sociology; Population; Research methodology","score_opus":0.07470431751550546,"score_gpt":0.3646026889106937,"score_spread":0.28989837139518826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388301516","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007596488,0.18446007,0.0682241,0.44943264,0.22190048,0.0005893229,0.0063981256,0.004600932,0.06363473],"genre_scores_gemma":[0.021182455,0.40260792,0.08164084,0.09769589,0.18529177,0.0015589058,0.012476903,0.008075878,0.18946946],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9910909,0.0037716276,0.0008481603,0.0006815237,0.0034438039,0.00016406561],"domain_scores_gemma":[0.90911335,0.03640297,0.0036279021,0.0043703676,0.043128744,0.0033565937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017217455,0.0011190701,0.0020134402,0.009179249,0.0015119303,0.006640629,0.0040505813,0.0036976102,0.090250805],"category_scores_gemma":[0.11850905,0.0006886412,0.001427765,0.0075122123,0.002709013,0.006297268,0.004491914,0.004090934,0.050070196],"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":[0.000007150349,0.0000049929286,0.00010775387,0.0011246322,0.000023482193,0.000048640457,0.000120101904,0.00021635523,0.000052640447,0.0036726391,0.9397063,0.05491539],"study_design_scores_gemma":[0.000005152512,0.0000065085483,0.00032771705,0.0018782772,0.000019261695,0.00012495412,0.000094208306,0.0002859699,0.000038591104,0.004636147,0.99256647,0.000016806454],"about_ca_topic_score_codex":0.006474547,"about_ca_topic_score_gemma":0.010143565,"teacher_disagreement_score":0.090250805,"about_ca_system_score_codex":0.0029055632,"about_ca_system_score_gemma":0.008042008,"threshold_uncertainty_score":0.30191904},"labels":[],"label_agreement":null},{"id":"W4388439247","doi":"10.7202/1091947ar","title":"Le risque de longévité : valorisation et outils de gestion (deuxième partie)","year":2009,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Political science; Humanities; Art","score_opus":0.04433899700929899,"score_gpt":0.32014558931285214,"score_spread":0.27580659230355314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388439247","genre_codex":"review","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":[0.014559722,0.89489365,0.024678035,0.028540416,0.0071724495,0.00011072411,0.00095277216,0.000112976886,0.028979165],"genre_scores_gemma":[0.15779339,0.73907095,0.030779764,0.009302891,0.018930562,0.00035020008,0.0018195434,0.00024541686,0.04170736],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987834,0.00039531698,0.00017093237,0.00021676772,0.00031714968,0.00011629238],"domain_scores_gemma":[0.99524313,0.0029841533,0.00048597658,0.00021866475,0.00091774785,0.00015033496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031987121,0.0010641773,0.0010626181,0.0038030092,0.000563261,0.003305335,0.00073559524,0.0018056708,0.007828404],"category_scores_gemma":[0.0069977725,0.00052183325,0.0018053666,0.0024212396,0.0021255245,0.0034105221,0.0020423438,0.004539492,0.0012557696],"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":[0.00037548688,0.00021739215,0.01467032,0.0052700676,0.00035943947,0.0005239478,0.002003825,0.002150089,0.0032930244,0.050282072,0.08503163,0.83582276],"study_design_scores_gemma":[0.00004623071,0.00084836606,0.1066954,0.009185731,0.00047463772,0.0052901786,0.0018912851,0.0023071824,0.004796351,0.052539263,0.8155753,0.00035001643],"about_ca_topic_score_codex":0.0074475445,"about_ca_topic_score_gemma":0.0065525803,"teacher_disagreement_score":0.007828404,"about_ca_system_score_codex":0.0017762072,"about_ca_system_score_gemma":0.0014663356,"threshold_uncertainty_score":0.026188672},"labels":[],"label_agreement":null},{"id":"W4388439667","doi":"10.7202/1092098ar","title":"Mesure de l’incertitude tendanciellesur la mortalité – Application à un régime de rentes en cours de service","year":2007,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Humanities; Physics; Political science; Philosophy","score_opus":0.018405054363808317,"score_gpt":0.3249753651585651,"score_spread":0.30657031079475683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388439667","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":[0.98896617,0.0017782521,0.000605528,0.0012510831,0.00006157624,0.00004327879,0.0004584871,0.000019341318,0.006816298],"genre_scores_gemma":[0.99423295,0.00080914196,0.00078238937,0.0001424375,0.00008175481,0.000049655468,0.00024840876,0.0000072632274,0.0036459914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987884,0.0005419119,0.000081374164,0.00012790896,0.00026681638,0.00019366742],"domain_scores_gemma":[0.99426275,0.002940804,0.0012943208,0.00025291363,0.00066591275,0.0005833555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002107327,0.00022173412,0.00041143,0.0005296867,0.00026457012,0.00046216458,0.0003380505,0.00058755727,0.009063882],"category_scores_gemma":[0.009227692,0.00009450316,0.0006044129,0.0005752638,0.00027408573,0.00041192974,0.0007497826,0.0007178827,0.0005666858],"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":[0.023895307,0.0036417623,0.46358892,0.00074877904,0.0008764201,0.0005678547,0.0045072227,0.0049467916,0.017098442,0.002188345,0.0067365025,0.47120363],"study_design_scores_gemma":[0.000112755624,0.003948865,0.98501724,0.00009526975,0.00012056614,0.00013784984,0.00068945525,0.0011159485,0.0019725675,0.00043824714,0.0063204914,0.000030617364],"about_ca_topic_score_codex":0.009394152,"about_ca_topic_score_gemma":0.012724756,"teacher_disagreement_score":0.009394152,"about_ca_system_score_codex":0.000809307,"about_ca_system_score_gemma":0.0005251379,"threshold_uncertainty_score":0.030321658},"labels":[],"label_agreement":null},{"id":"W4388455350","doi":"10.1007/s10614-023-10493-1","title":"Computing Longitudinal Moments for Heterogeneous Agent Models","year":2023,"lang":"en","type":"article","venue":"Computational Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Monte Carlo method; Computer science; Population; Mathematical optimization; Computation; Method of moments (probability theory); Markov chain Monte Carlo; Function (biology); Applied mathematics; Mathematics; Algorithm; Statistics","score_opus":0.08935507334048438,"score_gpt":0.3399895755705446,"score_spread":0.25063450223006023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388455350","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":[0.15224321,0.00073967234,0.8421428,0.0012479128,0.00012418127,0.0000553335,0.0006629723,0.0011672284,0.0016167026],"genre_scores_gemma":[0.88614196,0.000520876,0.10937741,0.00015756967,0.00022403647,0.00013301727,0.0012882514,0.00019128782,0.0019655435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991647,0.00037037214,0.00006699244,0.00015062767,0.0001291632,0.00011822675],"domain_scores_gemma":[0.98134816,0.015582958,0.0010169668,0.0008733265,0.00050135044,0.00067721016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003216183,0.000798095,0.0016408844,0.0014674747,0.0008548094,0.0022017593,0.001849644,0.0016555389,0.0036759793],"category_scores_gemma":[0.026733007,0.00132379,0.0013036962,0.0011884149,0.0010119159,0.0028841596,0.00218055,0.0020796906,0.0005559243],"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":[0.00014630496,0.00007900996,0.004205644,0.00005568888,0.000095577256,0.00014158161,0.0000729882,0.95711637,0.00030293246,0.023640266,0.0011905933,0.01295311],"study_design_scores_gemma":[0.0000075088105,0.000004670564,0.00010875124,0.0000027131719,0.000004519748,0.0000069630305,0.0000072585262,0.985297,0.00005020822,0.014425465,0.00008200681,0.0000029323405],"about_ca_topic_score_codex":0.008097717,"about_ca_topic_score_gemma":0.010193863,"teacher_disagreement_score":0.008097717,"about_ca_system_score_codex":0.0014367795,"about_ca_system_score_gemma":0.0014332073,"threshold_uncertainty_score":0.01700896},"labels":[],"label_agreement":null},{"id":"W4388493367","doi":"10.1007/s13385-023-00370-4","title":"A new approximation of annuity prices for age–period–cohort models","year":2023,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Log-normal distribution; Autoregressive model; Mathematics; Econometrics; Mathematical finance; Life annuity; Applied mathematics; Economics; Statistics; Financial economics","score_opus":0.0504834881384066,"score_gpt":0.31848149985306246,"score_spread":0.26799801171465587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388493367","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":[0.0058981446,0.0005710313,0.98936087,0.0004585166,0.00022823375,0.00004977898,0.00017685312,0.0001436912,0.003112774],"genre_scores_gemma":[0.41446793,0.004198331,0.5276278,0.0012804101,0.0015549373,0.00072071573,0.001936144,0.0009120951,0.04730159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982091,0.0008894937,0.000083766645,0.0002118497,0.00039842524,0.00020742611],"domain_scores_gemma":[0.9903206,0.0066643013,0.0005239914,0.0008075171,0.0011783608,0.00050518505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009026633,0.0011419854,0.002157846,0.0017383405,0.000828916,0.0028407048,0.004624008,0.0028247328,0.007986887],"category_scores_gemma":[0.029935611,0.001144101,0.0026311572,0.0021020751,0.0012248661,0.0034386104,0.0020947014,0.004268038,0.0022156455],"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":[0.000071456794,0.00006933219,0.0017960494,0.0001043449,0.00013452076,0.00032443742,0.00013050243,0.8599763,0.000586017,0.11094569,0.0049489886,0.020912243],"study_design_scores_gemma":[0.000009163994,0.0000069036905,0.0001236349,0.0000140808415,0.000015217021,0.00005683651,0.000008647004,0.98474336,0.00004222921,0.013794715,0.0011767138,0.0000086091695],"about_ca_topic_score_codex":0.014690466,"about_ca_topic_score_gemma":0.009428568,"teacher_disagreement_score":0.014690466,"about_ca_system_score_codex":0.0017098173,"about_ca_system_score_gemma":0.0026611052,"threshold_uncertainty_score":0.047737956},"labels":[],"label_agreement":null},{"id":"W4388639052","doi":"10.1007/s10680-023-09675-2","title":"Can We Rely on Projections of the Immigrant Population? The Case of Norway","year":2023,"lang":"en","type":"article","venue":"European Journal of Population / Revue européenne de Démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Universitetet i Oslo","keywords":"Norwegian; Immigration; Population; Geography; Projections of population growth; Population projection; European union; Probabilistic logic; Demographic economics; Demography; Population growth; Statistics; Economics; Sociology; Mathematics","score_opus":0.027525861452877172,"score_gpt":0.2874858769280812,"score_spread":0.25996001547520403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388639052","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":[0.6313122,0.010248737,0.1906932,0.06813354,0.002544492,0.0001653808,0.011862095,0.0008968524,0.084143505],"genre_scores_gemma":[0.972338,0.0020832468,0.020825284,0.0008300607,0.00027857695,0.000041350177,0.0016591407,0.00009281032,0.001851592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782395,0.0012718916,0.0001119435,0.00030895064,0.00031190456,0.00017137441],"domain_scores_gemma":[0.99186885,0.0044865827,0.0010957672,0.0011418815,0.0011696944,0.00023715806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007436273,0.0006084458,0.00060428615,0.0007185993,0.000394951,0.0021556458,0.0012528176,0.0011032057,0.002290926],"category_scores_gemma":[0.05395769,0.00045274937,0.0007524607,0.0009856462,0.0008864251,0.004633314,0.0013474263,0.0013062386,0.0005411132],"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":[0.00032676497,0.000034027584,0.15418284,0.00025286226,0.00038300516,0.0007267097,0.0018736665,0.6476044,0.00027104912,0.07701547,0.0140797775,0.10324945],"study_design_scores_gemma":[0.000070391754,0.00013884489,0.055213362,0.0009429134,0.00014463354,0.00041762594,0.0034071775,0.7049644,0.0005093643,0.19711359,0.036853883,0.00022381058],"about_ca_topic_score_codex":0.1932122,"about_ca_topic_score_gemma":0.10148468,"teacher_disagreement_score":0.1932122,"about_ca_system_score_codex":0.0015431643,"about_ca_system_score_gemma":0.0021871587,"threshold_uncertainty_score":0.38417518},"labels":[],"label_agreement":null},{"id":"W4388720918","doi":"10.1109/iecon51785.2023.10312336","title":"Lightweight Compressed Temporal and Compressed Spatial Attention with Augmentation Fusion in Remaining Useful Life Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National University of Singapore","keywords":"Computer science; Transformer; Artificial intelligence; Modular design; Data modeling; Machine learning; Preprocessor; Spatial analysis; Data mining; Engineering","score_opus":0.02469482609719478,"score_gpt":0.2809264686908105,"score_spread":0.25623164259361575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388720918","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":[0.13367784,0.0022503776,0.854066,0.00084233086,0.0003104918,0.0000794116,0.0011539866,0.003885542,0.0037340901],"genre_scores_gemma":[0.937593,0.0006113517,0.056764934,0.0003487313,0.00019569047,0.00007083263,0.0013614368,0.00010571036,0.0029483598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997433,0.00003972185,0.000014037371,0.000079065445,0.00007061143,0.000053277745],"domain_scores_gemma":[0.999395,0.0002805501,0.000057370937,0.000082780396,0.00015632456,0.000027896223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055882655,0.00089777174,0.00083337724,0.0008915751,0.00024914628,0.00054628536,0.0011419207,0.00061715976,0.001545306],"category_scores_gemma":[0.0020893826,0.00023726716,0.00067215424,0.0011091015,0.0003657645,0.0012650803,0.0011301043,0.0010114084,0.00035428433],"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":[0.00048771893,0.0002404799,0.0059218504,0.0001547372,0.00012558563,0.0003665152,0.00019688213,0.37068778,0.01619903,0.004722968,0.008625948,0.59227043],"study_design_scores_gemma":[0.0000051227526,0.000036703917,0.001008552,0.0000094542365,0.000025888272,0.000042383843,0.000018611056,0.99257517,0.0025254386,0.002885649,0.00085771724,0.000009414477],"about_ca_topic_score_codex":0.014309792,"about_ca_topic_score_gemma":0.013000975,"teacher_disagreement_score":0.014309792,"about_ca_system_score_codex":0.0006064799,"about_ca_system_score_gemma":0.00080851297,"threshold_uncertainty_score":0.028452992},"labels":[],"label_agreement":null},{"id":"W4388771460","doi":"10.1017/asb.2023.35","title":"Optimal performance of a tontine overlay subject to withdrawal constraints","year":2023,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Efficient frontier; Economics; Overlay; Econometrics; Fraction (chemistry); Mathematical optimization; Bond; Parametric statistics; Constant (computer programming); Time horizon; Computer science; Mathematics; Statistics; Finance","score_opus":0.012742289181465404,"score_gpt":0.27516652237220035,"score_spread":0.26242423319073493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388771460","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":[0.9061918,0.00041095496,0.081465684,0.0003345153,0.000052213887,0.00015112855,0.00012239661,0.00017563299,0.011095663],"genre_scores_gemma":[0.9955901,0.000056219615,0.0030807806,0.000030479885,0.000005790123,0.000017214386,0.000032886346,0.000008717189,0.0011778396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953926,0.00017134091,0.000023631575,0.000072757626,0.000059541482,0.00013344578],"domain_scores_gemma":[0.99796224,0.0011858611,0.00023816667,0.00009276391,0.00023156933,0.00028931786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014037244,0.00092144636,0.0011146712,0.00050704344,0.00036846756,0.0011706945,0.000724535,0.0014510073,0.004138849],"category_scores_gemma":[0.004969992,0.0003935315,0.00032257455,0.0002585001,0.0006002274,0.0007917126,0.0011835768,0.0006219231,0.00025036454],"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":[0.0009044482,0.0001819502,0.0016428774,0.000073587835,0.000054172728,0.00016903771,0.00005360572,0.9748648,0.0032377397,0.002315796,0.0004948567,0.016007222],"study_design_scores_gemma":[0.000044240824,0.00037895478,0.00092102354,0.000011775054,0.000025529014,0.000017454377,0.00006067597,0.99668497,0.00070407416,0.0009242554,0.00021615165,0.000010936709],"about_ca_topic_score_codex":0.0109082265,"about_ca_topic_score_gemma":0.0032250937,"teacher_disagreement_score":0.0109082265,"about_ca_system_score_codex":0.00078315026,"about_ca_system_score_gemma":0.0009047523,"threshold_uncertainty_score":0.021689475},"labels":[],"label_agreement":null},{"id":"W4388917393","doi":"10.23977/jeis.2023.080506","title":"Population prediction in China based on maximum information coefficient and NAR-BP neural network","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Census; Artificial neural network; Population; China; Computer science; Data mining; Geography; Statistics; Econometrics; Artificial intelligence; Mathematics; Demography","score_opus":0.006878572895146841,"score_gpt":0.26222470498546274,"score_spread":0.2553461320903159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388917393","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":[0.35616127,0.0015209622,0.6307847,0.00094311975,0.00019428978,0.0001107243,0.0010135862,0.0009770412,0.008294405],"genre_scores_gemma":[0.9531427,0.0007868167,0.04270318,0.00006684977,0.000048769765,0.00008788786,0.00075883965,0.000028145188,0.002376834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957305,0.00011131554,0.000030440278,0.00012033647,0.00012586966,0.000039041533],"domain_scores_gemma":[0.99924695,0.0003334875,0.00007876489,0.000031582924,0.00028316083,0.000026133868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000797773,0.0005490229,0.00057743286,0.0009009595,0.0002963877,0.00059549615,0.0006358136,0.00035480992,0.00070706534],"category_scores_gemma":[0.0030451543,0.00025219948,0.00048881583,0.0009126468,0.00022913632,0.0010882341,0.0005050568,0.0006452709,0.0001947654],"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":[0.00015062396,0.00010591051,0.027215606,0.00020938007,0.000116721865,0.0001812692,0.0001491508,0.8087935,0.002045777,0.0036973187,0.0028305533,0.1545043],"study_design_scores_gemma":[0.0000031197408,0.000016126138,0.0036469016,0.000006791,0.000010739528,0.000010211126,0.000013817973,0.99494743,0.00034815044,0.000767086,0.00022254551,0.00000695388],"about_ca_topic_score_codex":0.0289904,"about_ca_topic_score_gemma":0.01895741,"teacher_disagreement_score":0.0289904,"about_ca_system_score_codex":0.0006811744,"about_ca_system_score_gemma":0.00078133406,"threshold_uncertainty_score":0.057643294},"labels":[],"label_agreement":null},{"id":"W4388994252","doi":"10.1145/3604237.3626849","title":"The complexity of financial wellness: examining survey patterns via kernel metric learning and clustering of mixed-type data","year":2023,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Universitas Brawijaya","keywords":"Cluster analysis; Kernel (algebra); Metric (unit); Computer science; Artificial intelligence; Mathematics; Business; Marketing","score_opus":0.15013617421188852,"score_gpt":0.35059638641817675,"score_spread":0.20046021220628824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388994252","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":[0.9165179,0.00042427107,0.08033828,0.0005833989,0.000055565757,0.0002489275,0.0011529134,0.00017019734,0.0005084343],"genre_scores_gemma":[0.9696437,0.000069621085,0.028076129,0.00007765993,0.000023282964,0.00019560904,0.0016788124,0.000021093767,0.00021403939],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9848791,0.009785142,0.0012322328,0.0023842482,0.001154842,0.0005645997],"domain_scores_gemma":[0.9430797,0.035288334,0.008733122,0.0069599533,0.0048643313,0.0010746212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016137308,0.0006886878,0.0010869654,0.0037108017,0.00089374784,0.0020156214,0.0015409867,0.001539191,0.0011118916],"category_scores_gemma":[0.07532277,0.00037151415,0.0015880376,0.004217348,0.0013883779,0.0017601128,0.0018826545,0.0012727252,0.00038964732],"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":[0.0005962028,0.0004331769,0.90724176,0.00027103603,0.00082974037,0.00025163978,0.002916845,0.022567347,0.0011865217,0.0033984627,0.0023995042,0.05790771],"study_design_scores_gemma":[0.000049553568,0.00036526192,0.5003433,0.000117191485,0.00014707743,0.00047339298,0.0041124807,0.47825897,0.00095672574,0.012483103,0.002561663,0.00013123972],"about_ca_topic_score_codex":0.008657975,"about_ca_topic_score_gemma":0.006568483,"teacher_disagreement_score":0.016137308,"about_ca_system_score_codex":0.0013069708,"about_ca_system_score_gemma":0.00079364213,"threshold_uncertainty_score":0.08534324},"labels":[],"label_agreement":null},{"id":"W4389047219","doi":"10.3390/risks11120206","title":"On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"TD Bank Group","funders":"","keywords":"Longevity risk; Solvency; Actuarial science; Risk management; Risk analysis (engineering); Context (archaeology); Economic capital; Capital requirement; Insolvency; Capital (architecture); Economics; Life insurance; Business; Computer science; Econometrics; Finance; Pension; Microeconomics; Geography","score_opus":0.0954989682927925,"score_gpt":0.38197958889800143,"score_spread":0.28648062060520896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389047219","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":[0.285079,0.0011119492,0.6829227,0.0032374214,0.00020830536,0.00017780076,0.00053541467,0.00026719683,0.026460234],"genre_scores_gemma":[0.9636305,0.00062281743,0.030016322,0.0002106927,0.00008140981,0.00017710729,0.00020935095,0.000035274577,0.0050164172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994492,0.000332584,0.000020000763,0.000054636123,0.00006564029,0.00007789122],"domain_scores_gemma":[0.99547404,0.0034727347,0.00045921857,0.00010652197,0.00029730867,0.00019012608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020779269,0.000738771,0.0010490753,0.0010562954,0.0006639608,0.0014587089,0.0016143257,0.0022543233,0.003119043],"category_scores_gemma":[0.0071132677,0.0004568271,0.0010337293,0.0008203943,0.0013330817,0.0011142233,0.0015340245,0.0018370864,0.00017971448],"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":[0.000011330935,0.0000143043,0.00059585087,0.000007960388,0.000007631528,0.000023979765,0.000021176565,0.99003816,0.000057315778,0.008395091,0.00009360423,0.0007336332],"study_design_scores_gemma":[0.000003009622,0.0000054435927,0.00005990896,0.00000383999,0.000002716492,0.0000031937873,0.000009500305,0.99769247,0.000020430367,0.002108099,0.00008841821,0.0000029210232],"about_ca_topic_score_codex":0.02618137,"about_ca_topic_score_gemma":0.01137906,"teacher_disagreement_score":0.02618137,"about_ca_system_score_codex":0.0011827693,"about_ca_system_score_gemma":0.0018403962,"threshold_uncertainty_score":0.05205798},"labels":[],"label_agreement":null},{"id":"W4389163503","doi":"10.1134/s0006297923110093","title":"Actuarial Aging Rates in Human Cohorts","year":2023,"lang":"en","type":"article","venue":"Biochemistry (Moscow)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"National Institute on Aging; National Institutes of Health","keywords":"Cohort; Gompertz function; Demography; Life expectancy; Mortality rate; Cohort effect; Cohort study; Medicine; Gerontology; Population; Statistics; Internal medicine; Mathematics","score_opus":0.022284481430336317,"score_gpt":0.3343097789002741,"score_spread":0.3120252974699378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389163503","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":[0.83638304,0.008449242,0.1159614,0.00030378252,0.00028233873,0.00032827337,0.025666468,0.0007582152,0.01186727],"genre_scores_gemma":[0.97275645,0.0017974107,0.0121083185,0.00007044361,0.0000865765,0.00025435135,0.0107713435,0.00006838414,0.002086657],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974591,0.00078251475,0.0002691411,0.0008529477,0.00047167222,0.00016460882],"domain_scores_gemma":[0.99106616,0.0034886387,0.002280243,0.002110874,0.0008792672,0.00017495597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008558202,0.0003469123,0.00041426794,0.003558899,0.00024523452,0.00088544923,0.00062732346,0.00038564406,0.002363984],"category_scores_gemma":[0.020125855,0.00016210316,0.00074208126,0.0024277084,0.00035547692,0.0008980181,0.0009718382,0.00058571214,0.00080149143],"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":[0.00022587185,0.000035312223,0.8435389,0.00032621695,0.0011171454,0.00029503132,0.00080796314,0.03456081,0.0014503296,0.011097549,0.0036436354,0.102901235],"study_design_scores_gemma":[0.000018413271,0.00036814064,0.90604365,0.00019020168,0.00044885033,0.001635087,0.0005252554,0.039533097,0.0024618541,0.01381415,0.03485267,0.00010861018],"about_ca_topic_score_codex":0.0017956415,"about_ca_topic_score_gemma":0.0010803388,"teacher_disagreement_score":0.008558202,"about_ca_system_score_codex":0.0004967826,"about_ca_system_score_gemma":0.00033438441,"threshold_uncertainty_score":0.04526061},"labels":[],"label_agreement":null},{"id":"W4389204355","doi":"10.54254/2754-1169/55/20231009","title":"Analysis of China’s Birth Rate Prediction Based on Time Series","year":2023,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Total fertility rate; Fertility; Autoregressive integrated moving average; Birth rate; China; Childbirth; Demography; Population; Time series; Economics; Statistics; Geography; Mathematics; Family planning; Pregnancy; Biology; Sociology; Research methodology","score_opus":0.009389208419021997,"score_gpt":0.2811061526169596,"score_spread":0.27171694419793757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389204355","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":[0.98231214,0.00043663368,0.011806273,0.0007614202,0.00006706008,0.000036832622,0.0026091158,0.00019822687,0.0017723696],"genre_scores_gemma":[0.99356043,0.00032078358,0.0015619311,0.00003393151,0.00003371053,0.000025205993,0.0035878655,0.0000183331,0.0008577883],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992505,0.00014663758,0.00007148243,0.00019949388,0.00021678927,0.00011504174],"domain_scores_gemma":[0.99750584,0.0010928164,0.00036064626,0.00017786548,0.00073134893,0.00013153857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029079074,0.0006216777,0.00054877286,0.0022607753,0.00030458454,0.00082451873,0.0008702928,0.00054972566,0.0012947959],"category_scores_gemma":[0.0067700394,0.00029582507,0.0009923115,0.0016871908,0.0002131022,0.0007312859,0.00051118067,0.00069851923,0.00034966637],"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":[0.00012912732,0.00012106189,0.65335166,0.00014348267,0.00027539334,0.0007672464,0.00033394844,0.2899142,0.0012449542,0.0039455383,0.005283446,0.044489913],"study_design_scores_gemma":[0.000009043963,0.000033746524,0.14869729,0.000020226675,0.000046759564,0.000050372695,0.00006306178,0.8489413,0.0003543957,0.00063375203,0.0011260735,0.000024027904],"about_ca_topic_score_codex":0.07640548,"about_ca_topic_score_gemma":0.03060358,"teacher_disagreement_score":0.07640548,"about_ca_system_score_codex":0.0011748731,"about_ca_system_score_gemma":0.0012011251,"threshold_uncertainty_score":0.15192145},"labels":[],"label_agreement":null},{"id":"W4389208232","doi":"10.1007/s12561-023-09407-4","title":"Evaluating Effects of Various Exposures on Mortality Risk of Opioid Use Disorders with Linked Administrative Databases","year":2023,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Centre for Advancing Health Outcomes; St. Paul's Hospital; Simon Fraser University","funders":"National Institute of Nursing Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Biostatistics; Covariate; Medicine; Hazard; Proportional hazards model; Data mining; Econometrics; Actuarial science; Computer science; Statistics; Public health; Machine learning; Business; Mathematics","score_opus":0.09982524946763711,"score_gpt":0.4270462260048003,"score_spread":0.3272209765371632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389208232","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":[0.98575526,0.0015505058,0.0017557908,0.00042253666,0.00005466289,0.000108066066,0.009786643,0.00005536508,0.0005112133],"genre_scores_gemma":[0.99168444,0.00043350342,0.0015344683,0.00008868603,0.000049785278,0.00006741764,0.005985369,0.000014249555,0.00014211306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9717388,0.01753289,0.0033268484,0.003904366,0.0024114219,0.0010856425],"domain_scores_gemma":[0.86945003,0.109591864,0.010889019,0.0058162506,0.0026271406,0.0016257123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025946174,0.0009975253,0.001350639,0.0034074944,0.00054063014,0.002316824,0.0016161697,0.0010660432,0.0014939999],"category_scores_gemma":[0.08367671,0.00058635336,0.007684538,0.00580973,0.00054080505,0.0021924442,0.0020369086,0.0014568571,0.00018087971],"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":[0.0021323755,0.00024960196,0.97689724,0.0001761058,0.010119374,0.00011232215,0.00007206746,0.0030674988,0.00011765885,0.00015536656,0.00034551055,0.006554769],"study_design_scores_gemma":[0.00023807927,0.0014307621,0.9637024,0.00010032066,0.011402626,0.00019911781,0.00041631082,0.020469451,0.00052047294,0.0005670867,0.0009089917,0.000044347868],"about_ca_topic_score_codex":0.029825596,"about_ca_topic_score_gemma":0.0237045,"teacher_disagreement_score":0.029825596,"about_ca_system_score_codex":0.0010998914,"about_ca_system_score_gemma":0.0024903764,"threshold_uncertainty_score":0.13721812},"labels":[],"label_agreement":null},{"id":"W4389239385","doi":"10.11113/matematika.v39.n3.1496","title":"Multi-Population O’Hare with ARIMA, ARIMA-GARCH and ANN in Forecasting Mortality Rate","year":2023,"lang":"en","type":"article","venue":"MATEMATIKA","topic":"Insurance, Mortality, Demography, Risk Management","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":"Universiti Teknologi Malaysia","keywords":"Autoregressive integrated moving average; Life expectancy; Population; Statistics; Autoregressive conditional heteroskedasticity; Mean absolute percentage error; Mean squared error; Econometrics; Geography; Demography; Mathematics; Time series; Sociology; Volatility (finance)","score_opus":0.09375345205141045,"score_gpt":0.34914579118565425,"score_spread":0.2553923391342438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389239385","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":[0.20297441,0.0052062096,0.7828644,0.0011181335,0.0004837706,0.00009335782,0.00064302486,0.00083436427,0.005782339],"genre_scores_gemma":[0.9202219,0.0020672781,0.07235725,0.00016193795,0.00023896909,0.00007725196,0.00052708725,0.00005052652,0.004297776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915564,0.0003263463,0.00006770644,0.00020233622,0.0001723106,0.00007571006],"domain_scores_gemma":[0.9989826,0.0006069661,0.00012774207,0.000058648227,0.00018918669,0.00003480152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008355,0.0008023244,0.0007903343,0.00092498027,0.0003639223,0.0010193057,0.00085183827,0.00086313684,0.0008188468],"category_scores_gemma":[0.0035813898,0.0003115331,0.0012884585,0.0010121237,0.00025705618,0.0010738964,0.00054291467,0.0013777254,0.00021641071],"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":[0.00009968752,0.00008052718,0.016965939,0.0001086565,0.0003236338,0.00021971333,0.00009930133,0.89771545,0.0011498105,0.0051997886,0.0014892856,0.07654825],"study_design_scores_gemma":[0.0000037722093,0.00003582872,0.0029172376,0.00001463307,0.000041006795,0.000036693596,0.000021299724,0.9937052,0.0002662787,0.0021361343,0.00080256053,0.000019345738],"about_ca_topic_score_codex":0.017889727,"about_ca_topic_score_gemma":0.011129579,"teacher_disagreement_score":0.017889727,"about_ca_system_score_codex":0.0004778468,"about_ca_system_score_gemma":0.0006632854,"threshold_uncertainty_score":0.035571218},"labels":[],"label_agreement":null},{"id":"W4389376160","doi":"10.7888/juoeh.45.217","title":"Revisiting the Calculation for a Novel Measure of Average Lifespan Shortened: Real-World Examples From Cervical and Ovarian Cancers in Alberta, Canada, 2000 - 2020","year":2023,"lang":"en","type":"article","venue":"Journal of UOEH","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Confidence interval; Cervical cancer; Measure (data warehouse); Medicine; Years of potential life lost; Statistics; Demography; Gynecology; Cancer; Mathematics; Internal medicine; Computer science; Life expectancy; Population; Environmental health","score_opus":0.029206576635446305,"score_gpt":0.29316922427261366,"score_spread":0.26396264763716737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389376160","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":[0.69306505,0.0028515372,0.27058074,0.0029262304,0.00014428467,0.00021341552,0.013979304,0.00060189376,0.015637532],"genre_scores_gemma":[0.92539966,0.0005248663,0.06794744,0.00015751907,0.000036883852,0.00006142682,0.0048174136,0.00005678523,0.0009981095],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981408,0.00053809845,0.00015179704,0.00025782024,0.0007795595,0.00013184329],"domain_scores_gemma":[0.9890428,0.0054185507,0.0013004434,0.0007940631,0.0032448515,0.00019932202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062663085,0.0003686984,0.0003058069,0.0025843605,0.0005897066,0.0018666259,0.0015208494,0.0003621527,0.0008950245],"category_scores_gemma":[0.02846677,0.00014858847,0.00038754707,0.0035773763,0.00094676524,0.00067146437,0.001074981,0.00076137227,0.00011820672],"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":[0.00017183759,0.000042946245,0.6120314,0.00039565426,0.0002650783,0.00069199555,0.002042907,0.20388778,0.0010152248,0.049157396,0.011376323,0.11892152],"study_design_scores_gemma":[0.0000312511,0.00007925459,0.48682278,0.0003337389,0.00008647587,0.0005277019,0.0021034982,0.46490836,0.0019473474,0.018247725,0.024780227,0.0001316082],"about_ca_topic_score_codex":0.723634,"about_ca_topic_score_gemma":0.71410155,"teacher_disagreement_score":0.276366,"about_ca_system_score_codex":0.0058667427,"about_ca_system_score_gemma":0.0048509426,"threshold_uncertainty_score":0.5559871},"labels":[],"label_agreement":null},{"id":"W4389485471","doi":"10.1515/apjri-2023-0032","title":"Estimating Risk Relativity of Driving Records using Generalized Additive Models: A Statistical Approach for Auto Insurance Rate Regulation","year":2023,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Flexibility (engineering); Generalized linear model; Computer science; Class (philosophy); Theory of relativity; Statistical model; Econometrics; Estimation; Actuarial science; Mathematical optimization; Mathematics; Machine learning; Statistics; Economics; Artificial intelligence","score_opus":0.032738388290303996,"score_gpt":0.30291921488825524,"score_spread":0.27018082659795123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389485471","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":[0.19137429,0.00035267504,0.80470747,0.00054975535,0.00009765983,0.00019979951,0.0003801694,0.00035702216,0.0019811143],"genre_scores_gemma":[0.88239294,0.00019898971,0.115941465,0.000093896095,0.00008983543,0.00018380614,0.0003423621,0.000033985547,0.0007226941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9825209,0.011396321,0.0008186732,0.0023137832,0.0025397025,0.00041054247],"domain_scores_gemma":[0.9427011,0.040656917,0.0071525523,0.005542535,0.003372079,0.0005748766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023542434,0.00095659506,0.001305115,0.0052905553,0.0008773742,0.002945342,0.0019397496,0.0013213394,0.0014584381],"category_scores_gemma":[0.07140715,0.00054414483,0.0020935803,0.0038412246,0.0015761955,0.0018910429,0.0031958625,0.0023901432,0.0002562345],"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":[0.0004625823,0.00052887533,0.28040755,0.00034159512,0.001987556,0.00060067524,0.0024737117,0.3456104,0.0018151351,0.12234901,0.0023010937,0.24112189],"study_design_scores_gemma":[0.000012265033,0.0002688503,0.035094835,0.000119797885,0.00019184203,0.00021113615,0.00073937606,0.9067735,0.00067639345,0.05398902,0.0018186506,0.00010423628],"about_ca_topic_score_codex":0.006146508,"about_ca_topic_score_gemma":0.0054580993,"teacher_disagreement_score":0.023542434,"about_ca_system_score_codex":0.0012291962,"about_ca_system_score_gemma":0.0016967867,"threshold_uncertainty_score":0.12450576},"labels":[],"label_agreement":null},{"id":"W4389613579","doi":"10.52843/cassyni.w9k23k","title":"Presentation of EAJ Issue 13/2 - December 11th","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université du Québec à Montréal; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Presentation (obstetrics); Computer science; Medicine; Radiology","score_opus":0.056088149807611905,"score_gpt":0.37512790173533345,"score_spread":0.31903975192772155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389613579","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":[0.0033177345,0.00955023,0.0056777745,0.074672304,0.41883808,0.0007085268,0.0073256306,0.002499292,0.4774104],"genre_scores_gemma":[0.006662123,0.0020402714,0.0011731571,0.005199852,0.041152637,0.00019023777,0.002776045,0.0009982163,0.9398076],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873465,0.00015979545,0.000057835397,0.00031345355,0.00042453277,0.000309646],"domain_scores_gemma":[0.9980995,0.00026464346,0.000073354044,0.0001219267,0.000635793,0.000804684],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029340296,0.0016318735,0.0015409697,0.001738534,0.0019396435,0.006036906,0.0016620061,0.0039500664,0.53600174],"category_scores_gemma":[0.0038596054,0.00038215928,0.0012831943,0.0007936841,0.0005639725,0.0029878747,0.0037266605,0.0046463627,0.33266088],"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":[0.000097983255,0.000052986954,0.00009437373,0.00007712468,0.0000071150025,0.000068445755,0.000032408414,0.00005292872,0.0004103615,0.0016460704,0.9816809,0.01577925],"study_design_scores_gemma":[0.000017216185,0.000036096295,0.00051865197,0.00010919624,0.000004871578,0.00002960785,0.00007404338,0.0001773267,0.00015665243,0.0010643174,0.99780446,0.0000076149636],"about_ca_topic_score_codex":0.0009172183,"about_ca_topic_score_gemma":0.0027176943,"teacher_disagreement_score":0.53600174,"about_ca_system_score_codex":0.0015740651,"about_ca_system_score_gemma":0.0015258852,"threshold_uncertainty_score":0.6618372},"labels":[],"label_agreement":null},{"id":"W4389719704","doi":"10.4054/demres.2023.49.42","title":"Bayesian implementation of Rogers–Castro model migration schedules: An alternative technique for parameter estimation","year":2023,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Bayesian probability; Estimation; Econometrics; Bayes estimator; Estimation theory; Bayesian inference; Statistics; Economics; Computer science; Mathematics","score_opus":0.1259160796126696,"score_gpt":0.48361389086287504,"score_spread":0.35769781125020544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389719704","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":[0.0012406232,0.000065145745,0.99706334,0.00015643533,0.0000305219,0.000037780133,0.00010488298,0.00031850504,0.000982797],"genre_scores_gemma":[0.060728926,0.00032568973,0.93287313,0.0002347491,0.00013168294,0.0006337682,0.00062732556,0.00069013814,0.0037546232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9902671,0.006685319,0.00045608744,0.0011693087,0.0011542122,0.00026794514],"domain_scores_gemma":[0.9776227,0.01502075,0.0014394624,0.0033945907,0.0022091859,0.00031336083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018229991,0.0016614429,0.0018348605,0.0035511998,0.001133243,0.002904863,0.0044974317,0.0027673957,0.01580622],"category_scores_gemma":[0.0872024,0.0017463147,0.0030355903,0.0033717265,0.0014298442,0.003555706,0.0029777652,0.0048690713,0.0043153823],"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":[0.00014396993,0.00013487613,0.007768425,0.00041664875,0.0005011548,0.00026603288,0.0013993629,0.26705104,0.0016094961,0.49484292,0.014398719,0.21146736],"study_design_scores_gemma":[0.00007642774,0.000060940885,0.0015087483,0.00018889403,0.00010895722,0.00026922047,0.0001644325,0.7188706,0.0010014528,0.24930884,0.028341629,0.00009985135],"about_ca_topic_score_codex":0.020007737,"about_ca_topic_score_gemma":0.020584589,"teacher_disagreement_score":0.020007737,"about_ca_system_score_codex":0.0015834739,"about_ca_system_score_gemma":0.0037185643,"threshold_uncertainty_score":0.09641057},"labels":[],"label_agreement":null},{"id":"W4389805192","doi":"10.1007/s13385-023-00375-z","title":"Publisher Correction: A new approximation of annuity prices for age–period–cohort models","year":2023,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"","keywords":"Annuity; Mathematical finance; Economics; Period (music); Actuarial science; Econometrics; Cohort; Life annuity; Financial economics; Mathematics; Statistics; Finance; Philosophy","score_opus":0.053894599268005175,"score_gpt":0.310918211576084,"score_spread":0.25702361230807885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389805192","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":[0.0027030956,0.0034572673,0.14981245,0.043645374,0.7523552,0.00012923965,0.010804177,0.003571431,0.033521727],"genre_scores_gemma":[0.08491413,0.0054070326,0.11155288,0.015175885,0.11037136,0.00042252397,0.009946957,0.007316427,0.6548928],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970561,0.0007476572,0.00035842482,0.00049118814,0.0011868896,0.00015967316],"domain_scores_gemma":[0.9693257,0.008785115,0.0009430371,0.0053541604,0.014870718,0.0007212744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046288106,0.0013686925,0.001211749,0.0032064358,0.0013262099,0.0031334339,0.0038054015,0.0030982855,0.11649632],"category_scores_gemma":[0.081162125,0.0007997047,0.0020598322,0.0037183524,0.00089271006,0.0029766567,0.0015285638,0.0061874427,0.05859517],"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":[0.00006646195,0.000012614793,0.00039471436,0.00013877815,0.00005106824,0.00032401827,0.000043060896,0.0028110929,0.0001465447,0.020473959,0.9477832,0.027754497],"study_design_scores_gemma":[0.00010159682,0.00003891257,0.001958385,0.0003097934,0.00014928881,0.0014002923,0.00007002626,0.040163532,0.0011310733,0.0507954,0.9037753,0.00010647245],"about_ca_topic_score_codex":0.012094054,"about_ca_topic_score_gemma":0.0120133925,"teacher_disagreement_score":0.11649632,"about_ca_system_score_codex":0.001997471,"about_ca_system_score_gemma":0.0024355275,"threshold_uncertainty_score":0.389719},"labels":[],"label_agreement":null},{"id":"W4390322779","doi":"10.3917/popu.p2001.56n1-2.0049","title":"Principes de biodémographie avec référence particulière à la longévité humaine","year":2001,"lang":"fr","type":"article","venue":"Population","topic":"Insurance, Mortality, Demography, Risk Management","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":"Humanities; Philosophy","score_opus":0.019839852219520636,"score_gpt":0.3035530586048202,"score_spread":0.28371320638529957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390322779","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":[0.03075321,0.07518694,0.34054136,0.021597186,0.0031165294,0.00058441306,0.0024554383,0.00036611556,0.52539885],"genre_scores_gemma":[0.44177875,0.083458096,0.21180773,0.0074868384,0.0015478686,0.0014006464,0.0014574555,0.00023123552,0.2508314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978781,0.00071532815,0.00015581495,0.00039868624,0.0007717683,0.0000802486],"domain_scores_gemma":[0.9961755,0.0015399511,0.00048401058,0.00027829,0.0014257372,0.00009641552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031785439,0.00082408864,0.00065053586,0.0042781923,0.0018237995,0.005231521,0.0012822481,0.0010723798,0.008249165],"category_scores_gemma":[0.0039554695,0.00036046613,0.00077845313,0.0028731427,0.0065425215,0.0028684663,0.0024273274,0.002320443,0.0017154205],"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":[0.000046349924,0.00003740595,0.007435012,0.0008174151,0.000048413178,0.00028414602,0.004815244,0.0025696554,0.002180127,0.87040997,0.012286464,0.09906985],"study_design_scores_gemma":[0.000007657706,0.00012735657,0.013714368,0.0012475121,0.000056813406,0.00063070405,0.002958047,0.0015299318,0.0024140931,0.24497634,0.73224354,0.000093664465],"about_ca_topic_score_codex":0.027010785,"about_ca_topic_score_gemma":0.025455102,"teacher_disagreement_score":0.027010785,"about_ca_system_score_codex":0.004369162,"about_ca_system_score_gemma":0.004201358,"threshold_uncertainty_score":0.053707182},"labels":[],"label_agreement":null},{"id":"W4390343103","doi":"10.5539/ijsp.v12n6p1","title":"Modelling Factors that Predict Differences in Childhood Mortality in Lagos Communities Using Prognostic Logistic and Poisson Regression Models","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Tertiary Education Trust Fund","keywords":"Poisson regression; Covariate; Logistic regression; Child mortality; Demography; Regression analysis; Statistics; Poisson distribution; Medicine; Population; Environmental health; Mathematics","score_opus":0.14985040682845316,"score_gpt":0.35268186301234117,"score_spread":0.202831456183888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390343103","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":[0.97563577,0.0004996247,0.020382265,0.0010340593,0.000100772166,0.0001633259,0.0012434443,0.000120559744,0.0008201623],"genre_scores_gemma":[0.98816967,0.00041002218,0.008656754,0.00006958783,0.00004367143,0.00018378819,0.0012690471,0.000015674748,0.0011817927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99830496,0.00096546597,0.00010539103,0.0002581072,0.00010184452,0.0002641857],"domain_scores_gemma":[0.9925889,0.0057037147,0.00076284446,0.00017463589,0.0004542368,0.00031559748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056946827,0.0010807742,0.0009020616,0.0017146774,0.0007635782,0.001650868,0.0015942272,0.0012432779,0.003138144],"category_scores_gemma":[0.011621858,0.00061621703,0.0022032745,0.0013369285,0.00050619163,0.0010823028,0.0013916417,0.0021570341,0.00028464003],"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":[0.0007758537,0.00084954617,0.52220964,0.00022888208,0.00068772334,0.0009881419,0.0008249721,0.4474929,0.00040341567,0.0039856327,0.0016365929,0.019916615],"study_design_scores_gemma":[0.000051886047,0.0002545296,0.032416668,0.00007576299,0.0001456381,0.00011221108,0.0007486437,0.96307945,0.000110964815,0.0023965773,0.0005738836,0.00003378683],"about_ca_topic_score_codex":0.046500802,"about_ca_topic_score_gemma":0.024167527,"teacher_disagreement_score":0.046500802,"about_ca_system_score_codex":0.0011528593,"about_ca_system_score_gemma":0.0020876792,"threshold_uncertainty_score":0.092460275},"labels":[],"label_agreement":null},{"id":"W4390535457","doi":"10.3390/risks12010010","title":"Credibility Distribution Estimation with Weighted or Grouped Observations","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"University of Piraeus Research Centre","keywords":"Credibility; Estimator; Credibility theory; Econometrics; Estimation; Distribution (mathematics); Actuarial science; Projection (relational algebra); Mathematics; Economics; Computer science; Statistics; Algorithm","score_opus":0.098588550216306,"score_gpt":0.37496435487370405,"score_spread":0.2763758046573981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390535457","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":[0.019878669,0.00020397085,0.97854733,0.00023478043,0.000019471305,0.00003408266,0.000084512896,0.00007759001,0.0009195932],"genre_scores_gemma":[0.7604718,0.0006352849,0.23573016,0.00011317063,0.0001356739,0.00019318101,0.00047262077,0.00006527826,0.0021827894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951652,0.0026766043,0.00021657029,0.0009157679,0.00081087364,0.00021487304],"domain_scores_gemma":[0.94321597,0.04924181,0.0025552511,0.0027660422,0.0018985979,0.00032241308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016149743,0.0007986964,0.0013327276,0.0018078964,0.0004655249,0.0019114099,0.002109967,0.0020883826,0.0024116808],"category_scores_gemma":[0.09306412,0.0005141591,0.0011126996,0.0018313497,0.0018581456,0.005251388,0.002216393,0.0033528463,0.00037884098],"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":[0.00021099077,0.000060590293,0.0058450582,0.00016774955,0.00016941382,0.0004551863,0.0005389727,0.6765509,0.0012286034,0.2378629,0.0012037948,0.07570582],"study_design_scores_gemma":[0.000018587905,0.000030165913,0.00080709584,0.000039836665,0.000018166636,0.00007164394,0.00006917715,0.8611947,0.0005976293,0.13639721,0.00073127745,0.000024495846],"about_ca_topic_score_codex":0.002877297,"about_ca_topic_score_gemma":0.0012103331,"teacher_disagreement_score":0.016149743,"about_ca_system_score_codex":0.0010198132,"about_ca_system_score_gemma":0.00091057,"threshold_uncertainty_score":0.085409045},"labels":[],"label_agreement":null},{"id":"W4390962405","doi":"10.48550/arxiv.2401.07724","title":"A non-parametric estimator for Archimedean copulas under flexible censoring scenarios and an application to claims reserving","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Copula (linguistics); Estimator; Censoring (clinical trials); Bivariate analysis; Parametric statistics; Computer science; Univariate; Econometrics; Model selection; Goodness of fit; Nonparametric statistics; Parametric model; Statistics; Mathematics; Multivariate statistics; Artificial intelligence; Machine learning","score_opus":0.09043082734547293,"score_gpt":0.29174630397657386,"score_spread":0.20131547663110094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390962405","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":[0.012783649,0.00012617285,0.9862809,0.00018398758,0.000012598053,0.00005799566,0.00010301414,0.00012536206,0.00032625408],"genre_scores_gemma":[0.46672136,0.0006182551,0.5294371,0.00023300207,0.00013203455,0.00056992087,0.00089358864,0.00016201932,0.0012327625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99638236,0.0027576783,0.00010855576,0.00029482663,0.00032771623,0.00012882822],"domain_scores_gemma":[0.9665719,0.027167127,0.001951329,0.002736479,0.0012354562,0.00033776186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013198815,0.00075946504,0.0013643587,0.0015832689,0.000590063,0.0013152618,0.0025906884,0.0015587712,0.0019916205],"category_scores_gemma":[0.05285232,0.0005598283,0.001376827,0.0020983596,0.0009967578,0.0015444225,0.0018080351,0.002836035,0.00032287024],"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":[0.00007880509,0.00018458517,0.013725802,0.00017215766,0.00024651442,0.0004254838,0.00027602314,0.7675454,0.0016398191,0.13507526,0.002899127,0.07773109],"study_design_scores_gemma":[0.000016678654,0.000030086869,0.001171999,0.000017004772,0.0000143464495,0.000053659995,0.000030854302,0.9773532,0.00025313595,0.020412015,0.00063082256,0.00001616828],"about_ca_topic_score_codex":0.005031767,"about_ca_topic_score_gemma":0.004834856,"teacher_disagreement_score":0.013198815,"about_ca_system_score_codex":0.00088906154,"about_ca_system_score_gemma":0.0017610404,"threshold_uncertainty_score":0.06980282},"labels":[],"label_agreement":null},{"id":"W4391032270","doi":"10.1007/s13385-023-00373-1","title":"Evaluation of participating endowment life insurance policies in a stochastic environment","year":2024,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"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","keywords":"Actuarial science; Endowment policy; Endowment; Life insurance; Dividend; Economics; Stochastic modelling; Insurance policy; Finance","score_opus":0.07369921651465267,"score_gpt":0.3495630185620292,"score_spread":0.27586380204737654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391032270","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":[0.9867705,0.000097996744,0.010618493,0.0002514604,0.000018373627,0.000079728554,0.00018415018,0.00004178416,0.0019375555],"genre_scores_gemma":[0.99797374,0.000028896151,0.0014846785,0.0000098668115,0.0000039830334,0.000017472032,0.00008113777,0.000004377787,0.00039593125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971233,0.0018072786,0.00009806685,0.00022411598,0.00031521346,0.00043198475],"domain_scores_gemma":[0.97700787,0.018857455,0.0012088189,0.00051077333,0.0011481862,0.0012669122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008118083,0.00086130644,0.0012484096,0.00085627224,0.00047382203,0.0020777963,0.0011805103,0.0016270912,0.0026276119],"category_scores_gemma":[0.018904956,0.00036800257,0.0006953259,0.00065530493,0.0010889771,0.0014198729,0.0013271411,0.0010272445,0.000100517886],"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":[0.0011160658,0.00033852732,0.005015148,0.000036808433,0.000056426063,0.000089373345,0.000029758756,0.9858861,0.00054138515,0.003824272,0.00015053035,0.0029156771],"study_design_scores_gemma":[0.000088174784,0.00062645617,0.0019837664,0.000006471749,0.000043497344,0.000014071605,0.00008270475,0.9950122,0.0005474683,0.0014950252,0.00009005427,0.000010005477],"about_ca_topic_score_codex":0.009940674,"about_ca_topic_score_gemma":0.0042119403,"teacher_disagreement_score":0.009940674,"about_ca_system_score_codex":0.0024770894,"about_ca_system_score_gemma":0.0027889302,"threshold_uncertainty_score":0.042932987},"labels":[],"label_agreement":null},{"id":"W4391117154","doi":"10.1609/aaaiss.v2i1.27713","title":"SurvivalEVAL: A Comprehensive Open-Source Python Package for Evaluating Individual Survival Distributions","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Python (programming language); Open source; R package; Computer science; Software package; Open source software; Software engineering; Software; Data science; Programming language","score_opus":0.06270916326131269,"score_gpt":0.360950210917829,"score_spread":0.29824104765651627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391117154","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00753132,0.00040228415,0.6475743,0.0009141601,0.00028098223,0.0005190271,0.07957941,0.25014704,0.013051431],"genre_scores_gemma":[0.10758676,0.0013155452,0.64514196,0.0018479077,0.00036373737,0.0042415974,0.115295686,0.10519465,0.019012121],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855334,0.0004740647,0.00017870805,0.00019764056,0.00046709302,0.00012921313],"domain_scores_gemma":[0.9940568,0.0036664444,0.00052305753,0.0006328393,0.0008759423,0.00024485702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036364808,0.001162386,0.0008256481,0.0017046438,0.0005225682,0.0015382275,0.0021876362,0.00056883553,0.041811477],"category_scores_gemma":[0.019113127,0.0006525217,0.0015851649,0.0013832531,0.0006087991,0.0018467322,0.0026046652,0.0018187524,0.017602162],"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":[0.0003619943,0.00017442978,0.0165199,0.0018148319,0.0004014535,0.0003477144,0.0005782099,0.045362826,0.0025503617,0.036027778,0.69112086,0.20473972],"study_design_scores_gemma":[0.00030717053,0.00015184784,0.012640822,0.0005939071,0.00017730611,0.00083954725,0.00018576207,0.37771022,0.0069140685,0.16124636,0.43893173,0.0003012856],"about_ca_topic_score_codex":0.0045563797,"about_ca_topic_score_gemma":0.007210378,"teacher_disagreement_score":0.041811477,"about_ca_system_score_codex":0.0007567973,"about_ca_system_score_gemma":0.0032732468,"threshold_uncertainty_score":0.13987327},"labels":[],"label_agreement":null},{"id":"W4391347220","doi":"10.1093/jrsssa/qnae008","title":"On partial likelihood","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Mathematics; Computer science; Econometrics","score_opus":0.011061850388608796,"score_gpt":0.2952536295931763,"score_spread":0.2841917792045675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391347220","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":[0.001284403,0.0031579637,0.975274,0.004893205,0.00044707255,0.00006772683,0.00057294447,0.00019870725,0.014103973],"genre_scores_gemma":[0.23383601,0.0177111,0.6967035,0.011392575,0.0072842725,0.0014028677,0.0035524517,0.0017110518,0.026406165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9660415,0.024511445,0.0012912214,0.0029582125,0.0044918284,0.0007057929],"domain_scores_gemma":[0.8670719,0.11078691,0.003383364,0.01093597,0.006862105,0.00095972297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03443983,0.002122337,0.0025846274,0.0046061715,0.0018475138,0.0071199383,0.005062721,0.0038721985,0.01707182],"category_scores_gemma":[0.14482369,0.001316151,0.0028356952,0.007425546,0.009535636,0.014446122,0.0076608486,0.011401445,0.004454548],"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":[0.000018776827,0.000010470451,0.0007166988,0.00011094637,0.000044751938,0.00010736415,0.00018651319,0.0060942783,0.00006440255,0.95727277,0.008957979,0.026414972],"study_design_scores_gemma":[0.000007048185,0.00000793727,0.00010274084,0.00009209718,0.000012361213,0.00010796401,0.00003144774,0.013254363,0.000066763314,0.9749869,0.011314014,0.0000163187],"about_ca_topic_score_codex":0.0049227206,"about_ca_topic_score_gemma":0.0030701186,"teacher_disagreement_score":0.03443983,"about_ca_system_score_codex":0.004174476,"about_ca_system_score_gemma":0.0043211025,"threshold_uncertainty_score":0.18213743},"labels":[],"label_agreement":null},{"id":"W4391349978","doi":"10.7554/elife.79714.sa2","title":"Author response: Stable population structure in Europe since the Iron Age, despite high mobility","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Musée de la Civilisation","funders":"","keywords":"Age structure; Population; Geography; Economic geography; Demography; Sociology","score_opus":0.04825462374787979,"score_gpt":0.3662402200278969,"score_spread":0.3179855962800171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391349978","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005108333,0.0034635493,0.0012047518,0.83516777,0.12626112,0.00009538859,0.0038054702,0.00021061448,0.02468303],"genre_scores_gemma":[0.12660317,0.01233137,0.0031212266,0.50046116,0.11112163,0.00050904055,0.0056063896,0.0007152927,0.23953071],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99693155,0.0009291345,0.00026536835,0.00042734726,0.0011466908,0.00029989012],"domain_scores_gemma":[0.9560855,0.01324174,0.0020200931,0.0015849776,0.025048912,0.0020188342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005527553,0.00040236436,0.0006780515,0.0009908547,0.0010759552,0.0018076202,0.00091717223,0.0052922093,0.06435895],"category_scores_gemma":[0.0761648,0.00018529782,0.00036551664,0.0008305962,0.001172374,0.0016444793,0.0021938873,0.0032561712,0.016822524],"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":[0.000067698486,0.00000530645,0.00095924805,0.00027485617,0.000015947928,0.00016074441,0.00019665895,0.00008045645,0.00008122857,0.0009973597,0.9869971,0.010163436],"study_design_scores_gemma":[0.00009282255,0.000057659297,0.007239539,0.00092847744,0.000029902467,0.0004104657,0.0021173684,0.00018311579,0.00033602363,0.0032996777,0.9852598,0.00004518295],"about_ca_topic_score_codex":0.003041361,"about_ca_topic_score_gemma":0.0044149146,"teacher_disagreement_score":0.06435895,"about_ca_system_score_codex":0.0011293514,"about_ca_system_score_gemma":0.0035002604,"threshold_uncertainty_score":0.21530211},"labels":[],"label_agreement":null},{"id":"W4391350055","doi":"10.7554/elife.79714.sa0","title":"Editor's evaluation: Stable population structure in Europe since the Iron Age, despite high mobility","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","funders":"","keywords":"Population; Demography; Sociology","score_opus":0.03174359788876218,"score_gpt":0.34794930296655086,"score_spread":0.31620570507778867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391350055","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015915194,0.0016285707,0.00083497324,0.09339266,0.89969337,0.00015074242,0.0015687778,0.00036681775,0.002204876],"genre_scores_gemma":[0.0037701062,0.0039497833,0.0025240802,0.119782925,0.8485767,0.00053207675,0.0011364782,0.0006258661,0.019101985],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98764116,0.0036502455,0.0018245667,0.0014364576,0.004621376,0.0008262802],"domain_scores_gemma":[0.8784478,0.03877815,0.0050905854,0.0035966837,0.069104604,0.004982194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019431429,0.003676421,0.0037266125,0.0038952038,0.003059749,0.008660714,0.007372083,0.010906298,0.05960371],"category_scores_gemma":[0.13437101,0.0017659452,0.0026256195,0.0027098288,0.0027782524,0.005344995,0.0030100571,0.012118628,0.0227369],"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":[0.00007073643,0.000009783022,0.00007243476,0.0002929012,0.000019356938,0.000057449222,0.000013707968,0.00005391711,0.000024907193,0.00017950393,0.99531835,0.003886909],"study_design_scores_gemma":[0.00030778468,0.000057823254,0.0011769414,0.0017839383,0.00013814075,0.00019281736,0.00013472083,0.0008950842,0.00037470774,0.0022236023,0.9926307,0.00008369715],"about_ca_topic_score_codex":0.0061266674,"about_ca_topic_score_gemma":0.005947578,"teacher_disagreement_score":0.05960371,"about_ca_system_score_codex":0.00440533,"about_ca_system_score_gemma":0.0075333384,"threshold_uncertainty_score":0.19939429},"labels":[],"label_agreement":null},{"id":"W4391381539","doi":"10.1109/wsc60868.2023.10407759","title":"Cutting Through the Noise: Machine Learning Proxies for High Dimensional Nested Simulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Interpretability; Computer science; Transparency (behavior); Deep learning; Machine learning; Artificial intelligence; Stability (learning theory); Data modeling; Noise (video); Reliability (semiconductor); Computer security","score_opus":0.04020386282745392,"score_gpt":0.343662370279847,"score_spread":0.30345850745239306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391381539","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":[0.025157424,0.00015243019,0.97229046,0.0004880758,0.0000401786,0.000051019604,0.00007450118,0.00032254026,0.0014232983],"genre_scores_gemma":[0.78080803,0.00020072925,0.2162897,0.00030067377,0.00007927945,0.0002866701,0.00027012,0.00022259507,0.0015421181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993697,0.0045825304,0.00023327046,0.0005907321,0.00066653517,0.00023000376],"domain_scores_gemma":[0.9415995,0.04369194,0.004900201,0.0065009,0.0017957675,0.001511815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012419649,0.0010471705,0.0010248042,0.001298036,0.0008630912,0.0020977017,0.0025499444,0.0019090772,0.0027184372],"category_scores_gemma":[0.10419465,0.00077001576,0.0011681236,0.0008367742,0.004063691,0.003959928,0.0050207875,0.003831415,0.00030768968],"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":[0.000106391315,0.00009598683,0.00575439,0.000052216703,0.00006567813,0.000112891394,0.00021608677,0.6829446,0.0006781971,0.29796666,0.0006156171,0.011391243],"study_design_scores_gemma":[0.0000056193694,0.000017190318,0.0002122538,0.000013079028,0.000003621497,0.000011287876,0.000008674986,0.9331437,0.0002324871,0.06608916,0.00025497336,0.000008080671],"about_ca_topic_score_codex":0.0035349068,"about_ca_topic_score_gemma":0.0027057994,"teacher_disagreement_score":0.012419649,"about_ca_system_score_codex":0.0021421604,"about_ca_system_score_gemma":0.00213476,"threshold_uncertainty_score":0.06568223},"labels":[],"label_agreement":null},{"id":"W4391431695","doi":"10.3390/risks12020027","title":"LSTM-Based Coherent Mortality Forecasting for Developing Countries","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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":"Xi’an Jiaotong-Liverpool University; Concordia University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Life expectancy; Developing country; Benchmark (surveying); Computer science; Term (time); Econometrics; Economics; Demography; Geography; Economic growth; Population; Sociology","score_opus":0.20391683315919754,"score_gpt":0.4229996332222281,"score_spread":0.21908280006303057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391431695","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":[0.43458796,0.0015190797,0.55595106,0.0012565259,0.0001698705,0.000031892498,0.0015908588,0.0007442082,0.0041485685],"genre_scores_gemma":[0.9646091,0.0005116122,0.032529823,0.00010446563,0.000043084678,0.000028253815,0.0010161408,0.000024097862,0.0011332876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988174,0.000033750977,0.00001029644,0.00004070089,0.000015842224,0.000017744345],"domain_scores_gemma":[0.9997856,0.000083955376,0.000049473143,0.000017924001,0.000049960556,0.000013125791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005615497,0.0004970974,0.00032830433,0.0005537943,0.00017326917,0.00048437563,0.0005555554,0.0004663157,0.0009488573],"category_scores_gemma":[0.0016789536,0.00019677031,0.00038707795,0.000861748,0.00015798335,0.0010862066,0.00049737917,0.0006765846,0.00021216313],"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":[0.00007968675,0.000044229233,0.010532736,0.000067461915,0.00009701387,0.00011263556,0.000105039166,0.8563039,0.0016049028,0.005278546,0.0027383901,0.12303549],"study_design_scores_gemma":[0.0000025904426,0.000007030052,0.0012630379,0.0000059188365,0.000008866967,0.0000065534477,0.000012023795,0.996071,0.0002439928,0.0021647983,0.0002098971,0.0000043405003],"about_ca_topic_score_codex":0.0128535,"about_ca_topic_score_gemma":0.01086711,"teacher_disagreement_score":0.0128535,"about_ca_system_score_codex":0.000590662,"about_ca_system_score_gemma":0.00061651133,"threshold_uncertainty_score":0.025557399},"labels":[],"label_agreement":null},{"id":"W4391492722","doi":"10.2139/ssrn.4714596","title":"'Egalitarian Pooling and Sharing of Longevity Risk'A.K.A.'The Many Ways to Skin a Tontine Cat'","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Longevity; Pooling; Longevity risk; Economics; Psychology; Gerontology; Medicine; Computer science; Artificial intelligence","score_opus":0.015061746237866077,"score_gpt":0.29419812570977893,"score_spread":0.27913637947191283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391492722","genre_codex":"commentary","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":[0.20092644,0.02439301,0.16651477,0.37952453,0.0016182291,0.00009097141,0.001448577,0.00027710613,0.2252063],"genre_scores_gemma":[0.9577309,0.007322788,0.008468355,0.0047014505,0.0012419242,0.00006162028,0.00013048224,0.00005770209,0.02028479],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99807394,0.0009238194,0.00007164285,0.00037481112,0.00025684419,0.0002989016],"domain_scores_gemma":[0.99534816,0.0024119273,0.0009835422,0.00066705275,0.00037117943,0.00021816746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004806385,0.00038862292,0.00095982646,0.0006044253,0.0018944418,0.004016141,0.00085017306,0.004020244,0.016445292],"category_scores_gemma":[0.014981801,0.0003663354,0.00095161627,0.0011478935,0.0055750776,0.006235583,0.0028800387,0.0031437906,0.0011784941],"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":[0.00005084082,0.000018325887,0.0011529343,0.00004841328,0.00005458248,0.00006443464,0.00038340356,0.0015794748,0.0001273863,0.97216254,0.013389849,0.010967771],"study_design_scores_gemma":[0.0000120630975,0.0000093991775,0.0010938179,0.000025586334,0.000014068962,0.00003975039,0.00008854759,0.0014244479,0.00007250211,0.99175996,0.005449129,0.00001082875],"about_ca_topic_score_codex":0.0048839548,"about_ca_topic_score_gemma":0.003495261,"teacher_disagreement_score":0.016445292,"about_ca_system_score_codex":0.002505555,"about_ca_system_score_gemma":0.0013470536,"threshold_uncertainty_score":0.055014968},"labels":[],"label_agreement":null},{"id":"W4391715462","doi":"10.1007/978-3-031-17299-1_1647","title":"Life Quality Index","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Index (typography); Computer science; World Wide Web","score_opus":0.08867249009202682,"score_gpt":0.35659291053868714,"score_spread":0.2679204204466603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391715462","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":[0.011703021,0.011982601,0.0043621985,0.0032663848,0.0012214718,0.0004486414,0.13818026,0.0009936005,0.8278418],"genre_scores_gemma":[0.12639421,0.01451787,0.015188535,0.0037336024,0.0012607321,0.0015232268,0.21455793,0.00057178125,0.62225205],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999338,0.00008110292,0.000050546216,0.000057593727,0.00042636905,0.0000464379],"domain_scores_gemma":[0.99909866,0.00015862558,0.00011833715,0.00003949226,0.0004547788,0.00013008053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006355467,0.00045471365,0.0005821696,0.0028952044,0.00030592617,0.0009676477,0.0005579955,0.0002856274,0.09359323],"category_scores_gemma":[0.003096572,0.00007548013,0.0005115211,0.0027470042,0.00013459046,0.000771129,0.0007272221,0.00093357475,0.03682124],"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":[0.00017015797,0.0001142999,0.013669762,0.00031536768,0.000060637914,0.00003531252,0.00007548145,0.00033154932,0.00017128626,0.0076560653,0.6405747,0.33682543],"study_design_scores_gemma":[0.000049124126,0.00013501402,0.09086808,0.00036393208,0.00005807124,0.0003416384,0.00010205962,0.0006258075,0.00019385235,0.007438186,0.8997992,0.000024943043],"about_ca_topic_score_codex":0.0039651524,"about_ca_topic_score_gemma":0.004918014,"teacher_disagreement_score":0.09359323,"about_ca_system_score_codex":0.0010194537,"about_ca_system_score_gemma":0.0005566568,"threshold_uncertainty_score":0.31310058},"labels":[],"label_agreement":null},{"id":"W4391728583","doi":"10.1007/978-3-031-17299-1_1881","title":"Multiple Discrepancies Theory (MDT)","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Computer science","score_opus":0.03840556281726175,"score_gpt":0.28813458178233603,"score_spread":0.24972901896507427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391728583","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":[0.0055312584,0.0070785587,0.21273066,0.013816855,0.0015788813,0.000054039767,0.00024253542,0.00016786615,0.75879925],"genre_scores_gemma":[0.57571554,0.01047302,0.112992406,0.004202523,0.0019252776,0.0003788057,0.00040738366,0.0004825843,0.2934224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99905854,0.00043443474,0.000044568005,0.000121277175,0.0002861249,0.000055079137],"domain_scores_gemma":[0.99781054,0.0015688526,0.000112410344,0.00021707847,0.00023462357,0.00005660077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017519247,0.0006501752,0.0006494896,0.0015285078,0.0013988734,0.002751653,0.001507178,0.0022312552,0.02258976],"category_scores_gemma":[0.006891097,0.00043499173,0.0008354552,0.002226373,0.004956567,0.004785676,0.0017795372,0.0038273076,0.0027800351],"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":[0.0000011676601,0.0000035758762,0.0000365134,0.00001263947,0.000001961131,0.000017781538,0.00011345556,0.00032404985,0.000010873929,0.98855096,0.004363852,0.0065631806],"study_design_scores_gemma":[0.0000014410714,0.0000013114862,0.000042594416,0.000016649763,0.0000017193419,0.00004449457,0.00005279177,0.00086651015,0.00001704977,0.98464876,0.014304355,0.0000023898258],"about_ca_topic_score_codex":0.0025826662,"about_ca_topic_score_gemma":0.002322198,"teacher_disagreement_score":0.02258976,"about_ca_system_score_codex":0.0024116002,"about_ca_system_score_gemma":0.0016518311,"threshold_uncertainty_score":0.075570345},"labels":[],"label_agreement":null},{"id":"W4391729119","doi":"10.1007/978-3-031-17299-1_1851","title":"Mortality Rates","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto; Carleton University","funders":"","keywords":"Geography","score_opus":0.08463880298253072,"score_gpt":0.35743526713665696,"score_spread":0.27279646415412623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391729119","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":[0.0050483365,0.0074705263,0.0027468447,0.0021958689,0.0021461279,0.00010400508,0.051287655,0.00087080314,0.9281298],"genre_scores_gemma":[0.04779938,0.012237369,0.0024516976,0.0014374788,0.0016341844,0.000323175,0.053324185,0.0004337128,0.8803587],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903715,0.00015568563,0.00007131248,0.00014114568,0.0004901709,0.00010453485],"domain_scores_gemma":[0.999433,0.00009947771,0.00007849959,0.00006391144,0.00026325544,0.00006179175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086955755,0.00064316776,0.0005234685,0.0036155921,0.00041777192,0.0016086944,0.000857459,0.0005689052,0.11987255],"category_scores_gemma":[0.003110583,0.00020437494,0.0005623274,0.0036350521,0.00025035307,0.0009984743,0.0008615288,0.0012066334,0.115939826],"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":[0.000062366365,0.000048692764,0.0062377523,0.00031053615,0.00003847876,0.000051106483,0.00039819613,0.0005138843,0.00030651756,0.065716565,0.71203995,0.21427594],"study_design_scores_gemma":[0.000005607154,0.000033833887,0.012196118,0.00012600809,0.000012902217,0.00019101541,0.00015132538,0.00015348446,0.00011305951,0.005358997,0.98164797,0.000009685854],"about_ca_topic_score_codex":0.0043859016,"about_ca_topic_score_gemma":0.0055879084,"teacher_disagreement_score":0.11987255,"about_ca_system_score_codex":0.0008754135,"about_ca_system_score_gemma":0.0007730854,"threshold_uncertainty_score":0.4010136},"labels":[],"label_agreement":null},{"id":"W4391776341","doi":"10.2139/ssrn.4689090","title":"Identifying Risk Factor Regimes with Machine Learning: Implications for Tactical Asset Allocation","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Asset allocation; Asset (computer security); Factor (programming language); Computer science; Risk analysis (engineering); Business; Finance; Computer security; Portfolio","score_opus":0.023227170966092437,"score_gpt":0.3352273936634457,"score_spread":0.3120002226973533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391776341","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":[0.31912753,0.0029018826,0.6548606,0.012236569,0.00018872388,0.0001570632,0.0008986142,0.00048583603,0.009143169],"genre_scores_gemma":[0.93348897,0.0009183563,0.06327494,0.00041779908,0.00031602595,0.00009040076,0.00030452092,0.000034023884,0.0011549693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980274,0.0014202019,0.000085799344,0.0002644273,0.00011956674,0.00008266554],"domain_scores_gemma":[0.94961315,0.0449593,0.002420572,0.0015692213,0.0009156849,0.0005220332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008711343,0.0008280271,0.0016714185,0.001890475,0.0007268532,0.0037969023,0.0011269448,0.0015103954,0.0038271623],"category_scores_gemma":[0.053735636,0.000436788,0.0007960981,0.0020298557,0.0017702846,0.003983002,0.001538043,0.0026016259,0.00040223644],"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":[0.0003975735,0.00067657005,0.14294592,0.0002345194,0.00056238106,0.00021856504,0.00053686835,0.582376,0.00058291794,0.074963555,0.0050386526,0.19146648],"study_design_scores_gemma":[0.00002325618,0.000028987532,0.008209137,0.000058256977,0.000017505201,0.000028130695,0.00014225094,0.75788647,0.00009665368,0.23295651,0.0005266336,0.000026127036],"about_ca_topic_score_codex":0.00524848,"about_ca_topic_score_gemma":0.0045592007,"teacher_disagreement_score":0.008711343,"about_ca_system_score_codex":0.0010931892,"about_ca_system_score_gemma":0.0015024157,"threshold_uncertainty_score":0.046070516},"labels":[],"label_agreement":null},{"id":"W4391814456","doi":"10.1017/asb.2024.4","title":"Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data","year":2024,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Negative binomial distribution; Count data; Artificial neural network; Telematics; Computer science; Interpretability; Generalized linear model; Component (thermodynamics); Perceptron; Poisson distribution; Poisson regression; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Telecommunications","score_opus":0.061055876767558984,"score_gpt":0.3567739675076773,"score_spread":0.29571809074011834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391814456","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":[0.3633068,0.0007972761,0.62959975,0.0011864151,0.000121536985,0.0000758695,0.00054718775,0.0005385942,0.0038265819],"genre_scores_gemma":[0.97002935,0.00019471155,0.024261365,0.00010467041,0.000056194225,0.000081794635,0.00030102034,0.000021157832,0.0049498384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995291,0.00019702429,0.000023910989,0.00011548631,0.00007286945,0.00006146372],"domain_scores_gemma":[0.9975483,0.0015318408,0.00033871978,0.00016336175,0.00032406114,0.00009378106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002885914,0.0006182996,0.000588597,0.0006989058,0.00029306643,0.00076823804,0.0014837543,0.0011519706,0.0024633491],"category_scores_gemma":[0.0052619157,0.0003960344,0.0006559779,0.0006073349,0.0006964098,0.0011818751,0.00095952785,0.0015078656,0.00029934366],"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":[0.00009054158,0.00006120533,0.0036517116,0.000015544161,0.00004413983,0.000048554877,0.000026427946,0.97360283,0.00024707656,0.006030119,0.00039876477,0.015783137],"study_design_scores_gemma":[8.430881e-7,0.0000049537857,0.00017978283,0.0000011474234,0.000002153258,0.0000020979828,0.0000012358178,0.9989328,0.00004110165,0.0008042414,0.000028221151,0.0000014241549],"about_ca_topic_score_codex":0.013188336,"about_ca_topic_score_gemma":0.011518341,"teacher_disagreement_score":0.013188336,"about_ca_system_score_codex":0.0013952411,"about_ca_system_score_gemma":0.0006282347,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4391824537","doi":"10.1002/for.3086","title":"Space, mortality, and economic growth","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"St. Francis Xavier University","funders":"","keywords":"Gross domestic product; Econometrics; Model selection; Space (punctuation); Economics; Economic model; Growth model; Selection (genetic algorithm); Lag; Computer science; Statistics; Mathematics; Macroeconomics","score_opus":0.05966678313283622,"score_gpt":0.3299953345094367,"score_spread":0.27032855137660045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391824537","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":[0.9505174,0.0015733939,0.037242685,0.001558863,0.00007492589,0.000014439429,0.00045165306,0.00007768757,0.0084890295],"genre_scores_gemma":[0.9984458,0.0002719884,0.0006326957,0.000011987398,0.000013683461,0.0000040826085,0.00008508477,0.0000031801453,0.000531446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997427,0.00013511018,0.0000083076975,0.000037476333,0.000037471218,0.00003892925],"domain_scores_gemma":[0.9981483,0.0012283663,0.0002478355,0.00008850197,0.00019404822,0.0000929298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011127706,0.0003839744,0.0002513351,0.0009169417,0.00022912912,0.00089417084,0.0002999862,0.00041645658,0.0027982914],"category_scores_gemma":[0.0047674645,0.000091035276,0.00037443562,0.0008815205,0.0007292913,0.0008167338,0.0007544585,0.00061179436,0.00017406154],"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":[0.000114014314,0.000062712505,0.12114796,0.00005018939,0.000097482836,0.00016856893,0.00014983512,0.790412,0.00039161468,0.06425687,0.0015242716,0.021624481],"study_design_scores_gemma":[0.00000567904,0.00007733145,0.035344005,0.000026601861,0.000021770546,0.000046838857,0.0002426431,0.91591907,0.00024571878,0.046443623,0.0016112803,0.000015405625],"about_ca_topic_score_codex":0.009642974,"about_ca_topic_score_gemma":0.0048597436,"teacher_disagreement_score":0.009642974,"about_ca_system_score_codex":0.0008339091,"about_ca_system_score_gemma":0.0005995181,"threshold_uncertainty_score":0.019173682},"labels":[],"label_agreement":null},{"id":"W4392058980","doi":"10.1093/oso/9780198894131.001.0001","title":"Real-World Shocks and Retirement System Resiliency","year":2024,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"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":"Bard College; McGill University; Dartmouth College; Georgetown University; Johns Hopkins University; Harvard University; University of Connecticut; Wellesley College; National Institutes of Health; Ohio State University; University of Pennsylvania; U.S. Department of the Treasury","keywords":"Economics","score_opus":0.02072862571755345,"score_gpt":0.305754219756219,"score_spread":0.28502559403866556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392058980","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":[0.029810183,0.15430847,0.0069901273,0.03340144,0.004109174,0.000034123135,0.0028209102,0.00046632547,0.76805925],"genre_scores_gemma":[0.3503927,0.1914278,0.0043788343,0.0048645083,0.0043695983,0.00005458149,0.002741348,0.00032388777,0.44144666],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998958,0.000029763656,0.0000041355643,0.000013131321,0.00004772039,0.000009452869],"domain_scores_gemma":[0.9996038,0.0002670964,0.000027427273,0.000023539553,0.00004618954,0.000031984004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027191732,0.00023924437,0.00017860887,0.0010229109,0.000332192,0.0029504097,0.00027598286,0.00059425074,0.022842303],"category_scores_gemma":[0.0007899258,0.00010934977,0.00015749797,0.0016267473,0.00071338186,0.0018048666,0.00078810885,0.0007369835,0.003747211],"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":[0.00002867391,0.000041268115,0.004905526,0.00041243227,0.000028589211,0.0001492862,0.0015901176,0.0059645902,0.00039861625,0.3114957,0.39517286,0.2798124],"study_design_scores_gemma":[0.0000042218653,0.00003446445,0.014449173,0.0006416715,0.000013063253,0.000262594,0.001479761,0.0027828317,0.00027029318,0.1721881,0.80784476,0.000029102905],"about_ca_topic_score_codex":0.0029144387,"about_ca_topic_score_gemma":0.0061861007,"teacher_disagreement_score":0.022842303,"about_ca_system_score_codex":0.000870854,"about_ca_system_score_gemma":0.00053995935,"threshold_uncertainty_score":0.07641518},"labels":[],"label_agreement":null},{"id":"W4392515953","doi":"10.1007/s42650-024-00080-6","title":"Models for Estimating Intrinsic r and the Mean Age of a Population at Stability: Evaluations at the National and Sub-national Level","year":2024,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Concordia University; Concordia University of Edmonton","keywords":"Stability (learning theory); Statistics; Demography; Econometrics; Population; Mathematics; Geography; Computer science; Sociology; Machine learning","score_opus":0.20632550153716367,"score_gpt":0.41112944212203434,"score_spread":0.20480394058487067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392515953","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":[0.79722726,0.0022640075,0.18630618,0.0030517518,0.0000930891,0.00029582274,0.0022184516,0.00045452343,0.008088866],"genre_scores_gemma":[0.96090287,0.0005823716,0.035569206,0.00012903319,0.000028709876,0.00009926387,0.0009960348,0.000054438297,0.0016380101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99164385,0.005772923,0.00020448888,0.00094639225,0.00083387666,0.0005984039],"domain_scores_gemma":[0.93871987,0.05003404,0.002791576,0.0018672616,0.0057407687,0.0008465217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030030267,0.0009870694,0.0011918361,0.0028311484,0.0013010534,0.002455579,0.0033339902,0.0008956975,0.0024220552],"category_scores_gemma":[0.06279437,0.0005238516,0.0021160587,0.0037178758,0.001954396,0.0017080602,0.0015491865,0.0014772746,0.00030376067],"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":[0.00045722918,0.00012625514,0.3258541,0.00028247057,0.0014355995,0.00020279903,0.0014191268,0.56705153,0.00025756954,0.050592687,0.003103904,0.04921673],"study_design_scores_gemma":[0.000040379364,0.00019510095,0.049177095,0.000116769275,0.00039770463,0.00005429141,0.0009973423,0.9353578,0.00027490588,0.01174892,0.001569672,0.00006999141],"about_ca_topic_score_codex":0.8643645,"about_ca_topic_score_gemma":0.76478094,"teacher_disagreement_score":0.1356355,"about_ca_system_score_codex":0.013081075,"about_ca_system_score_gemma":0.018085558,"threshold_uncertainty_score":0.2728685},"labels":[],"label_agreement":null},{"id":"W4392577525","doi":"","title":"The definition of a French actuarial climate index; one more step towards a European index","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Concordia University","funders":"","keywords":"Index (typography); Actuarial science; Economics; Computer science","score_opus":0.036474791382337245,"score_gpt":0.2747958261549159,"score_spread":0.23832103477257863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392577525","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":[0.101176694,0.018238964,0.5014855,0.016502984,0.0064425017,0.0005091962,0.012378921,0.0019114827,0.34135377],"genre_scores_gemma":[0.73437816,0.008430886,0.1978702,0.0027100563,0.0034947502,0.0005543521,0.008752883,0.00060515566,0.04320347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967899,0.0009963083,0.00025841492,0.0003520765,0.0013662643,0.00023701867],"domain_scores_gemma":[0.9967393,0.0007036105,0.0006515691,0.00035963304,0.0012895467,0.0002562069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034762328,0.0008801294,0.00045824214,0.00385885,0.00080575416,0.003938571,0.0006605403,0.0009811191,0.004870266],"category_scores_gemma":[0.0075285165,0.00014216577,0.00084979326,0.0032959417,0.0007565125,0.0016991947,0.0010999113,0.0015007977,0.0016998248],"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":[0.00012931292,0.00010279574,0.045571025,0.00028202453,0.00027510416,0.00036465164,0.00075144775,0.029879322,0.0026706422,0.43633774,0.090810366,0.3928256],"study_design_scores_gemma":[0.00002269986,0.00029717386,0.095934175,0.000367058,0.00008994903,0.0010196251,0.0004339738,0.043780185,0.0032880267,0.054092348,0.80038744,0.00028741613],"about_ca_topic_score_codex":0.02629813,"about_ca_topic_score_gemma":0.010726117,"teacher_disagreement_score":0.02629813,"about_ca_system_score_codex":0.0029933583,"about_ca_system_score_gemma":0.0018695812,"threshold_uncertainty_score":0.05229014},"labels":[],"label_agreement":null},{"id":"W4392659751","doi":"10.1016/j.socscimed.2024.116751","title":"The contributions of avoidable causes of death to gender gap in life expectancy and life disparity in the US and Canada: 2001–2019","year":2024,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Life expectancy; Medicine; Demography; Cause of death; Public health; Years of potential life lost; Disease; Gerontology; Population; Environmental health","score_opus":0.03208607881499992,"score_gpt":0.3391517269238732,"score_spread":0.30706564810887327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392659751","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":[0.898374,0.016431697,0.00082415994,0.012850522,0.0002706936,0.0000980391,0.05474903,0.00008485159,0.01631696],"genre_scores_gemma":[0.98816186,0.0025155887,0.0002959859,0.0005464196,0.000046778787,0.00002162793,0.0071924133,0.000009854489,0.001209375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986633,0.00009865126,0.000076155775,0.00014397888,0.00045647038,0.00056138897],"domain_scores_gemma":[0.99577147,0.0002310676,0.0006510227,0.00009747267,0.0024478526,0.00080118724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012002147,0.00034649268,0.00038916676,0.0019356516,0.0014987241,0.0012009987,0.00093242544,0.00048296392,0.0015321872],"category_scores_gemma":[0.0055028326,0.00016304036,0.0011150908,0.003361797,0.00053041446,0.0006147016,0.0016614802,0.0014138119,0.00012705733],"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":[0.000097240794,0.000018745037,0.96653897,0.00013405657,0.00021753118,0.000102442515,0.0007214722,0.0008686874,0.000060022743,0.0015703771,0.008069475,0.021600999],"study_design_scores_gemma":[0.0000053773288,0.0000111397785,0.99101806,0.000113279726,0.000086520755,0.00008434402,0.001077351,0.0010344321,0.00010973826,0.0002522032,0.0061875065,0.000020137628],"about_ca_topic_score_codex":0.9881259,"about_ca_topic_score_gemma":0.990516,"teacher_disagreement_score":0.022128178,"about_ca_system_score_codex":0.022128178,"about_ca_system_score_gemma":0.04389923,"threshold_uncertainty_score":0.1605519},"labels":[],"label_agreement":null},{"id":"W4393099323","doi":"10.1093/ije/dyae024","title":"US exceptionalism? International trends in midlife mortality","year":2024,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":21,"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":"H2020 European Research Council; Leverhulme Trust","keywords":"Life expectancy; Demography; Exceptionalism; Medicine; Gerontology; High income countries; Political science; Population; Developing country; Economic growth; Sociology; Economics","score_opus":0.09328048437121927,"score_gpt":0.46595662609597954,"score_spread":0.37267614172476027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393099323","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":[0.66553414,0.09481251,0.0016151415,0.06936497,0.0031204482,0.00008189863,0.0864925,0.00030635725,0.07867204],"genre_scores_gemma":[0.95113724,0.020434778,0.00087970536,0.0038552328,0.00091396313,0.00008126016,0.020751236,0.000041532567,0.0019051008],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995921,0.00007638081,0.00007552011,0.00007928627,0.00007834596,0.00009841388],"domain_scores_gemma":[0.99745005,0.00030814347,0.0010178889,0.00013042602,0.0006451611,0.00044839722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096820603,0.00016365544,0.00021573127,0.0026952391,0.00037878845,0.0011995542,0.00032092733,0.0003273954,0.005468714],"category_scores_gemma":[0.004520801,0.000076929835,0.0003896477,0.0058585834,0.00027579057,0.0011827569,0.001039981,0.00076687423,0.00048350214],"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":[0.00015040762,0.000035897512,0.7839797,0.0011650338,0.00024827296,0.00026416287,0.0027382136,0.0002679358,0.00015086013,0.007373806,0.08483964,0.11878606],"study_design_scores_gemma":[0.0000038331814,0.0000375169,0.9334633,0.0009512052,0.000059531543,0.00034139873,0.003330156,0.00017013469,0.00008510008,0.0013262297,0.060215227,0.000016360607],"about_ca_topic_score_codex":0.019882511,"about_ca_topic_score_gemma":0.021101112,"teacher_disagreement_score":0.019882511,"about_ca_system_score_codex":0.0007839537,"about_ca_system_score_gemma":0.0007908119,"threshold_uncertainty_score":0.039533556},"labels":[],"label_agreement":null},{"id":"W4394598694","doi":"10.1063/5.0204804","title":"The calculation of critical illness insurance premiums with terminal illness condition","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Association of Canadian Archivists","funders":"","keywords":"Terminal (telecommunication); Critical illness; Actuarial science; Business; Computer science; Medicine; Intensive care medicine; Telecommunications; Critically ill","score_opus":0.016815085146062312,"score_gpt":0.3113919480603761,"score_spread":0.2945768629143138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394598694","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":[0.83025724,0.0012154434,0.123856634,0.00092465436,0.0002478241,0.0008236487,0.008947028,0.00048781096,0.033239767],"genre_scores_gemma":[0.96155417,0.00026768332,0.030260587,0.000042909665,0.000041563657,0.00020151644,0.0034891535,0.000033998214,0.00410847],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903023,0.00020676805,0.00009970236,0.00014461053,0.0003935468,0.00012509237],"domain_scores_gemma":[0.9970643,0.0016010622,0.00043909327,0.00016013408,0.0005869036,0.00014856452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015223795,0.00039069625,0.00033830534,0.0021372733,0.0003117815,0.0010077851,0.00086265785,0.0005708653,0.0054732524],"category_scores_gemma":[0.010774266,0.00023486782,0.0009339899,0.0011126974,0.0002242555,0.0008220017,0.00071308756,0.0009922321,0.0005284988],"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":[0.0005261973,0.0005438051,0.64041483,0.00043020368,0.00048642443,0.0015704323,0.00078760757,0.13948232,0.0020730281,0.038122326,0.012807822,0.16275506],"study_design_scores_gemma":[0.000040067134,0.00026702965,0.35525772,0.00015678542,0.0002513791,0.0015924129,0.0010146372,0.6077287,0.0018648105,0.01936849,0.012373121,0.00008482819],"about_ca_topic_score_codex":0.008875351,"about_ca_topic_score_gemma":0.0050485604,"teacher_disagreement_score":0.008875351,"about_ca_system_score_codex":0.0012791412,"about_ca_system_score_gemma":0.0010264443,"threshold_uncertainty_score":0.018309891},"labels":[],"label_agreement":null},{"id":"W4394684032","doi":"10.1093/forestscience/57.2.102","title":"Estimating a Multilevel Dominant Height–Age Model from Nested Data with Generalized Errors","year":2011,"lang":"en","type":"article","venue":"Forest Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Alberta","funders":"Forest Resource Improvement Association of Alberta; Government of Alberta","keywords":"Nested set model; Statistics; Mathematics; Multilevel model; Econometrics; Computer science; Data mining","score_opus":0.11677078518897419,"score_gpt":0.33650037201991223,"score_spread":0.21972958683093805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394684032","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":[0.42902753,0.0002813341,0.56754994,0.00042044837,0.000062306055,0.00008971379,0.0014111827,0.00042880332,0.0007287904],"genre_scores_gemma":[0.8342074,0.00019006786,0.16010495,0.00012914208,0.00006352775,0.00025216473,0.002069037,0.00011423311,0.002869507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962675,0.001983527,0.0002085576,0.0009186403,0.0002291453,0.0003927202],"domain_scores_gemma":[0.9666694,0.027360555,0.0019424292,0.0027455387,0.0007939492,0.00048816254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010036063,0.00081057346,0.0025788709,0.0013502203,0.00087912485,0.0017322493,0.0039818785,0.0021351266,0.0029763908],"category_scores_gemma":[0.02824309,0.001737791,0.0034577518,0.001730092,0.0012128702,0.002134756,0.0029377467,0.0025949725,0.00061940076],"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":[0.00061404926,0.00027560224,0.10478547,0.00017013823,0.0016437786,0.00040969465,0.0008250219,0.8062718,0.0009834729,0.0356354,0.0016352448,0.046750374],"study_design_scores_gemma":[0.000042133,0.00006600287,0.006057104,0.00002480029,0.000131623,0.00004490427,0.000062641986,0.97909504,0.00013271063,0.014083794,0.00023684122,0.00002244078],"about_ca_topic_score_codex":0.046597727,"about_ca_topic_score_gemma":0.05890504,"teacher_disagreement_score":0.046597727,"about_ca_system_score_codex":0.0015244139,"about_ca_system_score_gemma":0.0019240426,"threshold_uncertainty_score":0.09265298},"labels":[],"label_agreement":null},{"id":"W4394749666","doi":"10.31857/s0320972523110106","title":"Actuarial aging rates in human cohorts","year":2023,"lang":"en","type":"article","venue":"Биохимия","topic":"Insurance, Mortality, Demography, Risk Management","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":"Cohort; Gompertz function; Demography; Life expectancy; Cohort effect; Mortality rate; Cohort study; Medicine; Gerontology; Population; Statistics; Internal medicine; Mathematics","score_opus":0.032823629452280276,"score_gpt":0.36571150301738065,"score_spread":0.33288787356510036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394749666","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":[0.83638304,0.008449242,0.1159614,0.00030378252,0.00028233873,0.00032827337,0.025666468,0.0007582152,0.01186727],"genre_scores_gemma":[0.97275645,0.0017974107,0.0121083185,0.00007044361,0.0000865765,0.00025435135,0.0107713435,0.00006838414,0.002086657],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974591,0.00078251475,0.0002691411,0.0008529477,0.00047167222,0.00016460882],"domain_scores_gemma":[0.99106616,0.0034886387,0.002280243,0.002110874,0.0008792672,0.00017495597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008558202,0.0003469123,0.00041426794,0.003558899,0.00024523452,0.00088544923,0.00062732346,0.00038564406,0.002363984],"category_scores_gemma":[0.020125855,0.00016210316,0.00074208126,0.0024277084,0.00035547692,0.0008980181,0.0009718382,0.00058571214,0.00080149143],"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":[0.00022587185,0.000035312223,0.8435389,0.00032621695,0.0011171454,0.00029503132,0.00080796314,0.03456081,0.0014503296,0.011097549,0.0036436354,0.102901235],"study_design_scores_gemma":[0.000018413271,0.00036814064,0.90604365,0.00019020168,0.00044885033,0.001635087,0.0005252554,0.039533097,0.0024618541,0.01381415,0.03485267,0.00010861018],"about_ca_topic_score_codex":0.0017956415,"about_ca_topic_score_gemma":0.0010803388,"teacher_disagreement_score":0.008558202,"about_ca_system_score_codex":0.0004967826,"about_ca_system_score_gemma":0.00033438441,"threshold_uncertainty_score":0.04526061},"labels":[],"label_agreement":null},{"id":"W4394813672","doi":"10.3390/jrfm17040158","title":"A Discrete Risk-Theory Approach to Manage Equity-Linked Policies in an Incomplete Market","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actuarial science; Equity (law); Hedge; Arbitrage; Martingale (probability theory); Business; Risk management; Economics; Hedge fund; Incomplete markets; Financial economics; Finance; Microeconomics","score_opus":0.01805835347558196,"score_gpt":0.3082631903718312,"score_spread":0.29020483689624926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394813672","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":[0.026384976,0.0004959171,0.9594762,0.0025101535,0.000119389806,0.000067507564,0.00018840151,0.000058747093,0.010698774],"genre_scores_gemma":[0.82601756,0.0012006746,0.1532731,0.00042874028,0.00036580322,0.0003772817,0.00017379322,0.00005681023,0.018106315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980719,0.0009428251,0.00008235179,0.00029847567,0.00041296156,0.00019147841],"domain_scores_gemma":[0.9953945,0.002941178,0.00062889716,0.00032843862,0.00026488845,0.00044206195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043625664,0.0012426688,0.001851431,0.0013410482,0.00078090647,0.0035928732,0.003292762,0.003265751,0.005916788],"category_scores_gemma":[0.008047331,0.0008710104,0.0018490745,0.0009256014,0.0038738707,0.004627003,0.0023787946,0.004002059,0.00037533982],"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":[0.00002351819,0.00006896025,0.0003975521,0.000053995333,0.000041301857,0.00013691449,0.000093575174,0.2593022,0.0003951534,0.7363963,0.00049742096,0.0025930244],"study_design_scores_gemma":[0.000027422762,0.0000532587,0.00011645945,0.000020036628,0.000016288934,0.000035836496,0.000030370415,0.70736015,0.00012412826,0.2911448,0.0010506797,0.000020546162],"about_ca_topic_score_codex":0.00285631,"about_ca_topic_score_gemma":0.002447967,"teacher_disagreement_score":0.005916788,"about_ca_system_score_codex":0.0032977723,"about_ca_system_score_gemma":0.0023575984,"threshold_uncertainty_score":0.023927152},"labels":[],"label_agreement":null},{"id":"W4394863499","doi":"10.2139/ssrn.4785927","title":"A Bayesian Approach to Discrimination-free Insurance Pricing","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Download; Bayesian probability; Computer science; World Wide Web; Internet privacy; Artificial intelligence","score_opus":0.011917369530415993,"score_gpt":0.2827233303036859,"score_spread":0.2708059607732699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394863499","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":[0.008726878,0.0008790255,0.97751325,0.0033158858,0.00014697821,0.000057743055,0.00023142688,0.00012354508,0.009005263],"genre_scores_gemma":[0.64092445,0.0037370955,0.30201277,0.0013080917,0.0021871084,0.00055690337,0.0006965144,0.0002950668,0.048282027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99396634,0.0036421488,0.00022027764,0.0005644762,0.0011970619,0.00040968452],"domain_scores_gemma":[0.9732589,0.023156092,0.0007490215,0.0009925336,0.0013939206,0.0004496152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013097773,0.0013134648,0.003906404,0.0025966465,0.0013861293,0.004571149,0.0052873045,0.0045864345,0.011017602],"category_scores_gemma":[0.045747917,0.0025120405,0.0022332645,0.0032119374,0.004299541,0.007602368,0.0030455235,0.005701067,0.0011343975],"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":[0.000044022614,0.0000745875,0.0005294438,0.000060525268,0.000086755106,0.0001195647,0.00015337512,0.16856503,0.00010241815,0.81236225,0.003530566,0.014371457],"study_design_scores_gemma":[0.000028736029,0.000011048486,0.00017654245,0.00001910849,0.000024215155,0.000045846402,0.000017892075,0.4347287,0.000027252352,0.56372046,0.0011761973,0.000024034662],"about_ca_topic_score_codex":0.014917809,"about_ca_topic_score_gemma":0.012980262,"teacher_disagreement_score":0.014917809,"about_ca_system_score_codex":0.0035528906,"about_ca_system_score_gemma":0.0029533375,"threshold_uncertainty_score":0.069268525},"labels":[],"label_agreement":null},{"id":"W4394962853","doi":"10.3390/risks12040070","title":"Determining Safe Withdrawal Rates for Post-Retirement via a Ruin-Theory Approach","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Economics; Ruin theory; Actuarial science; Econometrics; Risk model","score_opus":0.04918025630331083,"score_gpt":0.39126112177867334,"score_spread":0.3420808654753625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394962853","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":[0.5832659,0.0006464919,0.40565085,0.0005410221,0.00006491354,0.00037791135,0.001176921,0.00072967523,0.0075463075],"genre_scores_gemma":[0.9610702,0.00021521209,0.035228178,0.00008498935,0.000028599025,0.00015458894,0.00075985433,0.000070762944,0.002387653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744934,0.0010408199,0.00016561907,0.0004762193,0.0005431616,0.00032483868],"domain_scores_gemma":[0.9726121,0.018612765,0.003945411,0.001781452,0.0025067371,0.0005416667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011000999,0.0006708954,0.001077577,0.0025546788,0.00050389674,0.0015313785,0.002142718,0.0013749675,0.0032405313],"category_scores_gemma":[0.036153525,0.00041720222,0.0013730315,0.0011396713,0.0008404175,0.0018048477,0.0011230835,0.0018636307,0.00089506106],"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":[0.00065516576,0.00038637622,0.18691541,0.00024533758,0.00023110743,0.00073405745,0.00088246516,0.673018,0.0020353764,0.041895952,0.004891983,0.0881088],"study_design_scores_gemma":[0.000013130227,0.00016342943,0.018411089,0.000060485396,0.000040576848,0.00018617333,0.00028869233,0.9704536,0.0011734405,0.008095949,0.001070421,0.00004305745],"about_ca_topic_score_codex":0.0084396675,"about_ca_topic_score_gemma":0.0063111247,"teacher_disagreement_score":0.011000999,"about_ca_system_score_codex":0.0014425005,"about_ca_system_score_gemma":0.0013348776,"threshold_uncertainty_score":0.058179557},"labels":[],"label_agreement":null},{"id":"W4395003049","doi":"10.3390/jrfm17040170","title":"Estimating Asset Parameters Using Levy’s Moment Matching Method","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Moment (physics); Matching (statistics); Asset (computer security); Econometrics; Mathematics; Computer science; Economics; Statistics; Physics","score_opus":0.022193631570047036,"score_gpt":0.331674461692073,"score_spread":0.30948083012202593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395003049","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":[0.01751321,0.00008292452,0.9813426,0.000047119192,0.000012152489,0.000036325706,0.0000736111,0.00025992215,0.00063217786],"genre_scores_gemma":[0.5208873,0.0002938692,0.4751557,0.00010053739,0.00007398162,0.00022738187,0.00062143157,0.0001312983,0.0025084158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983284,0.0006854617,0.000113590046,0.000332784,0.00038790095,0.00015195564],"domain_scores_gemma":[0.99571747,0.0027135978,0.00047563348,0.00047053085,0.0005389487,0.00008383396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031518063,0.0006060907,0.0012379516,0.0030292284,0.00047630267,0.0011173333,0.0012518407,0.0014054225,0.0030023954],"category_scores_gemma":[0.012629033,0.00053493166,0.0013026865,0.0022743584,0.0005641225,0.0018992309,0.0013006694,0.0013489736,0.0011438516],"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":[0.00018701327,0.00020518029,0.02231407,0.000119808625,0.00033817094,0.0004504839,0.00024613665,0.52500147,0.008586844,0.06789143,0.002539798,0.37211958],"study_design_scores_gemma":[0.000010704439,0.000030172345,0.0024463749,0.000012416324,0.000022553968,0.00011176674,0.0000196471,0.97534275,0.0017274087,0.019319844,0.0009196971,0.00003660225],"about_ca_topic_score_codex":0.0031163874,"about_ca_topic_score_gemma":0.0020458964,"teacher_disagreement_score":0.0031518063,"about_ca_system_score_codex":0.0007015133,"about_ca_system_score_gemma":0.0012196132,"threshold_uncertainty_score":0.016668558},"labels":[],"label_agreement":null},{"id":"W4395449878","doi":"10.1016/j.puhip.2024.100500","title":"Exploring the determinants associated with adult mortality in Malta: A cohort study between 2014 and 2020","year":2024,"lang":"en","type":"article","venue":"Public Health in Practice","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"Strong","keywords":"Medicine; Population; Logistic regression; Demography; Cohort; Acute coronary syndrome; Cause of death; Lung cancer; Epidemiology; Myocardial infarction; Disease; Internal medicine; Environmental health","score_opus":0.134866390598321,"score_gpt":0.406775659508496,"score_spread":0.271909268910175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395449878","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":[0.9960514,0.0003067492,0.0001259117,0.00013641515,0.00000710539,0.000045235065,0.0029943709,0.00000461641,0.00032820247],"genre_scores_gemma":[0.99574983,0.00035697274,0.0001798474,0.00008472089,0.0000082983215,0.00008579618,0.0024701348,0.0000037911987,0.0010605043],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99971324,0.00004910266,0.00002757106,0.000065536646,0.000037575574,0.00010697111],"domain_scores_gemma":[0.9997029,0.000013449577,0.00009398658,0.00003094655,0.00008869706,0.00006997283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041603975,0.00029104523,0.00036323463,0.0006990447,0.00061926706,0.00078653934,0.00046359486,0.00033758738,0.0012959605],"category_scores_gemma":[0.00074668264,0.00037559523,0.00051835633,0.001443483,0.00020841775,0.00048811204,0.001030621,0.0005687621,0.00036156888],"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":[0.00006372419,0.000042952463,0.9971608,0.000021161157,0.00006168279,0.00019052834,0.0002813907,0.00003616398,0.00021100511,0.000033641638,0.00044479017,0.0014522249],"study_design_scores_gemma":[0.0000029800783,0.000033342447,0.9985071,0.000012439727,0.000030852756,0.000102094265,0.0004957118,0.00013821787,0.000032239343,0.000017442266,0.0006234761,0.0000039818037],"about_ca_topic_score_codex":0.11546684,"about_ca_topic_score_gemma":0.1651125,"teacher_disagreement_score":0.11546684,"about_ca_system_score_codex":0.0014461584,"about_ca_system_score_gemma":0.0011187941,"threshold_uncertainty_score":0.22958946},"labels":[],"label_agreement":null},{"id":"W4396856509","doi":"10.1016/j.insmatheco.2024.04.005","title":"Coping with longevity via hedging: Fair dynamic valuation of variable annuities","year":2024,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"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":"Coping (psychology); Valuation (finance); Actuarial science; Annuity; Longevity; Economics; Business; Life annuity; Econometrics; Psychology; Medicine; Gerontology; Finance; Pension; Clinical psychology","score_opus":0.020571639020913877,"score_gpt":0.26606681041744684,"score_spread":0.24549517139653296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396856509","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":[0.32638565,0.0013334034,0.65442175,0.0028450603,0.00031272546,0.00006643432,0.00010778563,0.0000980837,0.014429035],"genre_scores_gemma":[0.98830825,0.00026736053,0.009117902,0.00004230641,0.00006505647,0.000013298585,0.000020716141,0.0000108682725,0.0021542963],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991716,0.0004221957,0.00003487402,0.0001227224,0.00012940675,0.000119261145],"domain_scores_gemma":[0.9970222,0.0014276588,0.00040859697,0.00045632132,0.00033558704,0.00034951218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004500501,0.00068616064,0.00081440655,0.00055473286,0.0005473574,0.0035402991,0.0012963446,0.0016733601,0.002616241],"category_scores_gemma":[0.012764149,0.00033948474,0.0006303846,0.00056916825,0.0018322417,0.00453824,0.0016637208,0.0016181946,0.00009973915],"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":[0.00016431251,0.00011721259,0.002560427,0.00005730076,0.00008005127,0.00029276527,0.00030355988,0.3333858,0.0019178276,0.6349226,0.0011316747,0.02506641],"study_design_scores_gemma":[0.000016306656,0.00006994332,0.00063518743,0.000017307879,0.000021717495,0.00006297268,0.000083803054,0.61386645,0.00026815722,0.38429454,0.0006444077,0.00001929797],"about_ca_topic_score_codex":0.0014801389,"about_ca_topic_score_gemma":0.0009714432,"teacher_disagreement_score":0.004500501,"about_ca_system_score_codex":0.0013826309,"about_ca_system_score_gemma":0.0009646882,"threshold_uncertainty_score":0.023801148},"labels":[],"label_agreement":null},{"id":"W4396860332","doi":"10.1007/978-3-031-49783-4_2","title":"Fundamentals of Actuarial Pricing","year":2024,"lang":"ca","type":"book-chapter","venue":"Springer Actuarial","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université du Québec à Montréal","funders":"","keywords":"Actuarial science; Economics; Econometrics; Financial economics","score_opus":0.026442857466070814,"score_gpt":0.29226504001702347,"score_spread":0.26582218255095263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396860332","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":[0.0011131702,0.042676654,0.06393194,0.007075433,0.0032008053,0.000044966597,0.00044988494,0.00061829103,0.8808888],"genre_scores_gemma":[0.089720525,0.064087994,0.034822386,0.002083707,0.010091082,0.00022465263,0.00056271645,0.00051512395,0.7978918],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993216,0.00012438984,0.00003060533,0.0000716074,0.0003988177,0.00005302666],"domain_scores_gemma":[0.999283,0.00034498904,0.000044940807,0.00011977838,0.00015516802,0.000052111067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034761,0.00095256243,0.0008519008,0.001978789,0.0007751886,0.0035490387,0.0010287949,0.0017205054,0.052920654],"category_scores_gemma":[0.0031885025,0.0005421245,0.00042278512,0.002757296,0.0015540215,0.0035665536,0.0010435823,0.003888523,0.022590717],"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":[0.000005181616,0.000030059344,0.00008639528,0.000060861155,0.000004552522,0.000021515969,0.00008134381,0.0014034598,0.00011174752,0.8091403,0.09732971,0.09172473],"study_design_scores_gemma":[0.0000032707417,0.000009289465,0.00035591554,0.00010068,0.0000039706174,0.00007408076,0.000028518845,0.0038467394,0.00006229706,0.5941232,0.40138054,0.000011570975],"about_ca_topic_score_codex":0.0018764006,"about_ca_topic_score_gemma":0.0015260995,"teacher_disagreement_score":0.052920654,"about_ca_system_score_codex":0.0015906018,"about_ca_system_score_gemma":0.001170195,"threshold_uncertainty_score":0.17703724},"labels":[],"label_agreement":null},{"id":"W4397001342","doi":"10.1016/j.jmva.2024.105331","title":"On the Mai–Wang stochastic decomposition for <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si3.svg\" display=\"inline\" id=\"d1e23\"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norm symmetric survival functions on the positive orthant","year":2024,"lang":"lv","type":"article","venue":"Journal of Multivariate Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Computer science; Scalable Vector Graphics; Mathematics; World Wide Web","score_opus":0.02093975372024775,"score_gpt":0.27633247258022187,"score_spread":0.2553927188599741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397001342","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":[0.017303202,0.0013329317,0.9624698,0.0020173653,0.00027052738,0.00007646171,0.00051273516,0.000094160074,0.015922796],"genre_scores_gemma":[0.5314739,0.007382883,0.37844822,0.0020122493,0.0017236781,0.0011224629,0.0027162556,0.0004975582,0.074622825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871695,0.0007260683,0.000046895413,0.00014921336,0.00020037343,0.00016049623],"domain_scores_gemma":[0.9945051,0.0034467084,0.00047366283,0.00034045716,0.00071462727,0.00051941525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008280063,0.0018855213,0.0010270801,0.0017617146,0.00081279245,0.0017194132,0.0011420175,0.0011937955,0.011822326],"category_scores_gemma":[0.01418807,0.00059523765,0.0018460812,0.0014488174,0.0022774357,0.002379922,0.002299666,0.0036217575,0.0016162598],"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":[0.000060007183,0.00003128489,0.0009921262,0.000073574796,0.00004326122,0.00007263584,0.00010251211,0.022743318,0.0004028632,0.9571854,0.0066156127,0.011677391],"study_design_scores_gemma":[0.000025520572,0.000050377148,0.0008633309,0.000067358225,0.00003390274,0.00005289877,0.000050827875,0.29293504,0.0003220204,0.6999554,0.00561029,0.000032927986],"about_ca_topic_score_codex":0.005454037,"about_ca_topic_score_gemma":0.005121481,"teacher_disagreement_score":0.011822326,"about_ca_system_score_codex":0.0018477584,"about_ca_system_score_gemma":0.0028335173,"threshold_uncertainty_score":0.043789685},"labels":[],"label_agreement":null},{"id":"W4397289787","doi":"10.1681/asn.20233411s1667c","title":"A Distribution-Based Approach to Age Associations Between Continuous Kidney Function and Adverse Events in the General Population","year":2023,"lang":"en","type":"article","venue":"Journal of the American Society of Nephrology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba; University of Calgary; University of Ottawa","funders":"","keywords":"Renal function; Medicine; Population; Function (biology); Distribution (mathematics); Adverse effect; Internal medicine; Mathematics; Environmental health; Biology; Evolutionary biology","score_opus":0.02337077290889839,"score_gpt":0.302860723204068,"score_spread":0.2794899502951696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397289787","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":[0.7139508,0.0024605284,0.25993264,0.0016472357,0.00033522036,0.0012935678,0.012957453,0.0007308443,0.0066917213],"genre_scores_gemma":[0.96947205,0.00040018547,0.023631185,0.00017537583,0.00009938905,0.00054834166,0.002498239,0.000052304917,0.0031229644],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99455196,0.0030361693,0.00027201735,0.0011224949,0.00070584234,0.00031160365],"domain_scores_gemma":[0.99064976,0.0057495246,0.0015523509,0.0011281181,0.00068536546,0.00023488434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010152809,0.00074524595,0.0009912835,0.0034550861,0.00044500013,0.001281679,0.0016375117,0.0008323166,0.0062524183],"category_scores_gemma":[0.02279551,0.00040889785,0.0029966189,0.0024949673,0.0008933839,0.00089255895,0.0012297332,0.0014650682,0.00065602607],"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":[0.0005230576,0.0001336577,0.93377036,0.00024832098,0.0028764603,0.00042166843,0.00053299416,0.02257752,0.000514933,0.008326723,0.0027176025,0.02735669],"study_design_scores_gemma":[0.00015596859,0.0007923125,0.7610356,0.00012156662,0.0009952907,0.00088339223,0.0007443145,0.20791051,0.0004043095,0.015630212,0.01125778,0.00006883676],"about_ca_topic_score_codex":0.061199788,"about_ca_topic_score_gemma":0.035679437,"teacher_disagreement_score":0.061199788,"about_ca_system_score_codex":0.0019594438,"about_ca_system_score_gemma":0.001644444,"threshold_uncertainty_score":0.121687114},"labels":[],"label_agreement":null},{"id":"W4398172021","doi":"10.1017/s1748499524000150","title":"On the benefits of pension plan consolidation: Understanding the impact of full plan mergers","year":2024,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Consolidation (business); Diversification (marketing strategy); Pension; Liability; Pension plan; Economies of scale; Business; Economics; Asset allocation; Actuarial science; Public economics; Finance; Portfolio; Microeconomics; Marketing","score_opus":0.16900731460164578,"score_gpt":0.3867768112232939,"score_spread":0.2177694966216481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398172021","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":[0.9876122,0.0003454361,0.003327309,0.0009953034,0.0000112405205,0.000052095038,0.000093274546,0.0000146185475,0.0075485194],"genre_scores_gemma":[0.99900514,0.00006738231,0.00053842133,0.00003361174,0.000005835995,0.000004870909,0.000023233684,0.0000024206638,0.0003191138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99756074,0.0009130454,0.00009439365,0.000138018,0.0005154933,0.0007782279],"domain_scores_gemma":[0.98933405,0.0056624296,0.0020461846,0.0006002138,0.0016485495,0.0007085682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005121328,0.0004660297,0.00062237197,0.0010877666,0.0009302558,0.0025168078,0.00089554396,0.0011305511,0.0027446446],"category_scores_gemma":[0.018510925,0.0002509687,0.0007144994,0.0010477803,0.0019351222,0.0022186623,0.0024183632,0.0011665846,0.00010379006],"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":[0.000782863,0.00048501062,0.14991286,0.0000977584,0.00020597612,0.00080045714,0.00079898105,0.7398641,0.0035191043,0.055025533,0.0011627679,0.04734468],"study_design_scores_gemma":[0.00008023013,0.0013433764,0.22345804,0.00010187658,0.00041041995,0.00028072615,0.0071346657,0.7149747,0.0038543732,0.043417893,0.0048190486,0.00012455911],"about_ca_topic_score_codex":0.08623006,"about_ca_topic_score_gemma":0.078319475,"teacher_disagreement_score":0.08623006,"about_ca_system_score_codex":0.0062998165,"about_ca_system_score_gemma":0.0041285907,"threshold_uncertainty_score":0.17145634},"labels":[],"label_agreement":null},{"id":"W4398458420","doi":"10.7910/dvn/ltqfm7","title":"Replication Data for: Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach","year":2022,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Replication (statistics); Computer science; Satellite; Markov model; Markov chain; Hidden Markov model; Artificial intelligence; Statistics; Mathematics; Machine learning; Physics; Astronomy","score_opus":0.06768329912757924,"score_gpt":0.32489277030576097,"score_spread":0.2572094711781817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398458420","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":[0.0010565942,0.00029410204,0.0012789752,0.0004386268,0.00008694577,0.00006149968,0.9954039,0.0007961449,0.0005832775],"genre_scores_gemma":[0.0042588115,0.00011713898,0.0044881892,0.00014725681,0.000027575274,0.00036209563,0.9898886,0.00014835347,0.0005620441],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974172,0.0011666076,0.00032000395,0.0004866737,0.0004468819,0.0001626529],"domain_scores_gemma":[0.9891511,0.0039619543,0.0009133554,0.003832329,0.001666943,0.0004742675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069302022,0.0014166792,0.0012812611,0.0027617589,0.00080163963,0.001892242,0.0044525038,0.0022408743,0.024815036],"category_scores_gemma":[0.034761894,0.000788917,0.0020910725,0.0049498645,0.00042374135,0.0013328266,0.0024615817,0.0021569785,0.020503795],"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":[0.00033504982,0.000061957064,0.006662416,0.0010580227,0.00027830058,0.00005960506,0.00007008432,0.0018334587,0.00014732838,0.0022799794,0.97643626,0.010777593],"study_design_scores_gemma":[0.0028559116,0.00009456167,0.035743535,0.0010169693,0.00037000648,0.0002640348,0.000224775,0.011428409,0.00105952,0.014928273,0.93184835,0.00016568543],"about_ca_topic_score_codex":0.03792064,"about_ca_topic_score_gemma":0.06244199,"teacher_disagreement_score":0.03792064,"about_ca_system_score_codex":0.0014269936,"about_ca_system_score_gemma":0.0026073125,"threshold_uncertainty_score":0.08301455},"labels":[],"label_agreement":null},{"id":"W4398504040","doi":"10.7910/dvn/hxnvtq","title":"Replication Data for: Declining US Life Expectancy: A First Look","year":2017,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Replication (statistics); Life expectancy; Demography; Biology; Sociology; Virology","score_opus":0.08327953094995857,"score_gpt":0.37309554123175276,"score_spread":0.28981601028179416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398504040","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":[0.00052038016,0.000056656296,0.00014024267,0.00024611803,0.00008417751,0.00006052402,0.9979678,0.0002457292,0.0006783953],"genre_scores_gemma":[0.0024821928,0.000058302754,0.00056308607,0.00025321337,0.000052625903,0.00068291393,0.993782,0.00019313436,0.001932554],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99719256,0.0007466856,0.00041976201,0.0005956559,0.00070907193,0.0003362688],"domain_scores_gemma":[0.9831685,0.0035573107,0.0016926852,0.0056159873,0.0048888186,0.0010767743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051977914,0.001364592,0.0013916522,0.0025201614,0.0008863583,0.0021394235,0.003299763,0.0022359544,0.08913227],"category_scores_gemma":[0.04286481,0.0007295579,0.0018924645,0.005136918,0.0005898021,0.0011978186,0.0021549813,0.0026527226,0.054547615],"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":[0.000115650786,0.00002496767,0.002220149,0.0001917538,0.00007005636,0.000016803362,0.00002258799,0.00012931477,0.000032463176,0.00033624808,0.9952348,0.0016052915],"study_design_scores_gemma":[0.0023174384,0.00008530152,0.03325566,0.00069631607,0.0002125289,0.00016537425,0.00023165146,0.0006828975,0.00043747807,0.0036153197,0.958187,0.000113131784],"about_ca_topic_score_codex":0.039369196,"about_ca_topic_score_gemma":0.06398058,"teacher_disagreement_score":0.08913227,"about_ca_system_score_codex":0.0015504011,"about_ca_system_score_gemma":0.004935116,"threshold_uncertainty_score":0.29817718},"labels":[],"label_agreement":null},{"id":"W4398520657","doi":"10.7910/dvn/db11yd","title":"PROSPERED Dataset: Paternity Leave","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Computer science","score_opus":0.034583175789737616,"score_gpt":0.30772306640532865,"score_spread":0.273139890615591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398520657","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":[0.0003742153,0.00010432653,0.0000724152,0.000120800774,0.000024093633,0.000007720238,0.9987288,0.00016368333,0.00040393503],"genre_scores_gemma":[0.00081291335,0.00006625478,0.0002637227,0.000084725885,0.000013138098,0.000052224095,0.9981647,0.000029763714,0.00051257166],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887687,0.000238078,0.00014609557,0.00035532925,0.0002265683,0.00015694764],"domain_scores_gemma":[0.9979086,0.0005340761,0.00038142994,0.00047123583,0.00049676135,0.00020793416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013721498,0.0016678355,0.0014593294,0.0022470239,0.000851637,0.0020845325,0.0030867145,0.0023759424,0.033288915],"category_scores_gemma":[0.007400383,0.00058153627,0.0014257005,0.004161264,0.00041564927,0.0011412536,0.0019719696,0.0021501705,0.042751547],"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":[0.00008244317,0.00003119197,0.0034167331,0.00035552518,0.00004894137,0.000038307655,0.000030405545,0.00038096422,0.000052728516,0.00059400965,0.99256164,0.0024071194],"study_design_scores_gemma":[0.0006987756,0.000044316115,0.02084325,0.00058526377,0.00008452617,0.0002480203,0.00017550612,0.0018324481,0.00027801658,0.0028731416,0.97226995,0.000066730456],"about_ca_topic_score_codex":0.038115412,"about_ca_topic_score_gemma":0.068689495,"teacher_disagreement_score":0.038115412,"about_ca_system_score_codex":0.0014960847,"about_ca_system_score_gemma":0.0019367213,"threshold_uncertainty_score":0.11136258},"labels":[],"label_agreement":null},{"id":"W4398582361","doi":"10.7910/dvn/o7eggp","title":"PROSPERED Dataset: Minimum Age for Marriage","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Demography; Computer science; Geography; Sociology","score_opus":0.030190676271314745,"score_gpt":0.31637005062994605,"score_spread":0.28617937435863133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398582361","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":[0.0003216191,0.000059584112,0.000052320232,0.00008461071,0.00001916332,0.000010546367,0.998887,0.00007661054,0.0004885778],"genre_scores_gemma":[0.00065493153,0.00004348541,0.00016562446,0.000057113142,0.000011293825,0.00007589648,0.9984049,0.000021665033,0.0005650853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894637,0.00021784432,0.00014849717,0.00027257827,0.00024559046,0.00016921766],"domain_scores_gemma":[0.9972772,0.0005916573,0.0005455434,0.0004650979,0.00080841786,0.00031209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013855017,0.0012867526,0.0013233603,0.0028316434,0.0007549893,0.0018299146,0.0026044906,0.0017795084,0.043355126],"category_scores_gemma":[0.0076444526,0.00052245264,0.0008939719,0.0057012616,0.0003049492,0.0009203926,0.001714321,0.0020447546,0.049931776],"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":[0.000049226546,0.000023346722,0.002781388,0.00021717079,0.000022632972,0.000024111223,0.000025379026,0.00017843687,0.000025714995,0.00048125765,0.99449676,0.001674643],"study_design_scores_gemma":[0.0004762779,0.00003328499,0.024291867,0.00042403533,0.000044765424,0.00013148923,0.00017684279,0.0008039673,0.00018444963,0.0016410392,0.9717506,0.000041343326],"about_ca_topic_score_codex":0.029476924,"about_ca_topic_score_gemma":0.043955185,"teacher_disagreement_score":0.043355126,"about_ca_system_score_codex":0.0011552827,"about_ca_system_score_gemma":0.0018917224,"threshold_uncertainty_score":0.14503735},"labels":[],"label_agreement":null},{"id":"W4398866354","doi":"10.7910/dvn/v5nber","title":"Replication Data for: Declining United States Life Expectancy: A First Look","year":2017,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Replication (statistics); Life expectancy; Demography; Biology; Sociology; Virology","score_opus":0.08125593582465464,"score_gpt":0.36995837505457585,"score_spread":0.2887024392299212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398866354","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":[0.00030392094,0.000083595645,0.00033874676,0.00030112133,0.00017898447,0.00025769783,0.9974794,0.00025958996,0.0007968981],"genre_scores_gemma":[0.0034438581,0.00016172734,0.0019768502,0.0004772169,0.000109780885,0.00486716,0.98569643,0.0003565544,0.0029104091],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99561715,0.0011811359,0.0010499823,0.0008361845,0.00093218207,0.0003833845],"domain_scores_gemma":[0.9720784,0.006790751,0.0019425531,0.009389678,0.00868781,0.0011108837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009071967,0.0016434464,0.0018121258,0.0029453167,0.0013652251,0.0022270575,0.0030611008,0.0023281202,0.16877325],"category_scores_gemma":[0.08275922,0.0012238852,0.002707375,0.006047006,0.00065484765,0.0016694028,0.0021302851,0.0032503558,0.07238253],"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":[0.000100126235,0.00002196463,0.0010839456,0.0004088414,0.00006426218,0.00002211,0.000025315201,0.00007804445,0.00003550699,0.00033356604,0.9958877,0.001938616],"study_design_scores_gemma":[0.00583514,0.00013321453,0.025990656,0.002221636,0.0004536654,0.00034839305,0.00027343107,0.00050021766,0.00047525138,0.006421091,0.9571679,0.00017945304],"about_ca_topic_score_codex":0.030588593,"about_ca_topic_score_gemma":0.04135413,"teacher_disagreement_score":0.16877325,"about_ca_system_score_codex":0.0016672512,"about_ca_system_score_gemma":0.0059884223,"threshold_uncertainty_score":0.56460273},"labels":[],"label_agreement":null},{"id":"W4399033088","doi":"10.1016/j.rbmo.2024.103996","title":"Using simulation to optimize IVF lab resources and meet physiological time constraints","year":2024,"lang":"en","type":"article","venue":"Reproductive BioMedicine Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"St. Thomas Hospital","funders":"","keywords":"Computer science; Biochemical engineering; Environmental science; Engineering","score_opus":0.06783351824730675,"score_gpt":0.39363610213044675,"score_spread":0.32580258388314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399033088","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":[0.61451036,0.0005529748,0.3434915,0.0026406338,0.00026635383,0.00030388133,0.0011048923,0.0010728441,0.036056563],"genre_scores_gemma":[0.97951496,0.000090452086,0.01853751,0.00010402984,0.000020633914,0.000103667546,0.00022279295,0.000047386482,0.0013585327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950874,0.0002701799,0.000021010985,0.000059667444,0.0000552902,0.000085150255],"domain_scores_gemma":[0.99550194,0.003673762,0.0002231202,0.00011868926,0.00026623457,0.00021627592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010565701,0.0006903227,0.0007981296,0.00068638235,0.0005207237,0.0013155449,0.0010116686,0.0014322393,0.004066353],"category_scores_gemma":[0.0055665583,0.0006152153,0.0006530832,0.00059170014,0.00048560573,0.0008450545,0.00097136607,0.00095386984,0.0002387991],"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":[0.000033085107,0.000028619974,0.00086615194,0.000007729578,0.000010599504,0.000017202066,0.000011713095,0.9972249,0.00008345429,0.00043957005,0.00012536123,0.0011515187],"study_design_scores_gemma":[0.000015705104,0.000017641396,0.00014151966,0.000003111959,0.000005761629,0.0000042080915,0.000014569297,0.99897695,0.00007264294,0.0005885282,0.00015642228,0.0000031002905],"about_ca_topic_score_codex":0.024114903,"about_ca_topic_score_gemma":0.014887477,"teacher_disagreement_score":0.024114903,"about_ca_system_score_codex":0.0010721831,"about_ca_system_score_gemma":0.0023121093,"threshold_uncertainty_score":0.047949076},"labels":[],"label_agreement":null},{"id":"W4399034923","doi":"10.57805/revstat.v16i2.240","title":"Semi-Parametric Likelihood Inference for Birnbaum–Saunders Frailty Model","year":2022,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Inference; Parametric statistics; Econometrics; Parametric model; Statistics; Computer science; Mathematics; Artificial intelligence","score_opus":0.34267100252748306,"score_gpt":0.581013435464089,"score_spread":0.23834243293660595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399034923","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":[0.02204259,0.00025695466,0.976541,0.00024970848,0.00002398538,0.0000594361,0.00010546605,0.00013542341,0.0005855055],"genre_scores_gemma":[0.65251124,0.0010213719,0.33918494,0.00027898478,0.00020321057,0.000667998,0.0009922057,0.00019911524,0.004940893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99500436,0.003486214,0.00015329485,0.0006386366,0.00048478044,0.00023277773],"domain_scores_gemma":[0.9420462,0.052553322,0.001638277,0.0020842731,0.0012828337,0.00039512062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020070296,0.0009869923,0.0023807317,0.0018634205,0.0008961803,0.0014868072,0.003911833,0.0018419508,0.0036590954],"category_scores_gemma":[0.07833419,0.00084251753,0.0020990877,0.0016063367,0.0024873838,0.0025737088,0.0022556055,0.0033376666,0.00059045624],"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":[0.00026716563,0.00014008208,0.016240077,0.00029470175,0.00034301085,0.00090933003,0.00085156615,0.68769085,0.001186903,0.22534834,0.0024407355,0.06428719],"study_design_scores_gemma":[0.000029338777,0.00004777014,0.0012753011,0.000032126423,0.000033144614,0.00012047242,0.000064754844,0.9312098,0.0002485557,0.06640083,0.0005099086,0.000027891965],"about_ca_topic_score_codex":0.010584242,"about_ca_topic_score_gemma":0.007498921,"teacher_disagreement_score":0.020070296,"about_ca_system_score_codex":0.0014448225,"about_ca_system_score_gemma":0.0019927628,"threshold_uncertainty_score":0.10614318},"labels":[],"label_agreement":null},{"id":"W4399161024","doi":"10.1515/9782760623958","title":"Vieillissement et évolution démographique au Canada","year":2003,"lang":"fr","type":"book","venue":"Les Presses de l'Université de Montréal eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.009440673626612757,"score_gpt":0.2062266677683599,"score_spread":0.19678599414174716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399161024","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":[0.42148098,0.11547167,0.011309646,0.024483686,0.00067965436,0.00022428496,0.03605426,0.00082320534,0.38947263],"genre_scores_gemma":[0.71001023,0.0556307,0.009752064,0.0007042399,0.00013824839,0.00009630983,0.0065326975,0.00021630009,0.21691923],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967384,0.000036848465,0.00001713456,0.00005401935,0.0001264896,0.00009167652],"domain_scores_gemma":[0.999363,0.00012286805,0.000050258976,0.000025118743,0.00035495794,0.00008369713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040205306,0.00041562095,0.00039036787,0.007334805,0.0029453267,0.0028443187,0.0006011542,0.0005256715,0.011930004],"category_scores_gemma":[0.001683461,0.00034259786,0.000430145,0.015825795,0.0016953932,0.00087583903,0.0005254213,0.0009205504,0.00040832447],"study_design_candidate":"not_applicable","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":[0.00020201303,0.00006838795,0.075959705,0.0005680193,0.00014645846,0.00054097647,0.015470395,0.015072321,0.0015269497,0.3438123,0.09946701,0.44716543],"study_design_scores_gemma":[0.000020293615,0.00002796985,0.2827445,0.0003481165,0.000059508307,0.00020755509,0.003994031,0.00452987,0.00043917063,0.014104418,0.693448,0.00007655569],"about_ca_topic_score_codex":0.9976132,"about_ca_topic_score_gemma":0.9980484,"teacher_disagreement_score":0.0633327,"about_ca_system_score_codex":0.0633327,"about_ca_system_score_gemma":0.06496217,"threshold_uncertainty_score":0.45951307},"labels":[],"label_agreement":null},{"id":"W4399352157","doi":"10.1007/s11113-024-09889-0","title":"Correction to: Housing Affordability Crisis and Delayed Fertility: Evidence from the USA","year":2024,"lang":"en","type":"article","venue":"Population Research and Policy Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University; Canada Mortgage and Housing Corporation","funders":"","keywords":"Fertility; Economics; Development economics; Population; Demographic economics; Medicine; Environmental health","score_opus":0.1763463121300909,"score_gpt":0.4967103702188511,"score_spread":0.32036405808876023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399352157","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":[0.00013787787,0.0059933728,0.00023293054,0.08804562,0.8977843,0.000062718784,0.0063289204,0.00014901982,0.0012652361],"genre_scores_gemma":[0.020021845,0.05760698,0.0027096644,0.21797256,0.61706454,0.0014336173,0.0070302146,0.0008454673,0.07531508],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933749,0.001401565,0.00194919,0.0007923609,0.001834727,0.0006472649],"domain_scores_gemma":[0.9354888,0.022229109,0.004899063,0.0032178517,0.031266127,0.0028990342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008082666,0.002824499,0.004311223,0.0056142462,0.002209919,0.00482083,0.004956894,0.009355374,0.08769792],"category_scores_gemma":[0.122693,0.0014273035,0.0024680416,0.008731721,0.0023553867,0.0028051522,0.0027543316,0.011264289,0.023222806],"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":[0.000050660754,0.000004539163,0.00018045235,0.0011771118,0.00004871379,0.00008396072,0.000032390646,0.00003396431,0.000009348339,0.0002719007,0.9940468,0.0040602107],"study_design_scores_gemma":[0.00054126343,0.000044569697,0.006512693,0.007301199,0.00046529717,0.00034586768,0.0005523446,0.0003021323,0.00020777767,0.0020399736,0.9815445,0.00014227892],"about_ca_topic_score_codex":0.05948178,"about_ca_topic_score_gemma":0.06582671,"teacher_disagreement_score":0.08769792,"about_ca_system_score_codex":0.004483806,"about_ca_system_score_gemma":0.014221643,"threshold_uncertainty_score":0.29337877},"labels":[],"label_agreement":null},{"id":"W4399570912","doi":"10.32614/cran.package.actuar","title":"actuar: Actuarial Functions and Heavy Tailed Distributions","year":2006,"lang":"en","type":"dataset","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua","funders":"","keywords":"Statistics; Mathematics; Environmental science","score_opus":0.013357957854869427,"score_gpt":0.28688023185768596,"score_spread":0.27352227400281653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399570912","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":[0.00042720104,0.00011245131,0.00074717624,0.00019251907,0.00004018916,0.000025442478,0.99645555,0.0010921645,0.0009073219],"genre_scores_gemma":[0.0019202555,0.00010005023,0.0014307942,0.000104066734,0.00002130688,0.00016287308,0.9952608,0.00017003219,0.00082986604],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979221,0.00047935254,0.00029779153,0.00036062222,0.00069237873,0.0002478351],"domain_scores_gemma":[0.99371016,0.002174441,0.0007237123,0.0018294117,0.0012625415,0.00029974748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030285476,0.0023059445,0.0014158519,0.004291002,0.00063053664,0.0024075853,0.004064754,0.0026177485,0.046895016],"category_scores_gemma":[0.017379215,0.0010030727,0.0017864277,0.0067109535,0.0004795803,0.0018947853,0.0019526306,0.003076379,0.06068139],"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":[0.000068740905,0.00004890818,0.0019716069,0.0004126831,0.000049061968,0.000024382447,0.00001626928,0.0017909048,0.000046318808,0.0012334925,0.9893869,0.0049506645],"study_design_scores_gemma":[0.0007825287,0.000048941776,0.014885069,0.00046148506,0.000060849103,0.00018242907,0.00009139108,0.008373387,0.0009088883,0.008336075,0.9657759,0.00009296033],"about_ca_topic_score_codex":0.017433986,"about_ca_topic_score_gemma":0.021193668,"teacher_disagreement_score":0.046895016,"about_ca_system_score_codex":0.0018245747,"about_ca_system_score_gemma":0.0022481296,"threshold_uncertainty_score":0.15687948},"labels":[],"label_agreement":null},{"id":"W4399653919","doi":"10.54097/6azcw585","title":"The Research of the Birth Rate in Canada from 1951 to 2022","year":2024,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Earl Haig Secondary School","funders":"","keywords":"Autoregressive integrated moving average; Birth rate; Pandemic; Demography; Population; Geography; Coronavirus disease 2019 (COVID-19); Time series; Research methodology; Statistics; Sociology; Medicine; Mathematics","score_opus":0.014120751046574887,"score_gpt":0.28724402327219345,"score_spread":0.27312327222561855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399653919","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":[0.8835245,0.018415492,0.0015473827,0.0076648174,0.00036907548,0.000091034024,0.044021003,0.000088841975,0.044277877],"genre_scores_gemma":[0.9769672,0.007876532,0.00075210055,0.00027427246,0.000043270327,0.00002086508,0.007299324,0.0000129400005,0.0067533883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913555,0.0000546568,0.00005018351,0.00010018948,0.00040387415,0.00025554578],"domain_scores_gemma":[0.9967939,0.00024461854,0.00031219888,0.000072094546,0.0022479242,0.0003292751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011037877,0.00023658178,0.00022581732,0.004990993,0.0018020355,0.0012763548,0.00070922385,0.0002776903,0.0024468668],"category_scores_gemma":[0.0046327775,0.00013985821,0.0005217313,0.009391934,0.00043618013,0.00039234536,0.00051355734,0.0009886195,0.00025897793],"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":[0.00007654373,0.000035653487,0.9100047,0.00026127332,0.00010809323,0.0004394076,0.0025797307,0.0015293104,0.0001897258,0.006192664,0.012092085,0.06649092],"study_design_scores_gemma":[0.000002121765,0.000018922414,0.9744631,0.00016092445,0.000050456452,0.00011321099,0.002581256,0.001246436,0.00022241868,0.00015613642,0.020966785,0.000018312969],"about_ca_topic_score_codex":0.99327105,"about_ca_topic_score_gemma":0.99250376,"teacher_disagreement_score":0.028113535,"about_ca_system_score_codex":0.028113535,"about_ca_system_score_gemma":0.05001355,"threshold_uncertainty_score":0.20397896},"labels":[],"label_agreement":null},{"id":"W4399747162","doi":"10.1186/s12963-024-00331-3","title":"The impact of different imputation methods on estimates and model performance: an example using a risk prediction model for premature mortality","year":2024,"lang":"en","type":"article","venue":"Population Health Metrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Schwartz/Reisman Emergency Medicine Institute; Trillium Health Centre; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Imputation (statistics); Statistics; Missing data; Medicine; Population; Econometrics; Mathematics","score_opus":0.1761302411805733,"score_gpt":0.49292066644380095,"score_spread":0.31679042526322765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399747162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80038875,0.0011335502,0.19378428,0.0016924201,0.00009599169,0.00021844762,0.0005218649,0.0002945892,0.0018701493],"genre_scores_gemma":[0.9172183,0.00023163621,0.081421874,0.0001535041,0.000035320925,0.00015870281,0.0003167878,0.000054974833,0.0004088187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9508132,0.043369684,0.0012506815,0.0015129807,0.0025392675,0.0005142753],"domain_scores_gemma":[0.7654918,0.21121712,0.005485978,0.00693049,0.010303332,0.00057129556],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07996379,0.00088129705,0.0014908349,0.0009377603,0.0011118905,0.0018561886,0.0018145785,0.0020884024,0.0009137026],"category_scores_gemma":[0.12445733,0.0005209896,0.002754954,0.0020514082,0.00071304955,0.0012725494,0.0015841351,0.0023221294,0.0001893186],"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":[0.0027865062,0.0005482908,0.18056202,0.00043669695,0.0020924443,0.0014660895,0.0012418297,0.69331914,0.0022306414,0.008413935,0.0030030594,0.10389947],"study_design_scores_gemma":[0.00020476306,0.0008609801,0.030572712,0.00014257162,0.0004549379,0.00029878665,0.00035970742,0.9552949,0.0028960612,0.0076368568,0.0011292738,0.0001483356],"about_ca_topic_score_codex":0.008671853,"about_ca_topic_score_gemma":0.006743297,"teacher_disagreement_score":0.9200362,"about_ca_system_score_codex":0.0013996009,"about_ca_system_score_gemma":0.0015948168,"threshold_uncertainty_score":0.422894},"labels":[],"label_agreement":null},{"id":"W4399817129","doi":"10.1080/03461238.2024.2365977","title":"Spatial natural hedging: a general framework with application to the mortality of U.S. states","year":2024,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"St. Francis Xavier University","funders":"","keywords":"Natural (archaeology); Econometrics; Geography; Mathematics; Computer science; Statistics","score_opus":0.010074672763429092,"score_gpt":0.3132846967513995,"score_spread":0.3032100239879704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399817129","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":[0.174604,0.002891187,0.79255986,0.004605027,0.00018322318,0.00015056341,0.0009038437,0.00019708823,0.023905225],"genre_scores_gemma":[0.9459937,0.0020875835,0.03966193,0.0003157579,0.00016680788,0.00014934775,0.0002400696,0.000032619664,0.011352194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99926966,0.0004020678,0.000030377338,0.00013084065,0.000081920705,0.00008515133],"domain_scores_gemma":[0.9982108,0.0010502121,0.0003248536,0.00010853386,0.0001716555,0.0001339394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027915891,0.0007504087,0.00073637837,0.0011680563,0.0007289224,0.002016052,0.0020799164,0.0016516139,0.0063768355],"category_scores_gemma":[0.0061352975,0.00043639968,0.0012645731,0.0011892663,0.0017779709,0.0017570984,0.002110975,0.001454901,0.00027974544],"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":[0.000028601702,0.00006172061,0.004816966,0.000065860964,0.0000653042,0.0003060691,0.00024600085,0.5339947,0.00034520266,0.45033097,0.0013300432,0.008408634],"study_design_scores_gemma":[0.000022527836,0.00007596471,0.0022805412,0.00003302774,0.000037484555,0.00012301125,0.00023499272,0.8358582,0.00009481182,0.15760347,0.0036024936,0.00003339068],"about_ca_topic_score_codex":0.024424868,"about_ca_topic_score_gemma":0.015069315,"teacher_disagreement_score":0.024424868,"about_ca_system_score_codex":0.0018055134,"about_ca_system_score_gemma":0.0013807492,"threshold_uncertainty_score":0.048565447},"labels":[],"label_agreement":null},{"id":"W4400134637","doi":"10.1111/2041-210x.14335","title":"Response to Discretising and validating Keyfitz' entropy for any demographic classification","year":2024,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Natural Environment Research Council; Sight Research UK; McGill University","keywords":"Survivorship curve; Discretization; Longevity; Mathematics; Life expectancy; Entropy (arrow of time); Pace; Population; Econometrics; Applied mathematics; Statistics; Demography; Gerontology; Sociology; Mathematical analysis; Geography; Medicine","score_opus":0.042480283526524144,"score_gpt":0.4240106601680814,"score_spread":0.38153037664155726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400134637","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.265805,0.0029385514,0.27902994,0.4004121,0.012408248,0.00031753702,0.0011685807,0.00076337595,0.03715663],"genre_scores_gemma":[0.9230902,0.00042663267,0.044025924,0.026145875,0.0025299168,0.0003843015,0.00029195854,0.00017266803,0.0029325224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97675294,0.015333628,0.0011922593,0.002433498,0.003796114,0.0004916084],"domain_scores_gemma":[0.7211644,0.22765876,0.0111095775,0.016150827,0.020911718,0.003004688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04818746,0.0005284197,0.0010243182,0.0014393918,0.002423407,0.003229576,0.0026225857,0.0046466803,0.0058311555],"category_scores_gemma":[0.34374937,0.00029740162,0.0009493333,0.0014757661,0.0073456005,0.006587331,0.0043510143,0.010413915,0.0011395584],"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":[0.0006370786,0.00009119783,0.023854364,0.00028121678,0.0001083873,0.00041040548,0.006267379,0.0071205446,0.00092162023,0.80406785,0.06764136,0.088598676],"study_design_scores_gemma":[0.00014474853,0.00028762504,0.011945769,0.0006353514,0.000043231794,0.0005529644,0.003710497,0.083575405,0.0029501254,0.8259181,0.06996727,0.00026897594],"about_ca_topic_score_codex":0.0034824396,"about_ca_topic_score_gemma":0.0012893142,"teacher_disagreement_score":0.04818746,"about_ca_system_score_codex":0.0051208227,"about_ca_system_score_gemma":0.0013779183,"threshold_uncertainty_score":0.2548427},"labels":[],"label_agreement":null},{"id":"W4400227174","doi":"10.1007/s42519-024-00392-5","title":"Optimizing Robust Shape Parameter: Improved Methodologies for Birnbaum–Saunders Distribution","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Mathematics; Applied mathematics; Statistics; Mathematical optimization","score_opus":0.10035625291875457,"score_gpt":0.4237678697744925,"score_spread":0.32341161685573794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400227174","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":[0.0013634796,0.00014465835,0.997951,0.000058996175,0.000020636993,0.000017282957,0.000025336267,0.00013229768,0.00028644758],"genre_scores_gemma":[0.06453851,0.000604727,0.929209,0.00013284736,0.00013655632,0.00022853988,0.0003874121,0.00085280585,0.003909577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975198,0.001265491,0.00012788973,0.00033985535,0.0006179978,0.00012899017],"domain_scores_gemma":[0.99196094,0.0047626104,0.0004101361,0.0010677039,0.0016056391,0.00019298554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009224436,0.0015461714,0.0019360246,0.0028460259,0.00088128913,0.0018152938,0.003593128,0.0024046425,0.005211951],"category_scores_gemma":[0.028078958,0.001021969,0.0019662413,0.0023445997,0.0012064305,0.002953965,0.0029198285,0.003616915,0.0018954771],"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":[0.00018005056,0.00017568114,0.002867347,0.0002914621,0.0001996109,0.00017680244,0.0003258462,0.47017878,0.0063146306,0.16708116,0.008694355,0.34351435],"study_design_scores_gemma":[0.000010326862,0.000019558516,0.00036778205,0.00002274072,0.000022917155,0.000044299606,0.000018737323,0.97252715,0.0008941286,0.023562863,0.0024873645,0.000022094307],"about_ca_topic_score_codex":0.007759663,"about_ca_topic_score_gemma":0.0063975286,"teacher_disagreement_score":0.009224436,"about_ca_system_score_codex":0.0015060769,"about_ca_system_score_gemma":0.0024193297,"threshold_uncertainty_score":0.048784018},"labels":[],"label_agreement":null},{"id":"W4400227238","doi":"10.1007/s11113-024-09899-y","title":"Application of the Extended Log Quad Model to Municipal Life Tables","year":2024,"lang":"en","type":"article","venue":"Population Research and Policy Review","topic":"Insurance, Mortality, Demography, Risk Management","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":"Keio University","keywords":"Life expectancy; Life table; Table (database); Statistics; Estimation; Geography; Demography; Econometrics; Computer science; Mathematics; Population; Database; Sociology; Economics; Management","score_opus":0.15010720594912405,"score_gpt":0.49998032874652937,"score_spread":0.34987312279740534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400227238","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":[0.11030311,0.0010409162,0.87433,0.0015504105,0.00015919229,0.00023573363,0.006038949,0.0006639189,0.0056777033],"genre_scores_gemma":[0.81792605,0.0008618209,0.16838609,0.00028651956,0.00014536828,0.0005388708,0.0068545192,0.00016302257,0.004837691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968599,0.0020729424,0.0001471578,0.0004637232,0.0002769966,0.0001793109],"domain_scores_gemma":[0.98763776,0.008848027,0.0010871388,0.00094177516,0.0012005058,0.00028471142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057046604,0.0005532317,0.0010610233,0.0020391906,0.00066313776,0.0022681293,0.0024655869,0.0009890853,0.007753185],"category_scores_gemma":[0.02622175,0.0005630168,0.0016281744,0.0033963446,0.00061471795,0.0028827637,0.0019952357,0.0016655006,0.0010670968],"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":[0.00021274119,0.00013759236,0.06865879,0.0002578944,0.00037226078,0.00044440926,0.0008399245,0.6220825,0.00018847224,0.17289299,0.011821487,0.122090966],"study_design_scores_gemma":[0.000013186722,0.000041311934,0.004121081,0.000045149605,0.000036339807,0.00008333113,0.000112894304,0.93689936,0.000030319641,0.05484576,0.0037549075,0.000016338543],"about_ca_topic_score_codex":0.03409613,"about_ca_topic_score_gemma":0.01795779,"teacher_disagreement_score":0.03409613,"about_ca_system_score_codex":0.0014649538,"about_ca_system_score_gemma":0.0013500802,"threshold_uncertainty_score":0.067795336},"labels":[],"label_agreement":null},{"id":"W4400642845","doi":"10.1038/s41467-024-50305-0","title":"Investigating grey matter volumetric trajectories through the lifespan at the individual level","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"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; Centre Hospitalier Universitaire Sainte-Justine; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Medical Research Council; Higher Education Discipline Innovation Project; Fédération pour la Recherche sur le Cerveau; National Institute of Mental Health; Science and Technology Commission of Shanghai Municipality; Fondation de France; National Institute for Health and Care Research; Bundesministerium für Bildung und Forschung; Fondation pour la Recherche Médicale; National Natural Science Foundation of China; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; European Commission; Deutsche Forschungsgemeinschaft; King's College London; National Institute on Drug Abuse; Mission Interministérielle de Lutte Contre les Drogues et les Conduites Addictives; Science Foundation Ireland; National Institutes of Health; Fondation de l'Avenir pour la Recherche Médicale Appliquée","keywords":"Grey matter; Computer science; Biology; Artificial intelligence; Evolutionary biology; Computational biology; Medicine; White matter; Magnetic resonance imaging","score_opus":0.0968685184608152,"score_gpt":0.3613946736063106,"score_spread":0.2645261551454954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400642845","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":[0.9975056,0.00028634458,0.0005341159,0.000050718867,0.0000029207715,0.000004960928,0.0011017177,0.000009211258,0.0005043627],"genre_scores_gemma":[0.99704355,0.0002918308,0.00085722323,0.000021880089,0.0000043649306,0.0000115320745,0.0013109907,0.00000938894,0.00044928116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998565,0.00002971518,0.00001077455,0.000056944453,0.00002017304,0.000026035415],"domain_scores_gemma":[0.99937624,0.0001315617,0.0002612921,0.00008326728,0.00008351182,0.00006421645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005069238,0.00018366754,0.00014700067,0.00086544605,0.00022105525,0.00043221432,0.00022245052,0.00021901773,0.00094038097],"category_scores_gemma":[0.0017717388,0.00010563748,0.00023218089,0.0006078511,0.00022013257,0.0003633195,0.00050005387,0.00026403336,0.00016866178],"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":[0.0000455004,0.000010688426,0.9903916,0.000019724772,0.000077432094,0.00007530564,0.00052293466,0.00013329308,0.002040969,0.00013836028,0.00019659109,0.006347618],"study_design_scores_gemma":[4.041646e-7,0.000020087613,0.9991122,0.000008266322,0.000011198079,0.000088411456,0.00012649644,0.00007711061,0.00021949889,0.000084211766,0.00025087746,0.0000013085058],"about_ca_topic_score_codex":0.009543259,"about_ca_topic_score_gemma":0.019283699,"teacher_disagreement_score":0.009543259,"about_ca_system_score_codex":0.00022132965,"about_ca_system_score_gemma":0.0002229172,"threshold_uncertainty_score":0.018975437},"labels":[],"label_agreement":null},{"id":"W4400981915","doi":"10.1037/pag0000833","title":"The Flynn effect and cognitive decline among americans aged 65 years and older.","year":2024,"lang":"en","type":"article","venue":"Psychology and Aging","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institute of Aging","funders":"Johns Hopkins Bloomberg School of Public Health; National Institute on Aging; National Institutes of Health; Johns Hopkins University","keywords":"Demography; Cohort; Cognitive decline; Gerontology; Psychology; Cognition; Confounding; Medicine; Cohort study; Health and Retirement Study; Disease; Psychiatry; Internal medicine; Dementia","score_opus":0.01225302556086834,"score_gpt":0.35575282257238544,"score_spread":0.3434997970115171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400981915","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":[0.9682073,0.013719649,0.0026324736,0.004444666,0.00041611044,0.000055293716,0.002843961,0.00004209815,0.007638491],"genre_scores_gemma":[0.9939628,0.0015863291,0.0009840484,0.00066435494,0.00012826378,0.000030904022,0.0007030444,0.000009733568,0.0019305779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983315,0.00056737167,0.00012085605,0.0005064054,0.0003025734,0.00017128706],"domain_scores_gemma":[0.98899615,0.0036294896,0.0048592584,0.0012325678,0.0006821023,0.000600348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004558011,0.00037976613,0.00039494026,0.0014580473,0.0009496074,0.0007405695,0.000592105,0.0008835012,0.0034058497],"category_scores_gemma":[0.015279499,0.00020902198,0.0013549183,0.0010314422,0.0006925806,0.00094635546,0.00091129605,0.0008560136,0.00020693899],"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":[0.00011517114,0.000015711124,0.9901974,0.000036669982,0.00042809144,0.00006879649,0.00024225489,0.0000795133,0.00008139385,0.0007047264,0.0009822127,0.0070480434],"study_design_scores_gemma":[0.0000043896152,0.00003646472,0.9975298,0.00003890277,0.00014186924,0.000117224496,0.00013169878,0.0001904862,0.000029388995,0.00033624558,0.0014367528,0.0000068040235],"about_ca_topic_score_codex":0.09178311,"about_ca_topic_score_gemma":0.15503387,"teacher_disagreement_score":0.09178311,"about_ca_system_score_codex":0.0007516694,"about_ca_system_score_gemma":0.0007802563,"threshold_uncertainty_score":0.18249774},"labels":[],"label_agreement":null},{"id":"W4401033546","doi":"10.47604/jsar.2754","title":"Modeling Longevity Risk in Pension Funds Using Population Dynamics in Canada","year":2024,"lang":"en","type":"article","venue":"Journal of statistics and actuarial research.","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Longevity risk; Pension; Longevity; Dynamics (music); Business; Population; Actuarial science; Economics; Finance; Gerontology; Medicine; Psychology; Environmental health","score_opus":0.0698827843947236,"score_gpt":0.3886278364289114,"score_spread":0.3187450520341878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401033546","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":[0.93522245,0.0021477868,0.037744585,0.0036600435,0.00011053059,0.00020231165,0.0072503644,0.0001810859,0.01348095],"genre_scores_gemma":[0.98598945,0.00091197836,0.0065010283,0.00011299561,0.000015982336,0.00006997141,0.0014543516,0.00001777739,0.0049264543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949837,0.0001319291,0.000021244408,0.00009251546,0.00009993133,0.00015602764],"domain_scores_gemma":[0.99860966,0.00043737516,0.00016172066,0.0000588113,0.00059499865,0.00013743367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017655267,0.0004661846,0.0005249968,0.0015785047,0.0014804305,0.0017290876,0.0015706477,0.0007367732,0.0017339092],"category_scores_gemma":[0.005684047,0.00025340365,0.0009521341,0.0015494403,0.00071618694,0.0006712033,0.0011133994,0.0007563355,0.00015718158],"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":[0.00009500127,0.00006196282,0.13385788,0.00008692975,0.00015948259,0.00022059595,0.0006695339,0.8213393,0.0002615119,0.02174715,0.0037158201,0.017784845],"study_design_scores_gemma":[0.000027247383,0.0000326809,0.037871853,0.000080416365,0.00010503575,0.000043997512,0.0008695828,0.94781303,0.00018204714,0.0055428613,0.007381447,0.00004977438],"about_ca_topic_score_codex":0.9831018,"about_ca_topic_score_gemma":0.96169657,"teacher_disagreement_score":0.024571074,"about_ca_system_score_codex":0.024571074,"about_ca_system_score_gemma":0.029012615,"threshold_uncertainty_score":0.17827648},"labels":[],"label_agreement":null},{"id":"W4401537380","doi":"10.1136/bmjopen-2023-079365","title":"Life expectancy and geographic variation in mortality: an observational comparison study of six high-income Anglophone countries","year":2024,"lang":"en","type":"article","venue":"BMJ Open","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"National Institute on Aging; National Institutes of Health","keywords":"Medicine; Life expectancy; Observational study; Geographic variation; Epidemiology; Demography; Public health; Environmental health; High income countries; Gerontology; Developing country; Population; Economic growth; Pathology","score_opus":0.1568777559879984,"score_gpt":0.4521656158099709,"score_spread":0.2952878598219725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401537380","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":[0.99899524,0.00010029662,0.00004745527,0.000013597408,0.0000022490533,0.000018208188,0.00063534634,6.257796e-7,0.00018688815],"genre_scores_gemma":[0.99812955,0.00014091635,0.00012017369,0.0000395913,0.0000061592123,0.00004794945,0.0014032441,9.4117564e-7,0.000111533765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99933475,0.00022434029,0.000083502935,0.00013074238,0.000101932266,0.00012465181],"domain_scores_gemma":[0.9986351,0.00021434127,0.0006307234,0.00008902175,0.0001979131,0.00023292367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082777935,0.00027237943,0.00033588655,0.00089003757,0.00057371124,0.0005345221,0.0003343924,0.00039185962,0.0009974721],"category_scores_gemma":[0.0019099967,0.00027159887,0.00041689174,0.0013580918,0.0003204898,0.00069762405,0.00090696017,0.00040579113,0.00023782658],"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":[0.000081289436,0.000042935742,0.9984794,0.000022332455,0.000066481276,0.0000665265,0.0003646628,0.000026189486,0.00005603536,0.000014223653,0.000107938075,0.00067191565],"study_design_scores_gemma":[0.000008485114,0.00010795693,0.9987243,0.000011714183,0.000020946694,0.00008487587,0.0007886552,0.000060471088,0.0000132907435,0.000007723648,0.00016789629,0.0000037411223],"about_ca_topic_score_codex":0.025469478,"about_ca_topic_score_gemma":0.039626796,"teacher_disagreement_score":0.025469478,"about_ca_system_score_codex":0.00054369244,"about_ca_system_score_gemma":0.00042076933,"threshold_uncertainty_score":0.05064249},"labels":[],"label_agreement":null},{"id":"W4401730693","doi":"10.2139/ssrn.4929303","title":"Can AI Distort Human Capital? *","year":2024,"lang":"it","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Quest University Canada","funders":"","keywords":"Human capital; Business; Economics; Market economy","score_opus":0.01114767276024136,"score_gpt":0.2941703787831009,"score_spread":0.28302270602285956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401730693","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":[0.4960281,0.0073508588,0.019377558,0.16679108,0.0016523564,0.00006364814,0.0011290396,0.00037769772,0.30722964],"genre_scores_gemma":[0.9847609,0.0015309588,0.00040764213,0.0019100256,0.00047760794,0.000008370808,0.000042657037,0.000017964978,0.010843862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992945,0.00025492042,0.000036808433,0.000113857954,0.00011593565,0.00018397547],"domain_scores_gemma":[0.9883189,0.007054693,0.002612575,0.0007318695,0.0007149096,0.0005669489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020929829,0.0002658853,0.00039036843,0.00071627944,0.00066723337,0.0037287325,0.000556608,0.0020195195,0.015670076],"category_scores_gemma":[0.017412877,0.00014891343,0.00027078803,0.0011420692,0.002411121,0.002396603,0.0009612446,0.0018895126,0.0012214541],"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":[0.00056412676,0.000197831,0.06150092,0.00029341658,0.00021021135,0.0009913676,0.0013914304,0.0131998,0.001301618,0.7714131,0.025624018,0.123312235],"study_design_scores_gemma":[0.00008712748,0.0002319074,0.043836694,0.00015069102,0.00010304183,0.00038813072,0.0019952,0.019318268,0.0011260395,0.8863994,0.04631162,0.000051915074],"about_ca_topic_score_codex":0.0062834676,"about_ca_topic_score_gemma":0.0037699006,"teacher_disagreement_score":0.015670076,"about_ca_system_score_codex":0.0015445087,"about_ca_system_score_gemma":0.000787595,"threshold_uncertainty_score":0.05242169},"labels":[],"label_agreement":null},{"id":"W4401912034","doi":"10.31107/2075-1990-2024-4-82-94","title":"Trends in the Architecture of the Most Sustainable Multi-tier Pension Systems","year":2024,"lang":"en","type":"article","venue":"Financial Journal","topic":"Insurance, Mortality, Demography, Risk Management","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":"Architecture; Pension; Business; Computer architecture; Computer science; Geography; Finance; Archaeology","score_opus":0.01603219030904773,"score_gpt":0.29366740756541937,"score_spread":0.27763521725637164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401912034","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":[0.9311087,0.012188243,0.006427661,0.0039017333,0.00006643401,0.00009051195,0.00061002036,0.00012667253,0.0454799],"genre_scores_gemma":[0.992592,0.0018535781,0.0035290485,0.00006042945,0.000012808461,0.000012241037,0.00018188763,0.0000073959363,0.0017506224],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99901927,0.00021739637,0.00011617174,0.00014897199,0.0002734814,0.00022482668],"domain_scores_gemma":[0.99784195,0.0002947524,0.0007726001,0.00016075555,0.00064179074,0.000288153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015581375,0.00012865014,0.00014227942,0.002780125,0.0007683076,0.0027568252,0.00036181952,0.00036685824,0.002151251],"category_scores_gemma":[0.0029775654,0.00011587945,0.0002102831,0.0028552455,0.00083010434,0.001841481,0.001441612,0.0005091281,0.00026749584],"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":[0.0002607396,0.00013036035,0.29824942,0.001172093,0.00008728466,0.00042908126,0.014931456,0.003615243,0.006657873,0.26978844,0.003204376,0.4014737],"study_design_scores_gemma":[0.000012223716,0.00027841382,0.80499095,0.0005303721,0.000055343673,0.0010382279,0.014344834,0.0022094497,0.0026619944,0.028567314,0.14524765,0.000063178064],"about_ca_topic_score_codex":0.0034802395,"about_ca_topic_score_gemma":0.0052892985,"teacher_disagreement_score":0.0034802395,"about_ca_system_score_codex":0.00281897,"about_ca_system_score_gemma":0.002425205,"threshold_uncertainty_score":0.020453155},"labels":[],"label_agreement":null},{"id":"W4401942733","doi":"10.1109/jsen.2024.3447717","title":"FPCA-SETCN: A Novel Deep Learning Framework for Remaining Useful Life Prediction","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","score_opus":0.04327807321042149,"score_gpt":0.33580307539409976,"score_spread":0.2925250021836783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942733","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":[0.028780041,0.0018989795,0.9626017,0.00048728127,0.00019832671,0.000089212896,0.0010967155,0.0024879954,0.002359807],"genre_scores_gemma":[0.75637186,0.0015729864,0.22812167,0.0005660974,0.00026186105,0.00034426202,0.0042040353,0.000228389,0.0083287945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969494,0.00006338849,0.000014737329,0.00009002415,0.00008021948,0.000056663874],"domain_scores_gemma":[0.99944764,0.0001984362,0.000051864496,0.000051086714,0.00021416367,0.00003677281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008583561,0.0013205443,0.00091540057,0.0011626962,0.00036052163,0.0007048347,0.002175615,0.001021712,0.0022967905],"category_scores_gemma":[0.0023712318,0.00038118233,0.0007545865,0.0009201715,0.00042384982,0.000955706,0.0009734701,0.0017719663,0.00043639785],"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":[0.00017519189,0.00020327652,0.004718388,0.00012569186,0.00021508666,0.00013323053,0.000046417146,0.642894,0.002922213,0.006766395,0.014758742,0.3270414],"study_design_scores_gemma":[0.0000029343958,0.00001101847,0.0002198086,0.0000060985312,0.0000074886843,0.000010592116,0.0000021465469,0.9974617,0.00037637018,0.0013866228,0.00051067927,0.0000045809816],"about_ca_topic_score_codex":0.028636683,"about_ca_topic_score_gemma":0.029586326,"teacher_disagreement_score":0.028636683,"about_ca_system_score_codex":0.0011568284,"about_ca_system_score_gemma":0.0018997823,"threshold_uncertainty_score":0.05694002},"labels":[],"label_agreement":null},{"id":"W4402029488","doi":"10.1080/10920277.2024.2325343","title":"A Markovian Aging Process Forecasting Model: Predicting U.S. Mortality","year":2024,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"","keywords":"Markov process; Process (computing); Computer science; Econometrics; Statistics; Economics; Mathematics","score_opus":0.03803916404277863,"score_gpt":0.34235266408883636,"score_spread":0.3043135000460577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402029488","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":[0.386562,0.0010151666,0.6009269,0.0024252494,0.00024587038,0.00010617073,0.0036469365,0.0008133951,0.004258341],"genre_scores_gemma":[0.93799925,0.00073308166,0.056336492,0.00019328638,0.00014201939,0.00013847083,0.0022748925,0.000026594082,0.0021558616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973637,0.00008284958,0.000015836054,0.00008307483,0.000042112282,0.00003982023],"domain_scores_gemma":[0.99898213,0.0006288491,0.00014383173,0.000042916945,0.00015401222,0.000048276062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001441531,0.00047547845,0.00053031085,0.0007535306,0.00034968602,0.0005637893,0.0009984534,0.0007172447,0.0011976268],"category_scores_gemma":[0.0034098206,0.00022597304,0.0006003029,0.00079549226,0.00021252796,0.0008774959,0.0004350324,0.0010084396,0.00023358018],"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":[0.00008336776,0.000092819784,0.021959528,0.00004995261,0.00006262725,0.00009544672,0.00011470635,0.91720223,0.00045068617,0.016910044,0.00407157,0.038907047],"study_design_scores_gemma":[0.0000036328695,0.000010750388,0.000807468,0.0000035806154,0.0000072447156,0.0000074123473,0.000006038415,0.99614066,0.00004466408,0.0026695933,0.00029493394,0.0000040691066],"about_ca_topic_score_codex":0.033905856,"about_ca_topic_score_gemma":0.025644703,"teacher_disagreement_score":0.033905856,"about_ca_system_score_codex":0.0007929308,"about_ca_system_score_gemma":0.0012385483,"threshold_uncertainty_score":0.067417026},"labels":[],"label_agreement":null},{"id":"W4402114191","doi":"10.1080/17441730.2024.2398275","title":"Estimating the stochastic uncertainty underlying sample-based estimates of infant mortality in the Philippines: a first-time application to a country in the Southeast Asia/Pacific Basin region","year":2024,"lang":"en","type":"article","venue":"Asian Population Studies","topic":"Insurance, Mortality, Demography, Risk Management","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":"Structural basin; Geography; Pacific basin; Sample (material); Southeast asia; Geology; Oceanography; Geomorphology; History; Ancient history","score_opus":0.05623862558520381,"score_gpt":0.3630267745098025,"score_spread":0.30678814892459866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402114191","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":[0.631446,0.00076109444,0.36440438,0.0005977731,0.000039875187,0.00021625539,0.0004194481,0.00025358782,0.0018615685],"genre_scores_gemma":[0.90575224,0.0006206556,0.092463106,0.00008574327,0.000047401947,0.00017070504,0.00033333042,0.000037421254,0.0004894815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996128,0.0031538706,0.00010344181,0.00024067692,0.00028759992,0.00008638281],"domain_scores_gemma":[0.9699214,0.02460424,0.0021980186,0.0012974493,0.0017321421,0.00024676137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0090069305,0.0005716771,0.0005894099,0.0010934534,0.00041289927,0.0009379417,0.0007524191,0.00042936805,0.00067401543],"category_scores_gemma":[0.048041936,0.0002692103,0.00066043675,0.0011617607,0.00064760464,0.00058936636,0.0014102854,0.0009583406,0.00007172409],"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":[0.00022085196,0.00011129206,0.33724537,0.00040466056,0.0013501388,0.0010993271,0.0024649827,0.4651767,0.0024086244,0.006623452,0.001103223,0.18179134],"study_design_scores_gemma":[0.000023397015,0.00027327245,0.08388843,0.00010606434,0.00016995534,0.00024348416,0.0014654177,0.9041667,0.0020028872,0.0058570495,0.0017116742,0.00009174946],"about_ca_topic_score_codex":0.037397567,"about_ca_topic_score_gemma":0.023519708,"teacher_disagreement_score":0.037397567,"about_ca_system_score_codex":0.000789767,"about_ca_system_score_gemma":0.0011901667,"threshold_uncertainty_score":0.074359775},"labels":[],"label_agreement":null},{"id":"W4402571891","doi":"10.1109/phm61473.2024.00075","title":"Remaining Useful Life Estimation Based on the System Trajectory in a Latent Space Representation","year":2024,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Hydro-Québec; École de Technologie Supérieure","funders":"Mitacs","keywords":"Trajectory; Representation (politics); Estimation; Computer science; Space (punctuation); Artificial intelligence; Engineering","score_opus":0.0435423324430684,"score_gpt":0.3167871723055089,"score_spread":0.2732448398624405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402571891","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":[0.07610662,0.00014256629,0.92231387,0.00014867065,0.000012237556,0.000028783381,0.00033974205,0.000096550866,0.00081087515],"genre_scores_gemma":[0.9301597,0.0003026727,0.067059845,0.000017599375,0.000018707053,0.00008827536,0.000820641,0.00003676486,0.0014958151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995871,0.00016888593,0.00002581489,0.00009520185,0.00007301064,0.000049840146],"domain_scores_gemma":[0.99831223,0.0010387169,0.00026067882,0.00013502498,0.00019278107,0.000060578404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014853341,0.00047796502,0.00053319405,0.0011257184,0.0002657046,0.0010347402,0.00066200685,0.00058193854,0.0017793722],"category_scores_gemma":[0.0046657533,0.00019504584,0.0006110575,0.001099031,0.00065326964,0.0016127771,0.00090297,0.00091078994,0.00025008872],"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":[0.000050419407,0.000030518015,0.009514174,0.000060864815,0.00004592758,0.0001037897,0.00017178856,0.92308176,0.0010835167,0.036473595,0.00040885297,0.02897481],"study_design_scores_gemma":[0.000001195016,0.000013191964,0.0008853275,0.000008638779,0.0000045390516,0.0000150957685,0.000023411218,0.99016213,0.00017407093,0.008498886,0.00020680681,0.000006662519],"about_ca_topic_score_codex":0.006348372,"about_ca_topic_score_gemma":0.0033892372,"teacher_disagreement_score":0.006348372,"about_ca_system_score_codex":0.0006456197,"about_ca_system_score_gemma":0.00097066385,"threshold_uncertainty_score":0.012622833},"labels":[],"label_agreement":null},{"id":"W4402603835","doi":"10.2139/ssrn.4958193","title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph; University of Waterloo","funders":"","keywords":"Risk aversion (psychology); Longevity; Longevity risk; Economics; Actuarial science; Business; Financial economics; Medicine; Expected utility hypothesis; Gerontology","score_opus":0.006153831257882553,"score_gpt":0.2651761641784564,"score_spread":0.25902233292057386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402603835","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":[0.9402509,0.00042680462,0.03826352,0.002221447,0.000027768694,0.00004413293,0.00018476596,0.000035927067,0.018544689],"genre_scores_gemma":[0.9940142,0.00018838244,0.0012409551,0.00006179123,0.000031927535,0.000020352463,0.00004792219,0.000007487539,0.0043870234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987066,0.000639006,0.00006440181,0.0001421038,0.00023089648,0.00021700586],"domain_scores_gemma":[0.97415835,0.02001583,0.0030182071,0.00082739774,0.00069696544,0.0012832702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049403473,0.00028022862,0.00073412317,0.0012339583,0.0006766046,0.0026362576,0.0005851859,0.0019780921,0.010130154],"category_scores_gemma":[0.026539044,0.00042831502,0.00042445023,0.0012142475,0.0024567156,0.0033340347,0.0015139212,0.0020989978,0.000495784],"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":[0.0005031504,0.0003695103,0.037771035,0.00007490076,0.00009114444,0.00030734256,0.0012455545,0.042858936,0.0011309608,0.8902461,0.0015201748,0.023881298],"study_design_scores_gemma":[0.00010942101,0.00014383571,0.018757964,0.000031228254,0.000038079357,0.00017191439,0.00042471703,0.05164351,0.00018536083,0.9275538,0.0009128732,0.000027368116],"about_ca_topic_score_codex":0.0017534522,"about_ca_topic_score_gemma":0.0014312733,"teacher_disagreement_score":0.010130154,"about_ca_system_score_codex":0.0017789267,"about_ca_system_score_gemma":0.0009703924,"threshold_uncertainty_score":0.033888757},"labels":[],"label_agreement":null},{"id":"W4402676146","doi":"10.2139/ssrn.4934382","title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; University of Waterloo","funders":"","keywords":"Longevity; Longevity risk; Risk aversion (psychology); Actuarial science; Economics; Risk analysis (engineering); Business; Financial economics; Expected utility hypothesis; Medicine; Gerontology","score_opus":0.006153831257882553,"score_gpt":0.2651761641784564,"score_spread":0.25902233292057386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402676146","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":[0.9402509,0.00042680462,0.03826352,0.002221447,0.000027768694,0.00004413293,0.00018476596,0.000035927067,0.018544689],"genre_scores_gemma":[0.9940142,0.00018838244,0.0012409551,0.00006179123,0.000031927535,0.000020352463,0.00004792219,0.000007487539,0.0043870234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987066,0.000639006,0.00006440181,0.0001421038,0.00023089648,0.00021700586],"domain_scores_gemma":[0.97415835,0.02001583,0.0030182071,0.00082739774,0.00069696544,0.0012832702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049403473,0.00028022862,0.00073412317,0.0012339583,0.0006766046,0.0026362576,0.0005851859,0.0019780921,0.010130154],"category_scores_gemma":[0.026539044,0.00042831502,0.00042445023,0.0012142475,0.0024567156,0.0033340347,0.0015139212,0.0020989978,0.000495784],"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":[0.0005031504,0.0003695103,0.037771035,0.00007490076,0.00009114444,0.00030734256,0.0012455545,0.042858936,0.0011309608,0.8902461,0.0015201748,0.023881298],"study_design_scores_gemma":[0.00010942101,0.00014383571,0.018757964,0.000031228254,0.000038079357,0.00017191439,0.00042471703,0.05164351,0.00018536083,0.9275538,0.0009128732,0.000027368116],"about_ca_topic_score_codex":0.0017534522,"about_ca_topic_score_gemma":0.0014312733,"teacher_disagreement_score":0.010130154,"about_ca_system_score_codex":0.0017789267,"about_ca_system_score_gemma":0.0009703924,"threshold_uncertainty_score":0.033888757},"labels":[],"label_agreement":null},{"id":"W4402694484","doi":"10.2139/ssrn.4962272","title":"As-If-Markov Reserves for Reserve-Dependent Payments","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Royal Ottawa Mental Health Centre","funders":"","keywords":"Markov chain; Payment; Excess reserves; Reserve requirement; Bank reserves; Economics; Business; Monetary economics; Mathematics; Central bank; Finance; Monetary policy; Statistics","score_opus":0.021010653535731758,"score_gpt":0.34103690083269605,"score_spread":0.3200262472969643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402694484","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":[0.093722135,0.0009695875,0.85859877,0.0031303533,0.00055234414,0.0001623587,0.0010706987,0.0011786994,0.040615037],"genre_scores_gemma":[0.89947885,0.00062819885,0.062073354,0.00030447985,0.0003955744,0.00017507983,0.00073143037,0.00041941588,0.035793606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998591,0.0005100746,0.00008072502,0.00023336317,0.00021248567,0.00037235802],"domain_scores_gemma":[0.98915124,0.006701528,0.00085185503,0.0014879145,0.0010207423,0.0007867161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004867531,0.00084023253,0.0017172392,0.0011213308,0.0010051951,0.0031909507,0.0022397628,0.0022798316,0.025847873],"category_scores_gemma":[0.026180651,0.00062517624,0.0012428452,0.0008492752,0.002078085,0.004926209,0.002850922,0.0034444996,0.002419386],"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":[0.00042377302,0.00009962471,0.0010294967,0.00013175946,0.000040901425,0.0002404738,0.00012563408,0.15887973,0.0006996765,0.80958045,0.00830284,0.020445649],"study_design_scores_gemma":[0.000033485787,0.000037130947,0.0002462633,0.00004576973,0.000016404383,0.00010615295,0.000035776688,0.5544914,0.00032667958,0.44292665,0.0017145916,0.00001975878],"about_ca_topic_score_codex":0.003245044,"about_ca_topic_score_gemma":0.0033586049,"teacher_disagreement_score":0.025847873,"about_ca_system_score_codex":0.0014306859,"about_ca_system_score_gemma":0.0028195798,"threshold_uncertainty_score":0.08646977},"labels":[],"label_agreement":null},{"id":"W4402838380","doi":"10.2139/ssrn.4937839","title":"Fertility Incentives in Canada: A Cohort Analysis","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Incentive; Fertility; Cohort; Political science; Economics; Demography; Medicine; Sociology; Population; Microeconomics","score_opus":0.006341533794046201,"score_gpt":0.2652242343420175,"score_spread":0.25888270054797125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402838380","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":[0.98612124,0.0017624219,0.00015979266,0.0008535143,0.000031213123,0.00010121302,0.00926637,0.000019637213,0.0016845872],"genre_scores_gemma":[0.99153566,0.0013119744,0.00020630009,0.00025738394,0.000016633774,0.000048608577,0.0036204169,0.000016386468,0.002986528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971105,0.00030114228,0.00018018213,0.00040899302,0.00054917904,0.0014500449],"domain_scores_gemma":[0.9887805,0.0009832019,0.0024629736,0.00077720283,0.0040357993,0.0029603725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024206536,0.00071079296,0.00095967785,0.0032505922,0.0048374124,0.0033532542,0.003015593,0.001978871,0.0052129324],"category_scores_gemma":[0.007239197,0.0009732456,0.0019949053,0.009438518,0.0011856384,0.0010171647,0.0019777967,0.0021515735,0.0005532913],"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":[0.00027578315,0.00011930613,0.99259984,0.000024548446,0.00020858756,0.00012042237,0.00070817716,0.00021499675,0.00004226779,0.00033461972,0.0020279007,0.0033235908],"study_design_scores_gemma":[0.000047175163,0.000073825446,0.99469864,0.00006353454,0.00017699623,0.00008371074,0.0018004363,0.00078384276,0.00005337593,0.00009777681,0.0020784833,0.000042047548],"about_ca_topic_score_codex":0.99534386,"about_ca_topic_score_gemma":0.99643636,"teacher_disagreement_score":0.044534575,"about_ca_system_score_codex":0.044534575,"about_ca_system_score_gemma":0.06568769,"threshold_uncertainty_score":0.3231225},"labels":[],"label_agreement":null},{"id":"W4403073968","doi":"10.28924/2291-8639-22-2024-172","title":"A Novel Distribution in the Family of Lifetime Distributions for Enhancing Predictive Modeling for Medical and Engineering Data","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Distribution (mathematics); Mathematics; Statistics; Data science; Econometrics; Computer science; Mathematical analysis","score_opus":0.022455932524075226,"score_gpt":0.34855233975777483,"score_spread":0.3260964072336996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403073968","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":[0.013090737,0.0010788412,0.9830126,0.00046961143,0.000095752315,0.00008387288,0.00029961136,0.00037559672,0.001493267],"genre_scores_gemma":[0.63919574,0.0051667173,0.34331378,0.0008555847,0.0006974307,0.0009083342,0.0017269675,0.00036864995,0.00776679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99713886,0.0010028335,0.00016678224,0.0007288133,0.0007460902,0.000216633],"domain_scores_gemma":[0.9873553,0.008689164,0.0010919571,0.0011862563,0.0014395013,0.00023791002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007124076,0.00089722354,0.00091448554,0.0022261972,0.0006905846,0.0020167525,0.0021325771,0.0017753713,0.0037052173],"category_scores_gemma":[0.02817445,0.00036919152,0.0014172285,0.0022204183,0.0017716805,0.0043942635,0.0017250839,0.0026061968,0.0009045756],"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":[0.00032116313,0.00014992684,0.01936367,0.00064651115,0.00021416266,0.0011131973,0.001049869,0.25542995,0.0078635905,0.4650732,0.009582703,0.2391921],"study_design_scores_gemma":[0.000038530026,0.00022935004,0.005135946,0.0002042488,0.000075637385,0.001410901,0.00021556029,0.7826497,0.0022180008,0.18964404,0.018081302,0.00009670573],"about_ca_topic_score_codex":0.0020262424,"about_ca_topic_score_gemma":0.0011566503,"teacher_disagreement_score":0.007124076,"about_ca_system_score_codex":0.0012054897,"about_ca_system_score_gemma":0.0011162636,"threshold_uncertainty_score":0.037676215},"labels":[],"label_agreement":null},{"id":"W4403096387","doi":"10.1016/j.dajour.2024.100522","title":"A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping","year":2024,"lang":"en","type":"article","venue":"Decision Analytics Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo","funders":"","keywords":"Bayesian probability; Actuarial science; Econometrics; Computer science; Artificial intelligence; Business; Economics","score_opus":0.021079736288491045,"score_gpt":0.2993781064943486,"score_spread":0.27829837020585757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403096387","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":[0.06190434,0.0012268394,0.9122321,0.005895373,0.00026157885,0.00017678765,0.00267405,0.00060679525,0.015022193],"genre_scores_gemma":[0.8461746,0.0015657402,0.12562248,0.00085928047,0.0002970489,0.0004053656,0.0020391918,0.00012854229,0.022907648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989968,0.000342781,0.000041339612,0.00030710574,0.00017064148,0.00014132987],"domain_scores_gemma":[0.9976705,0.0016130001,0.00020583763,0.00006741606,0.00028672777,0.00015653223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022059167,0.0008428909,0.0014533589,0.0014208434,0.0006604994,0.002079187,0.0023865802,0.002177473,0.0075564575],"category_scores_gemma":[0.0067272573,0.00083156826,0.0012288726,0.0014461564,0.0008842575,0.002481868,0.00135523,0.0021370722,0.0008788024],"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":[0.00016115513,0.00015106832,0.0077094967,0.00009565324,0.00012801819,0.0003164285,0.0002222164,0.80704516,0.00037522454,0.13323161,0.006751073,0.043813005],"study_design_scores_gemma":[0.00002332753,0.000016802784,0.00039176716,0.000016550108,0.000027511356,0.000047003923,0.00001886854,0.9565429,0.000051957395,0.041075706,0.0017746049,0.000012936314],"about_ca_topic_score_codex":0.02815067,"about_ca_topic_score_gemma":0.021473441,"teacher_disagreement_score":0.02815067,"about_ca_system_score_codex":0.0019941886,"about_ca_system_score_gemma":0.002526673,"threshold_uncertainty_score":0.05597365},"labels":[],"label_agreement":null},{"id":"W4403145731","doi":"10.1007/s00414-024-03347-4","title":"Correction to: A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year","year":2024,"lang":"en","type":"erratum","venue":"International Journal of Legal Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"St. Thomas Hospital","funders":"","keywords":"Key (lock); Medical law; Psychology; Medicine; Computer science; Computer security; Psychiatry","score_opus":0.05023512743058475,"score_gpt":0.38523157370778244,"score_spread":0.3349964462771977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403145731","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":[0.0009930081,0.0012945354,0.019993603,0.06990654,0.7875457,0.00026803988,0.10449809,0.0041991295,0.011301324],"genre_scores_gemma":[0.05079042,0.0046179364,0.07259186,0.059983864,0.12470028,0.0022509436,0.08227124,0.008732602,0.59406084],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959901,0.0012447725,0.0007524277,0.0005850507,0.0011873665,0.000240342],"domain_scores_gemma":[0.9558948,0.02063324,0.0021665806,0.0032427474,0.016894924,0.0011677014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059683747,0.0024051883,0.0015745889,0.0032389916,0.0020463392,0.002917607,0.0044781268,0.004010244,0.19263147],"category_scores_gemma":[0.10444886,0.0013706472,0.0021219815,0.0035119322,0.0010630065,0.0019275344,0.0021906267,0.0075453166,0.077781215],"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":[0.000030549236,0.0000045624033,0.00021261587,0.00010665325,0.000014441204,0.000060795617,0.000019346926,0.00020016011,0.000011959693,0.00097008137,0.9939301,0.00443883],"study_design_scores_gemma":[0.00026963826,0.000034486387,0.0026842447,0.0009376509,0.00009152003,0.0005746282,0.000115683775,0.0036445074,0.00030454964,0.0072407145,0.98401815,0.00008423633],"about_ca_topic_score_codex":0.07504097,"about_ca_topic_score_gemma":0.08004399,"teacher_disagreement_score":0.19263147,"about_ca_system_score_codex":0.0037485694,"about_ca_system_score_gemma":0.008680034,"threshold_uncertainty_score":0.64441645},"labels":[],"label_agreement":null},{"id":"W4403190166","doi":"10.1093/ije/dyae128","title":"Faltering mortality improvements at young-middle ages in high-income English-speaking countries","year":2024,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Australian Research Council","keywords":"Life expectancy; Demography; Cohort; Cohort study; Pandemic; Medicine; Life course approach; Longevity; Gerontology; Population; Disease; Coronavirus disease 2019 (COVID-19); Psychology; Sociology; Infectious disease (medical specialty)","score_opus":0.053024418016628515,"score_gpt":0.3827942717701954,"score_spread":0.32976985375356693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403190166","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":[0.98965913,0.002436943,0.00029443848,0.0014677584,0.000052475545,0.000014481937,0.0013479849,0.000015962494,0.0047108564],"genre_scores_gemma":[0.99800926,0.0008346224,0.00014617371,0.00021423677,0.00002014159,0.000008223718,0.00039811968,0.0000022709412,0.00036698286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996369,0.00009580637,0.000030037618,0.00004964073,0.000042981865,0.00014460868],"domain_scores_gemma":[0.9988826,0.00012432633,0.000363214,0.00006504168,0.00030225603,0.00026262863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013962893,0.00022243099,0.00023158616,0.0005228519,0.00036243888,0.00070453587,0.00025810557,0.00016676914,0.0016761842],"category_scores_gemma":[0.002393256,0.000077445664,0.00039466683,0.00055844296,0.00031999275,0.00073094136,0.00091406965,0.0003186557,0.00020497211],"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":[0.00012407663,0.000029393652,0.96742666,0.0002902289,0.00014009426,0.0004222403,0.002996466,0.0005958687,0.00045614678,0.001318772,0.002300308,0.023899708],"study_design_scores_gemma":[0.000004271198,0.000043639826,0.99481493,0.0001327358,0.000033455217,0.000066276974,0.0017806524,0.0001601113,0.000101207406,0.00016294364,0.0026941274,0.0000055209716],"about_ca_topic_score_codex":0.0502296,"about_ca_topic_score_gemma":0.06788123,"teacher_disagreement_score":0.0502296,"about_ca_system_score_codex":0.00076958217,"about_ca_system_score_gemma":0.0018065366,"threshold_uncertainty_score":0.0998745},"labels":[],"label_agreement":null},{"id":"W4403303539","doi":"10.4108/eai.24-5-2024.2350121","title":"Life Expectancy Regression Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Regression analysis; Computer science; Statistics; Expectancy theory; Life expectancy; Econometrics; Mathematics; Psychology; Demography; Sociology","score_opus":0.022224125972238745,"score_gpt":0.3566682954617952,"score_spread":0.33444416948955646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403303539","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":[0.5192852,0.0029173081,0.40980342,0.0014517998,0.0006240672,0.0009775044,0.03116534,0.004487276,0.029288132],"genre_scores_gemma":[0.9207663,0.0006920075,0.04382451,0.00018125626,0.00015596951,0.0008296416,0.009404762,0.000731303,0.023414243],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971059,0.0016216899,0.00016419095,0.00048134345,0.00038596705,0.00024101751],"domain_scores_gemma":[0.9906619,0.0066288793,0.00073830417,0.0008387244,0.0009667427,0.00016546661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045514167,0.0009432071,0.00075197325,0.002643323,0.00030690714,0.0006844501,0.0012097342,0.00040618802,0.021069326],"category_scores_gemma":[0.022634326,0.00018469385,0.0021824974,0.0023633442,0.00018153542,0.0005626563,0.00085122033,0.0015474485,0.0054669483],"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":[0.0015482906,0.0010889462,0.47216266,0.00086346915,0.004353692,0.00070666615,0.0010558979,0.050574403,0.003557567,0.018525233,0.02733042,0.4182327],"study_design_scores_gemma":[0.00016703681,0.0027349803,0.47354147,0.00038763252,0.0019210177,0.00208225,0.0013635013,0.3825043,0.0084437765,0.016876757,0.10978281,0.00019446253],"about_ca_topic_score_codex":0.0054608723,"about_ca_topic_score_gemma":0.0033955793,"teacher_disagreement_score":0.021069326,"about_ca_system_score_codex":0.00044771962,"about_ca_system_score_gemma":0.00082036346,"threshold_uncertainty_score":0.07048386},"labels":[],"label_agreement":null},{"id":"W4403590633","doi":"10.3386/w33066","title":"The Time of Your Life: The Mortality and Longevity of Canadians","year":2024,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Insurance, Mortality, Demography, Risk Management","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":"Longevity; Demography; Gerontology; Geography; Medicine; Sociology","score_opus":0.35217091739199924,"score_gpt":0.5365409491440257,"score_spread":0.1843700317520265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403590633","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":[0.85758567,0.006297435,0.014931075,0.007413872,0.00034061048,0.00040382473,0.06033336,0.00015741432,0.052536715],"genre_scores_gemma":[0.96161574,0.0020978304,0.01345836,0.0002962374,0.00007851674,0.00020193547,0.010281698,0.000036156867,0.011933457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918705,0.00009044807,0.000024342702,0.00011351851,0.00040839918,0.00017627842],"domain_scores_gemma":[0.99790597,0.00027296256,0.00022971486,0.00011621704,0.0011720213,0.0003031409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017337862,0.00028220084,0.00023504527,0.0029741502,0.0024858082,0.0012699697,0.001116588,0.00036955246,0.0025216173],"category_scores_gemma":[0.0071186777,0.00013609706,0.0006069168,0.004321656,0.00044356866,0.00047467303,0.0010279136,0.00056304305,0.00025260955],"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":[0.000045391647,0.000034039625,0.9091791,0.000082911414,0.0000711573,0.000061894214,0.0016430854,0.0019348116,0.00012545819,0.010392908,0.013159567,0.063269675],"study_design_scores_gemma":[0.0000059040576,0.000019288393,0.9732723,0.00007662693,0.000046665697,0.000044764172,0.0015084588,0.0030673128,0.00016761986,0.0010529911,0.020713372,0.00002471712],"about_ca_topic_score_codex":0.9900393,"about_ca_topic_score_gemma":0.99202394,"teacher_disagreement_score":0.0140982885,"about_ca_system_score_codex":0.0140982885,"about_ca_system_score_gemma":0.0237912,"threshold_uncertainty_score":0.10229069},"labels":[],"label_agreement":null},{"id":"W4403600943","doi":"10.2139/ssrn.4994003","title":"The Time of Your Life: The Mortality and Longevity of Canadians","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Longevity; Gerontology; Demography; Medicine; Sociology","score_opus":0.015354537584978218,"score_gpt":0.29737902895899015,"score_spread":0.2820244913740119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403600943","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":[0.9739386,0.0055777305,0.00007950403,0.0076432107,0.000118905016,0.000014299436,0.004886444,0.000006789074,0.007734513],"genre_scores_gemma":[0.99588215,0.0017314493,0.00006347881,0.00028193212,0.000039154624,0.000004031245,0.00077557034,0.0000034835057,0.0012186676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992086,0.00006672293,0.00003499706,0.00007397638,0.0002200252,0.0003957309],"domain_scores_gemma":[0.9965287,0.00017620136,0.0006168082,0.000077112905,0.0014120616,0.0011891818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029517,0.00022085974,0.00031390903,0.0019469674,0.00423415,0.0017945401,0.0011790629,0.0010179412,0.0037254696],"category_scores_gemma":[0.006518147,0.00016656143,0.00059233437,0.00565737,0.0008997447,0.00089907716,0.0009400509,0.0014936307,0.00019153702],"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":[0.00016217808,0.00005598741,0.9708565,0.000047627684,0.000076788456,0.00011696565,0.003957167,0.00016688343,0.00004426621,0.0013225339,0.00567595,0.017517127],"study_design_scores_gemma":[0.000005600273,0.000026884823,0.98796904,0.000096576936,0.000053764645,0.00008804011,0.0060363645,0.00023213672,0.0000225589,0.00029414624,0.005147274,0.000027616767],"about_ca_topic_score_codex":0.99437875,"about_ca_topic_score_gemma":0.99630797,"teacher_disagreement_score":0.02373504,"about_ca_system_score_codex":0.02373504,"about_ca_system_score_gemma":0.0372158,"threshold_uncertainty_score":0.17221057},"labels":[],"label_agreement":null},{"id":"W4403662228","doi":"10.48550/arxiv.2409.08914","title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Longevity; Risk aversion (psychology); Longevity risk; Actuarial science; Economics; Business; Financial economics; Expected utility hypothesis; Medicine; Gerontology","score_opus":0.030605603019337724,"score_gpt":0.20753629882107472,"score_spread":0.17693069580173698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403662228","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":[0.8960016,0.0003433437,0.08341881,0.00086819375,0.000018569732,0.00009075043,0.00008184463,0.000024618335,0.01915224],"genre_scores_gemma":[0.995175,0.0001455712,0.003285993,0.0000335444,0.000016524955,0.000027621858,0.00001994313,0.000004170655,0.0012915855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985682,0.0007341827,0.00006652969,0.0001532171,0.00023646056,0.00024139699],"domain_scores_gemma":[0.9883525,0.0071613896,0.0027835332,0.0004973334,0.0003383901,0.0008669299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004617181,0.00039441016,0.00061152416,0.0009730337,0.0007342512,0.0024453716,0.0006952192,0.001502488,0.006410284],"category_scores_gemma":[0.016466392,0.000388129,0.00059551286,0.00066729065,0.0026953367,0.0035871305,0.0016740918,0.0016697596,0.00027493446],"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":[0.0005397071,0.00043649407,0.033355944,0.00008909783,0.00012835105,0.00035614654,0.0010477407,0.11922368,0.0031662558,0.816573,0.0006687942,0.02441478],"study_design_scores_gemma":[0.00020560126,0.00042314955,0.025865579,0.000070123024,0.00007892826,0.00031435207,0.0010194593,0.20346962,0.00096277444,0.76530993,0.0022057039,0.000074759846],"about_ca_topic_score_codex":0.0010863324,"about_ca_topic_score_gemma":0.0006280165,"teacher_disagreement_score":0.006410284,"about_ca_system_score_codex":0.0016085765,"about_ca_system_score_gemma":0.0010302857,"threshold_uncertainty_score":0.024418294},"labels":[],"label_agreement":null},{"id":"W4404297600","doi":"10.1016/j.insmatheco.2024.11.001","title":"Comonotonicity and Pareto optimality, with application to collaborative insurance","year":2024,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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":"KU Leuven; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek; Fonds De La Recherche Scientifique - FNRS","keywords":"Pareto principle; Pareto optimal; Actuarial science; Computer science; Economics; Business; Mathematical optimization; Mathematics; Multi-objective optimization","score_opus":0.010509680373913621,"score_gpt":0.2718318416009693,"score_spread":0.26132216122705565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404297600","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":[0.072263464,0.003782297,0.8674121,0.0044709044,0.00040989253,0.000090682326,0.00015864584,0.000098408076,0.05131366],"genre_scores_gemma":[0.8840138,0.0043883654,0.084063865,0.0005332301,0.0010107424,0.00022267422,0.00015329168,0.00010984356,0.02550424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99787045,0.0011343204,0.00011812023,0.00027020925,0.0003432727,0.00026353236],"domain_scores_gemma":[0.9850212,0.011385767,0.001080186,0.0005796701,0.0010646925,0.00086855167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006214548,0.0015503982,0.0026037337,0.0025394196,0.0026768479,0.003922773,0.0017305136,0.0025865014,0.0062538707],"category_scores_gemma":[0.022191409,0.0007355206,0.0029639378,0.0035591982,0.006038351,0.0057228566,0.0052178036,0.0040587303,0.00036296356],"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":[0.00000826366,0.000023251941,0.00034800748,0.000025059931,0.000020568572,0.00006314743,0.00011087297,0.015414937,0.00006877461,0.98060673,0.0006176524,0.0026927749],"study_design_scores_gemma":[0.000008703518,0.000011979864,0.0002068886,0.000012594508,0.000014380497,0.000024943713,0.00007336582,0.06810504,0.000027750002,0.93066865,0.0008328805,0.00001290105],"about_ca_topic_score_codex":0.00987625,"about_ca_topic_score_gemma":0.008462379,"teacher_disagreement_score":0.00987625,"about_ca_system_score_codex":0.003914489,"about_ca_system_score_gemma":0.0034024767,"threshold_uncertainty_score":0.03286606},"labels":[],"label_agreement":null},{"id":"W4404315218","doi":"10.1017/9781009007245.013","title":"Longevity in Modern Populations","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 de Montréal","funders":"","keywords":"Longevity; Biology; Genetics","score_opus":0.0486442740254725,"score_gpt":0.2667551234831736,"score_spread":0.2181108494577011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404315218","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":[0.015366539,0.17757115,0.008841161,0.023029678,0.002240877,0.00004065815,0.00029462384,0.000113616,0.7725016],"genre_scores_gemma":[0.3350406,0.187673,0.010038097,0.009580555,0.005045396,0.00016147182,0.00033155477,0.0000844492,0.45204479],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9998023,0.000056054352,0.000006855976,0.000048914746,0.00006388185,0.000022002318],"domain_scores_gemma":[0.9998865,0.0000553246,0.000011263277,0.00001522274,0.000018055725,0.000013710865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043463465,0.00031467938,0.00022249186,0.0005304013,0.0007249301,0.0016082911,0.0002806762,0.00083538756,0.008729686],"category_scores_gemma":[0.00079662935,0.00009999837,0.0001308041,0.0006329399,0.0025568777,0.001908297,0.0010230805,0.0011087069,0.0020021067],"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":[0.000011946816,0.00001317661,0.0006149732,0.0001594094,0.0000054044117,0.0000921947,0.0032969392,0.00036541588,0.000253385,0.7918509,0.058697738,0.14463848],"study_design_scores_gemma":[0.0000029589298,0.000025143063,0.0018057047,0.00021837039,0.0000041996354,0.00025475165,0.00045920332,0.00012916308,0.000064301326,0.21686876,0.78016084,0.0000065574022],"about_ca_topic_score_codex":0.0014160905,"about_ca_topic_score_gemma":0.0016726357,"teacher_disagreement_score":0.008729686,"about_ca_system_score_codex":0.0009551657,"about_ca_system_score_gemma":0.0007633007,"threshold_uncertainty_score":0.029203773},"labels":[],"label_agreement":null},{"id":"W4404533016","doi":"10.1016/s0140-6736(24)02417-6","title":"Halving premature death and improving quality of life at all ages: cross-country analyses of past trends and future directions","year":2024,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Centre for Global Health Research; Public Health Ontario","funders":"Medical Research Council; Harvard University; Harvard T.H. Chan School of Public Health; Direktoratet for Utviklingssamarbeid; Norges Forskningsråd; Bill and Melinda Gates Foundation","keywords":"Environmental health; Cross-sectional study; Demography; Medicine; Gerontology; Geography; Sociology; Pathology","score_opus":0.062075225414578994,"score_gpt":0.39657653601206144,"score_spread":0.33450131059748245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404533016","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":[0.97756517,0.009262658,0.0031113192,0.00088414823,0.00010833637,0.00006124613,0.006150397,0.000024917981,0.002831739],"genre_scores_gemma":[0.992297,0.002431707,0.0016177789,0.00013833749,0.00004845403,0.00005888439,0.003195989,0.000009592896,0.00020229729],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963181,0.0019956178,0.0003867772,0.00052878575,0.00042479715,0.00034582926],"domain_scores_gemma":[0.9867313,0.005085213,0.0040808804,0.0013376547,0.0022039572,0.000561012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010313321,0.00048212812,0.000545903,0.0029826346,0.0004197171,0.0014010412,0.0005287608,0.0004327977,0.001463813],"category_scores_gemma":[0.013068147,0.0002715318,0.001899032,0.0049728067,0.0005102696,0.0015978653,0.0014901977,0.00080498593,0.00020078656],"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":[0.00006686465,0.000034737925,0.9871389,0.00018304473,0.002002946,0.000049565308,0.0003672478,0.0009984891,0.00006990369,0.00041133782,0.0005731375,0.008103876],"study_design_scores_gemma":[0.0000041878498,0.00016153023,0.9945756,0.00014898356,0.000557414,0.00007811345,0.0009547951,0.0011827297,0.00013178622,0.00022504215,0.0019621039,0.000017724651],"about_ca_topic_score_codex":0.014708409,"about_ca_topic_score_gemma":0.016949814,"teacher_disagreement_score":0.014708409,"about_ca_system_score_codex":0.0007558495,"about_ca_system_score_gemma":0.0006989434,"threshold_uncertainty_score":0.05454266},"labels":[],"label_agreement":null},{"id":"W4404738048","doi":"10.2139/ssrn.5025227","title":"Dual Role of Human Activities and Climate in Pre-Industrial Nitrogen Shifts in Ireland","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Trent University","funders":"","keywords":"Dual (grammatical number); Dual purpose; Climate change; Nitrogen; Environmental science; Economic geography; Natural resource economics; Geography; Economics; Ecology; Biology; Engineering; Chemistry; Art","score_opus":0.015141438593378194,"score_gpt":0.2973955089354618,"score_spread":0.2822540703420836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404738048","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":[0.99691975,0.00010640275,0.00006156539,0.00026509832,0.000004271142,0.0000022083545,0.00021784383,0.0000023010232,0.0024206163],"genre_scores_gemma":[0.99930847,0.000050105056,0.000018533092,0.000015307303,0.0000043693885,0.0000016876608,0.00010526371,0.0000017572006,0.0004945461],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996736,0.00007437505,0.000020652024,0.000046781683,0.000032738837,0.00015189752],"domain_scores_gemma":[0.9992944,0.00016633117,0.00019979244,0.000041142695,0.00010798415,0.0001904174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004965637,0.000087290646,0.00017992349,0.00067787286,0.0003578533,0.0009125372,0.00035143376,0.00037694143,0.0021613159],"category_scores_gemma":[0.0013567914,0.00011498446,0.00019153373,0.00092515786,0.0007628375,0.0004589943,0.0015023593,0.00047601172,0.0002051782],"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":[0.0003951835,0.000076035314,0.97966033,0.000047131773,0.000045904097,0.0004534737,0.002805062,0.0015292928,0.00091493543,0.0015578226,0.0005536107,0.011961305],"study_design_scores_gemma":[0.000001700523,0.000014659263,0.99709344,0.0000079605325,0.0000032304308,0.00001923751,0.0017721487,0.00021691027,0.000048094807,0.0001328044,0.0006860923,0.0000037633492],"about_ca_topic_score_codex":0.124499016,"about_ca_topic_score_gemma":0.2252534,"teacher_disagreement_score":0.124499016,"about_ca_system_score_codex":0.002646464,"about_ca_system_score_gemma":0.0015215179,"threshold_uncertainty_score":0.2475487},"labels":[],"label_agreement":null},{"id":"W4404764622","doi":"10.1080/03461238.2024.2431539","title":"Optimal robust reinsurance with multiple insurers*","year":2024,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Reinsurance; Actuarial science; Econometrics; Business; Mathematics; Computer science; Mathematical optimization","score_opus":0.017737845759925944,"score_gpt":0.2765905577878879,"score_spread":0.25885271202796195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404764622","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":[0.45309478,0.0014106563,0.5275402,0.003527034,0.00013759927,0.00019154887,0.00027125166,0.00030268767,0.0135242725],"genre_scores_gemma":[0.9796038,0.00021150924,0.015319466,0.00010099018,0.000045485125,0.00004944243,0.0000499736,0.000032593238,0.004586625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99746287,0.0011987276,0.00009364881,0.00038864507,0.00029520376,0.00056076277],"domain_scores_gemma":[0.99262214,0.0046972865,0.0012993988,0.00034268945,0.00032560073,0.0007128601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055535473,0.0017877012,0.0027847665,0.00072999217,0.00068274,0.00216703,0.0023111766,0.0038511741,0.004077764],"category_scores_gemma":[0.015934153,0.0015364367,0.0017075585,0.0005241867,0.0020273738,0.0030372285,0.0034310468,0.002182438,0.000270474],"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":[0.0002791182,0.000087124514,0.0009605534,0.00006574309,0.00012344604,0.00043358543,0.00010898409,0.9485605,0.0011770416,0.043462936,0.00049545686,0.0042455327],"study_design_scores_gemma":[0.00006532807,0.00013078685,0.0003384941,0.000013547314,0.000038104692,0.00006802594,0.00005152674,0.984009,0.00028142132,0.014719269,0.00025876422,0.000025587184],"about_ca_topic_score_codex":0.00730554,"about_ca_topic_score_gemma":0.0025617247,"teacher_disagreement_score":0.00730554,"about_ca_system_score_codex":0.0029181784,"about_ca_system_score_gemma":0.0016440237,"threshold_uncertainty_score":0.029370308},"labels":[],"label_agreement":null},{"id":"W4405011914","doi":"10.1111/joim.20031","title":"Race and ethnicity dynamics in survival to 100 years in the United States","year":2024,"lang":"en","type":"article","venue":"Journal of Internal Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"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 Montréal","funders":"National Institute on Aging; Evelyn F. McKnight Brain Research Foundation; Social Sciences and Humanities Research Council of Canada; McKnight Foundation; Brain Research Foundation","keywords":"Demography; Population; Medicine; Ethnic group; Cohort; Life expectancy; Mortality rate; Race (biology); Gerontology; Internal medicine; Biology","score_opus":0.024498564106967144,"score_gpt":0.36056385740635055,"score_spread":0.3360652932993834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405011914","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":[0.9929731,0.0005571465,0.00016150805,0.0010763082,0.000023964012,0.00000730905,0.0023912431,0.000016139542,0.002793373],"genre_scores_gemma":[0.99745554,0.0003294054,0.00007473616,0.00013224827,0.000013966409,0.000011215788,0.0015031331,0.000005666592,0.00047417392],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981207,0.00004219894,0.000011961064,0.00004682151,0.000026214337,0.000060742368],"domain_scores_gemma":[0.9989436,0.00019178313,0.0004231346,0.000046973655,0.00017581697,0.00021875644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068819016,0.00009196302,0.00012680292,0.0006115166,0.0003409357,0.00057014805,0.00029810166,0.00026392212,0.0032146426],"category_scores_gemma":[0.00283316,0.00008080797,0.0002935298,0.0006158348,0.00019838424,0.0005650742,0.0005626726,0.0005703434,0.00030366657],"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":[0.00010999871,0.00004705316,0.9840449,0.00002214471,0.000056834073,0.00009135256,0.0006428832,0.0006492472,0.0001425943,0.00081634853,0.0027651342,0.010611439],"study_design_scores_gemma":[0.0000032953153,0.000028528195,0.99634945,0.000040891573,0.000020507621,0.00007946043,0.00070163596,0.0006984271,0.00004567792,0.0003288964,0.0016972856,0.0000059470417],"about_ca_topic_score_codex":0.0403031,"about_ca_topic_score_gemma":0.045994226,"teacher_disagreement_score":0.0403031,"about_ca_system_score_codex":0.00063992623,"about_ca_system_score_gemma":0.0005944703,"threshold_uncertainty_score":0.080137014},"labels":[],"label_agreement":null},{"id":"W4405189768","doi":"10.1007/s44199-024-00088-6","title":"Assessing the Impact of Geopolitical Risk on Longevity Bond Pricing: Insights from Bayesian Multivariate Regression","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Toronto Metropolitan University","funders":"University of South Africa; Toronto Metropolitan University","keywords":"Deviance information criterion; Multivariate statistics; Bayesian probability; Akaike information criterion; Posterior probability; Bayesian information criterion; Econometrics; Marginal likelihood; Mathematics; Statistics; Bayesian inference; Computer science","score_opus":0.02132702116760536,"score_gpt":0.39880296240839447,"score_spread":0.3774759412407891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405189768","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":[0.6136914,0.0012385235,0.37905535,0.001478436,0.000036870933,0.000035836805,0.00016846608,0.00020951951,0.0040856316],"genre_scores_gemma":[0.9894079,0.00046247113,0.009150033,0.000046810266,0.000047252328,0.0000101255155,0.00008079193,0.000025758947,0.00076878874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981548,0.0011239236,0.00007253327,0.00021497805,0.0003102644,0.000123466],"domain_scores_gemma":[0.97839224,0.017128874,0.0023800402,0.00067731045,0.0010918858,0.00032959256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008156916,0.0006097119,0.0010506165,0.0013758981,0.00031794407,0.0016997764,0.00095405965,0.0010872419,0.0016611661],"category_scores_gemma":[0.034572463,0.00041654968,0.00075988594,0.0011557747,0.0008918114,0.0021047324,0.0011892216,0.0014459601,0.0001338192],"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":[0.00014103955,0.00009761273,0.03708516,0.00008083255,0.000208115,0.00028953995,0.00020625256,0.8387032,0.0012658986,0.090540074,0.0008875276,0.030494701],"study_design_scores_gemma":[0.0000032780927,0.000016614762,0.0035407068,0.000008735964,0.000020411475,0.000020732019,0.00002082005,0.98756903,0.000118136,0.008533876,0.00013625668,0.000011368642],"about_ca_topic_score_codex":0.01033953,"about_ca_topic_score_gemma":0.005056135,"teacher_disagreement_score":0.01033953,"about_ca_system_score_codex":0.0007408719,"about_ca_system_score_gemma":0.000772909,"threshold_uncertainty_score":0.043138385},"labels":[],"label_agreement":null},{"id":"W4405561009","doi":"10.4153/s000843952400095x","title":"A note on Hayman’s problem","year":2024,"lang":"en","type":"article","venue":"Canadian Mathematical Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mathematics; Mathematical economics","score_opus":0.015478748419709294,"score_gpt":0.2879140921351633,"score_spread":0.27243534371545397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405561009","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":[0.08093982,0.012026986,0.23441091,0.32244217,0.014696886,0.00010641995,0.0009467624,0.0004452821,0.33398473],"genre_scores_gemma":[0.7866713,0.006814606,0.05171946,0.043712083,0.01705274,0.00030506158,0.0004778403,0.00031232272,0.09293451],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99711394,0.0010933272,0.00013951835,0.00062138814,0.00067172927,0.00036009192],"domain_scores_gemma":[0.99111617,0.005819113,0.000565337,0.0007404044,0.0013240727,0.0004348526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052598417,0.0005297715,0.001135879,0.0019006633,0.0033735668,0.0023058744,0.0025242032,0.003980103,0.011686762],"category_scores_gemma":[0.024634527,0.00040078184,0.0010447227,0.0019811117,0.0057020537,0.009573626,0.0033201973,0.0070305686,0.0013263273],"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":[0.00002427824,0.000026065063,0.000279478,0.00002965144,0.000009420965,0.00009605087,0.00013771937,0.00071285723,0.00006116858,0.96452767,0.02535227,0.008743363],"study_design_scores_gemma":[0.000020340081,0.000011061495,0.000108813496,0.000023389506,0.00000463599,0.00007660024,0.00008399807,0.0031826997,0.00007074163,0.97798,0.018427057,0.000010543403],"about_ca_topic_score_codex":0.0052942657,"about_ca_topic_score_gemma":0.00239346,"teacher_disagreement_score":0.011686762,"about_ca_system_score_codex":0.0037331388,"about_ca_system_score_gemma":0.0023154952,"threshold_uncertainty_score":0.039096177},"labels":[],"label_agreement":null},{"id":"W4405961419","doi":"10.1093/geroni/igae098.2190","title":"EFFECTS OF ATTENTIONAL CONTROL DEMANDS IN PROCESSING SPEED TRAINING ACROSS THE ADULT LIFESPAN: FIRST FINDINGS","year":2024,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Economic and Social Research Council","keywords":"Training (meteorology); Psychology; Control (management); Attentional control; Cognitive psychology; Computer science; Neuroscience; Artificial intelligence; Cognition; Geography","score_opus":0.01844388684885686,"score_gpt":0.3250596074005841,"score_spread":0.30661572055172726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405961419","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":[0.9994444,0.0001996011,0.00006113326,0.000014506472,0.0000018147906,0.000009430741,0.000046575555,0.0000031006766,0.00021940256],"genre_scores_gemma":[0.9996284,0.0000548154,0.000076898796,0.00001378776,0.0000049537757,0.000011499041,0.00003398859,0.0000010017553,0.00017451342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997285,0.00007184652,0.000019638639,0.000074442716,0.000063607105,0.000041841937],"domain_scores_gemma":[0.997262,0.0009299501,0.0006446588,0.00043962162,0.00030401183,0.00041976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011645295,0.00031608623,0.00037951118,0.0004020661,0.00025268784,0.00030532127,0.00033555448,0.0005472351,0.0014481057],"category_scores_gemma":[0.0030181173,0.00017577458,0.00025478698,0.0002454846,0.00041900974,0.00039691367,0.00051573815,0.0004795817,0.00013759978],"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":[0.02768654,0.0059791273,0.8042631,0.00030753054,0.00041685195,0.00065971416,0.0037667735,0.0004471186,0.044261243,0.00019035768,0.000542981,0.111478746],"study_design_scores_gemma":[0.00004068936,0.0031932527,0.99507785,0.0000088765955,0.000050678536,0.00006933564,0.0001547057,0.00007718856,0.001174615,0.000035709516,0.00011332174,0.0000038822222],"about_ca_topic_score_codex":0.0037970105,"about_ca_topic_score_gemma":0.004614204,"teacher_disagreement_score":0.0037970105,"about_ca_system_score_codex":0.00027587474,"about_ca_system_score_gemma":0.00024417994,"threshold_uncertainty_score":0.0075498223},"labels":[],"label_agreement":null},{"id":"W4406203788","doi":"10.4054/demres.2025.52.3","title":"Jointly estimating subnational mortality for multiple populations","year":2025,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Toronto","funders":"National Institute on Aging","keywords":"Geography; Demography; Sociology","score_opus":0.2751081046690669,"score_gpt":0.5254014937431906,"score_spread":0.2502933890741237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406203788","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":[0.33923635,0.00075584784,0.6546702,0.0009238317,0.00005374743,0.00017888204,0.0021326004,0.0003257496,0.0017227767],"genre_scores_gemma":[0.8449269,0.00046922988,0.14842485,0.00015090096,0.000077328805,0.0003028439,0.0030048115,0.00006662315,0.0025764918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769527,0.0012625182,0.00012503665,0.0006033883,0.00018378231,0.00013007745],"domain_scores_gemma":[0.99349385,0.0043433374,0.00083399547,0.0006377121,0.0005063144,0.00018477067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006559341,0.0007938577,0.0016674498,0.0013100124,0.00040701186,0.0010635452,0.0015471685,0.00077291776,0.0022382694],"category_scores_gemma":[0.014311318,0.00065704784,0.0017280065,0.0011806536,0.00069102366,0.001430568,0.002603132,0.0014077942,0.00034428408],"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":[0.00015689126,0.000122298,0.16483311,0.00011763808,0.0010085093,0.00022168679,0.0004495464,0.73496866,0.00063396414,0.010726096,0.0019149601,0.084846646],"study_design_scores_gemma":[0.000018376617,0.00007661693,0.026967285,0.000036951005,0.00014741495,0.000086740896,0.00018643295,0.9552044,0.0003069258,0.015598332,0.0013390603,0.000031446525],"about_ca_topic_score_codex":0.030775515,"about_ca_topic_score_gemma":0.031347334,"teacher_disagreement_score":0.030775515,"about_ca_system_score_codex":0.00120048,"about_ca_system_score_gemma":0.0019322471,"threshold_uncertainty_score":0.06119275},"labels":[],"label_agreement":null},{"id":"W4406225346","doi":"10.1017/asb.2024.38","title":"Forecasting mortality rates with functional signatures","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Robustness (evolution); Outlier; Bootstrapping (finance); Computer science; Econometrics; Regression; Data mining; Statistics; Artificial intelligence; Mathematics; Biology","score_opus":0.031229378047147767,"score_gpt":0.2961986112930384,"score_spread":0.26496923324589067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406225346","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":[0.5513948,0.00025544033,0.44390106,0.0006893134,0.00008373676,0.00005016285,0.00085114787,0.000350368,0.002424001],"genre_scores_gemma":[0.9826493,0.00008380671,0.016179228,0.000026601769,0.000031263255,0.000023752731,0.0004572038,0.000010965019,0.0005377517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919385,0.00047910277,0.000038020353,0.00010037178,0.00012542811,0.000063223255],"domain_scores_gemma":[0.99592197,0.002402544,0.0006865087,0.00037674262,0.00046655507,0.00014564004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029979642,0.0004571743,0.00051277695,0.0015768004,0.00016126293,0.0007680078,0.0009372512,0.0007117948,0.0011571127],"category_scores_gemma":[0.013005498,0.00022209108,0.0005960112,0.0011969963,0.0003885627,0.0010394378,0.0007316634,0.00086378725,0.0002507666],"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":[0.0001291637,0.000056266872,0.030967372,0.000035864283,0.00006373938,0.0000734172,0.000076664,0.90275615,0.00068871636,0.011801357,0.0009775745,0.052373637],"study_design_scores_gemma":[0.0000020207726,0.000012698684,0.0017109221,0.0000033090187,0.0000025107586,0.000006699552,0.000008911885,0.9951723,0.0001205136,0.0028674353,0.000088288194,0.000004405153],"about_ca_topic_score_codex":0.006073632,"about_ca_topic_score_gemma":0.0030882705,"teacher_disagreement_score":0.006073632,"about_ca_system_score_codex":0.00066545873,"about_ca_system_score_gemma":0.00065168703,"threshold_uncertainty_score":0.015854955},"labels":[],"label_agreement":null},{"id":"W4406455758","doi":"10.46481/jnsps.2025.1976","title":"A new Maxwell-Log logistic distribution and its applications for mortality rate data","year":2025,"lang":"en","type":"article","venue":"Journal of the Nigerian Society of Physical Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Yayasan UTP; Universiti Teknologi Petronas","keywords":"Logistic regression; Distribution (mathematics); Statistics; Log-logistic distribution; Econometrics; Mortality rate; Mathematics; Computer science; Statistical physics; Demography; Probability distribution; Physics; Distribution fitting; Sociology; Mathematical analysis","score_opus":0.07117621290574126,"score_gpt":0.3929467513500212,"score_spread":0.32177053844427994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406455758","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":[0.06977491,0.0026224637,0.91351426,0.002676833,0.000353038,0.00038634337,0.0026868691,0.0015373607,0.006447892],"genre_scores_gemma":[0.75342286,0.0050397366,0.21935107,0.001332695,0.0009906752,0.0012300649,0.006246797,0.00052603474,0.011860105],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966769,0.0014087406,0.00021765726,0.0007573366,0.0006428236,0.00029659877],"domain_scores_gemma":[0.9878005,0.0070467843,0.001352531,0.0013297537,0.0020294506,0.00044100004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082188845,0.001031252,0.0009678695,0.0035431464,0.001047885,0.0025430915,0.0030286093,0.001563269,0.0062033497],"category_scores_gemma":[0.032238524,0.0004834429,0.0016128271,0.0042307256,0.0018345586,0.0053978655,0.0021116612,0.0026325479,0.0021890595],"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":[0.0007395731,0.00030367248,0.11151351,0.00077310594,0.00026800355,0.001869373,0.0011300538,0.24252713,0.002789011,0.2809058,0.03175395,0.32542682],"study_design_scores_gemma":[0.00006416368,0.00021147083,0.013336306,0.00017978835,0.000058296577,0.0017931428,0.0004977197,0.82446444,0.0010584308,0.13233776,0.02583674,0.00016173939],"about_ca_topic_score_codex":0.0063950433,"about_ca_topic_score_gemma":0.0040698885,"teacher_disagreement_score":0.0082188845,"about_ca_system_score_codex":0.0022848304,"about_ca_system_score_gemma":0.0018410236,"threshold_uncertainty_score":0.04346615},"labels":[],"label_agreement":null},{"id":"W4406488678","doi":"10.1016/s0967-0653(97)83579-x","title":"10.1016/s0967-0653(97)83579-x","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"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":"Monte Carlo method; Statistical physics; Statistics; Econometrics; Mathematics; Physics","score_opus":0.01071856404238305,"score_gpt":0.22158147541458278,"score_spread":0.21086291137219973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406488678","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":[0.00061412645,0.00043308124,0.0005490102,0.00051982736,0.0002774801,0.00012582209,0.0012126347,0.0008268768,0.9954412],"genre_scores_gemma":[0.0008038292,0.00021260441,0.00037890315,0.00029344024,0.00007692909,0.00007554251,0.00052805094,0.00015347709,0.99747723],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932075,0.0000599121,0.00006624888,0.00021937561,0.0001800993,0.00015366252],"domain_scores_gemma":[0.9976525,0.0007068034,0.0001907183,0.00028153026,0.00041812888,0.00075030717],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013215056,0.0025374577,0.0017624358,0.002983114,0.001749016,0.0032962207,0.0030963554,0.0055132546,0.98979],"category_scores_gemma":[0.002177099,0.00085382676,0.0012113773,0.002560536,0.001587631,0.005130403,0.0031328502,0.0025044433,0.99178857],"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":[0.0003489914,0.00028899475,0.0012449697,0.000498391,0.000033892746,0.0002221806,0.000102956925,0.0004202131,0.0011835474,0.0049348357,0.35719788,0.63352317],"study_design_scores_gemma":[0.00008322605,0.0001617312,0.0014500747,0.0005022851,0.000019331,0.00039171995,0.00014710464,0.00029031353,0.0003295115,0.00083410356,0.9957569,0.000033652344],"about_ca_topic_score_codex":0.0046561817,"about_ca_topic_score_gemma":0.003771359,"teacher_disagreement_score":0.010209978,"about_ca_system_score_codex":0.0010417704,"about_ca_system_score_gemma":0.0010968784,"threshold_uncertainty_score":0.014563322},"labels":[],"label_agreement":null},{"id":"W4406855546","doi":"10.1080/03461238.2025.2455056","title":"Optimal income drawdown and investment with longevity basis risk","year":2025,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","funders":"Kempestiftelserna","keywords":"Drawdown (hydrology); Longevity risk; Investment (military); Economics; Actuarial science; Longevity; Econometrics; Mathematics; Finance; Geology; Medicine; Pension; Political science","score_opus":0.006204589496422858,"score_gpt":0.2710748910923721,"score_spread":0.26487030159594926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406855546","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":[0.51814973,0.0016148781,0.46189415,0.0019517174,0.000067660905,0.00017176957,0.00023382167,0.000110127585,0.015806116],"genre_scores_gemma":[0.9834682,0.00034397125,0.01139538,0.0000450661,0.000016638667,0.0000628407,0.000050319235,0.000018260744,0.0045994013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994417,0.00030652023,0.000020007557,0.00007197842,0.00005710613,0.00010266212],"domain_scores_gemma":[0.9984542,0.0010476625,0.00018784752,0.000056634577,0.000076720906,0.00017692595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002345431,0.0007293035,0.00088037556,0.00048717236,0.00029400535,0.0015631015,0.0009336753,0.0019112861,0.0029014992],"category_scores_gemma":[0.007862362,0.0005502963,0.00058400555,0.00038495648,0.0010904606,0.0017881579,0.0011082909,0.0012419317,0.00018790113],"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":[0.00024399708,0.00010067203,0.0017019315,0.00011342784,0.00005895271,0.00027240618,0.00017755685,0.8468193,0.0022226223,0.13432586,0.0008193368,0.013143966],"study_design_scores_gemma":[0.00003813406,0.00012669188,0.0007841839,0.00002935656,0.000021459617,0.0000445401,0.00005201498,0.94959015,0.0004561778,0.048192825,0.0006493095,0.000015261561],"about_ca_topic_score_codex":0.001773894,"about_ca_topic_score_gemma":0.00085402414,"teacher_disagreement_score":0.0029014992,"about_ca_system_score_codex":0.0016730177,"about_ca_system_score_gemma":0.00088231586,"threshold_uncertainty_score":0.012403965},"labels":[],"label_agreement":null},{"id":"W4406972075","doi":"10.1080/10920277.2024.2442416","title":"A Nested GLM Framework with Neural Network Encoding and Spatially Constrained Clustering in Non-Life Insurance Ratemaking","year":2025,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Artificial neural network; Generalized linear model; Econometrics; Computer science; Mathematics; Artificial intelligence; Machine learning","score_opus":0.011400301916251784,"score_gpt":0.28645719134878367,"score_spread":0.27505688943253187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406972075","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":[0.022503965,0.0002827243,0.9739778,0.00050109235,0.000045377135,0.0000388503,0.00032447875,0.0005782005,0.0017474851],"genre_scores_gemma":[0.67081636,0.0004056496,0.32043546,0.0003093778,0.000093720046,0.00029145347,0.0008023659,0.00023203326,0.006613588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814427,0.0009082829,0.00008111612,0.0005204144,0.00020282734,0.00014297153],"domain_scores_gemma":[0.9978878,0.0011845797,0.00032165728,0.0002184775,0.00028530214,0.00010224232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027127992,0.0010538995,0.001103958,0.0012317431,0.000608961,0.0019361187,0.0032131958,0.0018654926,0.003744718],"category_scores_gemma":[0.008960665,0.00082745805,0.0016608349,0.0014478182,0.0014421523,0.0029920738,0.0020869353,0.0030378494,0.00062483316],"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":[0.000058865688,0.000054029115,0.0019223997,0.000060734987,0.00008295853,0.00012374998,0.00023863284,0.8818924,0.0006479742,0.077763505,0.0009941442,0.036160566],"study_design_scores_gemma":[0.0000028968527,0.0000084400845,0.00015402849,0.0000060292223,0.0000055991877,0.0000068295417,0.0000100805655,0.9765396,0.00007277803,0.022949947,0.00023596683,0.0000078318335],"about_ca_topic_score_codex":0.027382648,"about_ca_topic_score_gemma":0.029273106,"teacher_disagreement_score":0.027382648,"about_ca_system_score_codex":0.0024062789,"about_ca_system_score_gemma":0.0016605265,"threshold_uncertainty_score":0.05444652},"labels":[],"label_agreement":null},{"id":"W4406981415","doi":"10.1007/s13385-025-00407-w","title":"Fast estimation of the Renshaw-Haberman model and its variants","year":2025,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Estimation; Econometrics; Statistics; Mathematics; Economics; Management","score_opus":0.016099487427901207,"score_gpt":0.2874528873385753,"score_spread":0.27135339991067414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406981415","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":[0.02073749,0.00025857022,0.97795105,0.00017798916,0.000018645589,0.000039795428,0.00006488466,0.0001545288,0.00059693574],"genre_scores_gemma":[0.37757185,0.0008602881,0.6150493,0.00014362029,0.00007659302,0.0004014964,0.0005162031,0.00021680517,0.0051638857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663204,0.0023645808,0.000098948345,0.00041691877,0.00033498817,0.00015249375],"domain_scores_gemma":[0.9934529,0.005145648,0.0004381221,0.00041488637,0.00044700407,0.00010156441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0097984895,0.0009294429,0.001326345,0.0014390135,0.00060710247,0.0011856249,0.0022105826,0.001492882,0.0028431695],"category_scores_gemma":[0.015991976,0.0008486806,0.0012371376,0.0014919597,0.0010839428,0.001999493,0.0022745002,0.002394028,0.0006413791],"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":[0.00012304024,0.0000737961,0.0050894744,0.00011220275,0.00013696814,0.00014217709,0.00016720663,0.85416776,0.0011600088,0.06734185,0.001247625,0.07023787],"study_design_scores_gemma":[0.00000764166,0.00001867762,0.00062183675,0.000013972652,0.000011606758,0.000027817445,0.000014673407,0.9856172,0.00035883984,0.01269627,0.0005929721,0.000018481298],"about_ca_topic_score_codex":0.013153727,"about_ca_topic_score_gemma":0.01087518,"teacher_disagreement_score":0.013153727,"about_ca_system_score_codex":0.0008563738,"about_ca_system_score_gemma":0.0018281466,"threshold_uncertainty_score":0.05181992},"labels":[],"label_agreement":null},{"id":"W4407074616","doi":"10.1007/s13340-025-00801-5","title":"Premature mortality due to diabetes in Japan: a nationwide analysis from 2000 to 2020","year":2025,"lang":"en","type":"article","venue":"Diabetology International","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Alberta Health Services","funders":"","keywords":"Medicine; Diabetes mellitus; Gerontology; Endocrinology","score_opus":0.009026918650473658,"score_gpt":0.3145130311452954,"score_spread":0.3054861124948217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407074616","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":[0.99228716,0.0008783671,0.00008587806,0.00011776644,0.00002271102,0.000012912316,0.0061011445,0.000008942684,0.0004850924],"genre_scores_gemma":[0.9916654,0.00052699447,0.00008973708,0.00008186565,0.000025575577,0.000026143036,0.007335567,0.0000036951487,0.00024503164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994282,0.0000695765,0.00015095186,0.00012523554,0.0000713655,0.00015461532],"domain_scores_gemma":[0.99853396,0.00009322637,0.0005963621,0.000091538896,0.00030269017,0.0003822048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007627887,0.00058030436,0.000526153,0.0031498037,0.0006588252,0.0008729513,0.000555125,0.0006202721,0.00086858],"category_scores_gemma":[0.001384144,0.00056657975,0.002177787,0.004304946,0.00027374463,0.00083547627,0.0011111669,0.0007297137,0.00017251892],"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":[0.000077278004,0.000017746364,0.9985814,0.000023471359,0.00015035608,0.00009209798,0.000060296374,0.00005616275,0.000056913235,0.000009412359,0.00025972386,0.0006151303],"study_design_scores_gemma":[0.0000027787448,0.00001567462,0.99941015,0.000005464636,0.000091617556,0.0000646293,0.00016873947,0.000101855796,0.000009873902,0.0000051763304,0.000120711986,0.0000034094762],"about_ca_topic_score_codex":0.07607687,"about_ca_topic_score_gemma":0.10053539,"teacher_disagreement_score":0.07607687,"about_ca_system_score_codex":0.001310335,"about_ca_system_score_gemma":0.0015427874,"threshold_uncertainty_score":0.15126812},"labels":[],"label_agreement":null},{"id":"W4407220451","doi":"10.2139/ssrn.5126965","title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; University of Waterloo","funders":"","keywords":"Risk aversion (psychology); Longevity; Longevity risk; Actuarial science; Business; Risk analysis (engineering); Economics; Financial economics; Expected utility hypothesis; Medicine; Gerontology","score_opus":0.006021618031829321,"score_gpt":0.2683442456386229,"score_spread":0.2623226276067936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407220451","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":[0.94022757,0.0004313109,0.038244132,0.0022270181,0.000028071301,0.000044120512,0.00018588525,0.000036092802,0.018575909],"genre_scores_gemma":[0.9939374,0.00019208768,0.0012342633,0.00006171237,0.00003223372,0.000020316604,0.00004818101,0.000007494754,0.0044662533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872464,0.0006290361,0.00006381044,0.00014072082,0.00022852594,0.0002132466],"domain_scores_gemma":[0.9742054,0.01997074,0.0030283406,0.0008189205,0.00069673953,0.0012799135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049012993,0.00027940533,0.00073638535,0.0012369921,0.0006732855,0.0026251276,0.00058082974,0.0019601423,0.01018437],"category_scores_gemma":[0.026662268,0.0004252787,0.0004221518,0.001228277,0.002440514,0.0033194765,0.0015083518,0.0020914774,0.00049527147],"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":[0.0005009876,0.00036919798,0.038083363,0.000075786775,0.00009161913,0.0003086442,0.0012521907,0.04301386,0.0011301715,0.8895408,0.0015376217,0.024095712],"study_design_scores_gemma":[0.000108757034,0.0001453291,0.018947344,0.000031709496,0.00003820681,0.00017217704,0.00042817686,0.051722743,0.00018419091,0.9272759,0.0009179417,0.000027456159],"about_ca_topic_score_codex":0.0017630383,"about_ca_topic_score_gemma":0.00143651,"teacher_disagreement_score":0.01018437,"about_ca_system_score_codex":0.0017763383,"about_ca_system_score_gemma":0.0009666907,"threshold_uncertainty_score":0.034070134},"labels":[],"label_agreement":null},{"id":"W4407225401","doi":"10.2139/ssrn.5126755","title":"Pension Risk and Capital Structure Decisions","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Lethbridge","funders":"","keywords":"Pension; Business; Actuarial science; Capital (architecture); Capital structure; Risk analysis (engineering); Finance; History","score_opus":0.00993322920595818,"score_gpt":0.2897519991261928,"score_spread":0.27981876992023463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407225401","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":[0.9826195,0.00079612626,0.0016548282,0.0038027125,0.00002506947,0.000011520365,0.00041452734,0.000013548415,0.010662205],"genre_scores_gemma":[0.99489045,0.0002909699,0.00009613076,0.000056837842,0.000023370068,0.0000027457202,0.00007376607,0.0000020035884,0.00456366],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996886,0.000114680115,0.000014278535,0.000045695277,0.000037845773,0.00009887241],"domain_scores_gemma":[0.9967378,0.0020582697,0.0005990403,0.00008755169,0.000083961415,0.00043329556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013531935,0.00018792908,0.0003415725,0.00062523043,0.00038504825,0.0019995284,0.00025400522,0.0017667877,0.009640847],"category_scores_gemma":[0.006714802,0.00021372733,0.00025485572,0.0006554386,0.00071035686,0.0012890863,0.000506092,0.0010828318,0.00077253307],"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":[0.0026660045,0.0011895051,0.5370161,0.00015933778,0.00035035677,0.0017980854,0.0031465902,0.07045025,0.0027029642,0.2957271,0.009001885,0.07579187],"study_design_scores_gemma":[0.00017024222,0.0004895803,0.49274406,0.00007372658,0.00015070934,0.00042514084,0.0022529257,0.06122773,0.000817278,0.4320292,0.00954701,0.00007242888],"about_ca_topic_score_codex":0.0036973048,"about_ca_topic_score_gemma":0.006173057,"teacher_disagreement_score":0.009640847,"about_ca_system_score_codex":0.0010794254,"about_ca_system_score_gemma":0.00041404527,"threshold_uncertainty_score":0.032251835},"labels":[],"label_agreement":null},{"id":"W4407239725","doi":"10.2139/ssrn.5126966","title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph; University of Waterloo","funders":"","keywords":"Risk aversion (psychology); Longevity; Longevity risk; Actuarial science; Business; Economics; Financial economics; Medicine; Expected utility hypothesis; Gerontology","score_opus":0.006021618031829321,"score_gpt":0.2683442456386229,"score_spread":0.2623226276067936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407239725","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":[0.94022757,0.0004313109,0.038244132,0.0022270181,0.000028071301,0.000044120512,0.00018588525,0.000036092802,0.018575909],"genre_scores_gemma":[0.9939374,0.00019208768,0.0012342633,0.00006171237,0.00003223372,0.000020316604,0.00004818101,0.000007494754,0.0044662533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872464,0.0006290361,0.00006381044,0.00014072082,0.00022852594,0.0002132466],"domain_scores_gemma":[0.9742054,0.01997074,0.0030283406,0.0008189205,0.00069673953,0.0012799135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049012993,0.00027940533,0.00073638535,0.0012369921,0.0006732855,0.0026251276,0.00058082974,0.0019601423,0.01018437],"category_scores_gemma":[0.026662268,0.0004252787,0.0004221518,0.001228277,0.002440514,0.0033194765,0.0015083518,0.0020914774,0.00049527147],"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":[0.0005009876,0.00036919798,0.038083363,0.000075786775,0.00009161913,0.0003086442,0.0012521907,0.04301386,0.0011301715,0.8895408,0.0015376217,0.024095712],"study_design_scores_gemma":[0.000108757034,0.0001453291,0.018947344,0.000031709496,0.00003820681,0.00017217704,0.00042817686,0.051722743,0.00018419091,0.9272759,0.0009179417,0.000027456159],"about_ca_topic_score_codex":0.0017630383,"about_ca_topic_score_gemma":0.00143651,"teacher_disagreement_score":0.01018437,"about_ca_system_score_codex":0.0017763383,"about_ca_system_score_gemma":0.0009666907,"threshold_uncertainty_score":0.034070134},"labels":[],"label_agreement":null},{"id":"W4407833892","doi":"10.2139/ssrn.5101398","title":"A Natural Hedging Framework for Longevity Risk with Graphical Risk Assessment","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Longevity risk; Natural (archaeology); Longevity; Risk assessment; Risk analysis (engineering); Computer science; Actuarial science; Business; Environmental science; Medicine; Geology; Gerontology","score_opus":0.009397565069866082,"score_gpt":0.3333130846107864,"score_spread":0.32391551954092035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407833892","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":[0.004977938,0.00027538842,0.9908682,0.0005856124,0.00006826353,0.00002363004,0.00012423209,0.00009922533,0.0029775011],"genre_scores_gemma":[0.5616574,0.0015302866,0.41550258,0.0005083928,0.00069145235,0.00026377712,0.00051016023,0.0001757309,0.019160246],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99706537,0.0017065292,0.00013831662,0.00039965,0.00050977094,0.00018037412],"domain_scores_gemma":[0.9926519,0.0047607175,0.0006415404,0.00089624536,0.00067171204,0.0003779937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061231926,0.0013011495,0.0010855136,0.0017557315,0.00058907375,0.0034903884,0.0027789646,0.0024209828,0.007583967],"category_scores_gemma":[0.015721733,0.00076415506,0.0021358086,0.0016247922,0.0023684967,0.0044649853,0.0025701576,0.0029190097,0.00085263286],"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":[0.000020349962,0.00003835388,0.0005172614,0.000053495492,0.00004043933,0.00008564549,0.00008397735,0.06837784,0.0004482644,0.91712636,0.001199754,0.012008317],"study_design_scores_gemma":[0.000018682156,0.000040028957,0.00028400787,0.00002553745,0.000027403774,0.0000826754,0.000018225173,0.3670979,0.00013822713,0.6299429,0.0022996236,0.000024771982],"about_ca_topic_score_codex":0.0022173505,"about_ca_topic_score_gemma":0.002189595,"teacher_disagreement_score":0.007583967,"about_ca_system_score_codex":0.0013552686,"about_ca_system_score_gemma":0.0011302151,"threshold_uncertainty_score":0.032382965},"labels":[],"label_agreement":null},{"id":"W4408022963","doi":"10.2139/ssrn.5158101","title":"Hedging universal life insurance policies","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Université Laval","funders":"","keywords":"Life insurance; Actuarial science; Business; Economics","score_opus":0.014438209143475355,"score_gpt":0.3002082844990131,"score_spread":0.2857700753555377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408022963","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":[0.92756003,0.002163,0.050929986,0.0045680376,0.00021350555,0.000041858904,0.00077322056,0.00028294337,0.013467399],"genre_scores_gemma":[0.9927188,0.00063824945,0.0009852191,0.00008477531,0.00005512248,0.000008873981,0.00013900516,0.000011734351,0.0053580943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993801,0.00020260956,0.000033006498,0.00011633734,0.00010031258,0.00016758761],"domain_scores_gemma":[0.99660933,0.0020698032,0.0005730432,0.00034273032,0.0001378384,0.00026735122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393453,0.00051723525,0.0011244334,0.0007467723,0.0003148186,0.0022578686,0.0007299039,0.0020739094,0.0072477963],"category_scores_gemma":[0.01180874,0.00038237285,0.0005332512,0.0008309068,0.0009599494,0.0019755391,0.0013646497,0.0019334079,0.00038505852],"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":[0.00070179737,0.0005860841,0.056098167,0.00040619436,0.0003780732,0.00078866904,0.0009160674,0.37292683,0.0030768237,0.42305526,0.009477291,0.13158871],"study_design_scores_gemma":[0.000069543996,0.00027003555,0.023714948,0.00010106942,0.00011435574,0.00018152548,0.00047995357,0.45761552,0.00088951655,0.5133902,0.0031293232,0.000044065742],"about_ca_topic_score_codex":0.0041413144,"about_ca_topic_score_gemma":0.0030341474,"teacher_disagreement_score":0.0072477963,"about_ca_system_score_codex":0.0015278014,"about_ca_system_score_gemma":0.00093925407,"threshold_uncertainty_score":0.024246335},"labels":[],"label_agreement":null},{"id":"W4408154673","doi":"10.3390/appliedmath5010025","title":"Option Pricing with Given Risk Constraints and Its Application to Life Insurance Contracts","year":2025,"lang":"en","type":"article","venue":"AppliedMath","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life insurance; Actuarial science; Business; Economics","score_opus":0.007312491558079416,"score_gpt":0.26921905509728045,"score_spread":0.26190656353920105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408154673","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":[0.005977755,0.0002445528,0.9919831,0.000114800954,0.000025687112,0.000022302944,0.000013246572,0.000026219137,0.0015922916],"genre_scores_gemma":[0.42046824,0.0009947446,0.5728648,0.000103069324,0.00020006004,0.00018178986,0.0000708354,0.00008549761,0.005030979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99873835,0.0006528323,0.00006122457,0.00014214819,0.00035238054,0.00005302736],"domain_scores_gemma":[0.9972447,0.0020833188,0.00015647587,0.00015047449,0.0002201098,0.00014495259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026268112,0.0008149418,0.0010223653,0.00082384725,0.0004514944,0.0014620745,0.0012735133,0.0014785301,0.0022300698],"category_scores_gemma":[0.008477076,0.00042613735,0.001026283,0.00088313385,0.0013980231,0.0020563232,0.0015822448,0.002233999,0.00020048965],"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":[0.000041731946,0.00007015571,0.0005688857,0.0001291235,0.00006282447,0.0002772757,0.00022912322,0.32159898,0.0026370776,0.6182754,0.0006898737,0.055419594],"study_design_scores_gemma":[0.000007765511,0.000019832367,0.000082306506,0.000014932685,0.000006637223,0.000057966772,0.000011850876,0.8871646,0.0003510937,0.11101131,0.0012593538,0.000012396572],"about_ca_topic_score_codex":0.0010478512,"about_ca_topic_score_gemma":0.0006899326,"teacher_disagreement_score":0.0026268112,"about_ca_system_score_codex":0.00080188335,"about_ca_system_score_gemma":0.00084429595,"threshold_uncertainty_score":0.013892055},"labels":[],"label_agreement":null},{"id":"W4408720153","doi":"10.1017/asb.2025.8","title":"Joint mortality models based on subordinated linear hypercubes","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","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":"Joint (building); Hypercube; Computer science; Econometrics; Mathematics; Parallel computing; Engineering","score_opus":0.041448887228920976,"score_gpt":0.3161667668971937,"score_spread":0.2747178796682727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408720153","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":[0.12358989,0.0012462517,0.8644263,0.0015273759,0.00015681438,0.00010647387,0.0007015544,0.00024860867,0.007996748],"genre_scores_gemma":[0.94153446,0.0011353687,0.03394926,0.00023962437,0.00019179255,0.00030616228,0.00072006835,0.0001070059,0.021816108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981805,0.00091789855,0.00006211025,0.00032966898,0.0002359879,0.0002738121],"domain_scores_gemma":[0.9942449,0.0034510652,0.00079874106,0.00030747365,0.00068926543,0.00050862523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220216,0.00088684703,0.001720232,0.001398031,0.00063489255,0.0023773578,0.002356515,0.0016164775,0.006696786],"category_scores_gemma":[0.00947736,0.00073792203,0.0015339793,0.001523412,0.0021963916,0.0028447853,0.0024483604,0.00264027,0.0008942253],"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":[0.00006374358,0.000039082006,0.00096880866,0.000033075412,0.00003574477,0.000092683586,0.00010703136,0.80655265,0.0003517599,0.18691193,0.0012541895,0.0035893335],"study_design_scores_gemma":[0.000009870921,0.000016339372,0.00014324319,0.00000528106,0.0000046372875,0.000012584626,0.000017068405,0.96336746,0.0000627052,0.035938602,0.0004128814,0.000009363898],"about_ca_topic_score_codex":0.010314387,"about_ca_topic_score_gemma":0.004617611,"teacher_disagreement_score":0.010314387,"about_ca_system_score_codex":0.0021321876,"about_ca_system_score_gemma":0.0010949852,"threshold_uncertainty_score":0.022403002},"labels":[],"label_agreement":null},{"id":"W4408764116","doi":"10.1093/jrsssa/qnaf034","title":"A Bayesian generalized additive model approach for forecasting mortality improvement with external information","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Bayesian probability; Econometrics; Statistics; Computer science; Mathematics","score_opus":0.017820774631099198,"score_gpt":0.2872126060681424,"score_spread":0.2693918314370432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408764116","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":[0.068160646,0.0007821657,0.92494446,0.0014580839,0.00012815789,0.000096235875,0.00072077336,0.0003245923,0.003384937],"genre_scores_gemma":[0.8733291,0.0010087963,0.117816135,0.00028534053,0.00021680439,0.00029441252,0.0010504372,0.00007056271,0.0059285243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978459,0.0012481264,0.00010007361,0.0003414743,0.0003114685,0.00015308481],"domain_scores_gemma":[0.9937262,0.004785424,0.00057600706,0.00018491068,0.00059135875,0.00013613515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00656717,0.0011227657,0.0014830938,0.0023480756,0.0005377682,0.0022621579,0.0021726296,0.0027294464,0.0028371047],"category_scores_gemma":[0.0144924605,0.00080384884,0.001545581,0.0023208263,0.00083489896,0.0017056902,0.0015721531,0.0026402008,0.00053365267],"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":[0.0000476315,0.00004358146,0.0031420966,0.00005593383,0.00009704517,0.0001030044,0.00012733207,0.94468373,0.00032286876,0.033176046,0.0007899795,0.017410759],"study_design_scores_gemma":[0.0000046975165,0.000018553044,0.00043174284,0.000012056278,0.00001434854,0.0000111979,0.000013621404,0.9886031,0.00003939121,0.010499684,0.00033865302,0.00001292467],"about_ca_topic_score_codex":0.019554293,"about_ca_topic_score_gemma":0.01772258,"teacher_disagreement_score":0.019554293,"about_ca_system_score_codex":0.0013249303,"about_ca_system_score_gemma":0.0011506183,"threshold_uncertainty_score":0.038880944},"labels":[],"label_agreement":null},{"id":"W4408811758","doi":"10.2139/ssrn.5109814","title":"Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Canadian Armed Forces","funders":"","keywords":"Estimation; Economic geography; Geography; Econometrics; Economics","score_opus":0.014311342680573703,"score_gpt":0.32461328368534903,"score_spread":0.3103019410047753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408811758","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":[0.93686473,0.0011998982,0.049696088,0.0018668969,0.00009426307,0.00009484352,0.0012697404,0.0004267476,0.0084868455],"genre_scores_gemma":[0.98799753,0.0001180326,0.010507132,0.000111057045,0.00006448671,0.00002549931,0.00060997845,0.000030705432,0.00053554465],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9838213,0.0133186355,0.00050550245,0.0010246187,0.0007699506,0.00056009565],"domain_scores_gemma":[0.79680324,0.18091772,0.005941396,0.00833503,0.0051612263,0.002841398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036535513,0.0009675947,0.0010303977,0.0026040683,0.00058946013,0.002122855,0.0014622436,0.0012430239,0.0030913036],"category_scores_gemma":[0.12609005,0.0003915478,0.0014304393,0.0021352984,0.0010568907,0.003283256,0.0034482777,0.0012159663,0.001278962],"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":[0.001013098,0.00018953272,0.8725947,0.00015560738,0.000887006,0.00014226123,0.0012119046,0.024969744,0.00029760224,0.002721993,0.0023382048,0.09347837],"study_design_scores_gemma":[0.00008551562,0.0015136467,0.43512553,0.00022894837,0.00045496962,0.0004466476,0.0039331764,0.5392164,0.0013837276,0.0140244365,0.0034816214,0.00010532985],"about_ca_topic_score_codex":0.007984837,"about_ca_topic_score_gemma":0.007323867,"teacher_disagreement_score":0.036535513,"about_ca_system_score_codex":0.00049248035,"about_ca_system_score_gemma":0.0015414819,"threshold_uncertainty_score":0.19322062},"labels":[],"label_agreement":null},{"id":"W4408830389","doi":"10.3390/risks13040061","title":"An Optional Semimartingales Approach to Risk Theory","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Mathematics; Econometrics; Computer science; Applied mathematics","score_opus":0.033487629883784294,"score_gpt":0.3801935313910686,"score_spread":0.3467059015072843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408830389","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":[0.0047355555,0.0006716285,0.98852843,0.00043020415,0.000094863324,0.000019364292,0.000054643333,0.000047108686,0.0054181246],"genre_scores_gemma":[0.49306467,0.003202937,0.4824073,0.0005629269,0.0012473618,0.00029113193,0.00023865169,0.00019840093,0.018786583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974584,0.001259853,0.0001521817,0.00027980862,0.00069868326,0.000151037],"domain_scores_gemma":[0.9942105,0.0037456946,0.0004248334,0.0005859537,0.00070181827,0.00033109952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006555632,0.0012116273,0.0010559543,0.0022938617,0.0006723672,0.0023079945,0.0021027955,0.0011563926,0.003900788],"category_scores_gemma":[0.007919855,0.0005554768,0.002560838,0.001087963,0.0032319806,0.004581804,0.002162956,0.00355538,0.0005408234],"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":[0.0000031201344,0.000010673551,0.00010121656,0.00002806405,0.00001336727,0.000026453496,0.00004974882,0.0075164232,0.00018611197,0.9890576,0.00026609495,0.0027410262],"study_design_scores_gemma":[0.0000056689837,0.000027060558,0.00013806777,0.00003376723,0.000010746007,0.000047545327,0.000017266362,0.09797556,0.00019594641,0.8970005,0.004533654,0.000014163212],"about_ca_topic_score_codex":0.0009672269,"about_ca_topic_score_gemma":0.0010022942,"teacher_disagreement_score":0.006555632,"about_ca_system_score_codex":0.0017414363,"about_ca_system_score_gemma":0.001411584,"threshold_uncertainty_score":0.034669936},"labels":[],"label_agreement":null},{"id":"W4409015169","doi":"10.2118/0425-0076-jpt","title":"Technology Focus: History Matching and Forecasting (April 2025)","year":2025,"lang":"en","type":"article","venue":"Journal of Petroleum Technology","topic":"Insurance, Mortality, Demography, Risk Management","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":"Focus (optics); Matching (statistics); Computer science; Data science; Geology; Mathematics; Statistics","score_opus":0.01707051418972382,"score_gpt":0.2768233721306887,"score_spread":0.25975285794096487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409015169","genre_codex":"other","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":[0.0050651915,0.018029474,0.060078803,0.049432393,0.085556716,0.00046161094,0.02833603,0.011434197,0.7416055],"genre_scores_gemma":[0.028087027,0.012660491,0.013069313,0.007881909,0.009298197,0.00028931728,0.023589475,0.002166461,0.9029578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993468,0.00005970115,0.00003074538,0.00008625618,0.00035454554,0.00012196922],"domain_scores_gemma":[0.99873513,0.00014406767,0.00006949312,0.00012402507,0.0005790902,0.00034811435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017036184,0.0010288586,0.00049051055,0.0013324452,0.00062650256,0.00442928,0.0010660595,0.0022346186,0.17626041],"category_scores_gemma":[0.003398805,0.00029361376,0.00058433437,0.0011329541,0.0004599706,0.0040186574,0.0019489855,0.00202636,0.106851645],"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":[0.00008362597,0.000033865916,0.00034077105,0.00014027693,0.000009680188,0.00006701214,0.000017317288,0.0010770869,0.0013778916,0.010557562,0.83422154,0.15207331],"study_design_scores_gemma":[0.000009944688,0.00005326616,0.00065167935,0.00010006121,0.0000050173844,0.00004233413,0.000021267435,0.0019475518,0.00087567733,0.008552774,0.9877285,0.000011837528],"about_ca_topic_score_codex":0.0027257307,"about_ca_topic_score_gemma":0.004275613,"teacher_disagreement_score":0.17626041,"about_ca_system_score_codex":0.0012460948,"about_ca_system_score_gemma":0.001986212,"threshold_uncertainty_score":0.5896498},"labels":[],"label_agreement":null},{"id":"W4409187619","doi":"10.1016/j.matcom.2025.03.027","title":"A lattice-based approach for life insurance pricing in a stochastic correlation framework","year":2025,"lang":"en","type":"article","venue":"Mathematics and Computers in Simulation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Ministero dell'Istruzione e del Merito; FP7 Coordination of Research Activities; Ministero dell’Istruzione, dell’Università e della Ricerca; European Commission","keywords":"Correlation; Lattice (music); Life insurance; Mathematics; Computer science; Statistical physics; Econometrics; Applied mathematics; Actuarial science; Business; Physics; Geometry","score_opus":0.024239252302486608,"score_gpt":0.3179545210569291,"score_spread":0.29371526875444254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409187619","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":[0.005543475,0.00014604567,0.9901974,0.00022814414,0.000065143846,0.000024189185,0.000048051745,0.0000658589,0.0036816525],"genre_scores_gemma":[0.33085495,0.0006094892,0.65710664,0.0002836236,0.00020145308,0.00030992512,0.00019223374,0.00019068194,0.010251029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988796,0.0004988128,0.000051128038,0.00010928274,0.00034046717,0.00012076556],"domain_scores_gemma":[0.9985014,0.0007741465,0.00013604257,0.00018252626,0.00023313404,0.00017269164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017310847,0.0006402127,0.0012916187,0.0009627922,0.00084535737,0.0023686455,0.0024357743,0.0017735974,0.0059353164],"category_scores_gemma":[0.0040123467,0.0006885887,0.0022839762,0.0012658892,0.0018966293,0.0020434356,0.0018420228,0.0033098387,0.0009821118],"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":[0.000024684505,0.000074335236,0.00050170923,0.00004632712,0.000028897199,0.00013840199,0.00007116086,0.6268963,0.0011483724,0.35968995,0.0008957494,0.010484094],"study_design_scores_gemma":[0.000007724349,0.000010513103,0.000032713462,0.0000054151565,0.0000030527751,0.000024067098,0.0000088167835,0.9537424,0.00009642716,0.045194376,0.0008660409,0.0000084070825],"about_ca_topic_score_codex":0.006509245,"about_ca_topic_score_gemma":0.0051723686,"teacher_disagreement_score":0.006509245,"about_ca_system_score_codex":0.0015406434,"about_ca_system_score_gemma":0.0020678884,"threshold_uncertainty_score":0.019855618},"labels":[],"label_agreement":null},{"id":"W4409467695","doi":"10.1111/jtsa.12828","title":"Change Point Analysis for Functional Data Using Empirical Characteristic Functionals","year":2025,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Point (geometry); Applied mathematics; Functional data analysis; Statistics; Econometrics; Geometry","score_opus":0.142811188698133,"score_gpt":0.39789862436710605,"score_spread":0.2550874356689731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409467695","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":[0.065052375,0.00014075593,0.9337026,0.00011858548,0.000031663967,0.00008413245,0.00008448373,0.0002187251,0.00056665705],"genre_scores_gemma":[0.806968,0.00011709845,0.19151308,0.000088508554,0.000049169874,0.00025688735,0.000277259,0.000090219546,0.0006397692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953524,0.002645144,0.00021186988,0.00058171636,0.0010245929,0.00018425997],"domain_scores_gemma":[0.9433272,0.044826325,0.0038382034,0.0032072903,0.0042091263,0.0005918091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011294562,0.00067115086,0.0010163278,0.0044905627,0.00040284643,0.0015839655,0.0014225283,0.0010273844,0.0016477423],"category_scores_gemma":[0.064947344,0.00030142337,0.00086386007,0.0020407196,0.002323587,0.0021948863,0.0014790461,0.001848711,0.00021081892],"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":[0.00076685415,0.00035148035,0.07457767,0.00043063166,0.0006829628,0.0007438739,0.0009725317,0.39406627,0.013036718,0.25979048,0.0019895835,0.25259092],"study_design_scores_gemma":[0.00002031673,0.00012397363,0.008622419,0.000026482701,0.000022573337,0.00013285471,0.00007703037,0.9500433,0.002202753,0.037962634,0.00072502356,0.000040573035],"about_ca_topic_score_codex":0.00200176,"about_ca_topic_score_gemma":0.00087170786,"teacher_disagreement_score":0.011294562,"about_ca_system_score_codex":0.0010532456,"about_ca_system_score_gemma":0.0009739775,"threshold_uncertainty_score":0.05973202},"labels":[],"label_agreement":null},{"id":"W4409886604","doi":"10.1001/jamanetworkopen.2025.7695","title":"All-Cause Mortality and Life Expectancy by Birth Cohort Across US States","year":2025,"lang":"en","type":"article","venue":"JAMA Network Open","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute; National Institute on Drug Abuse; National Institutes of Health","keywords":"Life expectancy; Demography; Cohort; Medicine; Mortality rate; Population; Epidemiology; Cohort study; Cohort effect; Gerontology; Environmental health","score_opus":0.03603180756387899,"score_gpt":0.37424656886050756,"score_spread":0.3382147612966286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409886604","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":[0.9616304,0.0040387074,0.0026477692,0.000761227,0.00012037207,0.00007644517,0.027007557,0.00009237207,0.0036250935],"genre_scores_gemma":[0.9886418,0.0012440054,0.0007282493,0.000113060174,0.000033201864,0.000052874482,0.008661019,0.000011383335,0.0005142884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991868,0.00021982285,0.000103829036,0.00023657593,0.00015648812,0.000096454045],"domain_scores_gemma":[0.9972287,0.00052167365,0.0011037003,0.00024206656,0.00062795077,0.00027592242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025208378,0.00026478435,0.00022981677,0.0018453836,0.00030367382,0.00050103193,0.00046334873,0.0002045779,0.0016710375],"category_scores_gemma":[0.007424426,0.0001556267,0.0008117441,0.0020489537,0.00019272356,0.0005342028,0.0005873064,0.00043591057,0.00019612764],"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":[0.00003363919,0.000009452982,0.99523664,0.000029908817,0.00023304397,0.000015491842,0.00006033686,0.00023227117,0.000032021173,0.00012396551,0.0008792925,0.0031138149],"study_design_scores_gemma":[0.0000043003706,0.000023449345,0.9981273,0.000038802977,0.00010054337,0.000045362292,0.000085881526,0.00053914107,0.000036179325,0.00008820326,0.00090643385,0.0000044244316],"about_ca_topic_score_codex":0.05540703,"about_ca_topic_score_gemma":0.06059974,"teacher_disagreement_score":0.05540703,"about_ca_system_score_codex":0.0005710233,"about_ca_system_score_gemma":0.0008391392,"threshold_uncertainty_score":0.11016905},"labels":[],"label_agreement":null},{"id":"W4409891615","doi":"10.2139/ssrn.5230576","title":"The Effect of Inflation on US Insurance Markets: A Markov-Switching Model Analysis","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"HEC Montréal","funders":"","keywords":"Markov chain; Inflation (cosmology); Economics; Econometrics; Monetary economics; Mathematics; Statistics; Physics","score_opus":0.006046268819326564,"score_gpt":0.2898902621782566,"score_spread":0.28384399335893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409891615","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":[0.87817043,0.0013285234,0.10494797,0.0052563064,0.00015094099,0.000117054464,0.001253239,0.0003017031,0.008473815],"genre_scores_gemma":[0.98920244,0.00068801304,0.0021699946,0.00018747964,0.00012841064,0.00005964273,0.00032904363,0.000032710515,0.007202209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864453,0.00065554027,0.000045513014,0.000176946,0.00010322429,0.00037430043],"domain_scores_gemma":[0.9777892,0.018825077,0.0014765025,0.00044556017,0.0005730233,0.0008906756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053160265,0.0009532846,0.0029283827,0.0016671885,0.0009502659,0.0026715382,0.0016968814,0.003151403,0.010520814],"category_scores_gemma":[0.016314458,0.0010949032,0.002277465,0.0010593119,0.0020531544,0.0026483424,0.0017002376,0.0035022553,0.00049699977],"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":[0.00072747434,0.00048654038,0.01696176,0.00014142356,0.00049250986,0.00065603416,0.00039161183,0.83849144,0.001088317,0.12800741,0.004292965,0.008262427],"study_design_scores_gemma":[0.000072228,0.00006902265,0.0022833808,0.000013377947,0.00010162629,0.000032296783,0.000075034215,0.97925264,0.00009363395,0.017785827,0.00018664432,0.000034332865],"about_ca_topic_score_codex":0.038530998,"about_ca_topic_score_gemma":0.02321961,"teacher_disagreement_score":0.038530998,"about_ca_system_score_codex":0.0018642269,"about_ca_system_score_gemma":0.002342605,"threshold_uncertainty_score":0.076613486},"labels":[],"label_agreement":null},{"id":"W4409968360","doi":"10.1186/s44263-025-00140-2","title":"Analytic methodology for demographic variation analyses for wave 1 of the global flourishing study","year":2025,"lang":"en","type":"letter","venue":"BMC Global and Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"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":"Templeton World Charity Foundation; Templeton Religion Trust; Fetzer Institute; John Templeton Foundation","keywords":"Flourishing; Variation (astronomy); Statistics; Demography; Sociology; Psychology; Mathematics; Social psychology; Physics","score_opus":0.3537733304562,"score_gpt":0.47490263729949395,"score_spread":0.12112930684329393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409968360","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":[0.0032766568,0.0014750378,0.9234272,0.010017748,0.004943249,0.036011346,0.014072534,0.0012877102,0.0054884744],"genre_scores_gemma":[0.011272099,0.00085651845,0.8153958,0.005462727,0.00091641775,0.16103508,0.0028550478,0.00039898796,0.0018074589],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.645724,0.30873752,0.02088093,0.0047794124,0.018752681,0.001125552],"domain_scores_gemma":[0.5997965,0.29373536,0.019918777,0.050782174,0.034641936,0.0011252968],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20170194,0.0014088899,0.0017850618,0.004876831,0.0018344887,0.0019426867,0.002899694,0.0033416473,0.018338254],"category_scores_gemma":[0.46940148,0.0014734428,0.004006225,0.007870102,0.0016644995,0.0013469004,0.00271116,0.0058526406,0.005269821],"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":[0.002055296,0.00049319246,0.026103832,0.008702555,0.003025469,0.0007343187,0.0038570166,0.00506763,0.0019437266,0.10669353,0.46424365,0.37707984],"study_design_scores_gemma":[0.0038467287,0.0026218954,0.046264112,0.008906757,0.0022156688,0.0021415139,0.0013337749,0.032317907,0.0063704313,0.1827831,0.71061003,0.00058817014],"about_ca_topic_score_codex":0.002121532,"about_ca_topic_score_gemma":0.0032028882,"teacher_disagreement_score":0.20170194,"about_ca_system_score_codex":0.0018333896,"about_ca_system_score_gemma":0.006786241,"threshold_uncertainty_score":0.9844436},"labels":[],"label_agreement":null},{"id":"W4409978964","doi":"10.1016/j.ajog.2025.04.050","title":"A comparison of the accuracy of different labor curves to predict labor progress","year":2025,"lang":"en","type":"article","venue":"American Journal of Obstetrics and Gynecology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University Health Centre","funders":"NHLBI Division of Intramural Research; Eunice Kennedy Shriver National Institute of Child Health and Human Development; U.S. Department of Health and Human Services; National Institutes of Health; National Institute of Child Health and Human Development; Division of Intramural Research, National Institute of Allergy and Infectious Diseases","keywords":"Medicine","score_opus":0.014606001415169668,"score_gpt":0.344880322451555,"score_spread":0.3302743210363853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409978964","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":[0.9772584,0.0020165804,0.011452628,0.00042925283,0.00014662411,0.000088597466,0.0033029763,0.00037085055,0.0049342047],"genre_scores_gemma":[0.99480176,0.00038862645,0.003048933,0.000054827666,0.000035107416,0.000024494904,0.0013321161,0.000043062442,0.000271103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99576795,0.0024109099,0.00040392834,0.00045995272,0.000713677,0.0002435406],"domain_scores_gemma":[0.9238847,0.06281089,0.0030086532,0.0031463488,0.005742052,0.00140738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01295442,0.00094828964,0.00090887287,0.0060265115,0.00036867344,0.0018091523,0.0009001533,0.0012368178,0.0011794447],"category_scores_gemma":[0.061456654,0.00025900116,0.0012622128,0.0018207125,0.0005842647,0.0014530655,0.00088053744,0.00087123946,0.00052496226],"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":[0.003278475,0.00015002918,0.94169515,0.000090885944,0.0006879854,0.000061243714,0.00035943993,0.00907778,0.0004050896,0.00046951437,0.00095186033,0.04277252],"study_design_scores_gemma":[0.00020503337,0.0019586626,0.8751296,0.00018470461,0.00076348305,0.00038582154,0.0008842107,0.11463335,0.0011163407,0.0027127261,0.001893875,0.00013210734],"about_ca_topic_score_codex":0.0059839995,"about_ca_topic_score_gemma":0.0030763336,"teacher_disagreement_score":0.01295442,"about_ca_system_score_codex":0.00053689705,"about_ca_system_score_gemma":0.000664873,"threshold_uncertainty_score":0.068510294},"labels":[],"label_agreement":null},{"id":"W4410156588","doi":"10.1007/s13385-026-00457-8","title":"Modeling Transition and Physical Risks in Pension Plan Investment Strategies: A Multivariate Normal Regime Switching Approach","year":2025,"lang":"en","type":"preprint","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Pension; Investment (military); Transition (genetics); Pension plan; Plan (archaeology); Economics; Actuarial science; Investment portfolio; Business; Econometrics; Financial economics; Finance; Portfolio; Mathematics; Political science; Statistics; Geography; Chemistry","score_opus":0.06844062419436,"score_gpt":0.33154030661819206,"score_spread":0.2630996824238321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410156588","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":[0.57424444,0.00061794865,0.4183891,0.0017092662,0.00009697876,0.00006737186,0.00058030675,0.00025682175,0.004037673],"genre_scores_gemma":[0.98492914,0.0003490708,0.0077384566,0.00007602939,0.00007417198,0.000065615604,0.00022656337,0.000043082804,0.006497838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915445,0.0003982809,0.000030683666,0.00017463957,0.00007060357,0.00017132086],"domain_scores_gemma":[0.9931717,0.0055298833,0.0005716708,0.00020580582,0.00021233554,0.0003087084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038678218,0.0008072678,0.0015877972,0.001222967,0.00047216847,0.0019749985,0.0017834643,0.0024536347,0.0038960513],"category_scores_gemma":[0.011520784,0.00096588285,0.0017270114,0.0009483299,0.0015297768,0.0021026975,0.0015709334,0.002504322,0.00031901832],"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":[0.00007673915,0.00005773966,0.004158491,0.000019065812,0.00008105911,0.000062478386,0.00008494743,0.9718444,0.00019419401,0.019095084,0.00030879313,0.004017176],"study_design_scores_gemma":[0.0000049082123,0.00000835994,0.0004746538,0.0000025036204,0.00000992755,0.0000052003475,0.000010910053,0.99373615,0.00001899123,0.0056738285,0.000048922608,0.000005663349],"about_ca_topic_score_codex":0.01964315,"about_ca_topic_score_gemma":0.009796492,"teacher_disagreement_score":0.01964315,"about_ca_system_score_codex":0.0013064065,"about_ca_system_score_gemma":0.0009770725,"threshold_uncertainty_score":0.039057672},"labels":[],"label_agreement":null},{"id":"W4410251164","doi":"10.18502/ijph.v54i5.18641","title":"Population Forecasting with Alzheimer's Disease in Iran Using a System Dynamic Model","year":2025,"lang":"en","type":"article","venue":"Iranian Journal of Public Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Saskatchewan Health Authority; University of Saskatchewan","funders":"","keywords":"Disease; Computer science; Population; Medicine; Internal medicine; Environmental health","score_opus":0.12747167423655445,"score_gpt":0.3741412023919434,"score_spread":0.24666952815538892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410251164","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":[0.8103784,0.0014686269,0.16613767,0.0038296445,0.0003242451,0.00022079554,0.003724467,0.00048251782,0.013433719],"genre_scores_gemma":[0.98854536,0.0005225635,0.007936759,0.000071214454,0.000057951966,0.000116657175,0.0008489411,0.000008974384,0.0018915457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968195,0.000102254075,0.000016562062,0.00009254136,0.000040123505,0.00006653324],"domain_scores_gemma":[0.9991917,0.00048523533,0.00011199424,0.000014891111,0.0001474393,0.000048777023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009561759,0.00077032216,0.0007527145,0.0008451274,0.00052749424,0.0013105471,0.0009351372,0.0011255319,0.0018814014],"category_scores_gemma":[0.0024386493,0.00032982838,0.0009064219,0.00081522466,0.00033187607,0.00079264905,0.00071787206,0.0010823202,0.00021167575],"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":[0.000039784365,0.000047565176,0.012881329,0.000034582627,0.0000575469,0.0001278903,0.00007159676,0.9768645,0.00011080771,0.002606296,0.0010754504,0.006082652],"study_design_scores_gemma":[0.0000062542667,0.000011566346,0.0011033897,0.000003960971,0.000012249008,0.000009761813,0.000028958377,0.9977336,0.00001637202,0.0008413961,0.00022822712,0.000004373729],"about_ca_topic_score_codex":0.07353248,"about_ca_topic_score_gemma":0.033235632,"teacher_disagreement_score":0.07353248,"about_ca_system_score_codex":0.0016013623,"about_ca_system_score_gemma":0.0019898573,"threshold_uncertainty_score":0.14620888},"labels":[],"label_agreement":null},{"id":"W4410486452","doi":"10.61091/jcmcc126-09","title":"Application of stacked machine learning models in population development equations for population forecasting","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Population; Development (topology); Computer science; Machine learning; Artificial intelligence; Econometrics; Economics; Mathematics; Demography; Mathematical analysis; Sociology","score_opus":0.035450749579826815,"score_gpt":0.3058625338334362,"score_spread":0.2704117842536094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410486452","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":[0.06817515,0.0007931193,0.92777914,0.0003996181,0.00012374722,0.00003018222,0.00024277378,0.0003223357,0.0021338884],"genre_scores_gemma":[0.90042967,0.001195257,0.09411328,0.000119505574,0.00015245093,0.00013494224,0.00044660378,0.000041751122,0.0033666189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995913,0.00016548444,0.00003043758,0.00007909452,0.000087799155,0.000045966084],"domain_scores_gemma":[0.9991242,0.000517997,0.00010858107,0.000046331104,0.00017629442,0.00002651119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013694253,0.0007270413,0.0009065161,0.00068751693,0.0004355436,0.00074260484,0.0009095351,0.00071952306,0.0010581536],"category_scores_gemma":[0.0033158406,0.00039329374,0.0011141187,0.001113025,0.00027234256,0.0009535154,0.0006385661,0.0014664646,0.00023045529],"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":[0.000007710606,0.000012003359,0.0016898764,0.000014897293,0.00003286182,0.000027196756,0.000026587908,0.97735244,0.00019632412,0.0034961416,0.00029295287,0.016851032],"study_design_scores_gemma":[4.1661096e-7,0.0000027754775,0.0001325475,0.000001588654,0.0000039353586,0.0000024431517,0.0000019526228,0.998852,0.00003435643,0.00089510926,0.0000706852,0.0000022054594],"about_ca_topic_score_codex":0.025182674,"about_ca_topic_score_gemma":0.01923903,"teacher_disagreement_score":0.025182674,"about_ca_system_score_codex":0.0006018204,"about_ca_system_score_gemma":0.0010807501,"threshold_uncertainty_score":0.050072193},"labels":[],"label_agreement":null},{"id":"W4410720977","doi":"10.1111/jog.16327","title":"Premature mortality due to cervical and ovarian cancers in Japan, 2000 to 2020","year":2025,"lang":"en","type":"article","venue":"Journal of obstetrics and gynaecology research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Alberta Health Services","funders":"","keywords":"Medicine; Cervical cancer; Years of potential life lost; Confidence interval; Ovarian cancer; Demography; Population; Mortality rate; Ovary; Gynecology; Cancer; Obstetrics; Internal medicine; Life expectancy; Environmental health","score_opus":0.03901246278922261,"score_gpt":0.39107175185977383,"score_spread":0.35205928907055123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410720977","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":[0.9828652,0.0068932124,0.00023413105,0.000413431,0.00006865247,0.00002683032,0.0076043024,0.000021818345,0.0018724859],"genre_scores_gemma":[0.99018997,0.0025224977,0.00018666172,0.00014095462,0.000040167968,0.000032997843,0.00640991,0.0000035517423,0.00047331213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99974877,0.0000373619,0.000048827584,0.000052824114,0.0000643763,0.000047831873],"domain_scores_gemma":[0.99925476,0.000043476128,0.0003030757,0.000027669865,0.000247299,0.0001236905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007024197,0.00027014824,0.00017679267,0.0012869261,0.00026346406,0.00042942865,0.00026374203,0.00027349315,0.0006166244],"category_scores_gemma":[0.0019053647,0.0001454111,0.0005615119,0.0012871148,0.00015930012,0.0004330247,0.00048699917,0.0002775246,0.00015822018],"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":[0.000057887108,0.000009034934,0.99302495,0.000103055965,0.00010588067,0.00010161278,0.0001483686,0.00016497048,0.00018674432,0.000035587484,0.0009834381,0.0050785197],"study_design_scores_gemma":[0.0000025127893,0.000024955742,0.9984774,0.000022859524,0.000048490947,0.00011247753,0.00017046023,0.00017140931,0.00003350031,0.000020669728,0.00091182685,0.0000033800181],"about_ca_topic_score_codex":0.03971795,"about_ca_topic_score_gemma":0.04898965,"teacher_disagreement_score":0.03971795,"about_ca_system_score_codex":0.0011193148,"about_ca_system_score_gemma":0.0008875449,"threshold_uncertainty_score":0.07897353},"labels":[],"label_agreement":null},{"id":"W4410908216","doi":"10.1093/ntr/ntaf117","title":"Modeling the Impact of Smoking on Mortality in Argentina From 2000 to 2100. A Maximum Potential Reduction in Premature Mortality Analysis","year":2025,"lang":"en","type":"article","venue":"Nicotine & Tobacco Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Medicine; Tobacco control; Demography; Psychological intervention; Smoking prevalence; Life expectancy; Environmental health; Years of potential life lost; Status quo; Cohort; Cigarette smoking; Smoking cessation; Burden of disease; Mortality rate; Public health; Population; Surgery; Internal medicine","score_opus":0.06881413703264844,"score_gpt":0.43792660003410994,"score_spread":0.3691124630014615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410908216","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":[0.88060576,0.0024194242,0.07779756,0.002796362,0.00016976561,0.00018999197,0.008712494,0.0003560684,0.026952565],"genre_scores_gemma":[0.98665696,0.00060979364,0.005953015,0.0000957538,0.000027572674,0.00015372116,0.001691216,0.000031142434,0.004780828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997117,0.00014236028,0.000010437688,0.000057958325,0.000025366351,0.000052113443],"domain_scores_gemma":[0.99943906,0.00030983664,0.00010177145,0.000019933685,0.00008397215,0.000045364548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006454385,0.0006371533,0.00047500207,0.00044284025,0.00033230727,0.00072182657,0.0010334777,0.0009133035,0.003104538],"category_scores_gemma":[0.0019050863,0.00039593663,0.000976703,0.00037770378,0.00034453886,0.00042654687,0.00053294125,0.0005390214,0.00023533632],"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":[0.000066490655,0.000024847614,0.00837021,0.000040325696,0.00004602564,0.00006928726,0.0000353144,0.98495203,0.00023730312,0.0034483585,0.00074395374,0.0019658238],"study_design_scores_gemma":[0.000037876758,0.00006181492,0.0032105225,0.000024965175,0.00004447503,0.000031455544,0.000041321913,0.992812,0.00009026129,0.0020042846,0.0016302031,0.000010772148],"about_ca_topic_score_codex":0.08350124,"about_ca_topic_score_gemma":0.046138715,"teacher_disagreement_score":0.08350124,"about_ca_system_score_codex":0.0022066578,"about_ca_system_score_gemma":0.0016418487,"threshold_uncertainty_score":0.1660304},"labels":[],"label_agreement":null},{"id":"W4410977227","doi":"10.2139/ssrn.5279418","title":"Hedging targeted risks with reinforcement learning: application to life insurance contracts with embedded guarantees","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Reinforcement learning; Actuarial science; Reinforcement; Life insurance; Business; Risk analysis (engineering); Computer science; Artificial intelligence; Engineering","score_opus":0.013045252960577345,"score_gpt":0.29993708473524683,"score_spread":0.2868918317746695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410977227","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":[0.32856563,0.0011261076,0.661662,0.0012991735,0.00011985479,0.00014062902,0.00012254236,0.0005302877,0.0064337826],"genre_scores_gemma":[0.96500945,0.00020435208,0.032414965,0.00006776343,0.000039242484,0.00005551648,0.000032263644,0.000031059262,0.0021454396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940515,0.00036370187,0.000021994752,0.00007629125,0.00007388403,0.000058940997],"domain_scores_gemma":[0.99023986,0.008590131,0.00032019758,0.00022666078,0.00034007724,0.00028316307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035798363,0.0007265907,0.0012326932,0.0004645743,0.00038589563,0.0011637682,0.0011712026,0.0021526846,0.0031629677],"category_scores_gemma":[0.014246005,0.0003816352,0.00055731856,0.0006397881,0.0011716149,0.0012068477,0.0014205566,0.0021979953,0.00016805304],"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":[0.00011127206,0.00016113573,0.0015000614,0.000045453282,0.00002365748,0.00007975633,0.00006179111,0.9692069,0.00027968458,0.010638078,0.00041486864,0.017477363],"study_design_scores_gemma":[0.000014579775,0.000013562733,0.00008194709,0.0000021472154,0.0000027232593,0.0000035869937,0.000004143131,0.9961047,0.00003962721,0.003686487,0.00004406252,0.0000023706705],"about_ca_topic_score_codex":0.012138687,"about_ca_topic_score_gemma":0.0058008954,"teacher_disagreement_score":0.012138687,"about_ca_system_score_codex":0.0012394536,"about_ca_system_score_gemma":0.0012463755,"threshold_uncertainty_score":0.024136066},"labels":[],"label_agreement":null},{"id":"W4411158073","doi":"10.3390/math13121916","title":"ResPoNet: A Residual Neural Network for Efficient Valuation of Large Variable Annuity Portfolios","year":2025,"lang":"en","type":"article","venue":"Mathematics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"State Administration of Foreign Experts Affairs; Ministry of Education, India; Ministry of Education of the People's Republic of China","keywords":"Residual; Valuation (finance); Annuity; Actuarial science; Artificial neural network; Econometrics; Business; Computer science; Financial economics; Economics; Life annuity; Artificial intelligence; Finance; Pension; Algorithm","score_opus":0.031538742722346115,"score_gpt":0.34169192625729106,"score_spread":0.3101531835349449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411158073","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":[0.038645003,0.00045307114,0.9554047,0.00033523448,0.00009825993,0.00005708154,0.0002505817,0.0010190981,0.0037370354],"genre_scores_gemma":[0.7094511,0.000551231,0.28046516,0.00030370546,0.00009192095,0.00019204764,0.00083235576,0.00018021051,0.00793227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978656,0.00005938584,0.000014992827,0.000045884743,0.00006512152,0.000028055869],"domain_scores_gemma":[0.9993759,0.00028938334,0.000067145345,0.00007028911,0.00016039531,0.000036881283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012232495,0.0006755563,0.0005380238,0.00055232574,0.00018595428,0.00081775396,0.0015542894,0.00087149226,0.0030030578],"category_scores_gemma":[0.004275341,0.0003026042,0.00045208394,0.0005998602,0.000381643,0.0014523331,0.0012400604,0.0012188194,0.000540703],"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":[0.0000826074,0.00006217293,0.0011078392,0.00005365645,0.000052781936,0.0000669791,0.000022125649,0.8504714,0.0017847223,0.008076984,0.0026544498,0.13556437],"study_design_scores_gemma":[0.0000033003769,0.00001451439,0.000063876236,0.000004452865,0.0000029577166,0.00000898328,0.0000023562698,0.9973176,0.00030153678,0.002046261,0.00023193742,0.0000022600652],"about_ca_topic_score_codex":0.0039256867,"about_ca_topic_score_gemma":0.005057289,"teacher_disagreement_score":0.0039256867,"about_ca_system_score_codex":0.0006421118,"about_ca_system_score_gemma":0.0008129222,"threshold_uncertainty_score":0.010046244},"labels":[],"label_agreement":null},{"id":"W4411404731","doi":"10.1017/9781009403092.009","title":"Distributional Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.018083262913055002,"score_gpt":0.24003469779602438,"score_spread":0.22195143488296937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411404731","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":[0.006980566,0.0047786403,0.7595682,0.0058636214,0.00094095035,0.00031004875,0.007909022,0.0026032766,0.21104562],"genre_scores_gemma":[0.3258625,0.012100405,0.44043857,0.003830203,0.0021943613,0.0015718448,0.029459909,0.004489055,0.18005325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99423426,0.0018836119,0.00048949535,0.0011847975,0.0019719931,0.00023591348],"domain_scores_gemma":[0.98843145,0.0065007177,0.00065574225,0.0019155786,0.0022629658,0.00023358456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005140177,0.0007839888,0.0009568603,0.004533402,0.0016726379,0.0072265184,0.0016988656,0.0011695242,0.059981957],"category_scores_gemma":[0.030129,0.00032711588,0.0012408539,0.005420305,0.00259391,0.0055199806,0.00325413,0.0021428862,0.020877168],"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":[0.000036562305,0.000024898256,0.0016546191,0.0003184051,0.00006710832,0.00015219714,0.0011478062,0.0030209664,0.0004796617,0.7455426,0.06133814,0.1862171],"study_design_scores_gemma":[0.0000074384175,0.000018746123,0.0014851603,0.00029199643,0.000017483992,0.00052769325,0.001053038,0.011533918,0.00048637172,0.68929976,0.29524326,0.000035141977],"about_ca_topic_score_codex":0.0016715149,"about_ca_topic_score_gemma":0.001265848,"teacher_disagreement_score":0.059981957,"about_ca_system_score_codex":0.0021417222,"about_ca_system_score_gemma":0.0016335003,"threshold_uncertainty_score":0.20065963},"labels":[],"label_agreement":null},{"id":"W4411412855","doi":"10.1371/journal.pone.0324733","title":"Prevalence patterns of overweight and obesity in the world: An age-period-cohort analysis","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute for Medical Research Development","keywords":"Overweight; Obesity; Demography; Medicine; Subgroup analysis; Cohort; Body mass index; Trend analysis; Cohort study; Cohort effect; Gerontology; Confidence interval; Internal medicine; Statistics; Mathematics","score_opus":0.029724049197882182,"score_gpt":0.2886589500655938,"score_spread":0.2589349008677116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411412855","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":[0.99503255,0.0011064184,0.0013672187,0.00006448301,0.000021468493,0.000056589466,0.0018519668,0.000012331741,0.00048700825],"genre_scores_gemma":[0.99683064,0.0005185575,0.000809619,0.000023809216,0.000011677865,0.00004680452,0.0016186576,0.000007032234,0.00013326388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991359,0.00031884463,0.00011213377,0.00019950506,0.00012899294,0.00010473429],"domain_scores_gemma":[0.9984327,0.00023250077,0.0006590051,0.00032463935,0.0002323928,0.00011867516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028922257,0.0002904919,0.00033721008,0.002510723,0.00038555788,0.00057741936,0.00031359104,0.00021178658,0.0010682971],"category_scores_gemma":[0.0030648063,0.00020232398,0.0015287618,0.00258295,0.00019054028,0.00045733707,0.00065949204,0.0004722603,0.00011809744],"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":[0.00006733312,0.000012911251,0.99754506,0.00002260265,0.00031654057,0.000029283747,0.00006343194,0.00007758166,0.000062983585,0.000045336914,0.000083783285,0.0016731402],"study_design_scores_gemma":[0.000003520381,0.000072264294,0.9982418,0.000022014807,0.00023928285,0.00012981782,0.00018816863,0.0005621555,0.000043746822,0.00004161679,0.00045102302,0.0000046705836],"about_ca_topic_score_codex":0.013629307,"about_ca_topic_score_gemma":0.01606234,"teacher_disagreement_score":0.013629307,"about_ca_system_score_codex":0.0003351718,"about_ca_system_score_gemma":0.0006339777,"threshold_uncertainty_score":0.027099967},"labels":[],"label_agreement":null},{"id":"W4411625814","doi":"10.1007/s10479-025-06699-1","title":"Optimal smoothing mechanism for actuarial discount rate in pension liability valuation in North America","year":2025,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"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 Waterloo","funders":"Key Technologies Research and Development Program; Fundamental Research Funds for the Central Universities; Shanghai Office of Philosophy and Social Science; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Valuation (finance); Actuarial science; Liability; Pension; Economics; Mechanism (biology); Econometrics; Finance","score_opus":0.19287666443157714,"score_gpt":0.49615709386717866,"score_spread":0.3032804294356015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411625814","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":[0.4756231,0.0010303563,0.50887644,0.0027809327,0.00014204522,0.000203515,0.00021301297,0.00045055963,0.010680029],"genre_scores_gemma":[0.96507674,0.00032034577,0.02915188,0.000118921955,0.000057115998,0.00006884218,0.00005350049,0.000043233067,0.005109549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984499,0.0008269174,0.00009366016,0.00029531535,0.00013260756,0.00020160699],"domain_scores_gemma":[0.9866399,0.010164564,0.00093240285,0.00092446286,0.0008841799,0.00045460105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008723289,0.0005627606,0.0014822778,0.0010382017,0.00086966314,0.0030005763,0.0020737683,0.0017576095,0.0037136462],"category_scores_gemma":[0.024639018,0.00095331046,0.0008145273,0.0008429142,0.0013376736,0.0032946013,0.0012282325,0.00208296,0.00031612965],"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":[0.00068545924,0.000396546,0.0057296525,0.0001383495,0.0001634726,0.0002573844,0.001106341,0.41270518,0.004201725,0.4661219,0.0050736787,0.10342028],"study_design_scores_gemma":[0.00007790396,0.00007323867,0.00236692,0.00003974796,0.0000754529,0.00007673861,0.00019548554,0.8573656,0.00046617328,0.13791071,0.0012967957,0.00005521237],"about_ca_topic_score_codex":0.014027804,"about_ca_topic_score_gemma":0.009836918,"teacher_disagreement_score":0.014027804,"about_ca_system_score_codex":0.003434879,"about_ca_system_score_gemma":0.0025753458,"threshold_uncertainty_score":0.046133697},"labels":[],"label_agreement":null},{"id":"W4411715142","doi":"10.32920/29416613.v1","title":"Demography - The Life and Death Discipline","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Demography; History; Geography; Sociology","score_opus":0.03252380937937745,"score_gpt":0.3412527063385492,"score_spread":0.3087288969591718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411715142","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":[0.01004055,0.17653814,0.089623824,0.122015804,0.012893999,0.00022843062,0.0039392677,0.00043564066,0.5842844],"genre_scores_gemma":[0.2719714,0.27445036,0.06321132,0.025679905,0.012536305,0.00074635807,0.0038741971,0.00068355404,0.34684658],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983046,0.00098909,0.0000800256,0.000205852,0.00033095718,0.000089501766],"domain_scores_gemma":[0.9967434,0.002472254,0.00015886662,0.00022023595,0.00029321457,0.000112062364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017925877,0.0006864141,0.00066856376,0.002181047,0.0014160973,0.0035199765,0.0010992974,0.0014778122,0.007861116],"category_scores_gemma":[0.006400111,0.00034318236,0.0005341511,0.0032866097,0.005905062,0.005853908,0.001620853,0.0035536368,0.002108352],"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":[0.000014645418,0.000017008322,0.0015884192,0.00038598865,0.000017146576,0.00008628787,0.0037378918,0.0011956771,0.000055969755,0.7683252,0.15782198,0.06675374],"study_design_scores_gemma":[0.000004541305,0.000022334376,0.0021051327,0.00071191683,0.000008417816,0.00016381302,0.0024229316,0.00091695867,0.00010862678,0.23517816,0.75834167,0.000015462016],"about_ca_topic_score_codex":0.0050506084,"about_ca_topic_score_gemma":0.007363075,"teacher_disagreement_score":0.007861116,"about_ca_system_score_codex":0.0032175824,"about_ca_system_score_gemma":0.002277597,"threshold_uncertainty_score":0.026298046},"labels":[],"label_agreement":null},{"id":"W4411715159","doi":"10.32920/29416613","title":"Demography - The Life and Death Discipline","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Demography; Geography; History; Sociology","score_opus":0.03252380937937745,"score_gpt":0.3412527063385492,"score_spread":0.3087288969591718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411715159","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":[0.01004055,0.17653814,0.089623824,0.122015804,0.012893999,0.00022843062,0.0039392677,0.00043564066,0.5842844],"genre_scores_gemma":[0.2719714,0.27445036,0.06321132,0.025679905,0.012536305,0.00074635807,0.0038741971,0.00068355404,0.34684658],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983046,0.00098909,0.0000800256,0.000205852,0.00033095718,0.000089501766],"domain_scores_gemma":[0.9967434,0.002472254,0.00015886662,0.00022023595,0.00029321457,0.000112062364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017925877,0.0006864141,0.00066856376,0.002181047,0.0014160973,0.0035199765,0.0010992974,0.0014778122,0.007861116],"category_scores_gemma":[0.006400111,0.00034318236,0.0005341511,0.0032866097,0.005905062,0.005853908,0.001620853,0.0035536368,0.002108352],"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":[0.000014645418,0.000017008322,0.0015884192,0.00038598865,0.000017146576,0.00008628787,0.0037378918,0.0011956771,0.000055969755,0.7683252,0.15782198,0.06675374],"study_design_scores_gemma":[0.000004541305,0.000022334376,0.0021051327,0.00071191683,0.000008417816,0.00016381302,0.0024229316,0.00091695867,0.00010862678,0.23517816,0.75834167,0.000015462016],"about_ca_topic_score_codex":0.0050506084,"about_ca_topic_score_gemma":0.007363075,"teacher_disagreement_score":0.007861116,"about_ca_system_score_codex":0.0032175824,"about_ca_system_score_gemma":0.002277597,"threshold_uncertainty_score":0.026298046},"labels":[],"label_agreement":null},{"id":"W4412051859","doi":"10.2139/ssrn.5340611","title":"Optimal Contracts Under General Mixed Constraints: Continuity, Structure, and Applications","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Ottawa","funders":"","keywords":"Computer science; Mathematics; Mathematical optimization; Business","score_opus":0.009980379190194478,"score_gpt":0.294454979230812,"score_spread":0.28447460004061753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412051859","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":[0.18924366,0.0038659088,0.78172165,0.006321253,0.00018781768,0.00017267169,0.0006930449,0.00013020264,0.01766384],"genre_scores_gemma":[0.8570682,0.003738207,0.119585976,0.00062913337,0.0006812707,0.0003613715,0.00064383936,0.00017283522,0.017119216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99634016,0.0022024952,0.00016933869,0.000539571,0.00039337258,0.00035502683],"domain_scores_gemma":[0.94811654,0.04251753,0.004189208,0.0013654027,0.0016858656,0.0021254446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010376859,0.0012326298,0.003141217,0.0020583782,0.001052356,0.0044297194,0.0029443034,0.0043771467,0.007621192],"category_scores_gemma":[0.063247666,0.0018804041,0.0013904406,0.0033034633,0.004015269,0.010978562,0.0030746593,0.0051432624,0.00031433578],"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":[0.0001670028,0.000160286,0.0015373147,0.00019519748,0.00007969549,0.00015216092,0.00022256265,0.12680362,0.00038909717,0.8507776,0.002507001,0.017008526],"study_design_scores_gemma":[0.00006412162,0.000052556556,0.0004857663,0.000056160574,0.000017650016,0.00006252588,0.00008854151,0.2802153,0.0001226792,0.7179179,0.0008914527,0.00002531906],"about_ca_topic_score_codex":0.004720786,"about_ca_topic_score_gemma":0.0031171606,"teacher_disagreement_score":0.010376859,"about_ca_system_score_codex":0.0033521024,"about_ca_system_score_gemma":0.0033576041,"threshold_uncertainty_score":0.05487871},"labels":[],"label_agreement":null},{"id":"W4412370752","doi":"10.15672/hujms.1522471","title":"A study on life insurance premiums under asymmetric dependence using Canadian insurance data","year":2025,"lang":"en","type":"article","venue":"Hacettepe Journal of Mathematics and Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Kırıkkale Üniversitesi","keywords":"Mathematics; Actuarial science; Life insurance; Econometrics; Statistics; Business","score_opus":0.0797756075374817,"score_gpt":0.3626108428147052,"score_spread":0.2828352352772235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412370752","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":[0.9922775,0.0003779701,0.0034691666,0.000212039,0.000007929415,0.000039066515,0.0010771843,0.000028840284,0.0025102643],"genre_scores_gemma":[0.9966001,0.00018778269,0.0016441351,0.00002094864,0.0000062705412,0.0000060912416,0.0012324963,0.0000096953845,0.00029249856],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956999,0.0008452784,0.00015995401,0.00057700666,0.0021455204,0.00057233294],"domain_scores_gemma":[0.9839717,0.007805446,0.0018836232,0.0015108708,0.004449976,0.00037839063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074040056,0.00045093868,0.00048367662,0.0028338528,0.0010808716,0.0012851873,0.0011894612,0.00051294046,0.0010797734],"category_scores_gemma":[0.02844716,0.00027308668,0.0010557817,0.0036694885,0.0006878859,0.0011889085,0.00074681174,0.0008620737,0.00009727872],"study_design_candidate":"simulation_or_modeling","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":[0.0002963725,0.0001256743,0.87715495,0.00017331494,0.0004320388,0.0011988593,0.001412852,0.040565155,0.0023622648,0.010405212,0.0021245708,0.06374883],"study_design_scores_gemma":[0.000014556238,0.00006963572,0.83754516,0.000049096252,0.00017011013,0.0004819293,0.0009895613,0.15519789,0.001117871,0.0013534551,0.0029378117,0.000072979485],"about_ca_topic_score_codex":0.72993517,"about_ca_topic_score_gemma":0.63224053,"teacher_disagreement_score":0.27006483,"about_ca_system_score_codex":0.007263819,"about_ca_system_score_gemma":0.0052651986,"threshold_uncertainty_score":0.5433106},"labels":[],"label_agreement":null},{"id":"W4412891480","doi":"10.1515/9783111045283","title":"Stochastic Finance","year":2025,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Economics; Business","score_opus":0.013848897679815059,"score_gpt":0.29199257464098854,"score_spread":0.2781436769611735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412891480","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":[0.0018958516,0.06740136,0.07071027,0.013903835,0.005648566,0.00007116095,0.0025425758,0.0009311436,0.8368953],"genre_scores_gemma":[0.04251185,0.062147357,0.020907493,0.0031550415,0.0046900627,0.00014193199,0.0026022703,0.0005740852,0.86327004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99955195,0.000069076064,0.000015865553,0.00006958856,0.00026167673,0.000031874617],"domain_scores_gemma":[0.9996531,0.00013744454,0.000022210854,0.00005276041,0.00010014707,0.000034483946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005504767,0.00092472875,0.0007574019,0.00088245753,0.0005441393,0.00239197,0.0005955603,0.0010010655,0.065478265],"category_scores_gemma":[0.001921837,0.00032562608,0.00054693554,0.001129593,0.0008688585,0.0019102404,0.00090135785,0.0019275473,0.026234686],"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":[0.00000886165,0.000016464075,0.00018735531,0.00013991448,0.000015331738,0.000052221923,0.000065977816,0.0021701762,0.00020543768,0.5281464,0.36356112,0.1054308],"study_design_scores_gemma":[0.0000035960998,0.000009073252,0.0003121213,0.00011281259,0.0000034660388,0.00011508031,0.000020701114,0.001798935,0.000088039706,0.20413719,0.7933915,0.0000073847445],"about_ca_topic_score_codex":0.0016885936,"about_ca_topic_score_gemma":0.0020542685,"teacher_disagreement_score":0.065478265,"about_ca_system_score_codex":0.001479864,"about_ca_system_score_gemma":0.0009858378,"threshold_uncertainty_score":0.2190466},"labels":[],"label_agreement":null},{"id":"W4413030001","doi":"10.1080/03461238.2025.2537925","title":"Mortality prediction via age-specific band selection","year":2025,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"University of Waterloo; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Computer science; Econometrics; Mathematics; Statistics; Artificial intelligence","score_opus":0.02157850417526177,"score_gpt":0.31767387102028577,"score_spread":0.296095366845024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413030001","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":[0.07815869,0.00039498438,0.9184132,0.00023236191,0.00006714488,0.000060372873,0.0005699737,0.00069825246,0.0014050554],"genre_scores_gemma":[0.8471668,0.00036871497,0.1475139,0.00014813746,0.00017225134,0.00016388287,0.0023943337,0.000058443038,0.002013583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922776,0.00029161145,0.00003897895,0.00018673655,0.00014744856,0.00010752215],"domain_scores_gemma":[0.99813384,0.00089554675,0.0002599015,0.00023323455,0.00037131662,0.000106205785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019193955,0.00070866744,0.0010400807,0.0018891727,0.00031913878,0.0007960037,0.0015099383,0.00074436015,0.0016396577],"category_scores_gemma":[0.0047708144,0.0002443898,0.00079956034,0.0011011579,0.00028888835,0.0009773253,0.0011483184,0.0009828503,0.0006402011],"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":[0.00035677978,0.00033634095,0.06340603,0.000071222086,0.00024748058,0.00016029878,0.00011062381,0.5111461,0.0028464203,0.007035313,0.006378057,0.40790534],"study_design_scores_gemma":[0.000007894576,0.00003512897,0.0037163885,0.000007165999,0.000020880465,0.00003411893,0.000014154027,0.991827,0.00046381168,0.0034320059,0.0004282645,0.000013164247],"about_ca_topic_score_codex":0.004928414,"about_ca_topic_score_gemma":0.003392531,"teacher_disagreement_score":0.004928414,"about_ca_system_score_codex":0.00034076866,"about_ca_system_score_gemma":0.00065962336,"threshold_uncertainty_score":0.01015079},"labels":[],"label_agreement":null},{"id":"W4413090898","doi":"10.22541/au.175270366.69122163/v1","title":"Linking climate and demography to predict population dynamics and persistence under global change","year":2025,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Persistence (discontinuity); Population; Demography; Dynamics (music); Climate change; Geography; Ecology; Sociology; Biology","score_opus":0.024818543961828247,"score_gpt":0.30631715332149634,"score_spread":0.2814986093596681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413090898","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":[0.64619356,0.004201602,0.29492489,0.027591908,0.00028978198,0.00007449232,0.0039593964,0.0005877142,0.02217666],"genre_scores_gemma":[0.9793394,0.002674621,0.016246125,0.00028429343,0.00010482765,0.000033962235,0.000696413,0.000060597366,0.00055971544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993975,0.0003466197,0.000042952808,0.00010305917,0.00006175797,0.000048112568],"domain_scores_gemma":[0.99396247,0.004301229,0.00072389195,0.00047276486,0.0003932063,0.00014657149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037547518,0.00044590715,0.00031746065,0.0018316931,0.00039012678,0.002042791,0.0005678883,0.0013254598,0.0018956492],"category_scores_gemma":[0.023936199,0.00032051574,0.00056111097,0.0014237589,0.0010087203,0.0043796618,0.0014281152,0.0014891466,0.00033119653],"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":[0.00004581572,0.000051320127,0.29704624,0.00016932002,0.00035572101,0.00011981273,0.000727419,0.5675304,0.0004234325,0.06855418,0.002801447,0.062174868],"study_design_scores_gemma":[0.000008172024,0.000045461285,0.09161065,0.00023531858,0.00008424861,0.00009147743,0.0011236925,0.60766155,0.0004084888,0.29003325,0.008593506,0.000104225895],"about_ca_topic_score_codex":0.011928232,"about_ca_topic_score_gemma":0.010946988,"teacher_disagreement_score":0.011928232,"about_ca_system_score_codex":0.001148768,"about_ca_system_score_gemma":0.0006894496,"threshold_uncertainty_score":0.023717582},"labels":[],"label_agreement":null},{"id":"W4413135825","doi":"10.1139/cjfas-2025-0001","title":"Hidden Markov models with serial correlation for identifying stock–recruitment regime shifts","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada; Government of Newfoundland and Labrador; Memorial University of Newfoundland","funders":"","keywords":"Autocorrelation; Markov chain; Stock (firearms); Econometrics; Correlation; Markov model; Biology; Statistics; Mathematics; Geography","score_opus":0.07124638798989251,"score_gpt":0.309900534592941,"score_spread":0.23865414660304846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413135825","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":[0.026825221,0.0004115691,0.9703399,0.0002668512,0.000056292898,0.00004730271,0.00034791676,0.00037998924,0.0013248458],"genre_scores_gemma":[0.7846802,0.001470165,0.20470591,0.00020609824,0.00021391334,0.0003191141,0.0017032772,0.00014230376,0.006559027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988372,0.0006085262,0.0000737618,0.00023875042,0.00015960394,0.000082331986],"domain_scores_gemma":[0.99048245,0.0077501778,0.0008605315,0.00040298505,0.00037923918,0.0001245703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004816902,0.00092859234,0.0008338256,0.0012556011,0.0005695479,0.0010115443,0.0012729531,0.0011154382,0.0027464516],"category_scores_gemma":[0.01214257,0.0007605169,0.0012367943,0.0012672034,0.0006867661,0.0017717978,0.0012174033,0.0020780268,0.0007221396],"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":[0.00008341078,0.000074582706,0.010395291,0.00010512713,0.00016215944,0.00015542087,0.00014899431,0.8947776,0.0009000455,0.045754667,0.0010739055,0.046368755],"study_design_scores_gemma":[0.0000051908673,0.000013664249,0.000699374,0.000011127215,0.00002038458,0.000012838749,0.000007747443,0.9865584,0.00014406025,0.012207057,0.00030951097,0.000010686517],"about_ca_topic_score_codex":0.0166752,"about_ca_topic_score_gemma":0.02115975,"teacher_disagreement_score":0.0166752,"about_ca_system_score_codex":0.0011729967,"about_ca_system_score_gemma":0.0017520731,"threshold_uncertainty_score":0.033156276},"labels":[],"label_agreement":null},{"id":"W4413213233","doi":"10.2139/ssrn.5366394","title":"Modeling Excess Mortality and Interest Rates using Mixed Fractional Brownian Motions","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Fractional Brownian motion; Brownian motion; Interest rate; Econometrics; Statistical physics; Mathematics; Economics; Statistics; Physics; Monetary economics","score_opus":0.08614946792259914,"score_gpt":0.38125337211499505,"score_spread":0.2951039041923959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413213233","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":[0.41906562,0.0009797239,0.5749417,0.001471924,0.00020434015,0.000050373914,0.00028848296,0.00025018543,0.0027476568],"genre_scores_gemma":[0.9625193,0.00053280586,0.025401432,0.00013328482,0.00017030895,0.000080461214,0.00019919434,0.00007884192,0.010884399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902236,0.00047436898,0.000050531024,0.00021082764,0.00008851487,0.00015347856],"domain_scores_gemma":[0.99306035,0.005386117,0.0007225517,0.00023517448,0.00028053872,0.00031523348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00445894,0.0011807121,0.0013514007,0.0017556179,0.00049681717,0.0027720318,0.0020917263,0.0032147495,0.0025854155],"category_scores_gemma":[0.017040132,0.0012979971,0.0017961251,0.0010228938,0.0013971939,0.0030761533,0.0015879067,0.0019363814,0.00030153376],"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":[0.00007695453,0.00007088431,0.004824384,0.000034360084,0.00008708448,0.00014549357,0.00012187564,0.91398686,0.0005904547,0.07552503,0.00037744423,0.004159085],"study_design_scores_gemma":[0.000009291366,0.000012867286,0.00035189418,0.0000045443003,0.0000133624035,0.000016228247,0.000012056428,0.9855767,0.000060540886,0.013815126,0.00011863462,0.00000874879],"about_ca_topic_score_codex":0.008496326,"about_ca_topic_score_gemma":0.0047719036,"teacher_disagreement_score":0.008496326,"about_ca_system_score_codex":0.0013146193,"about_ca_system_score_gemma":0.0009330071,"threshold_uncertainty_score":0.023581386},"labels":[],"label_agreement":null},{"id":"W4413434016","doi":"10.1007/s42650-025-00094-8","title":"Life Expectancy Loss and Recovery by Age and Sex Following Catastrophic Events in Europe during the 19th and 20th Centuries","year":2025,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Life expectancy; Late 19th century; Demography; Expectancy theory; History; Psychology; Population; Sociology; Period (music); Philosophy; Social psychology","score_opus":0.015445343461639319,"score_gpt":0.29371043149918946,"score_spread":0.2782650880375501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413434016","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":[0.997747,0.0005463365,0.00021508931,0.00006323451,0.000009942337,0.0000035783062,0.0006073602,0.0000023903356,0.00080497924],"genre_scores_gemma":[0.9988949,0.0002057242,0.00006105026,0.00001541387,0.000010170493,0.000004412254,0.00059628044,0.0000015824271,0.00021070687],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99972385,0.00006679007,0.000034385037,0.00006911741,0.000032012686,0.000073741576],"domain_scores_gemma":[0.9993268,0.00019682985,0.0002700298,0.00005170524,0.00007397447,0.000080588754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008157958,0.00019134789,0.0002023701,0.0008361939,0.00019037629,0.00057590817,0.00022678354,0.00028484472,0.002241688],"category_scores_gemma":[0.0025216856,0.00005978482,0.00043095005,0.00069956615,0.00027643977,0.00041394203,0.00071527297,0.00034649696,0.00024101553],"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":[0.00022672437,0.000034332134,0.9855539,0.000036842554,0.00016761699,0.00021588641,0.00086015766,0.000958603,0.00020300409,0.00080270274,0.0003911954,0.010549058],"study_design_scores_gemma":[0.0000021221645,0.00003477397,0.9979272,0.000017771852,0.000023051163,0.00011538548,0.00044619778,0.0004256019,0.000065852226,0.00025230306,0.00068494154,0.000004699368],"about_ca_topic_score_codex":0.0033787505,"about_ca_topic_score_gemma":0.0024685606,"teacher_disagreement_score":0.0033787505,"about_ca_system_score_codex":0.00021313228,"about_ca_system_score_gemma":0.00014119374,"threshold_uncertainty_score":0.007499218},"labels":[],"label_agreement":null},{"id":"W4413445022","doi":"10.5539/ijef.v17n9p14","title":"Dirichlet Bayesian Model Averaging for Aggregate Mortgage Default Risk","year":2025,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Aggregate (composite); Bayesian probability; Econometrics; Economics; Dirichlet distribution; Mathematics; Statistics; Mathematical analysis","score_opus":0.012107566180244599,"score_gpt":0.2875528120336956,"score_spread":0.275445245853451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413445022","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":[0.008785286,0.00032956872,0.98990554,0.00014451923,0.000040697567,0.000017697826,0.000081281214,0.00018887261,0.00050654955],"genre_scores_gemma":[0.61974883,0.0020747702,0.3702357,0.0003300521,0.00065945176,0.00046447743,0.0014220967,0.00023956975,0.004825082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976821,0.0011954841,0.00012857882,0.00047460216,0.00037104453,0.00014819541],"domain_scores_gemma":[0.99570376,0.0032350498,0.00023552899,0.0003175585,0.00038753016,0.00012061694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050289864,0.0009994988,0.0026112464,0.0015911531,0.00069351983,0.0014193098,0.0026446623,0.001587974,0.0020993892],"category_scores_gemma":[0.013665073,0.00073194236,0.0019548025,0.0016040488,0.0009880645,0.003030457,0.0015024479,0.002309239,0.00054509396],"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":[0.00007582903,0.000043205833,0.0014284248,0.00008429125,0.00014099391,0.000072666306,0.00012300521,0.8727754,0.000967052,0.057371113,0.0019524746,0.064965665],"study_design_scores_gemma":[0.0000039057786,0.000007209227,0.00016611564,0.0000047738367,0.00000924933,0.00001421294,0.0000043753535,0.97916883,0.0001329143,0.020218378,0.00026052038,0.000009419316],"about_ca_topic_score_codex":0.0071912347,"about_ca_topic_score_gemma":0.0054943217,"teacher_disagreement_score":0.0071912347,"about_ca_system_score_codex":0.0010402208,"about_ca_system_score_gemma":0.0011582997,"threshold_uncertainty_score":0.026596189},"labels":[],"label_agreement":null},{"id":"W4413773115","doi":"10.3390/stats8030077","title":"A Markov Chain Monte Carlo Procedure for Efficient Bayesian Inference on the Phase-Type Aging Model","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Monte Carlo method; Inference; Bayesian inference; Markov chain; Hybrid Monte Carlo; Computer science; Bayesian probability; Statistical physics; Mathematics; Artificial intelligence; Statistics; Machine learning; Physics","score_opus":0.03146989526737381,"score_gpt":0.36860158399593995,"score_spread":0.33713168872856614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413773115","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":[0.001216551,0.000042566648,0.99825186,0.00004686017,0.000008237508,0.000037239843,0.00004756761,0.000107258886,0.00024182782],"genre_scores_gemma":[0.0820198,0.00023956104,0.9148904,0.00010298824,0.00007152596,0.0006426099,0.00050531764,0.00014170434,0.0013861159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971705,0.0019472213,0.00010813419,0.00030787123,0.0003573099,0.00010890615],"domain_scores_gemma":[0.98666537,0.011403353,0.00043891888,0.0006046703,0.0007071389,0.00018049839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008032686,0.00079983246,0.0014538704,0.0018244227,0.0010624952,0.0010826838,0.0019320474,0.001152375,0.0054444363],"category_scores_gemma":[0.025587536,0.0011116475,0.001149696,0.0018236638,0.0013790433,0.0014072238,0.0018324283,0.0031257428,0.00086386746],"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":[0.00013640907,0.000095141375,0.0016241696,0.00016142442,0.0001402956,0.00016358007,0.00017648017,0.66335076,0.0014812949,0.22397813,0.0027286238,0.10596364],"study_design_scores_gemma":[0.000024197918,0.000015540358,0.0001718681,0.000020036669,0.000013334462,0.000027337821,0.0000070844053,0.9519044,0.0003037356,0.0462864,0.001207047,0.00001907887],"about_ca_topic_score_codex":0.011246888,"about_ca_topic_score_gemma":0.015705898,"teacher_disagreement_score":0.011246888,"about_ca_system_score_codex":0.0013534416,"about_ca_system_score_gemma":0.0045247767,"threshold_uncertainty_score":0.042481363},"labels":[],"label_agreement":null},{"id":"W4413853996","doi":"10.5539/ijsp.v14n3p42","title":"Parameter Reduction of Complex Distributions Using a Tuning Method with Applications to Actuarial Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Reduction (mathematics); Mathematics; Statistics; Econometrics; Applied mathematics; Mathematical optimization; Computer science","score_opus":0.08686306566013241,"score_gpt":0.4219512477397813,"score_spread":0.3350881820796489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413853996","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":[0.0055768373,0.00015495958,0.9934396,0.00011396532,0.000022529364,0.00003285242,0.00003498246,0.00023408775,0.0003902506],"genre_scores_gemma":[0.21792118,0.00048689026,0.77867323,0.0001913112,0.0001382346,0.00027914703,0.00031476814,0.00027488332,0.0017203162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976059,0.0012649703,0.0001692785,0.00037686282,0.00046757262,0.00011532876],"domain_scores_gemma":[0.98874223,0.00852738,0.0006584787,0.0011109185,0.0008001295,0.00016081624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007392135,0.0007331504,0.0009334934,0.0015736507,0.00074879144,0.0011214125,0.0012064632,0.0012033684,0.0021546276],"category_scores_gemma":[0.023905402,0.00043570844,0.0014597615,0.0013087764,0.0009615563,0.0010727707,0.0019977384,0.0022214635,0.00050122495],"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":[0.00018098182,0.00019512232,0.005879955,0.00031702107,0.0002363012,0.0005715616,0.0005770284,0.51787454,0.008484449,0.073042646,0.0037978946,0.38884258],"study_design_scores_gemma":[0.000018769377,0.000034952933,0.0008625626,0.000029069943,0.000021662465,0.00017538157,0.000040065515,0.96577483,0.0015501359,0.029337604,0.0021143944,0.00004053416],"about_ca_topic_score_codex":0.0028116957,"about_ca_topic_score_gemma":0.0020655508,"teacher_disagreement_score":0.007392135,"about_ca_system_score_codex":0.00051680574,"about_ca_system_score_gemma":0.0010272949,"threshold_uncertainty_score":0.039093852},"labels":[],"label_agreement":null},{"id":"W4414055502","doi":"10.1080/10920277.2025.2546881","title":"Creating Complete Mortality Life Tables for CARICOM: The Cases of Trinidad &amp; Tobago and Jamaica","year":2025,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Table (database); Life expectancy; Developing country; Life table","score_opus":0.04812997403348506,"score_gpt":0.3485962502126978,"score_spread":0.30046627617921273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414055502","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":[0.50295264,0.0025973911,0.019227967,0.0052810814,0.0003134963,0.0018894044,0.37232333,0.0013001448,0.09411459],"genre_scores_gemma":[0.6526764,0.0028475395,0.05068049,0.00062143564,0.00008609627,0.0028498825,0.26568037,0.00029975444,0.024257911],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995634,0.00010464719,0.000061295956,0.00008000319,0.00011690435,0.000073822404],"domain_scores_gemma":[0.9971591,0.0006094711,0.00051725435,0.0004475787,0.0010954196,0.00017126759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010068762,0.00033339922,0.00022500621,0.005156897,0.0005159107,0.001024893,0.0007856225,0.00028372888,0.009979178],"category_scores_gemma":[0.006636135,0.00024446507,0.00030801518,0.0070431163,0.00022392305,0.00056565297,0.0012500826,0.0005705982,0.0010988164],"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":[0.00020582328,0.00012793773,0.45609996,0.0007695541,0.00017943137,0.0036748506,0.004141304,0.014715229,0.00080896,0.013970582,0.26213598,0.24317035],"study_design_scores_gemma":[0.000049156843,0.0000510892,0.6258196,0.0010781974,0.000070598435,0.001140654,0.0086745145,0.012064431,0.00059823995,0.0034979305,0.34685984,0.00009567786],"about_ca_topic_score_codex":0.52493864,"about_ca_topic_score_gemma":0.588653,"teacher_disagreement_score":0.52493864,"about_ca_system_score_codex":0.0049383296,"about_ca_system_score_gemma":0.0035840007,"threshold_uncertainty_score":0.95571816},"labels":[],"label_agreement":null},{"id":"W4414098759","doi":"10.3847/1538-4357/adfee0","title":"Erratum: “Tripling the Census of Dwarf AGN Candidates Using DESI Early Data” (2025, ApJ, 982, 10)","year":2025,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Census; Stars; Background radiation; Dwarf galaxy; Field (mathematics)","score_opus":0.0482900178459537,"score_gpt":0.33944456861697214,"score_spread":0.2911545507710184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414098759","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":[0.0024535863,0.002483927,0.0015681542,0.09907256,0.7866898,0.0002457169,0.06874246,0.0034029102,0.03534091],"genre_scores_gemma":[0.0225288,0.007190736,0.012526464,0.11685122,0.17826588,0.0007617715,0.18390013,0.0055025397,0.47247252],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99700516,0.00026719278,0.00042220255,0.00029689685,0.0017524075,0.00025617602],"domain_scores_gemma":[0.9776063,0.0021348638,0.0020290622,0.0012287933,0.015039942,0.001961031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025679388,0.0019660299,0.0010254957,0.006735681,0.0035250278,0.0041717775,0.0017168123,0.003356596,0.08483083],"category_scores_gemma":[0.019495463,0.0008031276,0.0014442331,0.0035833335,0.0010261075,0.002653737,0.0022730485,0.0054161036,0.089049906],"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":[0.000016636972,0.0000047661265,0.00020005008,0.000023163662,0.0000025820202,0.000026660678,0.000004435296,0.000011060396,0.000050536175,0.00010057183,0.99764556,0.0019139799],"study_design_scores_gemma":[0.000034439807,0.000033431916,0.00551406,0.00015263092,0.000021068903,0.00012330925,0.00010229952,0.00013372002,0.00045928004,0.00043159173,0.99294996,0.000044241966],"about_ca_topic_score_codex":0.032190736,"about_ca_topic_score_gemma":0.048535082,"teacher_disagreement_score":0.08483083,"about_ca_system_score_codex":0.003242901,"about_ca_system_score_gemma":0.004640659,"threshold_uncertainty_score":0.28378737},"labels":[],"label_agreement":null},{"id":"W4414378127","doi":"10.1093/geront/gnaf214","title":"Catching up with stagnation: cause-specific dynamics of change in life expectancy at age 65 in the United States, Canada, and Latin America, 2000–2019","year":2025,"lang":"en","type":"article","venue":"The Gerontologist","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Life expectancy; Latin Americans; Developing country; Developed country; Dynamics (music)","score_opus":0.05236529395990231,"score_gpt":0.30365955712802506,"score_spread":0.25129426316812276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414378127","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":[0.97791934,0.0036462522,0.00021951956,0.001933534,0.00003478329,0.000035521323,0.013032644,0.000036379024,0.0031421313],"genre_scores_gemma":[0.99250925,0.0011492627,0.0001357171,0.00012818385,0.000012182302,0.000018227434,0.005419789,0.000007598673,0.0006197496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937195,0.00005665605,0.000039542712,0.00010616031,0.00014249877,0.0002831892],"domain_scores_gemma":[0.9977743,0.00012045535,0.0005775353,0.000087461885,0.0009657408,0.00047445777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011600875,0.00039362925,0.00040217282,0.002318157,0.0010219777,0.00159812,0.00089866045,0.00045763384,0.0011264441],"category_scores_gemma":[0.0037834116,0.0001866266,0.0008104533,0.00511725,0.00056518597,0.0006831309,0.00149753,0.0009959689,0.00014346998],"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":[0.000037194804,0.0000093528815,0.99473757,0.00001911603,0.00007933983,0.00004444161,0.0003828931,0.00020723685,0.000029049948,0.00017801582,0.0010392932,0.0032365373],"study_design_scores_gemma":[0.000002423672,0.0000067878664,0.9974166,0.00002865825,0.000028495715,0.000037876653,0.0008752302,0.00039920825,0.000025571204,0.000050341714,0.0011210773,0.000007650429],"about_ca_topic_score_codex":0.96952885,"about_ca_topic_score_gemma":0.9688655,"teacher_disagreement_score":0.030471146,"about_ca_system_score_codex":0.012715749,"about_ca_system_score_gemma":0.018100662,"threshold_uncertainty_score":0.092259586},"labels":[],"label_agreement":null},{"id":"W4414692181","doi":"10.54254/2754-1169/2025.gl27550","title":"Bayesian Methods in Risk Assessment and Insurance Pricing: Strengths, Limitations, and Future Trends","year":2025,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Bayesian probability; Scope (computer science); Risk assessment; Solvency; Process (computing); Model risk; Bayesian inference; Risk management","score_opus":0.01600994972162727,"score_gpt":0.38724229029404045,"score_spread":0.37123234057241317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414692181","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":[0.0051465305,0.47958094,0.43839398,0.057218447,0.0011893348,0.00012056765,0.0001859012,0.00014242301,0.018021883],"genre_scores_gemma":[0.19904706,0.5183975,0.26324627,0.00837009,0.006376412,0.0005135342,0.00022467125,0.00018038282,0.0036441411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9767488,0.016129538,0.0009345848,0.0014279775,0.0044998424,0.00025933803],"domain_scores_gemma":[0.91081727,0.07620139,0.0023754952,0.0027369137,0.0070222393,0.00084671297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046976913,0.0014084184,0.0021428403,0.004589838,0.00087420945,0.007093442,0.0043072132,0.0042499653,0.0036848828],"category_scores_gemma":[0.074010484,0.0010612033,0.0015681299,0.0061076065,0.0051357923,0.011476303,0.0033028636,0.0087040495,0.0011227427],"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":[0.000076226,0.00010013473,0.0038660294,0.002651508,0.00034709802,0.00008036618,0.0007431428,0.017821604,0.00020035627,0.5157829,0.0070304917,0.45130014],"study_design_scores_gemma":[0.000040017625,0.00011286096,0.0020331168,0.0065552713,0.00014107581,0.00019440056,0.00081781717,0.055644453,0.0003258091,0.82240635,0.1115906,0.0001382361],"about_ca_topic_score_codex":0.011380493,"about_ca_topic_score_gemma":0.009220467,"teacher_disagreement_score":0.046976913,"about_ca_system_score_codex":0.004037696,"about_ca_system_score_gemma":0.005420984,"threshold_uncertainty_score":0.24844062},"labels":[],"label_agreement":null},{"id":"W4414700069","doi":"10.1016/j.insmatheco.2025.103164","title":"Robust parameter estimation for the Lee-Carter family: A probabilistic principal component approach","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Estimation theory; Probabilistic logic; Component (thermodynamics); Principal component analysis; Estimation; Robustness (evolution); Identification (biology); Bayesian probability","score_opus":0.05060191496105669,"score_gpt":0.27760461442436984,"score_spread":0.22700269946331314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414700069","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":[0.0019989961,0.00008456976,0.9975956,0.000045389264,0.000007928786,0.00001339707,0.000032062835,0.000065983455,0.00015604623],"genre_scores_gemma":[0.2855733,0.0012111756,0.7090527,0.00015402865,0.00019628643,0.00036618154,0.00079530576,0.00031238966,0.0023386732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969066,0.0017984344,0.00012656106,0.0005194799,0.00051676005,0.00013216565],"domain_scores_gemma":[0.9928919,0.00486273,0.0007435469,0.0007282004,0.0006616378,0.000111966474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008032717,0.001280171,0.0015706538,0.001942534,0.000567982,0.0014700987,0.0019792204,0.0012201344,0.0019192382],"category_scores_gemma":[0.021498404,0.00077304523,0.0018258803,0.00198549,0.0012754367,0.0019458446,0.0017630996,0.0026253061,0.0006282219],"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":[0.00008017347,0.00004989924,0.0033670429,0.00017634321,0.00033164624,0.00012686949,0.000140034,0.79418814,0.0020023882,0.097273506,0.0017902781,0.10047372],"study_design_scores_gemma":[0.0000035847163,0.000015242231,0.0004277124,0.000013273733,0.000014459116,0.00003643872,0.000013204279,0.9740021,0.00033256013,0.024342243,0.0007789537,0.000020168369],"about_ca_topic_score_codex":0.004359461,"about_ca_topic_score_gemma":0.003042658,"teacher_disagreement_score":0.008032717,"about_ca_system_score_codex":0.000644914,"about_ca_system_score_gemma":0.001662246,"threshold_uncertainty_score":0.0424816},"labels":[],"label_agreement":null},{"id":"W4414763148","doi":"10.1139/cjfas-2025-0164","title":"Performance of age-only state-space assessment models under diverse somatic growth scenarios","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington","keywords":"Sampling (signal processing); Population; Sampling scheme; Sampling design; Affect (linguistics); Importance sampling; Sample size determination","score_opus":0.026018131989028984,"score_gpt":0.2739424466178215,"score_spread":0.24792431462879255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414763148","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":[0.9561282,0.00017705432,0.039618596,0.00033997654,0.000029903773,0.00012791393,0.00060700474,0.00039728847,0.002574066],"genre_scores_gemma":[0.9910417,0.000051368486,0.007956725,0.00005352114,0.000004762033,0.00007206853,0.00031096258,0.000012269438,0.00049665576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987735,0.0006778367,0.000108502085,0.00021718646,0.000099264376,0.00012363892],"domain_scores_gemma":[0.9849331,0.01143418,0.0011276293,0.0005829546,0.0013884794,0.00053367665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006583613,0.0010325954,0.0010309515,0.0007830324,0.00061486266,0.0013374876,0.0012342968,0.0015121901,0.0013850249],"category_scores_gemma":[0.013559764,0.00052847964,0.0010756283,0.00046463314,0.0006054112,0.0017439015,0.0012395657,0.0010386731,0.00022929934],"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":[0.00009725135,0.000041727246,0.0047182986,0.000014043467,0.00003659625,0.000015031423,0.00003229845,0.9920874,0.00015036721,0.0005039578,0.00006928395,0.002233702],"study_design_scores_gemma":[0.000013032252,0.000059615923,0.0006387537,0.000003885645,0.000011185668,0.000003579098,0.000013347463,0.99862754,0.00012844475,0.00045252262,0.000040498737,0.000007659608],"about_ca_topic_score_codex":0.048495382,"about_ca_topic_score_gemma":0.028420724,"teacher_disagreement_score":0.048495382,"about_ca_system_score_codex":0.002007522,"about_ca_system_score_gemma":0.002240075,"threshold_uncertainty_score":0.09642619},"labels":[],"label_agreement":null},{"id":"W4415092550","doi":"10.1016/s0140-6736(25)01330-3","title":"Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950–2023: a demographic analysis for the Global Burden of Disease Study 2023","year":2025,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":103,"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":"Japan Science and Technology Agency; Biotechnology and Biological Sciences Research Council; National Science and Technology Council; Italfarmaco; H. Lundbeck A/S; National Institute on Aging; Suicide Prevention Australia; Casen Recordati; Templeton World Charity Foundation; Medical Research Council; Servier; Ministry of Education- New Zealand; University of the Philippines; Taipei Medical University; National Institutes of Health; Tehran University of Medical Sciences and Health Services; Santen; Novo Nordisk; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministero della Salute; Ministry of Education, Culture, Sports, Science and Technology; British Heart Foundation; National Institute for Health and Care Research; National Taiwan Normal University; AstraZeneca; European Commission; University of Alberta; Precursory Research for Embryonic Science and Technology; World Health Organization; British Pharmacological Society; Sociedad Madrileña de Nefrología; Esteve Pharmaceuticals; National Natural Science Foundation of China; Fresenius Medical Care North America; State Key Laboratory of Respiratory Disease; Seres Therapeutics; Amarin Corporation; Ministerio de Ciencia, Innovación y Universidades; Norwegian Institute of Public Health; International Association for Suicide Prevention; Ministry of Health, New Zealand; Wellcome Trust; Heart Research UK; Ministerul Cercetării, Inovării şi Digitalizării; Moderna; American Heart Association; Universidade da Beira Interior; Society of Cardiovascular Anesthesiologists; Biogen; King Saud University; Indian Council of Medical Research; Department of Health and Social Care; Teva Pharmaceutical Industries; Bill and Melinda Gates Foundation; Pfizer; International Union of Basic and Clinical Pharmacology; Universitat Politècnica de Catalunya; U.S. Department of Veterans Affairs; Daiichi Sankyo Europe; Yale University; Swedish Orphan Biovitrum; Astellas Pharma; Alberta Health Services; Eli Lilly and Company; National Science Foundation; National Research, Development and Innovation Office; National Health and Medical Research Council; Neuraxpharm; European Society of Cardiology; Horizon Pharmaceuticals; Amgen; GAVI Alliance; Marga und Walter Boll-Stiftung; Sanofi; Global Fund to Fight AIDS, Tuberculosis and Malaria","keywords":"Life expectancy; Burden of disease; Demographic analysis; Disease burden; Disease; Developed country; Disability-adjusted life year; Demographic change","score_opus":0.05319451141714938,"score_gpt":0.3647931629448835,"score_spread":0.31159865152773414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415092550","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":[0.5736471,0.004797351,0.05430036,0.0013936684,0.000228721,0.0004900728,0.35605752,0.000651676,0.008433508],"genre_scores_gemma":[0.7112568,0.003353103,0.04226989,0.0002220964,0.000151711,0.0007214393,0.240112,0.00010677879,0.001806269],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99897325,0.000400362,0.00011715142,0.00016986816,0.00024836246,0.00009092397],"domain_scores_gemma":[0.9983847,0.0003118995,0.0005009353,0.0002412829,0.000474407,0.00008675058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037243618,0.0006292633,0.00056051975,0.0034474782,0.0002263707,0.0006419567,0.0006281207,0.00027012327,0.0022140963],"category_scores_gemma":[0.005017642,0.00025718662,0.0017922027,0.0058414834,0.0002531687,0.0008438331,0.0013205834,0.0006142415,0.0011427592],"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":[0.00010123443,0.0000531523,0.91987795,0.0003881808,0.00092189736,0.00010546832,0.00035146554,0.01821639,0.00044003557,0.00124356,0.019866727,0.038433935],"study_design_scores_gemma":[0.00003322434,0.00013419756,0.95083416,0.0003450965,0.00039623788,0.00028797626,0.00071502407,0.01821636,0.00054025016,0.0011813745,0.02725071,0.000065446155],"about_ca_topic_score_codex":0.05143102,"about_ca_topic_score_gemma":0.044519693,"teacher_disagreement_score":0.05143102,"about_ca_system_score_codex":0.0009797418,"about_ca_system_score_gemma":0.0016874004,"threshold_uncertainty_score":0.10226333},"labels":[],"label_agreement":null},{"id":"W4415092555","doi":"10.1016/s0140-6736(25)01917-8","title":"Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023","year":2025,"lang":"en","type":"article","venue":"The Lancet","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":275,"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 the Fraser Valley; Canadian Red Cross Society; Alberta Health Services; McGill University; Centre for Addiction and Mental Health; Centre for Advancing Health Outcomes; Health Sciences North; University of Alberta; University Health Network; University of Manitoba; Université de Montréal; Population Health Research Institute; Western University; Hospital for Sick Children; University of New Brunswick; McMaster University; Impact; York University; BC Children's Hospital; University of Windsor; University of Calgary; University of Waterloo; University of British Columbia; Dalhousie University; University of Toronto; Centre for Global Health Research; Queen's University","funders":"Institut canadien d'information sur la santé; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Health Statistics; Biotechnology Industry Research Assistance Council; Faculty of Science, Agriculture and Engineering, Newcastle University; Sixth Framework Programme; Science and Engineering Research Board; National Health and Medical Research Council; Eurostat; Medical Research Council; Economic Growth Center, Yale University; Fifth Framework Programme; Seventh Framework Programme; Rollins School of Public Health; Centers for Disease Control and Prevention; National Institutes of Health; National Science and Technology Council; National Institute on Aging; Suicide Prevention Australia; Tasmanian Department of Health; National Institute for Medical Research; Universiteit van Tilburg; Ministry of Health and Family Welfare; Ministry of Health, State of Israel; University of Ghana; Ministry of Public Health; Novo Nordisk; Max-Planck-Gesellschaft; Ministry of Education, Culture, Sports, Science and Technology; Bundesministerium für Bildung und Forschung; Monash University; University of Tasmania; Socialdepartementet; AstraZeneca; Tufts University; European Commission; National Institute of Mental Health and Neurosciences; National Institute of Mental Health; Ministry of Health and Population; Ministerul Cercetării, Inovării şi Digitalizării; Sociedad Madrileña de Nefrología; United States Agency for International Development; Gilead Research Scholars; St. Jude Children's Research Hospital; Fresenius Medical Care North America; European Food Safety Authority; H. Lundbeck A/S; Department of Science and Technology, Ministry of Science and Technology, India; Institute for Health Metrics and Evaluation; University of Sydney; Ministry of Health, New Zealand; Precursory Research for Embryonic Science and Technology; World Health Organization; Wellcome Trust; Ministero della Salute; Nepal Health Research Council, Government of Nepal; Queensland Health; Eli Lilly and Company; Health Protection Agency; Norwegian Institute of Public Health; Addis Ababa University; Gilead Sciences; International Association for Suicide Prevention; Swedish Orphan Biovitrum; Astellas Pharma; Bayer; University of Michigan; Université de Lausanne; Universitatea 'Dunărea de Jos' Galați; Indian Council of Medical Research; Department of Health and Social Care; National Research University Higher School of Economics; Universidad de Costa Rica; Teva Pharmaceutical Industries; Yale University; Japan Science and Technology Agency; International Parkinson and Movement Disorder Society; Sanofi; The Wellcome Trust DBT India Alliance; Peking University; National Institute for Health and Care Research; University of California Berkeley; Pfizer; Bill and Melinda Gates Foundation; Public Health Institute; Biogen; Department of Biotechnology, Ministry of Science and Technology, India; GAVI Alliance; David and Lucile Packard Foundation","keywords":"Burden of disease; Disease burden; Disease; Global health; Double burden; Cause of death","score_opus":0.023213893249706116,"score_gpt":0.3359092762858264,"score_spread":0.31269538303612027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415092555","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":[0.07218483,0.6262723,0.006304538,0.0023353791,0.00056772475,0.0009666958,0.28779724,0.00029347735,0.0032778273],"genre_scores_gemma":[0.34778613,0.5073278,0.016125426,0.0018077748,0.00029693326,0.003676635,0.121699885,0.00013270992,0.0011467427],"study_design_codex":"observational","study_design_gemma":"systematic_review","domain_scores_codex":[0.9963504,0.0012960049,0.0007761001,0.00053389027,0.00088804995,0.00015547646],"domain_scores_gemma":[0.99482733,0.0016142857,0.0014049703,0.00030602067,0.0016492957,0.00019811897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065594255,0.0011273823,0.00279969,0.008411923,0.00038429775,0.000796056,0.0006715104,0.00065422914,0.0016992181],"category_scores_gemma":[0.009314873,0.00074088224,0.008388351,0.014024559,0.00038415653,0.00058689096,0.0013518648,0.00071480725,0.00048725528],"study_design_candidate":"systematic_review","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":[0.0009507699,0.00004729674,0.5381283,0.11188957,0.053015653,0.0004127621,0.0009538105,0.0052129105,0.0006555947,0.0016673519,0.079126015,0.20794001],"study_design_scores_gemma":[0.0005729578,0.00021627531,0.7667448,0.08244631,0.054162104,0.0011245352,0.00095075875,0.0037203413,0.0005758345,0.0016479874,0.08765792,0.00018014059],"about_ca_topic_score_codex":0.07464872,"about_ca_topic_score_gemma":0.117432036,"teacher_disagreement_score":0.07464872,"about_ca_system_score_codex":0.0018376015,"about_ca_system_score_gemma":0.0080044065,"threshold_uncertainty_score":0.14842844},"labels":[],"label_agreement":null},{"id":"W4415099990","doi":"10.1016/j.pacfin.2025.102965","title":"Population age structure, industry return, and portfolio strategy: Capitalizing on the trend of population aging in China","year":2025,"lang":"en","type":"article","venue":"Pacific-Basin Finance Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Windsor","funders":"Scientific and Innovative Action Plan of Shanghai; National Natural Science Foundation of China","keywords":"Population ageing; Portfolio; Population; Age structure; China; Projections of population growth; Population size","score_opus":0.014726596419192738,"score_gpt":0.29302710249717456,"score_spread":0.27830050607798185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415099990","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":[0.99858284,0.000121192716,0.00024943994,0.00027857782,0.0000051989873,0.0000042867623,0.00016565634,0.0000062703384,0.00058647967],"genre_scores_gemma":[0.9993325,0.00007504043,0.000037958456,0.000018318698,0.000008369981,0.000001496707,0.00014273413,0.0000013927155,0.00038212576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997482,0.000045646513,0.000019888914,0.000059078076,0.00003996237,0.00008730874],"domain_scores_gemma":[0.998334,0.00029664687,0.0005474084,0.00012635946,0.00030599048,0.0003895822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010456169,0.00028823302,0.00030000022,0.0012406434,0.00040033503,0.00092593743,0.0005576443,0.0005102195,0.0019487508],"category_scores_gemma":[0.0026762572,0.00016089492,0.0005415729,0.0013358862,0.0003719642,0.0010865973,0.00066723407,0.00054687116,0.00021382066],"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":[0.00001991363,0.000030569096,0.99199486,0.0000067090327,0.000069368456,0.000110188994,0.00020119498,0.0023312122,0.00013622832,0.0007378923,0.00027046597,0.004091434],"study_design_scores_gemma":[0.000003044763,0.000042644566,0.9806536,0.000006457491,0.00008219226,0.000035916284,0.0005123926,0.017412836,0.0000826506,0.0007465539,0.00041307806,0.000008537776],"about_ca_topic_score_codex":0.06832395,"about_ca_topic_score_gemma":0.095084354,"teacher_disagreement_score":0.06832395,"about_ca_system_score_codex":0.0011910371,"about_ca_system_score_gemma":0.0014963548,"threshold_uncertainty_score":0.13585252},"labels":[],"label_agreement":null},{"id":"W4415257848","doi":"10.1016/j.vhri.2025.101509","title":"Analysis of Mortality Trajectory Patterns in the Middle East and North Africa: Which Diseases Are the Deadliest?","year":2025,"lang":"en","type":"article","venue":"Value in Health Regional Issues","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Prince Edward Island","funders":"","keywords":"Middle East; Trajectory; Intervention (counseling); Mortality rate","score_opus":0.15212617419340577,"score_gpt":0.35738834770191613,"score_spread":0.20526217350851036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415257848","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":[0.9962203,0.00048757775,0.0013371215,0.00027274506,0.00000908145,0.00004149973,0.0009421473,0.000009057853,0.0006805858],"genre_scores_gemma":[0.9973356,0.00024867998,0.0013492822,0.000023873448,0.000006552425,0.000033359116,0.00082305644,0.0000031539573,0.00017632118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995635,0.00018072518,0.000037486876,0.00007677429,0.00005589701,0.00008552336],"domain_scores_gemma":[0.99920076,0.00017108471,0.00032082392,0.000065319095,0.0001677182,0.00007436271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094452995,0.00025631808,0.00032513688,0.0018556295,0.00057537813,0.0006773191,0.00024476243,0.00018935995,0.0011095252],"category_scores_gemma":[0.003273037,0.00009905143,0.00048831286,0.0023485795,0.00023184108,0.00061984867,0.000702639,0.0002575758,0.00015335491],"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":[0.00014282124,0.00002153814,0.98151106,0.00006501593,0.00012550542,0.00013624316,0.0010466329,0.0012059947,0.0004919283,0.000379253,0.00039025611,0.014483729],"study_design_scores_gemma":[0.000004733065,0.000055514352,0.9900574,0.00005057952,0.000046133355,0.00014927621,0.0040782886,0.0034902052,0.00018937165,0.00035892223,0.0015084489,0.000010993073],"about_ca_topic_score_codex":0.028531857,"about_ca_topic_score_gemma":0.037743624,"teacher_disagreement_score":0.028531857,"about_ca_system_score_codex":0.00070102804,"about_ca_system_score_gemma":0.0009999921,"threshold_uncertainty_score":0.056731522},"labels":[],"label_agreement":null},{"id":"W4415493694","doi":"10.5539/jms.v15n2p109","title":"Aging and Finance. A Literature Review on Risk Profiles, Decision-Making, and Investment Returns","year":2025,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"European Commission","keywords":"Financial inclusion; Financial services; Vulnerability (computing); Corporate governance; Population ageing; Financial risk; Financial security; Face (sociological concept)","score_opus":0.004990299625262225,"score_gpt":0.30077412922722274,"score_spread":0.29578382960196054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415493694","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":[0.0007008195,0.99586654,0.00011822055,0.0016150299,0.00013605908,0.0000042650713,0.000043010554,0.0000023058703,0.0015136575],"genre_scores_gemma":[0.006732108,0.99203485,0.00017272771,0.0004350453,0.00027850148,0.000007998052,0.000043948534,0.0000012857743,0.0002934912],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927884,0.0002816264,0.00013095944,0.00006701549,0.00019012865,0.000051542655],"domain_scores_gemma":[0.9950487,0.0037356515,0.0005068346,0.00007413503,0.0005240695,0.00011057523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018218005,0.00064542255,0.0008368599,0.0044665644,0.0005630012,0.0026352748,0.00045503624,0.0014521042,0.004679821],"category_scores_gemma":[0.0054940353,0.00031748944,0.00074845325,0.0070372294,0.001030091,0.003045794,0.0008110845,0.0011564322,0.00062178646],"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":[0.00009121867,0.00008798555,0.0059950175,0.022578077,0.00018817371,0.0002916115,0.0016471377,0.0005681635,0.0001237641,0.02261977,0.05091218,0.8948969],"study_design_scores_gemma":[0.000025709016,0.00015093219,0.047831718,0.102791645,0.0004192365,0.0024403657,0.003974236,0.00047002014,0.00018143465,0.030019922,0.8116258,0.00006896235],"about_ca_topic_score_codex":0.007737709,"about_ca_topic_score_gemma":0.011051456,"teacher_disagreement_score":0.007737709,"about_ca_system_score_codex":0.0015765595,"about_ca_system_score_gemma":0.0037156367,"threshold_uncertainty_score":0.015655577},"labels":[],"label_agreement":null},{"id":"W4415716084","doi":"10.1080/2325548x.2025.2464789","title":"Demography and the Making of the Modern World","year":2025,"lang":"en","type":"article","venue":"The AAG Review of Books","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Making-of; Politics; Policy making; Public policy; Population; Historical demography","score_opus":0.013452509954493106,"score_gpt":0.3123661936673992,"score_spread":0.2989136837129061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415716084","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":[0.0135677215,0.3888105,0.005545123,0.35761276,0.007389487,0.000023343056,0.00047191317,0.000071888855,0.22650725],"genre_scores_gemma":[0.41403896,0.48257327,0.00461454,0.039939858,0.013034262,0.00007344095,0.00041491233,0.00012142467,0.0451892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987727,0.0007142566,0.000036364694,0.000110821515,0.0002592188,0.00010668707],"domain_scores_gemma":[0.99877316,0.00067825645,0.00011602632,0.00011354379,0.00020941699,0.00010967126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023158754,0.0005022651,0.0004196435,0.0021158867,0.0021431188,0.0047856327,0.00049084757,0.0013394507,0.0035648118],"category_scores_gemma":[0.004577398,0.00022988186,0.00031238463,0.0023584946,0.01580568,0.007417766,0.0021139425,0.0037738266,0.0009793494],"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":[0.000017268007,0.00001035266,0.0013862959,0.00015145852,0.00002029314,0.000074060445,0.005119792,0.0009823674,0.000075296484,0.86957496,0.07857724,0.044010647],"study_design_scores_gemma":[0.0000045717243,0.000008655617,0.0022125768,0.0004985386,0.00000555374,0.00009148683,0.0030744919,0.00014762726,0.000045312852,0.51418036,0.47971368,0.00001708227],"about_ca_topic_score_codex":0.012478212,"about_ca_topic_score_gemma":0.013950508,"teacher_disagreement_score":0.012478212,"about_ca_system_score_codex":0.0035062016,"about_ca_system_score_gemma":0.0030614485,"threshold_uncertainty_score":0.025439382},"labels":[],"label_agreement":null},{"id":"W4416082637","doi":"10.1139/cjfas-2024-0409","title":"A PSA for OSA residuals: residual diagnostics for state–space stock assessment models","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada; Memorial University of Newfoundland","funders":"","keywords":"Residual; Studentized residual; Normality; Stock (firearms); Production model; Statistical model","score_opus":0.04231863291184191,"score_gpt":0.33231862253519195,"score_spread":0.28999998962335005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416082637","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":[0.07377162,0.00019095911,0.9194102,0.0006509116,0.000097727614,0.000120369456,0.00070908724,0.0026704953,0.0023785708],"genre_scores_gemma":[0.6400995,0.00012457656,0.3553108,0.00021015087,0.00007241741,0.00014045899,0.0013200315,0.0004783183,0.0022437228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961358,0.0024645615,0.00023829076,0.0003834101,0.0006621959,0.0001157695],"domain_scores_gemma":[0.9625478,0.027493158,0.002424678,0.003766406,0.003407495,0.0003604777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014775178,0.000823996,0.0008353024,0.0013519292,0.00047448464,0.0014394226,0.0014614083,0.00087629724,0.0034308285],"category_scores_gemma":[0.06301412,0.0004942856,0.001060259,0.0012865583,0.0008096976,0.002099774,0.0014942104,0.0024319678,0.0007023538],"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":[0.0006615747,0.0003335002,0.12051083,0.00049847586,0.0004476652,0.00040522288,0.0007095238,0.3933036,0.0069190958,0.06753238,0.012905049,0.3957731],"study_design_scores_gemma":[0.00004066639,0.0003910041,0.013152672,0.00006322244,0.000039052316,0.000096781776,0.0001199972,0.9623739,0.0023406956,0.017977357,0.0033383043,0.00006632059],"about_ca_topic_score_codex":0.009613456,"about_ca_topic_score_gemma":0.012120637,"teacher_disagreement_score":0.014775178,"about_ca_system_score_codex":0.0005654174,"about_ca_system_score_gemma":0.0020228713,"threshold_uncertainty_score":0.078139484},"labels":[],"label_agreement":null},{"id":"W4416228291","doi":"10.3390/jrfm18110640","title":"Dynamic Asset Allocation for Pension Funds: A Stochastic Control Approach Using the Heston Model","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Downside risk; Stochastic volatility; Risk aversion (psychology); Heston model; Volatility (finance); Asset allocation; Portfolio; Implied volatility; Volatility smile; Equity (law)","score_opus":0.01533196089716655,"score_gpt":0.2908718212580372,"score_spread":0.27553986036087064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416228291","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":[0.04762246,0.0004514115,0.9430836,0.0006500868,0.000065294524,0.000061915605,0.000053434887,0.000069967675,0.007941858],"genre_scores_gemma":[0.9557184,0.00054868904,0.034098983,0.00013943943,0.000076318895,0.00015729833,0.0000571619,0.000031369043,0.009172371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992706,0.00033930002,0.00002817405,0.0001222971,0.00015144324,0.00008819035],"domain_scores_gemma":[0.9991079,0.00048779932,0.00016000493,0.000042371743,0.00011868986,0.00008315526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019183069,0.0009648073,0.0012835176,0.0006274272,0.00037704653,0.0015944965,0.0013240151,0.0013222893,0.002984283],"category_scores_gemma":[0.0033649092,0.0005682736,0.0010021715,0.00042461837,0.0013213954,0.0014739078,0.0010844164,0.0012406004,0.00020874193],"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":[0.0000188911,0.000023066817,0.0002968731,0.00002087416,0.000032266278,0.00006601097,0.00003757019,0.9147953,0.00054323266,0.08014807,0.0002454641,0.003772377],"study_design_scores_gemma":[0.000009827868,0.000020793832,0.00007226363,0.0000053007507,0.000008724638,0.000008704957,0.000007468395,0.98410517,0.00011464454,0.015342629,0.0002967936,0.0000075601347],"about_ca_topic_score_codex":0.00535257,"about_ca_topic_score_gemma":0.0030529757,"teacher_disagreement_score":0.00535257,"about_ca_system_score_codex":0.0015856933,"about_ca_system_score_gemma":0.0014819885,"threshold_uncertainty_score":0.011505067},"labels":[],"label_agreement":null},{"id":"W4416394191","doi":"10.1016/j.jgo.2025.102390","title":"Predicting the Risk of Death with New Indexes in Community-dwelling Elderly (PREDIC): Development of new 5- and 10-year mortality prediction models","year":2025,"lang":"en","type":"article","venue":"Journal of Geriatric Oncology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Predictive modelling; Risk assessment; Prognostic model; MEDLINE","score_opus":0.03177242409298326,"score_gpt":0.3198694593403357,"score_spread":0.2880970352473525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416394191","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":[0.9691936,0.000552641,0.025644556,0.0004639552,0.00010325583,0.00014043621,0.002085524,0.00026393944,0.0015520491],"genre_scores_gemma":[0.981232,0.0003114131,0.01575136,0.000052817053,0.0000685633,0.00012556801,0.0018310376,0.000017614442,0.0006096934],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995084,0.00017473559,0.00006298483,0.000071347684,0.00013091447,0.00005155086],"domain_scores_gemma":[0.99708265,0.0015421635,0.00040446784,0.00015714217,0.0006084769,0.00020504763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003375446,0.00088606024,0.00066522445,0.0015228499,0.00032028515,0.001102764,0.00060261134,0.0004537206,0.00092790026],"category_scores_gemma":[0.0068469737,0.00024929264,0.0008719541,0.0006210029,0.00016625266,0.0009309549,0.0007204222,0.000956972,0.0003029662],"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":[0.00055319956,0.0005438172,0.9305392,0.00004202167,0.00033990148,0.00006175755,0.00008788247,0.019181656,0.0003251207,0.00030655222,0.0012559235,0.046763007],"study_design_scores_gemma":[0.000097907156,0.00085027167,0.38013646,0.00006469374,0.0005997564,0.00022437284,0.00021108605,0.6131516,0.0012585024,0.0020717087,0.0012811004,0.000052573818],"about_ca_topic_score_codex":0.0056571797,"about_ca_topic_score_gemma":0.008063742,"teacher_disagreement_score":0.0056571797,"about_ca_system_score_codex":0.00058681826,"about_ca_system_score_gemma":0.00088750344,"threshold_uncertainty_score":0.017851233},"labels":[],"label_agreement":null},{"id":"W4416416102","doi":"10.1016/j.insmatheco.2025.103181","title":"Mortality modeling via vitality: Model constructions and actuarial applications","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Guangdong Science and Technology Department; Research Grants Council, University Grants Committee; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Actuarial Analysis; Statistical model; Term (time); Life table","score_opus":0.025071229048074278,"score_gpt":0.2955242127839696,"score_spread":0.2704529837358953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416416102","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":[0.017591236,0.0005934843,0.9775418,0.001345111,0.0000690917,0.000017431008,0.00013479708,0.00008432281,0.0026226642],"genre_scores_gemma":[0.8418265,0.0034216198,0.14104153,0.00040753023,0.0006076423,0.00019786667,0.0003870042,0.00018282859,0.011927517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999215,0.00047306434,0.00003518764,0.000098971475,0.00010895972,0.00006882786],"domain_scores_gemma":[0.99418736,0.0043481356,0.00055649015,0.00031129995,0.0003652842,0.00023148899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044491673,0.0008552978,0.0010381052,0.0017046063,0.00078619545,0.002003256,0.0019831406,0.0016666823,0.0023520354],"category_scores_gemma":[0.013199822,0.0007100659,0.0017205038,0.0013635501,0.0021092643,0.0033731132,0.0021645175,0.0025696529,0.00025972098],"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":[0.000011981794,0.000028140854,0.0014721921,0.000030423978,0.000033607874,0.000044743858,0.00012988766,0.3388977,0.00020055006,0.65108526,0.0009493641,0.007116114],"study_design_scores_gemma":[0.0000034673863,0.000008684282,0.00014056226,0.000012495305,0.000013027107,0.000023774779,0.000016326163,0.7425785,0.000083561696,0.25644818,0.00066073344,0.000010572475],"about_ca_topic_score_codex":0.004402522,"about_ca_topic_score_gemma":0.003387072,"teacher_disagreement_score":0.0044491673,"about_ca_system_score_codex":0.0014776207,"about_ca_system_score_gemma":0.0012561171,"threshold_uncertainty_score":0.023529768},"labels":[],"label_agreement":null},{"id":"W4416427012","doi":"10.1108/978-1-64802-876-220251022","title":"Saving the World, One R at A Time!","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Victoria; University of Ottawa","funders":"","keywords":"","score_opus":0.02929460555642713,"score_gpt":0.273013974691378,"score_spread":0.24371936913495088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416427012","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":[0.0016479613,0.11762767,0.00443895,0.1933753,0.021030392,0.000029451356,0.00034645712,0.00036753487,0.66113627],"genre_scores_gemma":[0.016832966,0.058374483,0.003531773,0.04128431,0.0072260667,0.000054785694,0.00026683984,0.000303917,0.87212485],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996419,0.00012043442,0.000008441264,0.000035212142,0.00013715899,0.000056775916],"domain_scores_gemma":[0.99961346,0.00015649761,0.000024285853,0.000042164487,0.00008042896,0.00008314538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069793203,0.00077985233,0.0005675296,0.0007757816,0.001558042,0.0048453338,0.00063625304,0.0022643523,0.03509406],"category_scores_gemma":[0.0024682095,0.00024134436,0.00037591977,0.0012595083,0.0022673977,0.0069169574,0.001964567,0.0045047305,0.018872226],"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":[0.000012042534,0.000020664105,0.00013990044,0.00007179493,0.000006379927,0.000037476002,0.00062024547,0.00010609095,0.00012673977,0.13431294,0.78674877,0.07779696],"study_design_scores_gemma":[0.0000019522993,0.0000070589786,0.00018110778,0.00012245032,0.0000028696393,0.000052249154,0.00048215102,0.000048248567,0.00003232932,0.030521607,0.96854186,0.000005968306],"about_ca_topic_score_codex":0.0046490645,"about_ca_topic_score_gemma":0.010455032,"teacher_disagreement_score":0.03509406,"about_ca_system_score_codex":0.0010185839,"about_ca_system_score_gemma":0.0020446077,"threshold_uncertainty_score":0.1174013},"labels":[],"label_agreement":null},{"id":"W4416865717","doi":"10.2139/ssrn.5818202","title":"&lt;p&gt;Equitable Longevity Risk Sharing or, The Raison D’etre for a First Nations Pension Plan&lt;/p&gt;","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Pension; Life expectancy; Longevity risk; Legislation; Statutory law; Longevity; Population; Indigenous; Subsidy; Ex-ante","score_opus":0.024588727021869466,"score_gpt":0.30122297842244194,"score_spread":0.27663425140057246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416865717","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":[0.008819168,0.023885911,0.011557264,0.19579294,0.008687314,0.000120132936,0.012114238,0.001407467,0.7376155],"genre_scores_gemma":[0.25286147,0.018540263,0.009034834,0.011172964,0.0058725053,0.0002960624,0.0030268603,0.0010713297,0.69812363],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99949956,0.00015008125,0.000025899006,0.00008837189,0.00017866922,0.00005744154],"domain_scores_gemma":[0.99888617,0.0005659169,0.00013670065,0.00014974947,0.00015910616,0.00010239948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014498187,0.00044379788,0.00071928516,0.0008551299,0.0014316706,0.004078843,0.0005218835,0.0044822292,0.106100775],"category_scores_gemma":[0.0076378784,0.000283241,0.00036934201,0.0016268061,0.0007551357,0.0027801448,0.0009310951,0.002264264,0.016876372],"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":[0.00005431184,0.000015712121,0.00036453214,0.00007987618,0.0000122960855,0.0000757838,0.00009878595,0.00030504545,0.00012818034,0.24909398,0.6942558,0.05551557],"study_design_scores_gemma":[0.0000560834,0.00003157033,0.00373202,0.00018379939,0.000017882134,0.0001340173,0.00014207468,0.0022409274,0.00065849914,0.20243159,0.7903427,0.000028908795],"about_ca_topic_score_codex":0.009158962,"about_ca_topic_score_gemma":0.011216884,"teacher_disagreement_score":0.106100775,"about_ca_system_score_codex":0.0023637817,"about_ca_system_score_gemma":0.0017144148,"threshold_uncertainty_score":0.35494244},"labels":[],"label_agreement":null},{"id":"W4416916868","doi":"10.3390/risks13120233","title":"Optimal Investment Considerations for a Single Cohort Life Insurance Portfolio","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life annuity; Volatility (finance); Portfolio; Sharpe ratio; Stochastic volatility; Life insurance; Stock (firearms); Investment strategy; Present value","score_opus":0.06762674807421931,"score_gpt":0.35546046077406823,"score_spread":0.2878337126998489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416916868","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":[0.56660134,0.0024159867,0.4038254,0.0039763236,0.00008875259,0.00019884898,0.0003575853,0.0000933164,0.022442501],"genre_scores_gemma":[0.9612609,0.0011238764,0.028270045,0.00016837989,0.00006533129,0.000112754526,0.00015721215,0.00003078985,0.008810717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964714,0.00012855469,0.000016125347,0.000067912515,0.000056792513,0.0000834768],"domain_scores_gemma":[0.999116,0.000487727,0.00013407478,0.000034389443,0.000071459996,0.00015635412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016596888,0.0007219267,0.00095957454,0.0007380828,0.00039627324,0.0015158693,0.0008732212,0.0014207792,0.0040424503],"category_scores_gemma":[0.0049463827,0.0007160116,0.00075445167,0.0003326877,0.000644199,0.0015999159,0.00096652773,0.0011104711,0.00025134132],"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":[0.00013228209,0.00010704993,0.0034241981,0.00006760613,0.00008818149,0.00028557592,0.0000768302,0.91129273,0.0023177094,0.07058836,0.0008565677,0.010762933],"study_design_scores_gemma":[0.000034312066,0.00010623703,0.0012237262,0.000028029463,0.00003805461,0.00009096767,0.00004299386,0.96922004,0.00043168166,0.028115395,0.0006539674,0.000014549429],"about_ca_topic_score_codex":0.0030524866,"about_ca_topic_score_gemma":0.0020438586,"teacher_disagreement_score":0.0040424503,"about_ca_system_score_codex":0.0015320531,"about_ca_system_score_gemma":0.0015502217,"threshold_uncertainty_score":0.01352334},"labels":[],"label_agreement":null},{"id":"W4416920181","doi":"10.48550/arxiv.2504.19904","title":"Interpretable additive model for analyzing high-dimensional functional time series","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Functional principal component analysis; Bivariate analysis; Series (stratigraphy); Time series; Smoothing; Additive model; Generalized additive model; Identification (biology)","score_opus":0.034932803699505646,"score_gpt":0.2966707625938105,"score_spread":0.26173795889430485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416920181","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":[0.010045512,0.000115386574,0.98836297,0.0003014651,0.000036105655,0.00001877223,0.00017158332,0.00015576511,0.0007925715],"genre_scores_gemma":[0.77234447,0.000894282,0.21734428,0.00037929855,0.00030271598,0.0004413073,0.0010638422,0.000154751,0.0070749638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858356,0.00079440215,0.00007315999,0.00027879808,0.0001844976,0.00008559268],"domain_scores_gemma":[0.993634,0.0045719426,0.0007244924,0.0005728409,0.00039339814,0.000103356055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00397107,0.0014220454,0.0010722501,0.0016869603,0.0004113147,0.0018125586,0.0019249199,0.001852464,0.003289207],"category_scores_gemma":[0.014442645,0.00054906745,0.0017268709,0.0015711832,0.0014941293,0.0024124174,0.0015829967,0.0029485715,0.0005846053],"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":[0.0000509966,0.00008202465,0.0043702405,0.00010846145,0.00017429955,0.00023197713,0.00027058923,0.6569324,0.0014072271,0.29595706,0.0017538614,0.038660876],"study_design_scores_gemma":[0.000004663652,0.000021187929,0.0005210307,0.0000098911205,0.000016639226,0.000027656179,0.000017929664,0.923816,0.0001292196,0.07473787,0.0006859343,0.00001206789],"about_ca_topic_score_codex":0.003682233,"about_ca_topic_score_gemma":0.002946401,"teacher_disagreement_score":0.00397107,"about_ca_system_score_codex":0.0008518749,"about_ca_system_score_gemma":0.0007221434,"threshold_uncertainty_score":0.02100128},"labels":[],"label_agreement":null},{"id":"W49622706","doi":"10.1017/cbo9780511542428.011","title":"Markov chain Monte Carlo estimation of hazard model parameters in paleodemography","year":2002,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":40,"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 Manitoba","funders":"","keywords":"Markov chain Monte Carlo; Categorical variable; Gibbs sampling; Computer science; Monte Carlo method; Statistics; Hazard; Markov chain; Sample (material); Expectation–maximization algorithm; Econometrics; Mathematics; Maximum likelihood; Bayesian probability","score_opus":0.02738563763676673,"score_gpt":0.22790598471288848,"score_spread":0.20052034707612174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W49622706","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":[0.0057031433,0.00047779366,0.9923233,0.00032173842,0.000031521093,0.00004253195,0.00009128457,0.00014645974,0.0008622135],"genre_scores_gemma":[0.25606298,0.0020118416,0.7336603,0.0003127678,0.00021723477,0.0007999879,0.00085284124,0.00022912203,0.0058529265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954537,0.003582949,0.00010263202,0.00038264767,0.0003692271,0.00010888155],"domain_scores_gemma":[0.97306156,0.024959452,0.0006027867,0.0007543833,0.00048751957,0.00013425929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012566605,0.00076849427,0.0015071026,0.0016512901,0.0008284047,0.0017812423,0.0022420422,0.0019924773,0.003867166],"category_scores_gemma":[0.043785073,0.0013117171,0.0009744773,0.0020986681,0.0025998806,0.0028041813,0.0015541842,0.003683367,0.0007743642],"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":[0.00008157509,0.000062120045,0.0038069533,0.00015667279,0.000112479946,0.00013714805,0.00047248686,0.5249044,0.00031975456,0.41950637,0.0024040672,0.048036028],"study_design_scores_gemma":[0.000024033068,0.000016118998,0.0005961872,0.000060890736,0.000016312099,0.000041844363,0.000038524176,0.76609516,0.0001848866,0.23043506,0.002458277,0.000032676246],"about_ca_topic_score_codex":0.01356705,"about_ca_topic_score_gemma":0.013826779,"teacher_disagreement_score":0.01356705,"about_ca_system_score_codex":0.0019310758,"about_ca_system_score_gemma":0.0021202213,"threshold_uncertainty_score":0.06645936},"labels":[],"label_agreement":null},{"id":"W55367362","doi":"10.1007/978-3-642-11520-2_4","title":"The emergence of supercentenarians in Canada","year":2010,"lang":"en","type":"book-chapter","venue":"Demographic research monographs","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Milestone; Demography; Suspect; Population; Geography; Genealogy; History; Political science; Cartography; Sociology; Law","score_opus":0.061230725595900426,"score_gpt":0.34813502767278004,"score_spread":0.2869043020768796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W55367362","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":[0.89128315,0.0063088974,0.0005681975,0.00921831,0.0002000037,0.000057970734,0.0013953716,0.00008553747,0.09088261],"genre_scores_gemma":[0.9605731,0.0023809168,0.0004486945,0.0008652579,0.000024699826,0.000012126548,0.00035013637,0.000034596094,0.03531039],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99876875,0.00006078242,0.000018766055,0.00012972075,0.00022807793,0.00079372927],"domain_scores_gemma":[0.997959,0.00014830627,0.00014033068,0.0000680275,0.00084131665,0.00084298477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060347206,0.00019387125,0.00035503984,0.0021191672,0.011588598,0.0033696874,0.00134131,0.00079455395,0.011053113],"category_scores_gemma":[0.0019596675,0.00032375086,0.00028691755,0.003968336,0.0028504396,0.000876699,0.0018765372,0.001603883,0.00055685715],"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":[0.00045377007,0.00013071805,0.2652725,0.0002164571,0.000081934,0.0025681374,0.10399119,0.0011556942,0.0017263863,0.19804105,0.10446101,0.3219012],"study_design_scores_gemma":[0.000032256004,0.000060153572,0.41934687,0.0002495116,0.000032457658,0.00049473543,0.07022742,0.0016482637,0.00057805306,0.003968656,0.5032543,0.00010731155],"about_ca_topic_score_codex":0.99704915,"about_ca_topic_score_gemma":0.9991904,"teacher_disagreement_score":0.055744477,"about_ca_system_score_codex":0.055744477,"about_ca_system_score_gemma":0.08003384,"threshold_uncertainty_score":0.40445638},"labels":[],"label_agreement":null},{"id":"W57577915","doi":"10.2139/ssrn.1816632","title":"Yaari's Lifecycle Model in the 21st Century: Consumption Under a Stochastic Force of Mortality","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Consumption (sociology); Economics; Sociology; Social science","score_opus":0.04659157668186507,"score_gpt":0.3081419811784456,"score_spread":0.2615504044965805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W57577915","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":[0.4510203,0.0061574215,0.39989603,0.052568547,0.0005025972,0.00020066266,0.0050283647,0.0005172395,0.08410876],"genre_scores_gemma":[0.91300374,0.005243999,0.011317558,0.0009086613,0.00045701445,0.00018112865,0.0008784688,0.00009024872,0.0679192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931073,0.00028667736,0.00003347667,0.00013150368,0.000089056746,0.0001484781],"domain_scores_gemma":[0.99752337,0.001456416,0.00038782664,0.00015608716,0.00018157628,0.00029472125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025038277,0.00072915683,0.0017487602,0.00096857967,0.0009851997,0.003576805,0.002335309,0.0043630325,0.009546455],"category_scores_gemma":[0.006592641,0.00058220234,0.0013918072,0.0020204566,0.0022235275,0.0044406364,0.0017322346,0.0031433932,0.0010524386],"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":[0.00011444143,0.00008633139,0.0040341974,0.000077750614,0.00007025503,0.00023839247,0.0005523357,0.116814196,0.00020951447,0.8662071,0.0039949277,0.007600503],"study_design_scores_gemma":[0.00013129451,0.000115517454,0.0031663906,0.000058127112,0.000079538506,0.00019761246,0.00041820062,0.4091901,0.0000915528,0.57947516,0.007006649,0.00006993037],"about_ca_topic_score_codex":0.016346613,"about_ca_topic_score_gemma":0.01285177,"teacher_disagreement_score":0.016346613,"about_ca_system_score_codex":0.0021895128,"about_ca_system_score_gemma":0.0017992064,"threshold_uncertainty_score":0.03250289},"labels":[],"label_agreement":null},{"id":"W614538908","doi":"10.1017/cbo9780511807336","title":"Strategic Financial Planning over the Lifecycle: A Conceptual Approach to Personal Risk Management","year":2012,"lang":"en","type":"book","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"York University","funders":"","keywords":"Diversification (marketing strategy); Life insurance; Actuarial science; Debt; Portfolio; Balance sheet; Risk management; Economics; Personal consumption expenditures price index; Business; Finance; Marketing; Personal income; Macroeconomics","score_opus":0.04510227734986164,"score_gpt":0.2938906773534999,"score_spread":0.24878840000363825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W614538908","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":[0.014195206,0.01926666,0.28702724,0.015700601,0.0007032804,0.00012119312,0.00020986836,0.00026232094,0.6625136],"genre_scores_gemma":[0.6261811,0.031443045,0.10721891,0.001960663,0.00073384435,0.00044293067,0.000372926,0.00024346079,0.23140311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99956864,0.00017250597,0.000015369396,0.000048602127,0.00013860321,0.000056335004],"domain_scores_gemma":[0.99955994,0.00021652473,0.000047681668,0.00003807185,0.000050859686,0.00008686173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087123615,0.00050824974,0.00025896114,0.0006693189,0.0011355387,0.0050233565,0.0008010536,0.0012682696,0.0079360455],"category_scores_gemma":[0.0012995376,0.00029850067,0.0003922887,0.0013328824,0.0036821158,0.0041365013,0.0013810345,0.0023780083,0.0011094507],"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":[0.000001546769,0.000006788787,0.00008647832,0.000019254854,0.00000189245,0.000027429167,0.00053935876,0.0014201946,0.00003631841,0.9796878,0.0060006445,0.012172334],"study_design_scores_gemma":[0.0000030156532,0.000011948634,0.00022046457,0.00010460594,0.000003950728,0.00007126298,0.00060671667,0.0045138793,0.000061986015,0.8676846,0.12670968,0.000007792465],"about_ca_topic_score_codex":0.0023638997,"about_ca_topic_score_gemma":0.0032291305,"teacher_disagreement_score":0.0079360455,"about_ca_system_score_codex":0.0027662972,"about_ca_system_score_gemma":0.002800514,"threshold_uncertainty_score":0.026548684},"labels":[],"label_agreement":null},{"id":"W630834592","doi":"10.1007/s00362-015-0697-8","title":"Some results on the relative ordering of two frailty models","year":2015,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":24,"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 Alberta","funders":"Deanship of Scientific Research, King Saud University; Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Statistics; Variable (mathematics); Hazard; Hazard ratio; Econometrics; Mathematics; Gerontology; Computer science; Medicine; Algorithm; Confidence interval; Chemistry","score_opus":0.09574594065934255,"score_gpt":0.3560232538766365,"score_spread":0.26027731321729397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W630834592","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":[0.12329571,0.0064677275,0.8256453,0.007324982,0.0007393998,0.00010838316,0.000817395,0.00034439765,0.035256617],"genre_scores_gemma":[0.700459,0.008913037,0.26464954,0.0026724518,0.0032831286,0.0003622609,0.0026606002,0.0005516792,0.016448317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.991388,0.0050001116,0.00052079826,0.0008658381,0.0016576328,0.00056753325],"domain_scores_gemma":[0.8191162,0.16096282,0.006207165,0.006751775,0.0039024602,0.0030595902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027009614,0.0015118258,0.0020443664,0.005146838,0.0019649558,0.004459213,0.0034179483,0.0024441737,0.013059632],"category_scores_gemma":[0.110854365,0.0011488463,0.0034569765,0.0061573177,0.004440175,0.008171739,0.0031161637,0.00751191,0.00066922285],"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":[0.0002157738,0.00012113605,0.00446673,0.00028871497,0.00012697517,0.00026551593,0.00043747298,0.017000435,0.0006260316,0.951423,0.002759073,0.022269137],"study_design_scores_gemma":[0.000039728817,0.000072704075,0.0013696839,0.0000699814,0.00006692647,0.00011581704,0.00007976045,0.048116013,0.00029532643,0.947788,0.0019536272,0.000032562813],"about_ca_topic_score_codex":0.0024624893,"about_ca_topic_score_gemma":0.0018933887,"teacher_disagreement_score":0.027009614,"about_ca_system_score_codex":0.0030203306,"about_ca_system_score_gemma":0.0019023493,"threshold_uncertainty_score":0.14284223},"labels":[],"label_agreement":null},{"id":"W6885790614","doi":"10.1371/journal.pone.0246086.s002","title":"Average annual population in Ontario aged ≥65 years by characteristic, 2002–2016.","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Life expectancy; Census; Population ageing; Duration (music)","score_opus":0.02100528383366637,"score_gpt":0.2661880303571505,"score_spread":0.24518274652348412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6885790614","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":[0.27278236,0.0038320452,0.00040379295,0.0020529386,0.0001694143,0.00013675082,0.69430894,0.00027266034,0.026040995],"genre_scores_gemma":[0.797894,0.0041953926,0.0006952952,0.0006151817,0.00009287686,0.00024801152,0.169772,0.000072084804,0.026415244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995809,0.000023163931,0.000047419464,0.00006827804,0.0001522383,0.00012793309],"domain_scores_gemma":[0.99838614,0.00006768069,0.0003661136,0.000045276713,0.00079968624,0.00033513465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002561324,0.00030216397,0.00036462775,0.0012798754,0.0010693698,0.00069284637,0.00092132913,0.00030165247,0.008032728],"category_scores_gemma":[0.002371946,0.00027075343,0.0006011787,0.0037029772,0.00029940245,0.0004407136,0.0006700905,0.0004517779,0.0010059999],"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":[0.000320431,0.00005174839,0.703332,0.0010523334,0.00027065517,0.00025385604,0.0012585969,0.001061361,0.00037337805,0.00092568924,0.2708021,0.020297877],"study_design_scores_gemma":[0.000037875896,0.000016015976,0.96914613,0.00017287557,0.0000629475,0.000114821865,0.00077813416,0.0003483418,0.000047460875,0.00007564925,0.029183965,0.000015858865],"about_ca_topic_score_codex":0.98690516,"about_ca_topic_score_gemma":0.9941573,"teacher_disagreement_score":0.013094842,"about_ca_system_score_codex":0.011716734,"about_ca_system_score_gemma":0.019563744,"threshold_uncertainty_score":0.0850113},"labels":[],"label_agreement":null},{"id":"W6892634912","doi":"10.5281/zenodo.10535103","title":"Can incorporating parity information improve the reliability of fertility projections? Insights from a Bayesian generalized additive model approach","year":2024,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"UK Research and Innovation","keywords":"Fertility; Bayesian probability; Parity (physics); Generalized additive model; Total fertility rate; Population; Covariate; Population model","score_opus":0.03163762232064737,"score_gpt":0.2691747580649061,"score_spread":0.23753713574425872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892634912","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":[0.22666936,0.0020791846,0.7459675,0.010856913,0.0003100213,0.000095896525,0.0020659093,0.0007206851,0.011234559],"genre_scores_gemma":[0.94101554,0.0009103501,0.05490986,0.00036049797,0.00015238715,0.000051466603,0.0008102476,0.00013372433,0.0016559984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773777,0.0015645331,0.00008315241,0.00027099083,0.00018386343,0.00015971894],"domain_scores_gemma":[0.98042434,0.015822805,0.0011411955,0.0011207077,0.0011982607,0.0002927349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010147257,0.0007326964,0.0012492315,0.0012731482,0.00052935357,0.0021379446,0.0019685267,0.0017140099,0.003432839],"category_scores_gemma":[0.059171524,0.00088157004,0.0012205354,0.0011325037,0.00092715834,0.0030288512,0.0018849309,0.0017613155,0.00049337005],"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":[0.00012515535,0.000037986727,0.018107064,0.00012030473,0.00018810022,0.00013500037,0.0003201918,0.862315,0.00028052978,0.066850126,0.0030486817,0.04847192],"study_design_scores_gemma":[0.000024221126,0.000025670786,0.003005501,0.00007122944,0.00004792816,0.000040783267,0.0000648102,0.90825826,0.00013877708,0.08664828,0.0016325283,0.00004204963],"about_ca_topic_score_codex":0.04296069,"about_ca_topic_score_gemma":0.03121413,"teacher_disagreement_score":0.04296069,"about_ca_system_score_codex":0.0012928711,"about_ca_system_score_gemma":0.0017739006,"threshold_uncertainty_score":0.085421264},"labels":[],"label_agreement":null},{"id":"W6899079015","doi":"10.58079/ovm6","title":"Actuarial Science Workshop on 2023 SSC Meeting in Ottawa","year":2023,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Actuarial Analysis; Government (linguistics); Actuary","score_opus":0.030006019388171334,"score_gpt":0.31610654267906596,"score_spread":0.28610052329089464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6899079015","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":[0.009963058,0.04615308,0.0066509666,0.271326,0.11893247,0.00081074046,0.025802163,0.0008455198,0.51951593],"genre_scores_gemma":[0.01568326,0.0060313116,0.0016137172,0.006190359,0.0045406097,0.00014345963,0.00345949,0.00015727345,0.9621805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99548584,0.0006701357,0.0002336122,0.00045816976,0.002158176,0.0009940253],"domain_scores_gemma":[0.9932561,0.0005854102,0.00021161186,0.00034991992,0.0037207992,0.0018761777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063109873,0.0014328934,0.0008508543,0.0020283503,0.0060649165,0.0069237542,0.0020686286,0.0042094253,0.15593736],"category_scores_gemma":[0.0054431357,0.00078524114,0.0016304392,0.0021824653,0.002119126,0.001666925,0.0028968677,0.0044032726,0.05560381],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009332262,0.000028107646,0.0005099133,0.000036970345,0.000018039073,0.00007334342,0.00008142554,0.0002631177,0.00017735649,0.0028794843,0.97974914,0.016089754],"study_design_scores_gemma":[0.00001079072,0.000019114836,0.0015615782,0.00007122079,0.0000065500603,0.000017667411,0.00014093208,0.00012702776,0.00010605898,0.0007267448,0.99719715,0.000015172765],"about_ca_topic_score_codex":0.47809458,"about_ca_topic_score_gemma":0.7794045,"teacher_disagreement_score":0.5219054,"about_ca_system_score_codex":0.02252067,"about_ca_system_score_gemma":0.041248996,"threshold_uncertainty_score":0.9506235},"labels":[],"label_agreement":null},{"id":"W6901502440","doi":"10.60692/gk6w3-rzz18","title":"The burden of premature mortality from cardiovascular diseases: A systematic review of years of life lost","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Insurance, Mortality, Demography, Risk Management","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":"Years of potential life lost; Mortality rate; Population; Life expectancy; Disease; Cause of death; Burden of disease; Quality of life (healthcare)","score_opus":0.024700273145584914,"score_gpt":0.2474257115190437,"score_spread":0.22272543837345876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901502440","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":[0.00068494544,0.9982322,0.00010085704,0.0001781893,0.0000775344,0.00015924145,0.00040960897,0.0000059773743,0.00015149867],"genre_scores_gemma":[0.011846647,0.9861151,0.0005458122,0.00041138625,0.00007618514,0.0006176858,0.000294504,0.000005570489,0.00008718532],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9872866,0.0049583176,0.004418076,0.00081861136,0.002231027,0.00028736837],"domain_scores_gemma":[0.96448696,0.025782922,0.0064285104,0.0004979587,0.0024440256,0.00035953778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012158521,0.0014882123,0.008868039,0.012676367,0.0006848136,0.00322399,0.0019252902,0.0017496645,0.004186633],"category_scores_gemma":[0.0530177,0.0011012366,0.0109581975,0.012257204,0.000884863,0.0030214349,0.0019754628,0.0017918515,0.00032723782],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017471428,0.000009875835,0.00069032505,0.96333784,0.011390396,0.00006187897,0.00018065388,0.00007476176,0.000064215776,0.00016058239,0.0010950245,0.02275979],"study_design_scores_gemma":[0.00022909581,0.00019704331,0.005382307,0.9004558,0.07640623,0.0003307657,0.0002838089,0.00007667742,0.00008390264,0.00036810114,0.016144995,0.000041294523],"about_ca_topic_score_codex":0.007885669,"about_ca_topic_score_gemma":0.025080804,"teacher_disagreement_score":0.012676367,"about_ca_system_score_codex":0.004423682,"about_ca_system_score_gemma":0.010222853,"threshold_uncertainty_score":0.06430119},"labels":[],"label_agreement":null},{"id":"W6901934352","doi":"10.6084/m9.figshare.21435534","title":"Additional file 1 of The temporal trend of cause-specific mortality: comparing Estonia and Lithuania, 2001 – 2019","year":2022,"lang":"en","type":"article","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Data file; Term (time); Trend analysis; Table (database); Set (abstract data type)","score_opus":0.08100690743845286,"score_gpt":0.3233019781946647,"score_spread":0.24229507075621187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901934352","genre_codex":"dataset","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":[0.00030794143,0.000012222896,0.0000334874,0.000040946354,0.000007663824,0.000024822246,0.99905723,0.000030658193,0.00048514068],"genre_scores_gemma":[0.012511155,0.00015319737,0.000626516,0.00014608732,0.00005112521,0.00078865304,0.9793559,0.00009243094,0.006275077],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9994524,0.00010042753,0.00012960938,0.000110294706,0.00010573851,0.00010161076],"domain_scores_gemma":[0.9927626,0.004175923,0.00094144244,0.00031403283,0.0015231455,0.0002828608],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001056859,0.00054494775,0.00078605383,0.002282486,0.00050281384,0.0009720464,0.0012070736,0.00064450415,0.68729705],"category_scores_gemma":[0.014619365,0.00032191005,0.0005369515,0.005435974,0.00015131035,0.0010934747,0.0008004313,0.000607587,0.053473208],"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":[0.00029653223,0.000097748,0.009401966,0.0022389235,0.00006821093,0.00008500458,0.00010126904,0.00058196107,0.00004685141,0.0009778609,0.978789,0.0073145945],"study_design_scores_gemma":[0.005212199,0.00046500168,0.198972,0.006148846,0.000374478,0.0007525994,0.0017966082,0.002434632,0.0006915605,0.0055046645,0.7774814,0.00016602899],"about_ca_topic_score_codex":0.024615807,"about_ca_topic_score_gemma":0.022976851,"teacher_disagreement_score":0.68729705,"about_ca_system_score_codex":0.0009485269,"about_ca_system_score_gemma":0.0022167473,"threshold_uncertainty_score":0.44603276},"labels":[],"label_agreement":null},{"id":"W6902278005","doi":"10.6084/m9.figshare.28529933","title":"Additional file 1 of Development and internal validation of a new life expectancy estimator for multimorbid older adults","year":2025,"lang":"en","type":"article","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Life expectancy; Estimator; Missing data; Expectancy theory; Table (database); Risk assessment; Life table","score_opus":0.030252704000010365,"score_gpt":0.3367334770911957,"score_spread":0.3064807730911854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902278005","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":[0.001825515,0.000023800632,0.0034894398,0.00010836064,0.000053181426,0.00074438116,0.99141335,0.0006695084,0.0016724771],"genre_scores_gemma":[0.02372701,0.000101420424,0.028324377,0.0005029865,0.00012468384,0.018075299,0.91754454,0.001428836,0.010170859],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982476,0.00063006516,0.0003297443,0.00032646323,0.0003730351,0.00009302302],"domain_scores_gemma":[0.9420949,0.046462107,0.0018537752,0.002388998,0.006648141,0.00055211846],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006413914,0.0010619593,0.00083719403,0.0022878305,0.00083744904,0.0012686332,0.0016521526,0.0009315628,0.71887994],"category_scores_gemma":[0.08491757,0.00053375086,0.0007437227,0.0025288598,0.00023347106,0.0010119895,0.0012137624,0.00080349535,0.10928068],"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":[0.0005375637,0.00027620074,0.009486496,0.0012686364,0.00007974238,0.00006331538,0.00014439343,0.0007869031,0.00016716418,0.0008333878,0.95304364,0.033312596],"study_design_scores_gemma":[0.00995768,0.0012705409,0.15643798,0.0043761865,0.0006006111,0.0010904939,0.0009438285,0.01252387,0.0032812164,0.014476198,0.7946337,0.0004076003],"about_ca_topic_score_codex":0.005783319,"about_ca_topic_score_gemma":0.011166958,"teacher_disagreement_score":0.71887994,"about_ca_system_score_codex":0.0009919698,"about_ca_system_score_gemma":0.0019391029,"threshold_uncertainty_score":0.40098363},"labels":[],"label_agreement":null},{"id":"W6920520721","doi":"10.6084/m9.figshare.13088990.v1","title":"The GLM framework of the Lee–Carter model: a multi-country study","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","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":"Deviance (statistics); Generalized linear model; Negative binomial distribution; Log-linear model; Quasi-likelihood; Statistical model; Linear model; Count data","score_opus":0.059721289735365654,"score_gpt":0.3285793857727753,"score_spread":0.2688580960374096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920520721","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":[0.8931159,0.0018831312,0.09377473,0.0011737155,0.00011607173,0.00029548004,0.0023158337,0.00016457238,0.007160547],"genre_scores_gemma":[0.9749203,0.0005853075,0.021649282,0.000120595934,0.000044944132,0.00023229251,0.0011530622,0.0000626275,0.0012315338],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99256164,0.0063639656,0.00012153835,0.0004354187,0.00022382996,0.00029356],"domain_scores_gemma":[0.98354787,0.012764081,0.0014714673,0.0011825223,0.0007752716,0.00025882994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010971823,0.00059187924,0.0012765336,0.0024716284,0.0006808281,0.0012522157,0.0011606285,0.0008465508,0.0050652823],"category_scores_gemma":[0.01927083,0.00025707384,0.002144354,0.0044006435,0.0006708225,0.001313166,0.0017139773,0.0017386747,0.00050079223],"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":[0.001247584,0.00062918325,0.5490722,0.0006149471,0.0041783075,0.0025375609,0.0029476138,0.28454584,0.0006634883,0.06427607,0.010733464,0.07855373],"study_design_scores_gemma":[0.0002235934,0.0015673118,0.24853818,0.00036668175,0.0013812121,0.0012340178,0.0061154934,0.6905943,0.00050334353,0.03337419,0.015839769,0.00026186174],"about_ca_topic_score_codex":0.021148248,"about_ca_topic_score_gemma":0.013164316,"teacher_disagreement_score":0.021148248,"about_ca_system_score_codex":0.0009319795,"about_ca_system_score_gemma":0.00079565035,"threshold_uncertainty_score":0.0580253},"labels":[],"label_agreement":null},{"id":"W6923392528","doi":"10.1371/journal.pone.0279275.s001","title":"Estimated number of children aged 2–12 years old by geographic region and year.","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Statistical analysis; Age groups; Data collection; Incidence (geometry)","score_opus":0.03356190705020157,"score_gpt":0.3042486319172316,"score_spread":0.27068672486703005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923392528","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":[0.013896741,0.0012712781,0.0003565634,0.00046109606,0.0002267899,0.00018746327,0.9765414,0.00020274814,0.0068559744],"genre_scores_gemma":[0.13756377,0.0071093743,0.0021251265,0.00084515533,0.0001428853,0.0012669093,0.8205488,0.00019024401,0.030207753],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99940395,0.000051948362,0.00006713763,0.00007403827,0.00020060995,0.0002021957],"domain_scores_gemma":[0.9979977,0.00015531466,0.00024144967,0.00006338458,0.0013412558,0.0002009166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007238653,0.0008327485,0.00066020957,0.0041445624,0.0005272572,0.0006602429,0.0019749703,0.00041455514,0.040658586],"category_scores_gemma":[0.0039277025,0.00054447656,0.0011179487,0.0050325,0.00026127076,0.0008273504,0.0007594357,0.0013914403,0.008248645],"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":[0.00014315722,0.000036554888,0.102194935,0.001139143,0.00013937912,0.000109871886,0.00034704548,0.0010144461,0.00014471934,0.0012058604,0.8776828,0.015841974],"study_design_scores_gemma":[0.0001763443,0.000038106227,0.69933265,0.0014555206,0.00025271962,0.00032271064,0.0028626497,0.0012728992,0.00046463197,0.00042760614,0.29333174,0.00006233567],"about_ca_topic_score_codex":0.9016923,"about_ca_topic_score_gemma":0.8969134,"teacher_disagreement_score":0.9016923,"about_ca_system_score_codex":0.006226784,"about_ca_system_score_gemma":0.008417469,"threshold_uncertainty_score":0.19777334},"labels":[],"label_agreement":null},{"id":"W6925221105","doi":"10.17605/osf.io/xrfyp","title":"OPTIKNEE Consensus","year":2020,"lang":"en","type":"other","venue":"Open Science Framework","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Consensus conference; Osteoarthritis; Rehabilitation; MEDLINE; Scientific consensus; Systematic review","score_opus":0.03977131286566293,"score_gpt":0.38229379907144634,"score_spread":0.3425224862057834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925221105","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":[0.003526095,0.042574044,0.112975135,0.19077359,0.16361283,0.021580894,0.089246064,0.005717667,0.36999357],"genre_scores_gemma":[0.05087432,0.050887205,0.21900648,0.08903153,0.024748264,0.10357382,0.08630728,0.008615266,0.36695582],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9412469,0.02318849,0.014839012,0.005510497,0.013248035,0.0019669975],"domain_scores_gemma":[0.844697,0.047415793,0.010715581,0.019315833,0.07109073,0.006765067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05593569,0.0009228772,0.0020694635,0.008618839,0.002895214,0.010260231,0.004378319,0.005897411,0.24367224],"category_scores_gemma":[0.22779198,0.0010255006,0.0033909772,0.0070376634,0.002125927,0.0045230333,0.006790479,0.007870803,0.062124938],"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":[0.00036436337,0.000032996042,0.0002931097,0.011761261,0.00020673334,0.00016739748,0.0006511782,0.00036209667,0.00038629427,0.024027934,0.82657826,0.13516833],"study_design_scores_gemma":[0.0001972977,0.000020331878,0.00030744183,0.009947416,0.000104536695,0.00017681016,0.00017846849,0.00015878872,0.00019695895,0.01641018,0.9722711,0.00003053737],"about_ca_topic_score_codex":0.0023200004,"about_ca_topic_score_gemma":0.0036338952,"teacher_disagreement_score":0.24367224,"about_ca_system_score_codex":0.004810118,"about_ca_system_score_gemma":0.03174828,"threshold_uncertainty_score":0.8151648},"labels":[],"label_agreement":null},{"id":"W6928927651","doi":"10.4224/21275411","title":"Learning and performance support systems: personal learning record: user studies white paper","year":2015,"lang":"en","type":"report","venue":"NPARC","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Context (archaeology); Learning analytics; Experiential learning; Learning Management; Work (physics); Active learning (machine learning); Learning design; Point (geometry); Online learning","score_opus":0.05639547408472852,"score_gpt":0.3425176832298052,"score_spread":0.2861222091450767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6928927651","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":[0.081081286,0.0036895834,0.040186007,0.029583804,0.001698038,0.0023453806,0.37597695,0.061391316,0.40404767],"genre_scores_gemma":[0.20005913,0.0042096293,0.03219174,0.002944526,0.0009535616,0.001699645,0.35213995,0.0094241435,0.3963777],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9964265,0.0008276637,0.0003009518,0.00028491425,0.0019053409,0.00025457115],"domain_scores_gemma":[0.9699442,0.010388286,0.00069578266,0.0069873575,0.009077746,0.0029066454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009463311,0.00052210985,0.0008502071,0.002259042,0.0013994296,0.0032577082,0.0012697865,0.001209149,0.07411153],"category_scores_gemma":[0.02255379,0.00036310722,0.00041526568,0.004540524,0.00031413953,0.0037544433,0.0021616318,0.0011350249,0.0609394],"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":[0.00021973629,0.0003264714,0.0054615643,0.0002268923,0.000018233492,0.000065779284,0.00039640864,0.00024083513,0.00054162904,0.0014738997,0.8738025,0.11722604],"study_design_scores_gemma":[0.00006864662,0.00014449407,0.016270109,0.00014092655,0.000025850435,0.00012698548,0.00033359395,0.0027602369,0.0018938519,0.0006737505,0.97751325,0.00004834096],"about_ca_topic_score_codex":0.016853793,"about_ca_topic_score_gemma":0.024207445,"teacher_disagreement_score":0.07411153,"about_ca_system_score_codex":0.0012214308,"about_ca_system_score_gemma":0.0025741581,"threshold_uncertainty_score":0.24792773},"labels":[],"label_agreement":null},{"id":"W6929276810","doi":"10.48550/arxiv.1202.5684","title":"Fractional Order Modeling of a PHWR Under Step-Back Condition and Control of Its Global Power with a Robust PIλDμ Controller","year":2012,"lang":"en","type":"preprint","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","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":"Control theory (sociology); Robustness (evolution); Parametric statistics; PID controller; Control system; Robust control; Estimator; Step response","score_opus":0.04709351770990543,"score_gpt":0.30474668271749966,"score_spread":0.2576531650075942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929276810","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":[0.10700308,0.00013240936,0.8873876,0.00010847631,0.00003941079,0.0000361471,0.000057629404,0.0005637854,0.004671458],"genre_scores_gemma":[0.9813989,0.000066920846,0.016507832,0.000017365886,0.000007656897,0.00003289952,0.000030203199,0.000012082623,0.0019261346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998512,0.000021549882,0.000006160247,0.00004325878,0.00005964686,0.000018114037],"domain_scores_gemma":[0.99986994,0.00004228582,0.00003746209,0.00001552154,0.00003046143,0.0000043253417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002464802,0.00043362568,0.0002842086,0.00017494573,0.00022050246,0.0005076736,0.0005682492,0.00036565066,0.00076969404],"category_scores_gemma":[0.0005179879,0.00014893318,0.00033678228,0.00012827787,0.00030120305,0.00024509008,0.00027515265,0.00041419486,0.00013174416],"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":[0.00013091431,0.000043116645,0.0012141826,0.0001080737,0.000034891356,0.00012474263,0.0001503847,0.882869,0.06974058,0.004230133,0.00033994336,0.041013993],"study_design_scores_gemma":[0.0000054271195,0.00004792653,0.00049569656,0.0000022547522,0.000008655903,0.000019316481,0.000008571776,0.99237084,0.006153892,0.00036501704,0.0005169852,0.000005464335],"about_ca_topic_score_codex":0.0052885734,"about_ca_topic_score_gemma":0.0034297213,"teacher_disagreement_score":0.0052885734,"about_ca_system_score_codex":0.0003776192,"about_ca_system_score_gemma":0.00036499157,"threshold_uncertainty_score":0.010515571},"labels":[],"label_agreement":null},{"id":"W6929430723","doi":"10.48448/mg6h-a517","title":"Counter Turing Test ($CT^2$): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index ($ADI_{hi}$)","year":2024,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Hindi; Ranking (information retrieval); Index (typography); Dimension (graph theory); Rank (graph theory); Code (set theory); Test (biology)","score_opus":0.020649735370560196,"score_gpt":0.32439382600090305,"score_spread":0.30374409063034286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929430723","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":[0.8957815,0.0029397234,0.03587004,0.0014780358,0.0017216356,0.001283705,0.025496745,0.018148104,0.017280566],"genre_scores_gemma":[0.86628664,0.00037681387,0.06167644,0.0009740334,0.00028791605,0.0007045402,0.059415717,0.0004626279,0.009815269],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9972671,0.0009430487,0.00028742236,0.00068568485,0.00060161593,0.00021501846],"domain_scores_gemma":[0.9856162,0.008468745,0.00074410375,0.0020533188,0.0021014186,0.0010162392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040094536,0.0018712158,0.0007210709,0.0025026891,0.0008856686,0.0021516576,0.0018861907,0.001579703,0.003921512],"category_scores_gemma":[0.020881219,0.00022553976,0.00090589264,0.0010831485,0.0010326928,0.0022074727,0.0017953217,0.0017951244,0.003945383],"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":[0.004525085,0.0028304055,0.110804185,0.0036974957,0.0007353598,0.0018405212,0.0026302568,0.031645607,0.027135905,0.0054512145,0.20668769,0.6020162],"study_design_scores_gemma":[0.00093091454,0.004981796,0.12061392,0.0005158546,0.000352171,0.0028963163,0.0048610563,0.6481863,0.11565183,0.0071958248,0.093381554,0.00043250283],"about_ca_topic_score_codex":0.009375077,"about_ca_topic_score_gemma":0.012240935,"teacher_disagreement_score":0.009375077,"about_ca_system_score_codex":0.0010874661,"about_ca_system_score_gemma":0.0012667755,"threshold_uncertainty_score":0.021204293},"labels":[],"label_agreement":null},{"id":"W6929675354","doi":"10.5064/f6gzgcjb/olcoyn","title":"Lum_53080.Patient_SC_2018.02.26_Alberta.pdf","year":2022,"lang":"nl","type":"dataset","venue":"Syracuse University Qualitative Data Repository","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Nursing Research; National Institutes of Health","keywords":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.05923726397270716,"score_gpt":0.3457377124521482,"score_spread":0.286500448479441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929675354","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":[0.000054271,0.000018052202,0.00003881682,0.000087876564,0.000013861512,0.000011009116,0.99845755,0.00018260286,0.0011359602],"genre_scores_gemma":[0.0007329613,0.00009714522,0.00034873546,0.00015230867,0.00001561056,0.00023622876,0.994835,0.0001425228,0.0034394641],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989844,0.00020128563,0.00014910806,0.00022657325,0.0002029365,0.00023564586],"domain_scores_gemma":[0.9944622,0.0020249735,0.0004031937,0.0010663217,0.0012694696,0.00077386503],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0021020933,0.0011457813,0.0011542387,0.0028221728,0.0010203021,0.0033689942,0.0027004532,0.0019282455,0.35345381],"category_scores_gemma":[0.013256374,0.0012461358,0.0011855734,0.0063022673,0.00048386015,0.0013058231,0.0023879237,0.0014752347,0.22603942],"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":[0.000046234363,0.000009339459,0.00056547084,0.00028491078,0.000009514504,0.0000081518265,0.000021852567,0.00007759268,0.000018077324,0.00037575635,0.99633276,0.0022502467],"study_design_scores_gemma":[0.00069482136,0.000021650187,0.005730703,0.0008382948,0.000027278864,0.00004827551,0.00017156484,0.00044879163,0.00024910385,0.0015426911,0.990182,0.00004494133],"about_ca_topic_score_codex":0.12457622,"about_ca_topic_score_gemma":0.18267412,"teacher_disagreement_score":0.6465462,"about_ca_system_score_codex":0.003968647,"about_ca_system_score_gemma":0.005815751,"threshold_uncertainty_score":0.92221963},"labels":[],"label_agreement":null},{"id":"W6929756206","doi":"10.5061/dryad.4jh42","title":"Data from: A systematic review of methods for studying consumer health YouTube videos, with implications for systematic reviews","year":2013,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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; Sunnybrook Health Science Centre; Health Sciences Centre; Children's Hospital of Eastern Ontario","funders":"","keywords":"Systematic review; Transparency (behavior); Health care; Inclusion (mineral); Social media; Government (linguistics); Relevance (law); Data extraction; Inclusion and exclusion criteria","score_opus":0.17665601130040692,"score_gpt":0.45448866241762514,"score_spread":0.2778326511172182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929756206","genre_codex":"review","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00318197,0.7416819,0.018314868,0.007951702,0.0027811846,0.18057363,0.040024642,0.0005019817,0.004988228],"genre_scores_gemma":[0.02829329,0.42686817,0.06081116,0.006141264,0.00053580356,0.46726295,0.008331002,0.0002925949,0.0014638649],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.63300353,0.14109036,0.17151766,0.0116892895,0.04017649,0.0025227498],"domain_scores_gemma":[0.48318857,0.35767084,0.07598417,0.020121079,0.060522504,0.0025127614],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19127207,0.0045708497,0.017709818,0.059381675,0.0047107083,0.010915627,0.0064719403,0.0061983867,0.020830112],"category_scores_gemma":[0.5665734,0.00445558,0.01713551,0.045627903,0.00491692,0.013449434,0.008800974,0.0049280166,0.0034113263],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012559319,0.000010457252,0.00045074717,0.97381514,0.003453139,0.00006383664,0.00069086364,0.000058931175,0.000103419145,0.00053785305,0.005026226,0.015663825],"study_design_scores_gemma":[0.0002742073,0.000054220163,0.0007701842,0.97679,0.008416028,0.00006453343,0.0004113376,0.000054719156,0.000120655866,0.0006354968,0.012365587,0.0000431409],"about_ca_topic_score_codex":0.019447347,"about_ca_topic_score_gemma":0.044225905,"teacher_disagreement_score":0.8087279,"about_ca_system_score_codex":0.020572431,"about_ca_system_score_gemma":0.07515625,"threshold_uncertainty_score":0.9973055},"labels":[],"label_agreement":null},{"id":"W6929932533","doi":"10.5281/zenodo.11092574","title":"DESIGN OF STOVE FUELED BY USED LUBRICATING OIL FOR INDUSTRIAL SALT DRYING","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Encana (Canada)","funders":"","keywords":"Stove; Pellet; Salt (chemistry); Combustor; Fuel oil; Lubricant; Kiln; Fuel efficiency","score_opus":0.08581326396947052,"score_gpt":0.3038188653308878,"score_spread":0.21800560136141728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929932533","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":[0.7562959,0.0014224953,0.22199772,0.000096164185,0.00010423046,0.00035120695,0.00046750417,0.0005639575,0.018700788],"genre_scores_gemma":[0.9372929,0.000480041,0.056558743,0.000023547305,0.000008583687,0.00016208315,0.0002731052,0.00004256764,0.005158437],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988127,0.000019240035,0.000010513201,0.000028384156,0.0000396797,0.000020898968],"domain_scores_gemma":[0.9999318,0.000008791735,0.000013838781,0.0000056298395,0.000031032614,0.0000089857995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017174604,0.00041947773,0.00040145885,0.00064245245,0.00025700865,0.00047051618,0.00063761283,0.00045057878,0.0014568099],"category_scores_gemma":[0.00015705498,0.00017813843,0.00040253607,0.0002606162,0.00012585598,0.0002424082,0.0003153467,0.00011416092,0.00037248587],"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":[0.0012014399,0.00052910944,0.0137698185,0.0014599109,0.00012466343,0.0012214461,0.00022967678,0.20295468,0.6272659,0.0063563683,0.0016913393,0.14319566],"study_design_scores_gemma":[0.00028329712,0.0036050114,0.029001659,0.0001428717,0.00035097645,0.001414697,0.00040317938,0.5332142,0.39072937,0.0021968605,0.038542613,0.00011528568],"about_ca_topic_score_codex":0.0008492793,"about_ca_topic_score_gemma":0.0011548338,"teacher_disagreement_score":0.0014568099,"about_ca_system_score_codex":0.00020394864,"about_ca_system_score_gemma":0.00035920256,"threshold_uncertainty_score":0.0048734546},"labels":[],"label_agreement":null},{"id":"W6930307310","doi":"10.5281/zenodo.11874713","title":"nelson math 6 textbook pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Lifelong learning; Focus (optics); Everyday life; Division (mathematics); Simple (philosophy)","score_opus":0.030337896241211547,"score_gpt":0.282347793225776,"score_spread":0.2520098969845645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930307310","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":[0.00020355967,0.00041884807,0.0013368607,0.0009612631,0.001346204,0.00017284107,0.004029496,0.0048787533,0.98665214],"genre_scores_gemma":[0.00068330596,0.0004213636,0.0010245318,0.00057652046,0.00021862902,0.00006432471,0.002247025,0.0017447987,0.9930195],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992562,0.000041088617,0.000052440795,0.000115509734,0.00046452926,0.00007022027],"domain_scores_gemma":[0.99689674,0.0003409716,0.00012507234,0.0003569969,0.0016995968,0.00058057863],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004740653,0.0017122771,0.001287758,0.0020054127,0.0017616575,0.0074469587,0.0026935376,0.0021515847,0.9357725],"category_scores_gemma":[0.004288041,0.00094080897,0.0009442361,0.0025485489,0.00064065884,0.006766126,0.003155293,0.0031870902,0.91982013],"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":[0.00002147604,0.000043320324,0.00003497904,0.00012357594,0.0000012230047,0.000039417995,0.000018226237,0.00005590685,0.00023499444,0.0020224345,0.95604736,0.041357122],"study_design_scores_gemma":[0.000008011382,0.000013335564,0.00012935317,0.000064615204,0.000001578194,0.000047233076,0.000034539127,0.00004717073,0.00017351529,0.0011671411,0.9983065,0.000006946988],"about_ca_topic_score_codex":0.003604783,"about_ca_topic_score_gemma":0.008661783,"teacher_disagreement_score":0.06422752,"about_ca_system_score_codex":0.0018943191,"about_ca_system_score_gemma":0.0028422016,"threshold_uncertainty_score":0.091612756},"labels":[],"label_agreement":null},{"id":"W6930315657","doi":"10.5281/zenodo.13657201","title":"CURRICULUM VITAE (francais et anglais combine)","year":2024,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Position (finance); Point (geometry); Curriculum; Government (linguistics)","score_opus":0.026480958705592587,"score_gpt":0.28110884117184726,"score_spread":0.2546278824662547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930315657","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":[0.0023047351,0.006499084,0.0016945387,0.017477456,0.017836623,0.00052928337,0.028475286,0.0025437493,0.9226392],"genre_scores_gemma":[0.01024222,0.003259029,0.0018960066,0.0018500361,0.0014114066,0.00054031465,0.011695104,0.0016854532,0.9674205],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973803,0.00033425272,0.00017859518,0.00029104177,0.0011543311,0.00066144345],"domain_scores_gemma":[0.99216753,0.00042112014,0.00030891172,0.00038421337,0.004479341,0.0022387896],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0031527996,0.0010620169,0.000964176,0.003034713,0.0027567793,0.009915285,0.0011357307,0.0014894004,0.6080862],"category_scores_gemma":[0.0077935453,0.00047247755,0.00055552943,0.0043691164,0.00091839966,0.0027950956,0.0036302214,0.0024052327,0.4651347],"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":[0.000011798747,0.000031936925,0.00029296422,0.00012051168,0.0000014020695,0.000016631444,0.00014506806,0.000046636167,0.00011555283,0.0027106586,0.9392822,0.057224672],"study_design_scores_gemma":[0.0000021816065,0.0000057095417,0.0009877678,0.00009809454,4.7131851e-7,0.000017734199,0.000071335155,0.000018615612,0.000036182388,0.00020272135,0.99855644,0.0000027902697],"about_ca_topic_score_codex":0.03786747,"about_ca_topic_score_gemma":0.04242377,"teacher_disagreement_score":0.39191377,"about_ca_system_score_codex":0.0064255022,"about_ca_system_score_gemma":0.012922857,"threshold_uncertainty_score":0.5590174},"labels":[],"label_agreement":null},{"id":"W6930347257","doi":"10.5281/zenodo.12444476","title":"cfe exam prep course free download pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Download; Certification; Test (biology); Plan (archaeology); Online course; Multiple choice; Frequently asked questions","score_opus":0.02792557294114132,"score_gpt":0.2831659774860127,"score_spread":0.25524040454487135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930347257","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":[0.00032307915,0.00018443746,0.0005932375,0.000972819,0.0016043333,0.00020742083,0.0030578692,0.0036039713,0.98945284],"genre_scores_gemma":[0.0009078835,0.000115758725,0.00030519837,0.00046052405,0.00019089975,0.00005671272,0.0012383035,0.0009257833,0.995799],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99941087,0.00003634541,0.000021584296,0.00007525983,0.00034786054,0.00010812164],"domain_scores_gemma":[0.9965179,0.00030782967,0.00007375198,0.00028304668,0.001994009,0.0008234733],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006437626,0.0011927248,0.0010855007,0.0017569779,0.0020698593,0.0050110524,0.0017518839,0.0025109176,0.96615446],"category_scores_gemma":[0.004834521,0.000579901,0.0009632954,0.0012990173,0.0005902781,0.0038551546,0.0023524717,0.0023210505,0.94547963],"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":[0.000016676548,0.000027913266,0.000028501101,0.00004869237,6.772384e-7,0.000025339275,0.0000074713694,0.000015837433,0.00014381741,0.0002927126,0.9818409,0.017551444],"study_design_scores_gemma":[0.000015253557,0.00003402842,0.00042015204,0.000065319335,0.0000016036856,0.00006021202,0.00005543892,0.000040099254,0.0002143919,0.00038257704,0.99870145,0.00000949142],"about_ca_topic_score_codex":0.0045701726,"about_ca_topic_score_gemma":0.01130903,"teacher_disagreement_score":0.033845544,"about_ca_system_score_codex":0.0015435229,"about_ca_system_score_gemma":0.0014551829,"threshold_uncertainty_score":0.048276484},"labels":[],"label_agreement":null},{"id":"W6930377698","doi":"10.5281/zenodo.13848189","title":"FIGURE 5 in Chironomidae (Diptera: Insecta) of Alaska, USA, with descriptions of new species and a checklist","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Trent University","funders":"","keywords":"Checklist; Chironomidae; Scale (ratio); Taxonomy (biology)","score_opus":0.03445565365915218,"score_gpt":0.2604681572153957,"score_spread":0.2260125035562435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930377698","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":[0.034243423,0.009001783,0.008329737,0.0008056417,0.0032116568,0.00046830106,0.29183775,0.0040856525,0.64801604],"genre_scores_gemma":[0.09732959,0.010193979,0.033524934,0.0006169778,0.00053818023,0.0007848438,0.37011117,0.0009425566,0.48595765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983,0.000017254788,0.000019372013,0.000049927432,0.00005557031,0.000027774864],"domain_scores_gemma":[0.99971336,0.000025600502,0.000056287576,0.000026552767,0.0001313419,0.00004690622],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036447385,0.0008069494,0.00023160625,0.0038022143,0.0011323899,0.0005767159,0.0005479577,0.00040805442,0.1517427],"category_scores_gemma":[0.00051347807,0.0002615574,0.00034231434,0.0045280405,0.00031607147,0.00091243326,0.00062779477,0.00066602405,0.049284756],"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":[0.0001047871,0.00006556709,0.019678468,0.0014671473,0.000028161836,0.0003205672,0.0010974385,0.00028732113,0.0023543858,0.0019226155,0.7754244,0.19724922],"study_design_scores_gemma":[0.0000075799117,0.000031002448,0.07820101,0.00043773509,0.0000374909,0.00042818463,0.0009845509,0.00014017712,0.0005028076,0.00058908586,0.9186259,0.000014359053],"about_ca_topic_score_codex":0.051751263,"about_ca_topic_score_gemma":0.10953313,"teacher_disagreement_score":0.8482573,"about_ca_system_score_codex":0.00031290296,"about_ca_system_score_gemma":0.0010372645,"threshold_uncertainty_score":0.5076299},"labels":[],"label_agreement":null},{"id":"W6930397236","doi":"10.5281/zenodo.11521745","title":"SIDRRpy v1.0: SIDRR dataset analysis code","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University; Environment and Climate Change Canada","funders":"","keywords":"Python (programming language); Code (set theory); Process (computing); Source code; Deformation monitoring; Toolbox","score_opus":0.04307340391146578,"score_gpt":0.3164744892103995,"score_spread":0.2734010852989337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930397236","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005966188,0.00006758094,0.0066465284,0.0001091427,0.00009819934,0.00010307291,0.9527571,0.03790889,0.0017127446],"genre_scores_gemma":[0.002384311,0.00007038693,0.011933106,0.00019494338,0.000028673532,0.00055209204,0.9732843,0.009667701,0.0018844435],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896705,0.0001493054,0.0001560353,0.0003434969,0.0002519785,0.00013211512],"domain_scores_gemma":[0.9986137,0.0003866392,0.00010623395,0.00040103553,0.00037637676,0.00011603048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017138827,0.001995454,0.0011336149,0.0024226638,0.0007079766,0.001973423,0.0020615258,0.00094985985,0.08573295],"category_scores_gemma":[0.00585531,0.00096927944,0.002037634,0.002407947,0.0003935435,0.0013118783,0.0023005547,0.0019199081,0.1009822],"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":[0.0000972417,0.00002598765,0.0012619771,0.00051894074,0.00006593334,0.000041904223,0.00005742006,0.0009027295,0.000840818,0.00085610535,0.9875902,0.0077407453],"study_design_scores_gemma":[0.0002479145,0.00003768562,0.005407614,0.00019913155,0.00006225467,0.0001318887,0.00009226081,0.0077225273,0.0036893927,0.0078118076,0.9744927,0.00010481474],"about_ca_topic_score_codex":0.008040885,"about_ca_topic_score_gemma":0.016715163,"teacher_disagreement_score":0.08573295,"about_ca_system_score_codex":0.0007704855,"about_ca_system_score_gemma":0.0018289012,"threshold_uncertainty_score":0.28680527},"labels":[],"label_agreement":null},{"id":"W6930517664","doi":"10.5281/zenodo.14154533","title":"PM_037190_B_Oudenaarde","year":2009,"lang":"nl","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Vault (architecture); Period (music); Quarter (Canadian coin)","score_opus":0.030931015073883426,"score_gpt":0.2749645037908683,"score_spread":0.24403348871698485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930517664","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":[0.00055607397,0.00023276288,0.000760374,0.0004048913,0.0006798998,0.00009279096,0.060879227,0.004785801,0.93160814],"genre_scores_gemma":[0.002695334,0.00024289443,0.00047447358,0.00008551608,0.00010791118,0.00007374359,0.013651879,0.0026671342,0.98000103],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997037,0.0000264203,0.000014668737,0.00007925248,0.00012445617,0.000051472744],"domain_scores_gemma":[0.99882835,0.00024431685,0.00005147836,0.00018110743,0.0004068363,0.00028793025],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039526436,0.0006045192,0.0006774017,0.0011339092,0.0010275401,0.0040717367,0.0010183627,0.0009166154,0.9361471],"category_scores_gemma":[0.002939057,0.00043719538,0.00037201884,0.003394221,0.00028148337,0.0012910141,0.0014007831,0.0008282311,0.89370424],"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":[0.00003648604,0.000012562953,0.00014508818,0.00005282033,0.0000011690182,0.000015167086,0.000027934719,0.000030286867,0.00008618608,0.00046228804,0.9752465,0.023883525],"study_design_scores_gemma":[0.000016842298,0.000008219513,0.001008936,0.00003787111,0.0000012078427,0.000023140341,0.000039960127,0.00005183475,0.00014563225,0.0001564122,0.99850583,0.000004116831],"about_ca_topic_score_codex":0.014877592,"about_ca_topic_score_gemma":0.0146560585,"teacher_disagreement_score":0.063852906,"about_ca_system_score_codex":0.0009956562,"about_ca_system_score_gemma":0.00085210294,"threshold_uncertainty_score":0.09107834},"labels":[],"label_agreement":null},{"id":"W6930607320","doi":"10.5281/zenodo.15034061","title":"ResearchBox 567, 'Construal-level Perspective on Intergroup Conflict', https://researchbox.org/567","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University","funders":"","keywords":"Perspective (graphical); Group conflict; Resolution (logic); Conflict resolution; Negotiation","score_opus":0.0856139038845178,"score_gpt":0.33665456023262813,"score_spread":0.25104065634811035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930607320","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":[0.001379217,0.0043934127,0.0063503184,0.011104114,0.0032595252,0.00040400703,0.22679716,0.007355744,0.73895645],"genre_scores_gemma":[0.03481157,0.013715361,0.020297429,0.004887444,0.0033728061,0.002512696,0.13818544,0.016698519,0.76551867],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99925977,0.0002232885,0.000055144686,0.00009175145,0.00032205498,0.000047880247],"domain_scores_gemma":[0.99301845,0.0042477814,0.00027614477,0.0007261769,0.0011228771,0.00060843665],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025436564,0.000565256,0.00086235546,0.004027385,0.0011605147,0.0030032503,0.0013768853,0.0017505792,0.7477075],"category_scores_gemma":[0.013515393,0.00036055275,0.00047359488,0.006889233,0.00071241276,0.004742433,0.0016862063,0.0012067012,0.4168693],"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":[0.00003053613,0.000020103373,0.00016686344,0.0003935094,0.0000023668151,0.000018930377,0.00022542263,0.000028111865,0.0000644742,0.006815739,0.96751636,0.02471764],"study_design_scores_gemma":[0.000053122636,0.000014271512,0.0017326293,0.0005608846,0.0000066187285,0.000069464804,0.0003781358,0.000084414714,0.00019572242,0.0097883055,0.9871049,0.00001159393],"about_ca_topic_score_codex":0.004612741,"about_ca_topic_score_gemma":0.007822115,"teacher_disagreement_score":0.7477075,"about_ca_system_score_codex":0.0012945924,"about_ca_system_score_gemma":0.0020474799,"threshold_uncertainty_score":0.35986465},"labels":[],"label_agreement":null},{"id":"W6930847907","doi":"10.5281/zenodo.14522679","title":"FIGURES 99–112 in Insights into Nitzschia amphibia Grunow (Bacillariophyta, Bacillariaceae): Lectotypification and a comparative study with N. amphibioides Hustedt and N. semirobusta Lange-Bertalot","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Continental (Canada)","funders":"","keywords":"Scale (ratio); Line drawings; Taxonomy (biology); Nitzschia; Line (geometry)","score_opus":0.03686179611064997,"score_gpt":0.2798775763423815,"score_spread":0.2430157802317315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930847907","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":[0.066612594,0.0077488977,0.009796826,0.0011885522,0.0012224138,0.00041320038,0.064857,0.0018756337,0.8462849],"genre_scores_gemma":[0.5519763,0.0072051715,0.01778743,0.00035254707,0.00046888524,0.00031371452,0.06351793,0.0011534301,0.35722458],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999571,0.000004901093,0.000002879539,0.000014461355,0.000014455812,0.0000063092025],"domain_scores_gemma":[0.99995923,0.000007649179,0.000009012342,0.000004629279,0.000009533139,0.000009887253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005146896,0.00033533477,0.0001936608,0.0018252392,0.00039951786,0.00029751225,0.0003724392,0.00021698861,0.119037904],"category_scores_gemma":[0.00015595008,0.00009781945,0.00015611657,0.0016410899,0.00029613604,0.0004290109,0.00031797375,0.00043175102,0.01912885],"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":[0.00065949047,0.000084188476,0.019330667,0.0020644548,0.000073030176,0.0022155435,0.0025739702,0.0014809873,0.040034205,0.010611525,0.41285318,0.5080187],"study_design_scores_gemma":[0.000016684407,0.00002509006,0.15727092,0.00013636163,0.000026028001,0.0017281041,0.0006508999,0.00037241867,0.001552489,0.0011848756,0.8370257,0.000010500189],"about_ca_topic_score_codex":0.009877831,"about_ca_topic_score_gemma":0.0307891,"teacher_disagreement_score":0.119037904,"about_ca_system_score_codex":0.0005110024,"about_ca_system_score_gemma":0.00015138972,"threshold_uncertainty_score":0.39822143},"labels":[],"label_agreement":null},{"id":"W6930889470","doi":"10.5281/zenodo.15155066","title":"TABLE 3 in Morphology of the larvae of Rhantaticus congestus (Klug, 1833) and phylogenetic comparison with other known Aciliini (Coleoptera: Dytiscidae: Dytiscinae)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Laurentian University","funders":"","keywords":"Cladistics; Table (database); Phylogenetic tree; Morphology (biology); Larva","score_opus":0.03382163505726831,"score_gpt":0.28690849080435404,"score_spread":0.2530868557470857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930889470","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7057764,0.002696698,0.0039211647,0.00019550024,0.00027770607,0.00040316427,0.17213133,0.0013888093,0.11320928],"genre_scores_gemma":[0.85662526,0.00091445044,0.012497983,0.00014182058,0.000040555315,0.000263842,0.075654976,0.00046592666,0.053395204],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999052,0.000010240246,0.000012783706,0.00003757211,0.00001888848,0.000015368576],"domain_scores_gemma":[0.99984646,0.000041930805,0.00004630185,0.000014893566,0.000025373472,0.000025048486],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006993997,0.00036243474,0.00025296406,0.0016777873,0.00068361574,0.00033616205,0.00023807366,0.00022672451,0.045329288],"category_scores_gemma":[0.00029142702,0.00016565372,0.0003693508,0.0013309543,0.00023160735,0.00036723597,0.00039411135,0.00028534894,0.010183727],"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":[0.0017272102,0.00018635386,0.32430595,0.0034398441,0.00032172556,0.0026177252,0.003789162,0.0027717648,0.13924845,0.0016692814,0.066897325,0.4530253],"study_design_scores_gemma":[0.000011780445,0.00019975704,0.8801923,0.00008509208,0.000060007118,0.0014511743,0.0007599538,0.00038978586,0.004671541,0.00024501365,0.11190159,0.00003202501],"about_ca_topic_score_codex":0.0042170975,"about_ca_topic_score_gemma":0.014474336,"teacher_disagreement_score":0.9546707,"about_ca_system_score_codex":0.000278189,"about_ca_system_score_gemma":0.00018453332,"threshold_uncertainty_score":0.1516416},"labels":[],"label_agreement":null},{"id":"W6930984694","doi":"10.5281/zenodo.2222426","title":"Bargaining Update: ULFA Fall General Meeting","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Lethbridge","funders":"","keywords":"Government (linguistics); Context (archaeology); Work (physics); Subject (documents)","score_opus":0.03729499142278734,"score_gpt":0.29072710797046414,"score_spread":0.2534321165476768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930984694","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":[0.0025108685,0.064295106,0.005256032,0.2559699,0.22639601,0.0007302516,0.023309119,0.0030783599,0.4184544],"genre_scores_gemma":[0.014476128,0.024822887,0.0042985184,0.07271837,0.06322175,0.001718699,0.026718393,0.0015152104,0.79051006],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954554,0.000690446,0.00038657113,0.0003299072,0.0022867438,0.0008508934],"domain_scores_gemma":[0.9881159,0.0022921492,0.00068643497,0.0006345989,0.0062759155,0.0019950944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010583438,0.0015583371,0.0017425017,0.0046630995,0.002575705,0.0057533,0.0048680315,0.010716735,0.1862846],"category_scores_gemma":[0.02443659,0.0007852632,0.0016013705,0.0035112046,0.0007249076,0.003751352,0.0037395908,0.0069671897,0.1135644],"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":[0.000033651253,0.000018385546,0.000078379366,0.000048600905,0.0000030146223,0.000022469969,0.00001373174,0.00004862283,0.000017696675,0.00033546973,0.9800489,0.019331051],"study_design_scores_gemma":[0.000027143464,0.000017966897,0.00060036883,0.00026421028,0.000006317611,0.00003845485,0.000042678723,0.000046158144,0.000035870915,0.0005506604,0.9983606,0.000009371177],"about_ca_topic_score_codex":0.01614994,"about_ca_topic_score_gemma":0.02196303,"teacher_disagreement_score":0.1862846,"about_ca_system_score_codex":0.004235085,"about_ca_system_score_gemma":0.006531578,"threshold_uncertainty_score":0.6231841},"labels":[],"label_agreement":null},{"id":"W6931010728","doi":"10.5281/zenodo.16795405","title":"Fig. 2. Bayesian inference phylogenetic tree obtained from the 16S in Revision of the species of Caridina (Decapoda: Atyidae) from the Western Indian Ocean islands with description of a new species","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Cégep Saint-Jean-sur-Richelieu","funders":"","keywords":"Phylogenetic tree; Bayesian probability; Clade; Tree (set theory); Bayesian inference; Inference; Phylogenetics","score_opus":0.031606983036960294,"score_gpt":0.24901272128010674,"score_spread":0.21740573824314643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931010728","genre_codex":"dataset","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":[0.15010415,0.004950855,0.24180108,0.0050644507,0.0020943156,0.0024662493,0.4548573,0.01178472,0.12687688],"genre_scores_gemma":[0.36435658,0.0032210795,0.28212318,0.001001959,0.00042532518,0.0016712014,0.3133931,0.0020833395,0.031724215],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936944,0.00021766928,0.000050564486,0.00019163245,0.00012210198,0.000048651094],"domain_scores_gemma":[0.9986493,0.0006192681,0.00017452912,0.000090106754,0.0003581129,0.00010872598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016855749,0.0009815645,0.0005129867,0.0050969115,0.0013218977,0.0011732242,0.00076333084,0.0008485714,0.060142566],"category_scores_gemma":[0.004997471,0.00045816222,0.0008333986,0.0045741117,0.00043071105,0.0005684144,0.000554661,0.0011456208,0.014916232],"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":[0.0027000464,0.0002579869,0.07669784,0.0050567174,0.00076448143,0.0031582655,0.002258914,0.03371878,0.034311023,0.018363629,0.41169247,0.41101992],"study_design_scores_gemma":[0.00074151834,0.0002742663,0.15670907,0.0024452526,0.0011561256,0.0026612594,0.0017469333,0.08010833,0.0067197364,0.024658352,0.7224636,0.0003155445],"about_ca_topic_score_codex":0.0076038972,"about_ca_topic_score_gemma":0.009740481,"teacher_disagreement_score":0.060142566,"about_ca_system_score_codex":0.0008903022,"about_ca_system_score_gemma":0.001297542,"threshold_uncertainty_score":0.20119691},"labels":[],"label_agreement":null},{"id":"W6931104423","doi":"10.5281/zenodo.165536","title":"FIGURE 20 in Guide to the Parasites of Fishes of Canada Part V: Nematoda","year":2016,"lang":"en","type":"other","venue":"INFM-OAR (INFN Catania)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Taxonomy (biology); Capillaria; Single specimen; Key (lock)","score_opus":0.009901691220676408,"score_gpt":0.2927031892279342,"score_spread":0.2828014980072578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931104423","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001896567,0.02792424,0.009396893,0.0024873759,0.0040524136,0.0015314067,0.1455522,0.0043434133,0.8028155],"genre_scores_gemma":[0.0057915226,0.024919465,0.019175755,0.0013918403,0.00047928086,0.00039494975,0.06294439,0.0008862474,0.8840165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99966085,0.000014473087,0.000020019123,0.000044446268,0.00019537662,0.000064881504],"domain_scores_gemma":[0.99908733,0.000049806295,0.000061767365,0.000059218288,0.00060529,0.00013659104],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041077394,0.001543234,0.00079039944,0.007922126,0.0026419142,0.0015040046,0.0020406633,0.0007955381,0.34565178],"category_scores_gemma":[0.0010384411,0.0007132068,0.00061933964,0.006392205,0.0009089503,0.0014644406,0.0015116443,0.001300086,0.2011379],"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":[0.000013919353,0.000019923877,0.00086573575,0.00032890472,0.000004843054,0.00005812987,0.00013171422,0.00011595242,0.0006382753,0.0012085418,0.8597908,0.13682334],"study_design_scores_gemma":[0.0000015980427,0.000005063592,0.0027723385,0.00011350998,0.0000032023722,0.00007006244,0.00006047466,0.00002483485,0.000084673185,0.00022618224,0.9966329,0.0000051284374],"about_ca_topic_score_codex":0.63110185,"about_ca_topic_score_gemma":0.8790418,"teacher_disagreement_score":0.36889815,"about_ca_system_score_codex":0.0048200046,"about_ca_system_score_gemma":0.008487085,"threshold_uncertainty_score":0.9333483},"labels":[],"label_agreement":null},{"id":"W6931152606","doi":"10.5281/zenodo.4552096","title":"Portage Progress Report Presentation, November 2017","year":2017,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","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":"Work (physics); MEDLINE; Documentation; Government (linguistics)","score_opus":0.0803680925985408,"score_gpt":0.3975151708728305,"score_spread":0.3171470782742897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931152606","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":[0.00251543,0.0021060854,0.0009548912,0.040513046,0.026045324,0.0013100161,0.10308375,0.002537499,0.8209339],"genre_scores_gemma":[0.0023045198,0.0010894368,0.00061796635,0.0016285662,0.0011059422,0.0002703454,0.020986928,0.0004394075,0.9715568],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952043,0.0001936856,0.00020142096,0.00026712407,0.003548936,0.0005845397],"domain_scores_gemma":[0.98309267,0.0011797239,0.0003572436,0.0008392608,0.011335086,0.003196075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0054710675,0.00086629344,0.00075305067,0.003129888,0.0035009345,0.0077159926,0.0023418285,0.0035993524,0.48351422],"category_scores_gemma":[0.015939888,0.0006319396,0.00083711947,0.003027992,0.000831367,0.0029856083,0.004231491,0.0036566171,0.2572005],"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":[0.00002660844,0.00002083374,0.00014890205,0.000025169527,0.0000012342634,0.000022056816,0.000018272094,0.000015774318,0.000024247001,0.00028351007,0.9943329,0.0050806403],"study_design_scores_gemma":[0.000016790138,0.000013875657,0.0012651461,0.00007587597,0.0000019858744,0.00001631026,0.00011379909,0.000031436568,0.00010652078,0.0001802734,0.9981694,0.000008626016],"about_ca_topic_score_codex":0.16052093,"about_ca_topic_score_gemma":0.26292482,"teacher_disagreement_score":0.48351422,"about_ca_system_score_codex":0.004872839,"about_ca_system_score_gemma":0.039426796,"threshold_uncertainty_score":0.73670423},"labels":[],"label_agreement":null},{"id":"W6931157229","doi":"10.5281/zenodo.5278059","title":"Idiomacromerus perplexus","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Nearctic ecozone; Period (music); Identification (biology); Term (time)","score_opus":0.03561902268938198,"score_gpt":0.29410888558475645,"score_spread":0.25848986289537446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931157229","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":[0.69605136,0.010188235,0.0053247768,0.001166015,0.00079507957,0.00049153063,0.0027988923,0.0004833203,0.28270078],"genre_scores_gemma":[0.9661039,0.0020909049,0.0050087264,0.00062222255,0.00012577005,0.00014500375,0.00089357956,0.000016866503,0.024993066],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99983263,0.000019812898,0.0000093434855,0.0000759322,0.000030380541,0.000031962718],"domain_scores_gemma":[0.9998863,0.000021742831,0.000041211566,0.000011537051,0.000025936615,0.000013176091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013014942,0.0007363991,0.00029506907,0.0021415346,0.0015021767,0.00039802105,0.000595813,0.00056579936,0.009459356],"category_scores_gemma":[0.0003384879,0.00017108586,0.000100164245,0.0010084944,0.0008118075,0.00072935555,0.001044266,0.00060934504,0.0024938614],"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":[0.001296652,0.00036745795,0.06068024,0.001082501,0.00008263031,0.007576324,0.0031605598,0.002399217,0.07767039,0.032285683,0.03185605,0.78154224],"study_design_scores_gemma":[0.00020302118,0.0004559185,0.4056452,0.00048087907,0.00014933397,0.020956818,0.002502492,0.0022120543,0.010866762,0.0070163775,0.5494047,0.000106445856],"about_ca_topic_score_codex":0.010438179,"about_ca_topic_score_gemma":0.017357852,"teacher_disagreement_score":0.010438179,"about_ca_system_score_codex":0.00092049554,"about_ca_system_score_gemma":0.00047750236,"threshold_uncertainty_score":0.031644642},"labels":[],"label_agreement":null},{"id":"W6931216558","doi":"10.5281/zenodo.4587920","title":"What is Vashikaran ? Get your love back +91 98726 65620","year":2021,"lang":"hi","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Nucleofection; Scapegoat; Context (archaeology)","score_opus":0.0542585141198623,"score_gpt":0.29568764270694614,"score_spread":0.24142912858708385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931216558","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":[0.0024901875,0.005716087,0.00048147247,0.023237688,0.0073315557,0.00006691705,0.00052901875,0.00077583536,0.95937115],"genre_scores_gemma":[0.005099313,0.0019245468,0.0002674981,0.0032700137,0.00032277938,0.00001781516,0.00017311276,0.00020150842,0.9887234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999579,0.00006468591,0.000013134886,0.00007781076,0.00015388337,0.00011160725],"domain_scores_gemma":[0.9987399,0.00008810384,0.000043798296,0.000046921545,0.00033369867,0.0007475832],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005547942,0.0005460711,0.00054146553,0.00051265355,0.005535692,0.0041817,0.0007715171,0.0014726621,0.64230335],"category_scores_gemma":[0.0017229108,0.00037687024,0.0002903529,0.0006265565,0.00063846627,0.0023942261,0.0023337698,0.0024686132,0.40427908],"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":[0.000038065646,0.00002091578,0.00027443672,0.000068916546,0.000002252442,0.00015682034,0.00041971705,0.000012483287,0.00028453235,0.0011589046,0.94565547,0.051907483],"study_design_scores_gemma":[0.0000036760478,0.000013340562,0.0005205001,0.000054978755,0.0000019052796,0.00019338887,0.0008718736,0.000015306645,0.000077320336,0.00017823908,0.9980636,0.0000057639963],"about_ca_topic_score_codex":0.008240609,"about_ca_topic_score_gemma":0.029582657,"teacher_disagreement_score":0.35769665,"about_ca_system_score_codex":0.0013235568,"about_ca_system_score_gemma":0.0018446952,"threshold_uncertainty_score":0.5102109},"labels":[],"label_agreement":null},{"id":"W6931269208","doi":"10.5281/zenodo.5579826","title":"Guide pour la curation dans Dataverse","year":2021,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McMaster University; Dalhousie University; Université de Montréal; Wilfrid Laurier University; Queen's University","funders":"","keywords":"Data curation; Context (archaeology); Digital curation","score_opus":0.03936704093020085,"score_gpt":0.2901634733984232,"score_spread":0.25079643246822236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931269208","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":[0.0009808298,0.0035089194,0.7971168,0.0058125355,0.0013530754,0.0016937783,0.03642579,0.10487584,0.04823241],"genre_scores_gemma":[0.0054381196,0.0034263984,0.8218336,0.0041124057,0.0003787196,0.0026888878,0.044630643,0.042248543,0.07524264],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98229885,0.0054944097,0.0027134323,0.002053764,0.0069298674,0.0005097525],"domain_scores_gemma":[0.9565487,0.016994469,0.0012490014,0.0115198605,0.012443632,0.0012444024],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.016475722,0.0022003404,0.00176407,0.007624662,0.002541169,0.009734123,0.003298366,0.0044128904,0.118709125],"category_scores_gemma":[0.053952128,0.0031600567,0.003060635,0.0051265783,0.0020873006,0.007936395,0.008200066,0.006145872,0.13012104],"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":[0.00019779688,0.00010223276,0.0008146634,0.002205212,0.00015524054,0.0006317116,0.0032815458,0.0011935876,0.011082598,0.03700682,0.6891775,0.254151],"study_design_scores_gemma":[0.00002178334,0.000011439813,0.0003261827,0.00040991543,0.000012139828,0.0002938458,0.00016412449,0.0006843871,0.0017631571,0.006378687,0.98988956,0.00004483047],"about_ca_topic_score_codex":0.011472052,"about_ca_topic_score_gemma":0.01569505,"teacher_disagreement_score":0.99670166,"about_ca_system_score_codex":0.0017944331,"about_ca_system_score_gemma":0.007749804,"threshold_uncertainty_score":0.39712155},"labels":[],"label_agreement":null},{"id":"W6931605237","doi":"10.5683/sp3/vi2j14","title":"Entrepreneurship Among Afro-Descendant Communities: Practices, Motivations, and Support Strategies | L'Entreprenneuriat au sein des communautés afrodescendantes: pratiques, motivations et strategies d'accompagnement","year":2025,"lang":"en","type":"dataset","venue":"Borealis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 de Sherbrooke","funders":"","keywords":"Entrepreneurship; Face (sociological concept); Order (exchange); Portrait; Ethnic group","score_opus":0.05561050165658528,"score_gpt":0.355714131268018,"score_spread":0.3001036296114327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931605237","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925875,0.0003385641,0.00007602987,0.0011195996,0.000014013665,0.000013189955,0.000007385457,0.0000015699013,0.005842189],"genre_scores_gemma":[0.9967102,0.00046392094,0.00011083995,0.00011511779,0.000009236306,0.000014997325,0.000008799974,0.0000014991082,0.002565401],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993462,0.00030532922,0.000018064617,0.00004346526,0.00006728444,0.00021961649],"domain_scores_gemma":[0.9986706,0.0004812875,0.00023670797,0.00003883569,0.000094833245,0.0004777211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016427969,0.00022554555,0.0001535952,0.0006583281,0.005892373,0.0033611804,0.00036669092,0.00063712354,0.0032343992],"category_scores_gemma":[0.0022835874,0.00014459176,0.00011134932,0.00056559284,0.0017901022,0.0013952199,0.0023840566,0.0007118114,0.0002961547],"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":[0.000057535246,0.00033247186,0.20776239,0.000105994106,0.000011397135,0.0012684914,0.6988161,0.00004226924,0.0012631853,0.0050248243,0.0019117079,0.08340357],"study_design_scores_gemma":[0.0000047142726,0.000102679754,0.09918189,0.00018223218,0.000006622783,0.0004598155,0.8784133,0.00009442917,0.00022121075,0.00078747125,0.020530276,0.000015316575],"about_ca_topic_score_codex":0.014627995,"about_ca_topic_score_gemma":0.037966486,"teacher_disagreement_score":0.014627995,"about_ca_system_score_codex":0.0008901424,"about_ca_system_score_gemma":0.0021670652,"threshold_uncertainty_score":0.029085696},"labels":[],"label_agreement":null},{"id":"W6931608238","doi":"10.5683/sp3/pugbpp","title":"Données et code de réplication pour : Intégration régionale de travailleurs (im)migrants sous interdiction de changer d’employeur au pays : impact salarial sur les autres employés au sein des occupations affectées.","year":2022,"lang":"fr","type":"dataset","venue":"Borealis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 du Québec à Montréal","funders":"","keywords":"Interdiction; Power (physics); Deductive method; Reproduction","score_opus":0.07045672291049385,"score_gpt":0.34435565261755025,"score_spread":0.2738989297070564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931608238","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008062784,0.000044321063,0.00005817155,0.00007331802,0.000031264433,0.000019443596,0.99911755,0.00013597678,0.00043933626],"genre_scores_gemma":[0.0005167062,0.000058546037,0.00041757198,0.00007529945,0.000012496737,0.00024902113,0.9977842,0.00008300352,0.0008030586],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962993,0.0007443169,0.00075881416,0.0008811956,0.00084827887,0.00046813785],"domain_scores_gemma":[0.9901861,0.0034048161,0.00089563016,0.0019045977,0.0030816724,0.0005271999],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003775661,0.0017631717,0.0013692009,0.0043268446,0.0013136087,0.0036675069,0.002954476,0.0034372448,0.08701221],"category_scores_gemma":[0.029341267,0.0009991656,0.0020700742,0.00727112,0.0006806244,0.0016675125,0.0022517636,0.002522043,0.0701431],"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":[0.000045674395,0.000014656556,0.0012412563,0.0004846931,0.000034655182,0.000015483405,0.000037741815,0.00013410709,0.00004260949,0.00037529678,0.9961224,0.0014514207],"study_design_scores_gemma":[0.00046044067,0.000017190256,0.011453447,0.0008424363,0.00006121439,0.00006707523,0.00017392317,0.00042192836,0.00022174178,0.0012631388,0.98496485,0.000052606898],"about_ca_topic_score_codex":0.11975495,"about_ca_topic_score_gemma":0.14992362,"teacher_disagreement_score":0.99622434,"about_ca_system_score_codex":0.0032188226,"about_ca_system_score_gemma":0.006282923,"threshold_uncertainty_score":0.29108483},"labels":[],"label_agreement":null},{"id":"W6931611829","doi":"10.5281/zenodo.7629561","title":"Fig. 98. Mesepisternum. A in Revision of the Nearctic species of the Lasioglossum (Dialictus) gemmatum species complex (Hymenoptera: Halictidae)","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Nearctic ecozone; Species complex; Taxonomy (biology); Scale (ratio)","score_opus":0.05531219761596202,"score_gpt":0.2801432802466582,"score_spread":0.22483108263069618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931611829","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":[0.1080597,0.018905794,0.010240125,0.0016526431,0.0021896283,0.00085990294,0.02393524,0.0020085895,0.83214843],"genre_scores_gemma":[0.45711526,0.009804553,0.023161309,0.0008833481,0.000856767,0.0004628358,0.02583896,0.00037404132,0.48150292],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999516,0.000008346159,0.0000046884993,0.000015862532,0.0000129331465,0.000006681468],"domain_scores_gemma":[0.9999429,0.000009057119,0.000011483017,0.0000072263733,0.000019217236,0.000010132906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010967496,0.0004876263,0.00016862123,0.0011566774,0.00049005094,0.00027202308,0.00039657444,0.0002945488,0.061534952],"category_scores_gemma":[0.00018506704,0.00013012461,0.00015266538,0.0006739705,0.00041018048,0.0005004007,0.0004676743,0.0003964958,0.014848917],"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":[0.0005404861,0.000057402278,0.012013168,0.0010109456,0.000033559012,0.00086337887,0.00068951317,0.0006412854,0.015512523,0.010728449,0.153567,0.80434227],"study_design_scores_gemma":[0.000026744101,0.000052371644,0.03849771,0.00012517345,0.00002365747,0.0010280366,0.00020836582,0.00023527817,0.0011721669,0.0007642879,0.9578593,0.00000681406],"about_ca_topic_score_codex":0.003640863,"about_ca_topic_score_gemma":0.008205478,"teacher_disagreement_score":0.061534952,"about_ca_system_score_codex":0.00033548445,"about_ca_system_score_gemma":0.00024096867,"threshold_uncertainty_score":0.20585495},"labels":[],"label_agreement":null},{"id":"W6931783047","doi":"10.5683/sp3/f1qnjb","title":"Pest management in field vegetables and sugarbeets (2021)","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Guelph","funders":"Ontario Agri-Food Innovation Alliance","keywords":"Downy mildew; Fungicide; Pseudoperonospora cubensis; Integrated pest management; Mancozeb; Host resistance; Chemical control; Disease management","score_opus":0.01322854625937498,"score_gpt":0.2912696279712986,"score_spread":0.2780410817119236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931783047","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":[0.00026347372,0.00005102569,0.000029348656,0.000026616653,0.000007783623,0.000016963473,0.9991948,0.000052747793,0.00035729443],"genre_scores_gemma":[0.0010246644,0.000050323444,0.0002264541,0.00006674935,0.0000045824145,0.00016980633,0.9976063,0.00002810656,0.00082305056],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99901867,0.00019279002,0.00013102677,0.00027248386,0.0002199685,0.00016503499],"domain_scores_gemma":[0.99697316,0.00073449913,0.00055064907,0.0005003426,0.00093804835,0.00030332783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014593051,0.0016368881,0.0010991833,0.0021008032,0.0004581286,0.0013432032,0.0024987522,0.0016778584,0.057634484],"category_scores_gemma":[0.0061131404,0.0007102323,0.0015365953,0.004337206,0.00026321298,0.0007322964,0.0012077183,0.0011736953,0.02926699],"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":[0.0003426586,0.000073078634,0.007608926,0.0023906464,0.00027706975,0.00003897074,0.000037255213,0.001010216,0.00019390867,0.000438387,0.9828483,0.0047406247],"study_design_scores_gemma":[0.0025320025,0.00013489467,0.060215052,0.0017715099,0.00046706392,0.00011087512,0.00020449495,0.0015531264,0.00062954164,0.0010119107,0.93129325,0.00007624326],"about_ca_topic_score_codex":0.10498829,"about_ca_topic_score_gemma":0.1381926,"teacher_disagreement_score":0.10498829,"about_ca_system_score_codex":0.0017177649,"about_ca_system_score_gemma":0.0026432425,"threshold_uncertainty_score":0.20875436},"labels":[],"label_agreement":null},{"id":"W6931838411","doi":"10.5281/zenodo.7674055","title":"International Student Satisfaction: A Comparative Analysis of Student Perceptions, Expectation and Reality, and their Relationship with Recruitment Policies and Approach","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Higher education; Perception; International education; SERVQUAL; Assertion; Qualitative research","score_opus":0.1634476579873941,"score_gpt":0.3864380190221888,"score_spread":0.22299036103479467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931838411","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":[0.9965939,0.0001208433,0.00024391679,0.000087636785,0.000008463511,0.000017818727,0.00009857874,0.0000049508435,0.0028239728],"genre_scores_gemma":[0.9990876,0.0001054484,0.00011027305,0.00002710803,0.0000047834915,0.000016664704,0.0001272596,0.0000030194808,0.00051795825],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9981452,0.0008106659,0.00018595274,0.00009343605,0.00043381928,0.00033091067],"domain_scores_gemma":[0.99497294,0.0012393795,0.0011240137,0.00016884736,0.0013362551,0.0011584491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037052808,0.00015058047,0.0003269643,0.0017584899,0.0008074274,0.0014252166,0.00022853087,0.00024591776,0.0026416907],"category_scores_gemma":[0.0072172084,0.00010129362,0.0004624072,0.0028763588,0.00055955147,0.0008093135,0.0012994027,0.00049923017,0.00031703935],"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":[0.000221677,0.00040764126,0.9494632,0.000049866267,0.000038997558,0.000060785685,0.012595111,0.000066508874,0.0002190804,0.00035328625,0.00053339545,0.035990395],"study_design_scores_gemma":[0.0000052819105,0.0008282001,0.9608312,0.000032694592,0.000024665664,0.00009486495,0.035651725,0.00020881587,0.00019893565,0.00007950481,0.0020296632,0.000014449851],"about_ca_topic_score_codex":0.0047442876,"about_ca_topic_score_gemma":0.0059538935,"teacher_disagreement_score":0.0047442876,"about_ca_system_score_codex":0.00083310687,"about_ca_system_score_gemma":0.0011530328,"threshold_uncertainty_score":0.019595623},"labels":[],"label_agreement":null},{"id":"W6931865582","doi":"10.5281/zenodo.5968585","title":"Clytia longitheca Fraser 1914","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Antipodes; Square (algebra); Population","score_opus":0.03841918452802431,"score_gpt":0.29131738110732275,"score_spread":0.25289819657929846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931865582","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":[0.19340904,0.019962145,0.0037403554,0.0017241171,0.0014443863,0.00024892992,0.0026994392,0.00043052543,0.77634096],"genre_scores_gemma":[0.89776695,0.0040525496,0.0030852247,0.0008626632,0.00033180695,0.00010407674,0.0018184407,0.00007951217,0.09189886],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976856,0.000015698286,0.000011340398,0.00008438153,0.00007536851,0.000044742093],"domain_scores_gemma":[0.99984074,0.000025716014,0.000053060736,0.000012544224,0.00005514161,0.000012702747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013984609,0.0004907177,0.00023150291,0.0009108975,0.0031104172,0.0005415693,0.00065971236,0.0006527375,0.013948488],"category_scores_gemma":[0.00055388117,0.00024706367,0.00018889955,0.0007826877,0.000768799,0.0008045466,0.0009933572,0.0009807845,0.002867479],"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":[0.00024090531,0.00005352606,0.024072468,0.0007361989,0.000049333616,0.002832625,0.004286782,0.0008515142,0.022337308,0.023352388,0.11855091,0.80263615],"study_design_scores_gemma":[0.000037942707,0.000108560525,0.092838086,0.00041139804,0.00004171967,0.002683333,0.0011816054,0.00023672867,0.0019885916,0.0010441373,0.8993979,0.000029971647],"about_ca_topic_score_codex":0.06292291,"about_ca_topic_score_gemma":0.13395701,"teacher_disagreement_score":0.06292291,"about_ca_system_score_codex":0.001605038,"about_ca_system_score_gemma":0.0008657561,"threshold_uncertainty_score":0.12511331},"labels":[],"label_agreement":null},{"id":"W6939217508","doi":"10.60692/w3gbd-smd69","title":"The burden of premature mortality from cardiovascular diseases: A systematic review of years of life lost","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Insurance, Mortality, Demography, Risk Management","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":"Years of potential life lost; Mortality rate; Population; Life expectancy; Disease; Cause of death; Burden of disease; Quality of life (healthcare)","score_opus":0.024700273145584914,"score_gpt":0.2474257115190437,"score_spread":0.22272543837345876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939217508","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":[0.00068494544,0.9982322,0.00010085704,0.0001781893,0.0000775344,0.00015924145,0.00040960897,0.0000059773743,0.00015149867],"genre_scores_gemma":[0.011846647,0.9861151,0.0005458122,0.00041138625,0.00007618514,0.0006176858,0.000294504,0.000005570489,0.00008718532],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9872866,0.0049583176,0.004418076,0.00081861136,0.002231027,0.00028736837],"domain_scores_gemma":[0.96448696,0.025782922,0.0064285104,0.0004979587,0.0024440256,0.00035953778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012158521,0.0014882123,0.008868039,0.012676367,0.0006848136,0.00322399,0.0019252902,0.0017496645,0.004186633],"category_scores_gemma":[0.0530177,0.0011012366,0.0109581975,0.012257204,0.000884863,0.0030214349,0.0019754628,0.0017918515,0.00032723782],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017471428,0.000009875835,0.00069032505,0.96333784,0.011390396,0.00006187897,0.00018065388,0.00007476176,0.000064215776,0.00016058239,0.0010950245,0.02275979],"study_design_scores_gemma":[0.00022909581,0.00019704331,0.005382307,0.9004558,0.07640623,0.0003307657,0.0002838089,0.00007667742,0.00008390264,0.00036810114,0.016144995,0.000041294523],"about_ca_topic_score_codex":0.007885669,"about_ca_topic_score_gemma":0.025080804,"teacher_disagreement_score":0.012676367,"about_ca_system_score_codex":0.004423682,"about_ca_system_score_gemma":0.010222853,"threshold_uncertainty_score":0.06430119},"labels":[],"label_agreement":null},{"id":"W6939434635","doi":"10.6084/m9.figshare.22729171","title":"Additional file 1 of Identifying dementia using medical data linkage in a longitudinal cohort study: Lothian Birth Cohort 1936","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Cohort; Record linkage; Dementia; Cohort study; Linkage (software); Longitudinal data; Cohort effect; Medical record","score_opus":0.13994692591024716,"score_gpt":0.37847569945824816,"score_spread":0.238528773548001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939434635","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":[0.00028235573,0.000011708298,0.00011104202,0.00004551622,0.000010286906,0.000096111966,0.9985977,0.000039436713,0.00080590724],"genre_scores_gemma":[0.008578987,0.0001358892,0.0014619076,0.00030925244,0.000075824755,0.0026474593,0.97934204,0.00014048092,0.0073081474],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991704,0.00018018919,0.0001843395,0.00019450589,0.00014393733,0.0001266669],"domain_scores_gemma":[0.9899255,0.0056796963,0.0014458074,0.00072307675,0.0018020952,0.00042382878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014787208,0.0007256721,0.0009289436,0.0022076997,0.0012044975,0.0008552399,0.0013188387,0.0008411213,0.69541556],"category_scores_gemma":[0.018256908,0.00044853872,0.00060556125,0.005216148,0.00019720964,0.0009975282,0.0007876892,0.0006374931,0.061813395],"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":[0.00022121969,0.000073342475,0.005960777,0.0009650736,0.000045503475,0.000060786522,0.00007462011,0.00019117526,0.00006619687,0.00057073904,0.9872361,0.0045345295],"study_design_scores_gemma":[0.009832388,0.00068907253,0.18388319,0.005931318,0.0005392225,0.0012998378,0.0012155172,0.0024588099,0.0010176502,0.007969021,0.78496575,0.00019831098],"about_ca_topic_score_codex":0.024951201,"about_ca_topic_score_gemma":0.037969165,"teacher_disagreement_score":0.69541556,"about_ca_system_score_codex":0.0012238421,"about_ca_system_score_gemma":0.0020709066,"threshold_uncertainty_score":0.4344527},"labels":[],"label_agreement":null},{"id":"W6939711011","doi":"10.6084/m9.figshare.19367426.v1","title":"Additional file 1 of Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Simon Fraser University; University of Saskatchewan; Children's Hospital of Eastern Ontario","funders":"","keywords":"Trajectory; Nonlinear system; Longitudinal data; Cohort; Log-linear model; Cohort study","score_opus":0.07154777194446767,"score_gpt":0.3043424142848177,"score_spread":0.23279464234035002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939711011","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":[0.0003115108,0.000018899913,0.0010794852,0.00006259636,0.000016465912,0.000113596696,0.99710995,0.00049610424,0.000791333],"genre_scores_gemma":[0.015913986,0.00022305883,0.016652303,0.00048484028,0.00010662647,0.004806263,0.94562274,0.0027543053,0.013435932],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890363,0.00035817985,0.00018833145,0.00027320965,0.00017146695,0.000105085484],"domain_scores_gemma":[0.9575442,0.036632914,0.0012439785,0.0021290053,0.0019412425,0.0005087367],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038109336,0.0010116204,0.0012276173,0.0024430125,0.0008880964,0.0018271575,0.0020365317,0.0012467217,0.8543993],"category_scores_gemma":[0.055063415,0.0008505069,0.0013117372,0.0039554993,0.00024175475,0.0014381162,0.0011428201,0.0010560065,0.14618188],"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":[0.00034923767,0.00010665163,0.0052507143,0.0018731393,0.00010985771,0.000072995535,0.000107634085,0.001043005,0.000060899732,0.001450817,0.97510195,0.014473111],"study_design_scores_gemma":[0.008754494,0.0005114058,0.04757376,0.0050887167,0.00067321607,0.00085947104,0.0006199911,0.01156334,0.00085673895,0.022464935,0.9007739,0.00025991155],"about_ca_topic_score_codex":0.012868519,"about_ca_topic_score_gemma":0.027827308,"teacher_disagreement_score":0.8543993,"about_ca_system_score_codex":0.00086672395,"about_ca_system_score_gemma":0.0021539582,"threshold_uncertainty_score":0.2076816},"labels":[],"label_agreement":null},{"id":"W6958276859","doi":"10.6084/m9.figshare.13323899.v1","title":"Additional file 1 of Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Saskatchewan","funders":"","keywords":"Exponential smoothing; Autoregressive integrated moving average; Autoregressive model; Series (stratigraphy); Time series; Artificial neural network; Exponential function","score_opus":0.06288930374277486,"score_gpt":0.30321393159085047,"score_spread":0.24032462784807562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958276859","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":[0.00041688172,0.000010286,0.00008747579,0.00004408041,0.000006722854,0.000025039872,0.998906,0.000057114074,0.00044634248],"genre_scores_gemma":[0.012511073,0.00012578853,0.0017478945,0.000075375974,0.000028300165,0.00067601365,0.9785871,0.00014201643,0.006106479],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971205,0.000060754308,0.000058655463,0.000056591558,0.00007125361,0.000040658524],"domain_scores_gemma":[0.9930099,0.0048371693,0.00057094084,0.00030013334,0.0011523548,0.00012945422],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009356617,0.00049517263,0.0005186922,0.001575533,0.00033006998,0.00067366,0.0011795615,0.0004979987,0.69042975],"category_scores_gemma":[0.012447831,0.00028570174,0.0003911475,0.004013687,0.00009106071,0.000941594,0.00050575536,0.0005930385,0.06298233],"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":[0.00022246223,0.00010639321,0.0074849124,0.001726533,0.00004396508,0.00007446123,0.0000791619,0.001871703,0.00007070964,0.0010848904,0.9721086,0.015126357],"study_design_scores_gemma":[0.0029483156,0.00040904916,0.13228245,0.0033684003,0.00023868342,0.00040288686,0.0014149343,0.009975359,0.0010256624,0.008061891,0.83973926,0.00013298677],"about_ca_topic_score_codex":0.019491037,"about_ca_topic_score_gemma":0.031781286,"teacher_disagreement_score":0.69042975,"about_ca_system_score_codex":0.0006528159,"about_ca_system_score_gemma":0.0013031021,"threshold_uncertainty_score":0.44156438},"labels":[],"label_agreement":null},{"id":"W6958478423","doi":"10.6084/m9.figshare.22729171.v1","title":"Additional file 1 of Identifying dementia using medical data linkage in a longitudinal cohort study: Lothian Birth Cohort 1936","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto","funders":"","keywords":"Cohort; Record linkage; Dementia; Cohort study; Linkage (software); Longitudinal data; Cohort effect; Medical record","score_opus":0.13994692591024716,"score_gpt":0.37847569945824816,"score_spread":0.238528773548001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958478423","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":[0.00028235573,0.000011708298,0.00011104202,0.00004551622,0.000010286906,0.000096111966,0.9985977,0.000039436713,0.00080590724],"genre_scores_gemma":[0.008578987,0.0001358892,0.0014619076,0.00030925244,0.000075824755,0.0026474593,0.97934204,0.00014048092,0.0073081474],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991704,0.00018018919,0.0001843395,0.00019450589,0.00014393733,0.0001266669],"domain_scores_gemma":[0.9899255,0.0056796963,0.0014458074,0.00072307675,0.0018020952,0.00042382878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014787208,0.0007256721,0.0009289436,0.0022076997,0.0012044975,0.0008552399,0.0013188387,0.0008411213,0.69541556],"category_scores_gemma":[0.018256908,0.00044853872,0.00060556125,0.005216148,0.00019720964,0.0009975282,0.0007876892,0.0006374931,0.061813395],"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":[0.00022121969,0.000073342475,0.005960777,0.0009650736,0.000045503475,0.000060786522,0.00007462011,0.00019117526,0.00006619687,0.00057073904,0.9872361,0.0045345295],"study_design_scores_gemma":[0.009832388,0.00068907253,0.18388319,0.005931318,0.0005392225,0.0012998378,0.0012155172,0.0024588099,0.0010176502,0.007969021,0.78496575,0.00019831098],"about_ca_topic_score_codex":0.024951201,"about_ca_topic_score_gemma":0.037969165,"teacher_disagreement_score":0.69541556,"about_ca_system_score_codex":0.0012238421,"about_ca_system_score_gemma":0.0020709066,"threshold_uncertainty_score":0.4344527},"labels":[],"label_agreement":null},{"id":"W6958531524","doi":"10.6084/m9.figshare.7159415.v1","title":"Additional file 7: of Revising the motivation and confidence domain of the Canadian assessment of physical literacy","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","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":"Domain (mathematical analysis); Exploratory research; Literacy; Process (computing)","score_opus":0.02584762798514404,"score_gpt":0.31816646572288854,"score_spread":0.2923188377377445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958531524","genre_codex":"dataset","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":[0.0015334727,0.00003468498,0.0005172843,0.00031168177,0.0000706943,0.0004735105,0.989917,0.00024975452,0.006892051],"genre_scores_gemma":[0.06467208,0.00032480212,0.013238663,0.0008730354,0.00011285667,0.011065088,0.862275,0.0013582796,0.046080165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986299,0.00029810448,0.00015080944,0.00014554829,0.0005040966,0.0002716297],"domain_scores_gemma":[0.9572599,0.027253604,0.0008352302,0.0017836691,0.01215531,0.0007122823],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004138397,0.0013029154,0.0010313891,0.0045082173,0.0024060053,0.0028283428,0.0027058509,0.0009658654,0.69424915],"category_scores_gemma":[0.06185379,0.00071680767,0.0025484676,0.0049510296,0.00071633374,0.0024604753,0.0020433043,0.0017152074,0.07280471],"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":[0.0001736381,0.00009961759,0.01180058,0.0013326741,0.000044817698,0.00006024172,0.0007174936,0.00040418198,0.00003435502,0.0010884118,0.97118706,0.013056988],"study_design_scores_gemma":[0.0018856891,0.00014911957,0.27776396,0.007989587,0.00041255145,0.00034907667,0.01124072,0.0038544734,0.0008899595,0.007799251,0.6871696,0.0004959378],"about_ca_topic_score_codex":0.66004944,"about_ca_topic_score_gemma":0.74474174,"teacher_disagreement_score":0.69424915,"about_ca_system_score_codex":0.006894387,"about_ca_system_score_gemma":0.01565056,"threshold_uncertainty_score":0.6839052},"labels":[],"label_agreement":null},{"id":"W6958664121","doi":"10.6084/m9.figshare.26574637.v1","title":"Additional file 2 of Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Trillium Health Centre; University of Toronto; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Socioeconomic status; Working age; Working population; Age adjustment; Distribution (mathematics); Population","score_opus":0.02033929074115702,"score_gpt":0.24366549058213716,"score_spread":0.22332619984098012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958664121","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":[0.00024171735,0.000011750947,0.00002533832,0.000047333247,0.000010360468,0.000042781114,0.9987431,0.00002157263,0.0008559561],"genre_scores_gemma":[0.01322142,0.0001701468,0.00090280036,0.00023599395,0.00005098152,0.0010759393,0.97229326,0.00012393786,0.011925539],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9990895,0.00007142594,0.00014620987,0.00015673648,0.00027756026,0.00025854836],"domain_scores_gemma":[0.9878929,0.0034371172,0.0008952842,0.000593741,0.0063818013,0.000799071],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009332569,0.0007456484,0.0012705359,0.0028554695,0.002008126,0.0016595244,0.0019104613,0.0007396132,0.65311754],"category_scores_gemma":[0.01746211,0.00055544736,0.0012325653,0.008409152,0.00034257176,0.0011330346,0.0011370503,0.0007347902,0.041961808],"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":[0.000089753536,0.000029829796,0.008706619,0.0009366073,0.00003525881,0.000034408426,0.00013216388,0.00019209606,0.000019072579,0.00040410607,0.9862342,0.0031858787],"study_design_scores_gemma":[0.003262462,0.000092584676,0.34371892,0.005381543,0.0002794309,0.00030048518,0.002258508,0.001711206,0.00026177883,0.0023158446,0.6402326,0.00018463407],"about_ca_topic_score_codex":0.89693815,"about_ca_topic_score_gemma":0.9361356,"teacher_disagreement_score":0.65311754,"about_ca_system_score_codex":0.007961505,"about_ca_system_score_gemma":0.016400855,"threshold_uncertainty_score":0.49478573},"labels":[],"label_agreement":null},{"id":"W6958718683","doi":"10.6084/m9.figshare.26735007","title":"Additional file 1 of The impact of different imputation methods on estimates and model performance: an example using a risk prediction model for premature mortality","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Institute for Clinical Evaluative Sciences; Trillium Health Centre; University of Toronto","funders":"","keywords":"Imputation (statistics); Predictive modelling; Statistical model; Risk assessment; Estimation","score_opus":0.11065697930502022,"score_gpt":0.42709387371354846,"score_spread":0.31643689440852824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958718683","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":[0.0011265242,0.000053859778,0.0037137878,0.00033090007,0.00007615472,0.00015179922,0.99123794,0.0012175376,0.0020915573],"genre_scores_gemma":[0.03749378,0.00025412597,0.053492136,0.0015108787,0.0002486232,0.003760779,0.874702,0.005253767,0.023283828],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815303,0.0007976991,0.0002764164,0.0003134285,0.00034143563,0.00011803386],"domain_scores_gemma":[0.85020405,0.13840453,0.001974273,0.003303231,0.005672836,0.00044102507],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0051868604,0.0009812836,0.0010338599,0.0014490796,0.000632117,0.0013928887,0.0019738334,0.0019237853,0.8439617],"category_scores_gemma":[0.07482726,0.0006894748,0.0010622026,0.0020999198,0.00022314003,0.0011167907,0.00079388643,0.0013077615,0.1511251],"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":[0.00047820044,0.00026513007,0.0026097207,0.0016428886,0.00011010436,0.000118156735,0.000070762835,0.0031686523,0.00012532699,0.0013762639,0.96752155,0.022513265],"study_design_scores_gemma":[0.017428802,0.001087594,0.052202716,0.005396358,0.0007677228,0.001793153,0.000830549,0.07922642,0.004593907,0.05032887,0.78584397,0.00049978984],"about_ca_topic_score_codex":0.00839271,"about_ca_topic_score_gemma":0.012523319,"teacher_disagreement_score":0.8439617,"about_ca_system_score_codex":0.0010006875,"about_ca_system_score_gemma":0.0016820033,"threshold_uncertainty_score":0.22256958},"labels":[],"label_agreement":null},{"id":"W6967472066","doi":"10.5281/zenodo.10535104","title":"Can incorporating parity information improve the reliability of fertility projections? Insights from a Bayesian generalized additive model approach","year":2024,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Economic and Social Research Council; UK Research and Innovation","keywords":"Fertility; Bayesian probability; Parity (physics); Generalized additive model; Total fertility rate; Population; Covariate; Population model","score_opus":0.03163762232064737,"score_gpt":0.2691747580649061,"score_spread":0.23753713574425872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967472066","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":[0.22666936,0.0020791846,0.7459675,0.010856913,0.0003100213,0.000095896525,0.0020659093,0.0007206851,0.011234559],"genre_scores_gemma":[0.94101554,0.0009103501,0.05490986,0.00036049797,0.00015238715,0.000051466603,0.0008102476,0.00013372433,0.0016559984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773777,0.0015645331,0.00008315241,0.00027099083,0.00018386343,0.00015971894],"domain_scores_gemma":[0.98042434,0.015822805,0.0011411955,0.0011207077,0.0011982607,0.0002927349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010147257,0.0007326964,0.0012492315,0.0012731482,0.00052935357,0.0021379446,0.0019685267,0.0017140099,0.003432839],"category_scores_gemma":[0.059171524,0.00088157004,0.0012205354,0.0011325037,0.00092715834,0.0030288512,0.0018849309,0.0017613155,0.00049337005],"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":[0.00012515535,0.000037986727,0.018107064,0.00012030473,0.00018810022,0.00013500037,0.0003201918,0.862315,0.00028052978,0.066850126,0.0030486817,0.04847192],"study_design_scores_gemma":[0.000024221126,0.000025670786,0.003005501,0.00007122944,0.00004792816,0.000040783267,0.0000648102,0.90825826,0.00013877708,0.08664828,0.0016325283,0.00004204963],"about_ca_topic_score_codex":0.04296069,"about_ca_topic_score_gemma":0.03121413,"teacher_disagreement_score":0.04296069,"about_ca_system_score_codex":0.0012928711,"about_ca_system_score_gemma":0.0017739006,"threshold_uncertainty_score":0.085421264},"labels":[],"label_agreement":null},{"id":"W6976773390","doi":"10.6084/m9.figshare.19185751.v1","title":"Additional file 1 of The predictive power of geographic health care utilization for unintentional fatal fall rates","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 de Sherbrooke; University of Toronto; Queen's University","funders":"","keywords":"Linear regression; Predictive power; Mortality rate; Regression analysis; Table (database); Cause of death; Regression; Logistic regression; Standard error","score_opus":0.03735000795266646,"score_gpt":0.3050505090088681,"score_spread":0.26770050105620163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976773390","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":[0.0002912678,0.000009291592,0.00010916013,0.0000579608,0.000009087871,0.00003838479,0.9988481,0.00007228736,0.0005644726],"genre_scores_gemma":[0.014221964,0.00011431004,0.002090941,0.0003689857,0.000081677885,0.0017067576,0.9736395,0.00031181631,0.007464102],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99933213,0.00014056156,0.000111443565,0.00015742192,0.00016224981,0.000096269185],"domain_scores_gemma":[0.9782362,0.015855437,0.0015178078,0.0010049574,0.0029903904,0.00039517749],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013356832,0.00086464407,0.0008650051,0.00210559,0.0006693119,0.0011552018,0.0015033754,0.00090958795,0.76800925],"category_scores_gemma":[0.033651136,0.00040633013,0.0008653562,0.0036398326,0.0001458598,0.0013827045,0.00091800507,0.0010047173,0.11345556],"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":[0.00010488699,0.00006938797,0.007493591,0.0007536089,0.00004961598,0.00002798607,0.0000448208,0.0003900828,0.00002196011,0.00047292662,0.98492014,0.0056509916],"study_design_scores_gemma":[0.0051806415,0.00045709143,0.16985492,0.006428453,0.00055001467,0.0007218143,0.001290554,0.009961492,0.00077605597,0.010353089,0.79422307,0.00020278967],"about_ca_topic_score_codex":0.022236435,"about_ca_topic_score_gemma":0.027285853,"teacher_disagreement_score":0.76800925,"about_ca_system_score_codex":0.00078397914,"about_ca_system_score_gemma":0.001699134,"threshold_uncertainty_score":0.33090663},"labels":[],"label_agreement":null},{"id":"W6976954848","doi":"10.6084/m9.figshare.26574637","title":"Additional file 2 of Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Trillium Health Centre; University of Toronto; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Socioeconomic status; Working age; Working population; Age adjustment; Distribution (mathematics); Population","score_opus":0.02033929074115702,"score_gpt":0.24366549058213716,"score_spread":0.22332619984098012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976954848","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":[0.00024171735,0.000011750947,0.00002533832,0.000047333247,0.000010360468,0.000042781114,0.9987431,0.00002157263,0.0008559561],"genre_scores_gemma":[0.01322142,0.0001701468,0.00090280036,0.00023599395,0.00005098152,0.0010759393,0.97229326,0.00012393786,0.011925539],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990895,0.00007142594,0.00014620987,0.00015673648,0.00027756026,0.00025854836],"domain_scores_gemma":[0.9878929,0.0034371172,0.0008952842,0.000593741,0.0063818013,0.000799071],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009332569,0.0007456484,0.0012705359,0.0028554695,0.002008126,0.0016595244,0.0019104613,0.0007396132,0.65311754],"category_scores_gemma":[0.01746211,0.00055544736,0.0012325653,0.008409152,0.00034257176,0.0011330346,0.0011370503,0.0007347902,0.041961808],"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":[0.000089753536,0.000029829796,0.008706619,0.0009366073,0.00003525881,0.000034408426,0.00013216388,0.00019209606,0.000019072579,0.00040410607,0.9862342,0.0031858787],"study_design_scores_gemma":[0.003262462,0.000092584676,0.34371892,0.005381543,0.0002794309,0.00030048518,0.002258508,0.001711206,0.00026177883,0.0023158446,0.6402326,0.00018463407],"about_ca_topic_score_codex":0.89693815,"about_ca_topic_score_gemma":0.9361356,"teacher_disagreement_score":0.65311754,"about_ca_system_score_codex":0.007961505,"about_ca_system_score_gemma":0.016400855,"threshold_uncertainty_score":0.49478573},"labels":[],"label_agreement":null},{"id":"W6977578777","doi":"10.6084/m9.figshare.29826007.v1","title":"Additional file 1 of Contributions of injury deaths to changes in life expectancy and disparity: A comparative analysis of G7 countries over two decades","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"","keywords":"Life expectancy; Poison control; MEDLINE; Occupational safety and health; Mortality rate","score_opus":0.023495346438426747,"score_gpt":0.36164627719503384,"score_spread":0.33815093075660707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977578777","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":[0.0008681881,0.000016603051,0.00005375353,0.00004031632,0.000009500483,0.000025823001,0.998228,0.000047088408,0.00071071245],"genre_scores_gemma":[0.032003745,0.00016531348,0.0012282776,0.0002088515,0.000060877523,0.0007023873,0.9577015,0.00019536173,0.0077335928],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999271,0.00014086884,0.000121603065,0.00015377859,0.00013279838,0.00017993289],"domain_scores_gemma":[0.9849133,0.00929907,0.0021828602,0.0009850977,0.002099883,0.00051970495],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011711731,0.00063611876,0.00074199116,0.004189377,0.00045994142,0.00092212873,0.0015617037,0.0007824815,0.70666283],"category_scores_gemma":[0.01842998,0.00049052504,0.0011065287,0.010074443,0.00021007769,0.0011988385,0.0010058015,0.0006884913,0.07404837],"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":[0.0004192728,0.00013226154,0.03504664,0.0022989258,0.00021835689,0.0001319374,0.00017127737,0.001074492,0.000056106022,0.0009441409,0.9482248,0.011281853],"study_design_scores_gemma":[0.0039874916,0.00045323404,0.5325905,0.0038440647,0.0007287612,0.0009603355,0.002894618,0.0032773854,0.00047941136,0.004233184,0.4463945,0.00015652478],"about_ca_topic_score_codex":0.039217524,"about_ca_topic_score_gemma":0.03976557,"teacher_disagreement_score":0.29333717,"about_ca_system_score_codex":0.0008750828,"about_ca_system_score_gemma":0.0015827465,"threshold_uncertainty_score":0.41840982},"labels":[],"label_agreement":null},{"id":"W6980606930","doi":"","title":"Comparing forecasting ability of parametric and non-parametric methods: An applications with Canadian monthly interest rates","year":2003,"lang":"en","type":"article","venue":"Dspace Repository (Marmara Üniversitesi)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Parametric statistics; Interest rate; Nonparametric statistics; Economic forecasting; Parametric model; Measure (data warehouse)","score_opus":0.04888129534142673,"score_gpt":0.3149771188404559,"score_spread":0.2660958234990292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980606930","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":[0.95096415,0.002251625,0.040045056,0.0009761087,0.000106351275,0.00008512915,0.0012172582,0.00046636848,0.0038878776],"genre_scores_gemma":[0.96428484,0.0009078382,0.032067455,0.000037761354,0.000029982624,0.000042989148,0.00094538025,0.00007454897,0.001609065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840885,0.00076813804,0.000104038896,0.00017897383,0.00037450524,0.00016546091],"domain_scores_gemma":[0.9595576,0.03502333,0.000683475,0.0010385258,0.0033337465,0.00036334846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0109390775,0.0007613218,0.0008049327,0.001652465,0.00088652724,0.0013620984,0.0013348853,0.0011955001,0.001626364],"category_scores_gemma":[0.050336596,0.0002759234,0.0008118702,0.0027238477,0.00050907355,0.0011692602,0.0007219005,0.0010251577,0.0001418918],"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":[0.0018588424,0.0006549924,0.08769994,0.00047746056,0.00037590353,0.0003736049,0.0014442544,0.6066273,0.0017504258,0.015377915,0.008485282,0.27487415],"study_design_scores_gemma":[0.00011002362,0.00017543403,0.037534192,0.00005001415,0.000110667126,0.000064472326,0.00048877526,0.9563616,0.0012586507,0.002209694,0.0015525144,0.00008405329],"about_ca_topic_score_codex":0.70049155,"about_ca_topic_score_gemma":0.59855235,"teacher_disagreement_score":0.29950845,"about_ca_system_score_codex":0.004251356,"about_ca_system_score_gemma":0.006155809,"threshold_uncertainty_score":0.6025446},"labels":[],"label_agreement":null},{"id":"W6982952605","doi":"","title":"La desconocida mortalidad de la población en las residencias de personas mayores de España","year":2023,"lang":"es","type":"article","venue":"Scientific Electronic library online (Sciences Carlos III Health Institute)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 de Montréal","funders":"","keywords":"Persona; Context (archaeology); Coronavirus disease 2019 (COVID-19)","score_opus":0.02136034685514877,"score_gpt":0.366054196111807,"score_spread":0.34469384925665825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6982952605","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":[0.9751902,0.0033737104,0.00036588675,0.0013307718,0.000060883413,0.000021270096,0.0031806803,0.000034699784,0.016441736],"genre_scores_gemma":[0.9888206,0.0036650233,0.00027535716,0.00022700886,0.000050450857,0.000028793691,0.0017183804,0.000005450678,0.0052089985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995726,0.00010767769,0.00004096873,0.000074797856,0.00011576393,0.000088156026],"domain_scores_gemma":[0.9988225,0.00028294686,0.00034977583,0.000068523965,0.00033180442,0.00014434186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093524414,0.00025028552,0.00022102006,0.0013748457,0.00046664118,0.0006910965,0.00038147697,0.00044338327,0.008359387],"category_scores_gemma":[0.0027417748,0.000108093984,0.00033652072,0.0011856084,0.00035652085,0.00044691673,0.0008071916,0.000376049,0.00075594825],"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":[0.00016882241,0.00005415044,0.9487111,0.00018658018,0.00008213588,0.00025016905,0.0012755907,0.00014155339,0.0006009505,0.00038235675,0.0028468969,0.04529956],"study_design_scores_gemma":[0.0000029419168,0.00008401079,0.9936214,0.0001534592,0.000038054044,0.00020534464,0.0019184762,0.00008699144,0.0001599241,0.00015467974,0.0035689692,0.0000057234106],"about_ca_topic_score_codex":0.051174384,"about_ca_topic_score_gemma":0.06123462,"teacher_disagreement_score":0.051174384,"about_ca_system_score_codex":0.0008384726,"about_ca_system_score_gemma":0.0008958124,"threshold_uncertainty_score":0.101753},"labels":[],"label_agreement":null},{"id":"W6989450306","doi":"","title":"ASSET LIABILITY MANAGEMENT AND JOINT MORTALITY MODELLING IN OLD-AGE INSURANCE","year":2016,"lang":"en","type":"dissertation","venue":"IRIS","topic":"Insurance, Mortality, Demography, Risk Management","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":"Actuary; Pension; Liability; Asset (computer security); Context (archaeology); Asset management; Population; Asset allocation","score_opus":0.0420426593483902,"score_gpt":0.32409676469509424,"score_spread":0.28205410534670405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989450306","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":[0.109954655,0.005581921,0.8567358,0.0033240563,0.0001873261,0.00010573686,0.0010900735,0.00013693587,0.022883464],"genre_scores_gemma":[0.9219977,0.004297471,0.052183125,0.00021741574,0.0002658402,0.00025880092,0.00068579376,0.000060500337,0.020033292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991341,0.00041917898,0.00005117717,0.00012479912,0.00015032358,0.0001203589],"domain_scores_gemma":[0.9975897,0.0015709216,0.00043417263,0.000073063,0.00020191375,0.00013021695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002403397,0.00063637766,0.00069483335,0.0008763115,0.00039324723,0.0021404864,0.001427521,0.0017768735,0.0032071976],"category_scores_gemma":[0.006292662,0.00036959563,0.0010071459,0.0009465398,0.0010887105,0.0015210385,0.0015893733,0.0016225799,0.00028481486],"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":[0.000016822605,0.00003065651,0.0025827733,0.000078069344,0.00003257793,0.00013042672,0.00014507421,0.77655315,0.00020989418,0.21067944,0.0013113634,0.008229752],"study_design_scores_gemma":[0.0000040865375,0.00002109718,0.0006886612,0.000044857596,0.000012154751,0.000042187257,0.000042310192,0.9234164,0.000084547406,0.073680304,0.0019501778,0.000013252042],"about_ca_topic_score_codex":0.009478386,"about_ca_topic_score_gemma":0.0045055966,"teacher_disagreement_score":0.009478386,"about_ca_system_score_codex":0.0012414468,"about_ca_system_score_gemma":0.001522199,"threshold_uncertainty_score":0.018846452},"labels":[],"label_agreement":null},{"id":"W6990294012","doi":"","title":"Demographic Decisions and Demographic Well-Being","year":2006,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Surprise; Honor; Government (linguistics); Presidential system; Population; Social policy; Presidential address","score_opus":0.011069445073691163,"score_gpt":0.26764497380907254,"score_spread":0.25657552873538136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990294012","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":[0.91032,0.003913204,0.00055581634,0.03087388,0.00011831598,0.000016792732,0.0012976003,0.000013724218,0.052890684],"genre_scores_gemma":[0.995494,0.0016170954,0.000077219804,0.00033716552,0.0000587904,0.000005339754,0.0001844659,0.0000025554048,0.002223447],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993812,0.00030932893,0.000018551578,0.000043223845,0.00006597371,0.00018163034],"domain_scores_gemma":[0.9964394,0.00091424753,0.0009867346,0.00008807998,0.0003026319,0.0012688653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008928715,0.00011618604,0.00018260795,0.0008844113,0.00089552643,0.0012801638,0.00017712354,0.00057089434,0.009519077],"category_scores_gemma":[0.0046555763,0.00011483675,0.00021575473,0.0013358715,0.0011043824,0.0013497794,0.000776286,0.00087840937,0.00074421294],"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":[0.00011637353,0.0002529197,0.8884672,0.00006198053,0.00012835732,0.00037926855,0.00659512,0.0011612625,0.00014086599,0.06283893,0.010933945,0.028923718],"study_design_scores_gemma":[0.0000058895794,0.00008464999,0.94674313,0.000099729135,0.000030253688,0.00022845976,0.010014348,0.00083851867,0.00007438955,0.021192193,0.020651095,0.000037331825],"about_ca_topic_score_codex":0.017507868,"about_ca_topic_score_gemma":0.029365772,"teacher_disagreement_score":0.017507868,"about_ca_system_score_codex":0.00076318503,"about_ca_system_score_gemma":0.00040308404,"threshold_uncertainty_score":0.034811974},"labels":[],"label_agreement":null},{"id":"W6992392279","doi":"","title":"The Legend of HemLoft, A Secret Treehouse Hidden in Whistler BC","year":2022,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Girl; Wilderness; Nothing; Legend; White (mutation); Social media; Rumor","score_opus":0.008195998111082804,"score_gpt":0.2145214057498688,"score_spread":0.206325407638786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992392279","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":[0.0030913623,0.036824737,0.0012587607,0.019388549,0.012116882,0.00006961876,0.0019859294,0.00065598765,0.9246081],"genre_scores_gemma":[0.01617662,0.01746905,0.00051839655,0.002838449,0.0005723712,0.000013770505,0.00067980547,0.00040925614,0.9613222],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993844,0.000040197265,0.000012442133,0.000059191174,0.00037909186,0.00012472125],"domain_scores_gemma":[0.9989982,0.000069313486,0.000020846686,0.000054587774,0.00068804173,0.00016891542],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039473304,0.000575419,0.00028920913,0.0010728064,0.0070819687,0.00617747,0.0007329035,0.0013792464,0.13293828],"category_scores_gemma":[0.0016721883,0.00028932383,0.00021270839,0.0016913128,0.0020383059,0.0022758846,0.0012978851,0.0023249912,0.030781338],"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":[0.000017247949,0.000004071038,0.0001470213,0.00015008324,0.000002305493,0.0001823823,0.0009808962,0.000043227476,0.00032637705,0.012067226,0.94775575,0.038323414],"study_design_scores_gemma":[4.9918486e-7,0.0000011406597,0.00016462182,0.00007178111,0.0000010123472,0.000045802935,0.00036494673,0.000005201517,0.00008988611,0.00008975455,0.9991621,0.0000032895343],"about_ca_topic_score_codex":0.78867406,"about_ca_topic_score_gemma":0.93464553,"teacher_disagreement_score":0.86706173,"about_ca_system_score_codex":0.013415918,"about_ca_system_score_gemma":0.015596324,"threshold_uncertainty_score":0.44472283},"labels":[],"label_agreement":null},{"id":"W6996132590","doi":"","title":"Regional disparities in Canadian adult and old-age mortality: A comparative study based on smoothed mortality ratio surfaces and age at death distributions","year":2012,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Mortality rate; Cause of death; Population; Standardized mortality ratio; Excess mortality; Life expectancy; Developed country; Epidemiology","score_opus":0.321646190518451,"score_gpt":0.5478623732128239,"score_spread":0.22621618269437294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996132590","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":[0.9940065,0.0006030592,0.00031855283,0.000082814186,0.000004364819,0.000015207121,0.0037054268,0.00001739096,0.0012466551],"genre_scores_gemma":[0.9970999,0.00024992225,0.00032543545,0.000010755611,0.0000026260132,0.000004951935,0.0020747012,0.0000060735188,0.0002257248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992292,0.00008466076,0.000049944658,0.00013694672,0.00030971575,0.0001895285],"domain_scores_gemma":[0.9981229,0.0002709334,0.0003746026,0.00017061745,0.0008588692,0.00020216852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012401904,0.0002760434,0.0003540024,0.0036995292,0.0012066955,0.000956916,0.0008210216,0.00022887628,0.001532971],"category_scores_gemma":[0.0047405045,0.00017199923,0.00063812104,0.009325569,0.0006096419,0.00043108605,0.0008449274,0.0003071176,0.00013530953],"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":[0.00010239006,0.00001456362,0.98537946,0.00003574318,0.000117399184,0.0000573452,0.0011078833,0.000475564,0.00014664014,0.00038588836,0.0006327618,0.011544458],"study_design_scores_gemma":[0.000001808429,0.000009017121,0.99832016,0.0000073776896,0.000017165607,0.000030009498,0.0006122774,0.0004039974,0.000030032772,0.000031200594,0.0005292565,0.00000767861],"about_ca_topic_score_codex":0.9857723,"about_ca_topic_score_gemma":0.9900733,"teacher_disagreement_score":0.014227688,"about_ca_system_score_codex":0.010846049,"about_ca_system_score_gemma":0.012342748,"threshold_uncertainty_score":0.078693986},"labels":[],"label_agreement":null},{"id":"W6997143190","doi":"","title":"Úmrtnost podle příčin v České republice, Německu a Francii v uplynulých čtyřech desetiletích","year":2010,"lang":"cs","type":"dissertation","venue":"Digital Repository (National Repository of Grey Literature)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Czech; Table (database); Bachelor; Quarter (Canadian coin); German","score_opus":0.00844815654778467,"score_gpt":0.270614246415664,"score_spread":0.2621660898678793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997143190","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":[0.23550802,0.16311179,0.0050845896,0.055053197,0.005935503,0.00020489098,0.004415679,0.00029541747,0.5303909],"genre_scores_gemma":[0.6062965,0.06746806,0.0034915959,0.0028512147,0.0010371068,0.00017473554,0.0021928435,0.0004258842,0.31606206],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990332,0.00019647444,0.000050877505,0.00020156958,0.00029143627,0.00022647198],"domain_scores_gemma":[0.9993048,0.0001395417,0.00007532407,0.000078380675,0.00021626147,0.000185811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011173066,0.00030707984,0.0005882521,0.0012381055,0.00258514,0.0047683315,0.00036583998,0.000647071,0.01966168],"category_scores_gemma":[0.0024248844,0.00023167803,0.0002865657,0.002164701,0.001487123,0.0020694782,0.0024943408,0.0021419565,0.0035009591],"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":[0.00051487394,0.0002019625,0.025803737,0.0016119349,0.00009876002,0.001181952,0.018920043,0.0015796209,0.0054221074,0.22460729,0.24625935,0.47379845],"study_design_scores_gemma":[0.000011657359,0.000048404472,0.030433139,0.0008983784,0.0000151685645,0.00031102556,0.004877209,0.00013589762,0.0010390359,0.0060975295,0.9561001,0.00003234029],"about_ca_topic_score_codex":0.018299503,"about_ca_topic_score_gemma":0.035134472,"teacher_disagreement_score":0.01966168,"about_ca_system_score_codex":0.003188728,"about_ca_system_score_gemma":0.008279573,"threshold_uncertainty_score":0.06577492},"labels":[],"label_agreement":null},{"id":"W7006934443","doi":"","title":"WORLD POPULATION IN 2050: ASSESSING THE PROJECTIONS","year":2011,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dependency ratio; Population; Projections of population growth; Quarter (Canadian coin); Population projection; World population","score_opus":0.08668983263663514,"score_gpt":0.37235755075133714,"score_spread":0.285667718114702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006934443","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":[0.1870719,0.18962167,0.11893127,0.10446822,0.013974389,0.0010663047,0.10571385,0.0029045618,0.27624777],"genre_scores_gemma":[0.6608301,0.17416489,0.10232779,0.0046061273,0.0016519945,0.0017533185,0.04578643,0.0003095026,0.008569949],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988293,0.0004889015,0.000085460866,0.00008026108,0.00041933972,0.000096719494],"domain_scores_gemma":[0.9981957,0.0003985185,0.00018808998,0.000054051838,0.0010223149,0.00014130789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033578435,0.0017527705,0.00048282996,0.0036928144,0.0006473322,0.0022721684,0.0009853471,0.0011683807,0.0051279212],"category_scores_gemma":[0.009480208,0.00030405275,0.0011159584,0.0039817006,0.00034187356,0.0047652978,0.0017350708,0.0015147792,0.0020448023],"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":[0.00036645902,0.00013627991,0.05261239,0.0036865226,0.0003966972,0.0008086875,0.00084605446,0.12906036,0.00054255856,0.090286545,0.24410151,0.47715595],"study_design_scores_gemma":[0.00006417471,0.00080410374,0.07865854,0.006400547,0.00050149934,0.0015045318,0.011885997,0.15529653,0.001626132,0.14106175,0.6017904,0.00040569316],"about_ca_topic_score_codex":0.028401248,"about_ca_topic_score_gemma":0.023050861,"teacher_disagreement_score":0.028401248,"about_ca_system_score_codex":0.0030745454,"about_ca_system_score_gemma":0.0031261768,"threshold_uncertainty_score":0.056471884},"labels":[],"label_agreement":null},{"id":"W7008357978","doi":"","title":"CAN IMMIGRATION COMPENSATE FOR BELOW-REPLACEMENT FERTILITY?: THE CONSEQUENCES OF THE UNBALANCED SETTLEMENT OF IMMIGRANTS IN CANADIAN CITIES, 2001-2051.","year":2006,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Immigration; Settlement (finance); Distribution (mathematics); Internal migration; Population; Age structure; Population growth; Fertility","score_opus":0.052292884131141595,"score_gpt":0.3061771859043804,"score_spread":0.25388430177323884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008357978","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":[0.98438925,0.0012082907,0.00028429774,0.004616091,0.00005239211,0.000031880933,0.0024421124,0.000016991928,0.006958762],"genre_scores_gemma":[0.99744654,0.0004971542,0.00015779102,0.00014213541,0.000007916072,0.000004870369,0.0005388871,0.0000036004644,0.0012010263],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992631,0.00006408922,0.0000173985,0.00006385166,0.0001598402,0.0004317217],"domain_scores_gemma":[0.9987614,0.000086016196,0.000317541,0.000058550475,0.00047823592,0.0002982552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000934434,0.00036175264,0.0002921634,0.00062331016,0.0030134663,0.0019528051,0.0013416109,0.0008951541,0.002571203],"category_scores_gemma":[0.0035700493,0.0002187604,0.00072425336,0.0015002977,0.0009048652,0.0008721706,0.001242654,0.000768502,0.00020758618],"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":[0.0002537024,0.000057036865,0.93396556,0.00013678962,0.00015104079,0.0010089375,0.0021391315,0.0108947605,0.0007518932,0.00605939,0.0075695156,0.037012197],"study_design_scores_gemma":[0.000016048642,0.00007226246,0.9760641,0.00006144983,0.000116579045,0.0002542101,0.0067630257,0.0057489057,0.000298755,0.0010457485,0.009526946,0.00003201213],"about_ca_topic_score_codex":0.9750049,"about_ca_topic_score_gemma":0.9902029,"teacher_disagreement_score":0.029945228,"about_ca_system_score_codex":0.029945228,"about_ca_system_score_gemma":0.025517343,"threshold_uncertainty_score":0.21726882},"labels":[],"label_agreement":null},{"id":"W7010887149","doi":"","title":"La mauvaise perception des risques de longévité et de dépendance ne suffit pas à expliquer la faiblesse du marché de l'assurance dépendance (au Canada)","year":2023,"lang":"en","type":"book","venue":"Toulouse 1 Capitole Publications (Université Toulouse I Capitole)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Perception; Risk perception; Public policy; Insurance policy; General insurance","score_opus":0.016533864424659865,"score_gpt":0.2632129719555877,"score_spread":0.2466791075309278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010887149","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":[0.5009082,0.060652215,0.006423788,0.03248698,0.00057143753,0.00006299991,0.0061768657,0.00025560884,0.39246187],"genre_scores_gemma":[0.8953044,0.020813411,0.0019725212,0.00056741684,0.000121138,0.00001567978,0.000799724,0.000034719164,0.08037098],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974054,0.00002018286,0.000006408215,0.000029892239,0.00016790182,0.0000350485],"domain_scores_gemma":[0.99886864,0.00042059628,0.00015535549,0.000035014193,0.0003940607,0.00012632374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004449579,0.000235826,0.00017276849,0.0009227013,0.0011303697,0.0018316278,0.00042386295,0.00039764974,0.011387983],"category_scores_gemma":[0.0022975313,0.000102424405,0.00023500649,0.0014315691,0.00095906045,0.00066739146,0.00028640017,0.00048268292,0.00070272],"study_design_candidate":"theoretical_or_conceptual","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":[0.00014371477,0.000048870752,0.34627995,0.00065523176,0.000093839044,0.00046117365,0.016493425,0.0023960173,0.0024171225,0.06653003,0.086644776,0.47783583],"study_design_scores_gemma":[0.000010303776,0.000062619685,0.6868383,0.0003719271,0.00006269354,0.0006159573,0.0065888036,0.0016844444,0.0007752269,0.00403134,0.2988992,0.000059115377],"about_ca_topic_score_codex":0.9627709,"about_ca_topic_score_gemma":0.97810525,"teacher_disagreement_score":0.03722912,"about_ca_system_score_codex":0.015686382,"about_ca_system_score_gemma":0.01040603,"threshold_uncertainty_score":0.11381316},"labels":[],"label_agreement":null},{"id":"W7014530375","doi":"","title":"A probabilistic forecast of the immigrant population of Norway","year":2023,"lang":"en","type":"other","venue":"Econstor (Econstor)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Probabilistic logic; Immigration; Population; Fertility; Statistical model; Population projection","score_opus":0.01577716760424043,"score_gpt":0.2614924864921598,"score_spread":0.24571531888791937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014530375","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":[0.8504597,0.0005601039,0.0853146,0.0020561859,0.0005665681,0.00010386593,0.039481062,0.0008761086,0.020581953],"genre_scores_gemma":[0.96477115,0.0003538084,0.012917402,0.000101208425,0.000084547646,0.00008102954,0.017913405,0.00006960764,0.0037079598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997086,0.00006904941,0.00001552974,0.000083468876,0.0000737785,0.0000496159],"domain_scores_gemma":[0.9992519,0.0002400157,0.00012132505,0.00006605054,0.00024324475,0.00007734619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007660099,0.0004536285,0.0002837021,0.0005852629,0.0002946842,0.00089538656,0.00066469464,0.0006324322,0.0027767958],"category_scores_gemma":[0.002977646,0.0003114972,0.0007855858,0.0005836658,0.0003592731,0.0007168996,0.000449303,0.00065759756,0.000613526],"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":[0.00007200196,0.000014777399,0.0216641,0.000013685264,0.00001939985,0.00008950318,0.000060844017,0.9652716,0.00023550427,0.004685666,0.004254954,0.0036179044],"study_design_scores_gemma":[0.000032792388,0.00004408673,0.01427077,0.000031100375,0.000018162618,0.000053877895,0.00013178847,0.9767908,0.00034262377,0.0035613647,0.004672563,0.00005008962],"about_ca_topic_score_codex":0.14248319,"about_ca_topic_score_gemma":0.06101966,"teacher_disagreement_score":0.14248319,"about_ca_system_score_codex":0.0013616385,"about_ca_system_score_gemma":0.0012902042,"threshold_uncertainty_score":0.28330767},"labels":[],"label_agreement":null},{"id":"W7016228715","doi":"","title":"Want to Build a Solid Retirement Corpus? Buy These 3 Canadian Stocks Right Now! By The Motley Fool","year":2021,"lang":"en","type":"other","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Motley; Work (physics); Point (geometry)","score_opus":0.015254934153573789,"score_gpt":0.2996384081907695,"score_spread":0.2843834740371957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7016228715","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":[0.0017410337,0.00382883,0.0007481073,0.08799908,0.009395914,0.00014731802,0.01849085,0.0018924625,0.8757563],"genre_scores_gemma":[0.0029910041,0.0008017028,0.00051197386,0.004396634,0.0004767072,0.000029020097,0.001713473,0.00040054548,0.9886789],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998858,0.000054347678,0.000023413866,0.000053150416,0.00071415544,0.00029683197],"domain_scores_gemma":[0.9931409,0.00030584467,0.00009563464,0.00026236908,0.0039139153,0.0022814085],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015950251,0.00069975614,0.00046539135,0.0023944972,0.00703283,0.0061396863,0.001085297,0.0021855005,0.3757028],"category_scores_gemma":[0.008046745,0.0004359012,0.0006565727,0.0023002382,0.0013761531,0.0025781805,0.0024656386,0.0027330508,0.15314622],"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":[0.0000040738287,0.0000027106225,0.00011508088,0.0000047804256,5.9322684e-7,0.0000047631374,0.000021596225,0.000007915539,0.000009206501,0.0011811544,0.9899406,0.008707455],"study_design_scores_gemma":[0.0000055830146,0.0000031307065,0.0011431093,0.000039639428,0.0000017628658,0.0000068516106,0.00017319109,0.000038740796,0.000030913612,0.00080095575,0.99774593,0.000010064628],"about_ca_topic_score_codex":0.8945499,"about_ca_topic_score_gemma":0.9611735,"teacher_disagreement_score":0.3757028,"about_ca_system_score_codex":0.01375881,"about_ca_system_score_gemma":0.039828338,"threshold_uncertainty_score":0.8904842},"labels":[],"label_agreement":null},{"id":"W7017413999","doi":"","title":"On basis risk in mortality CAT bonds","year":2015,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Manitoba","funders":"","keywords":"Population; Basis (linear algebra); Mortality rate; Bond; Hedge; Basis risk; Basis point","score_opus":0.023040042123038285,"score_gpt":0.2732327873701039,"score_spread":0.25019274524706564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017413999","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":[0.80641586,0.00065984187,0.18001674,0.00080107735,0.000049085367,0.000052600735,0.00013081457,0.000066874236,0.01180705],"genre_scores_gemma":[0.9915714,0.00020385574,0.006486222,0.000043201206,0.00002055721,0.000028605986,0.000051615898,0.000016984892,0.0015774738],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868685,0.00063064933,0.000060872106,0.00021008898,0.0003003296,0.00011119044],"domain_scores_gemma":[0.9930264,0.0035998106,0.0020036479,0.0006473097,0.00044774543,0.00027512523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036798143,0.00041382152,0.00050917786,0.0009181288,0.00051994354,0.0018549998,0.0007383621,0.0010693122,0.0027596557],"category_scores_gemma":[0.016342703,0.00027814496,0.0004933195,0.0007408208,0.0014364566,0.0027658802,0.0018416292,0.0018620507,0.000161513],"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":[0.00018816316,0.0001132578,0.062811024,0.000094477145,0.00016029515,0.00049641187,0.0012216466,0.26894468,0.0035278308,0.625013,0.0017529613,0.035676256],"study_design_scores_gemma":[0.000016414497,0.0002563348,0.026732353,0.00007288111,0.000056444667,0.00045098737,0.0007051424,0.650128,0.0016287884,0.31738892,0.002490324,0.00007347479],"about_ca_topic_score_codex":0.0017208764,"about_ca_topic_score_gemma":0.0009947929,"teacher_disagreement_score":0.0036798143,"about_ca_system_score_codex":0.00090051576,"about_ca_system_score_gemma":0.00036200273,"threshold_uncertainty_score":0.019460917},"labels":[],"label_agreement":null},{"id":"W7017901432","doi":"","title":"Calculation of function for population dynamics model with continuous-time age","year":2008,"lang":"en","type":"article","venue":"The scientific electronic library of periodicals of the National Academy of Sciences of Ukraine (National Academy of Sciences of Ukraine)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Function (biology); Identification (biology); Process (computing); Dynamics (music)","score_opus":0.034395264220084105,"score_gpt":0.30370554711837555,"score_spread":0.26931028289829145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017901432","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":[0.009445467,0.00014254083,0.9884974,0.00008142516,0.00002568096,0.0000180997,0.00004158846,0.00015928398,0.0015885626],"genre_scores_gemma":[0.58181936,0.0011328724,0.4054751,0.00012257409,0.000136643,0.000500732,0.00040791108,0.00032320688,0.010081651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997781,0.00007524434,0.000010914174,0.00004447425,0.00005989061,0.00003123716],"domain_scores_gemma":[0.9993742,0.00036115022,0.000056646284,0.00004847427,0.00012659395,0.000032963337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091090106,0.0007961975,0.0007882498,0.0011463129,0.00063016027,0.00077783136,0.0012129246,0.0011683684,0.0031692944],"category_scores_gemma":[0.0027219497,0.0002678176,0.0010720902,0.0005360112,0.00048545512,0.0013844775,0.0007273656,0.0010567865,0.000681553],"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":[0.00001904581,0.000029594532,0.0015919125,0.00012243557,0.000029268891,0.00021034165,0.00012840514,0.90530276,0.00133076,0.0686707,0.000807141,0.02175764],"study_design_scores_gemma":[0.0000029198527,0.000007853639,0.00012385326,0.0000056584145,0.0000062255685,0.000037479585,0.000009619534,0.9897827,0.00019353384,0.009204108,0.0006210199,0.00000511681],"about_ca_topic_score_codex":0.0063397507,"about_ca_topic_score_gemma":0.002583073,"teacher_disagreement_score":0.0063397507,"about_ca_system_score_codex":0.0007757661,"about_ca_system_score_gemma":0.0011530936,"threshold_uncertainty_score":0.012605727},"labels":[],"label_agreement":null},{"id":"W7018728039","doi":"","title":"Does the recent evolution of Canadian mortality agree with the epidemiologic transition theory?","year":2008,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Transition (genetics); Epidemiological transition; Process (computing); Demographic transition; Epidemiology","score_opus":0.2620022850131893,"score_gpt":0.5126357683035362,"score_spread":0.2506334832903469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018728039","genre_codex":"commentary","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":[0.32831904,0.039402097,0.02361638,0.36616528,0.0020967156,0.00020892893,0.006257371,0.00030205733,0.23363213],"genre_scores_gemma":[0.95964235,0.015230458,0.005921139,0.009923444,0.0006347213,0.00003514524,0.0012075292,0.000053029235,0.007352291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99754167,0.00022698168,0.00011106115,0.0004368199,0.0009806951,0.00070275995],"domain_scores_gemma":[0.9886889,0.001983146,0.0016169957,0.0007846924,0.0060353875,0.0008907725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006123989,0.0003802619,0.00093237654,0.003846715,0.004643706,0.0046408107,0.0033078794,0.0016752364,0.005948339],"category_scores_gemma":[0.029158767,0.00024034227,0.001062686,0.008666112,0.004871514,0.0034490805,0.0019075404,0.0027810598,0.00033446],"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":[0.00032400718,0.00006756793,0.29257002,0.0006168642,0.00032948202,0.0005457837,0.010432143,0.0044787573,0.00032291672,0.5619102,0.020131173,0.10827106],"study_design_scores_gemma":[0.000062237945,0.000086026565,0.6911534,0.00067267416,0.0002798043,0.0004783194,0.0106289135,0.009016126,0.00032038815,0.16961133,0.11746065,0.00023008515],"about_ca_topic_score_codex":0.97447443,"about_ca_topic_score_gemma":0.9660908,"teacher_disagreement_score":0.042454917,"about_ca_system_score_codex":0.042454917,"about_ca_system_score_gemma":0.030976374,"threshold_uncertainty_score":0.3080334},"labels":[],"label_agreement":null},{"id":"W7019260699","doi":"","title":"Fact book on aging in British Columbia, 3rd Edition","year":2000,"lang":"en","type":"book","venue":"Summit (Simon Fraser University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Ethnic group; Poverty; Population; Distribution (mathematics); Population ageing; Marital status; Incidence (geometry); Household income","score_opus":0.011440831267672433,"score_gpt":0.22256071755057516,"score_spread":0.21111988628290274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019260699","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":[0.0009428951,0.18437321,0.0013633074,0.03325447,0.06222528,0.00037547597,0.08344931,0.0013917508,0.6326244],"genre_scores_gemma":[0.0021567103,0.072960794,0.0011365953,0.004593121,0.0043297745,0.00021222622,0.027956732,0.00035211892,0.886302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99947053,0.00003072735,0.000046455843,0.00006346673,0.00031901745,0.00006977608],"domain_scores_gemma":[0.9970709,0.0002245948,0.00008534337,0.000101239275,0.0021925361,0.00032545612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000628153,0.001428018,0.0011970412,0.004347675,0.0019552147,0.0035131255,0.0015454862,0.0014668284,0.20549028],"category_scores_gemma":[0.003593985,0.00043265725,0.00051350915,0.008583711,0.00049591396,0.001303052,0.0007969539,0.0017767332,0.12767477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034119355,0.0000024444632,0.000048549675,0.00007389025,0.0000010075904,0.000013008046,0.000011610526,0.000018486291,0.000016214375,0.00025365947,0.9804725,0.019085195],"study_design_scores_gemma":[0.0000021922435,0.0000027739948,0.00079088606,0.00029400017,0.0000027255292,0.000045302153,0.00003888039,0.000015670239,0.00001356771,0.00022072262,0.99856794,0.000005338129],"about_ca_topic_score_codex":0.4744902,"about_ca_topic_score_gemma":0.6786203,"teacher_disagreement_score":0.52550983,"about_ca_system_score_codex":0.0068812543,"about_ca_system_score_gemma":0.018186567,"threshold_uncertainty_score":0.9434567},"labels":[],"label_agreement":null},{"id":"W7028460538","doi":"","title":"Faculty of Arts Weekly Bulletin : 2009-03-31","year":2009,"lang":"en","type":"other","venue":"oURspace (University of Regina)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Saskatchewan Health Research Foundation","keywords":"The arts; Snapshot (computer storage); Student life","score_opus":0.016907824154035574,"score_gpt":0.24769454098686183,"score_spread":0.23078671683282626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028460538","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019208303,0.00059138326,0.00018001236,0.0036699413,0.0034613763,0.00018084413,0.015664546,0.0012199066,0.97311133],"genre_scores_gemma":[0.0026317537,0.00021091216,0.00013181224,0.00026430227,0.0002147308,0.000043236574,0.002981212,0.00022513166,0.9932969],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961793,0.000054480704,0.000019516812,0.000047190508,0.00018204625,0.000078726596],"domain_scores_gemma":[0.99836487,0.0001073961,0.00007304917,0.000120149525,0.00063124945,0.00070324534],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007892761,0.0004855382,0.00036385484,0.0013781262,0.0017312338,0.0034276047,0.0006792759,0.0009751976,0.6493178],"category_scores_gemma":[0.0023066874,0.00022565202,0.00025168277,0.0014645425,0.0002937924,0.00114296,0.0012719975,0.0009732302,0.47044194],"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":[0.000026030362,0.000023924806,0.00017051904,0.000028360599,7.248102e-7,0.000014116943,0.000033688393,0.000009924007,0.00005378689,0.00037139704,0.983723,0.015544581],"study_design_scores_gemma":[0.000004227479,0.0000075384464,0.0010493752,0.000021133408,4.91515e-7,0.000009573879,0.00006107875,0.000012129809,0.000045663222,0.000035986835,0.9987502,0.0000025646632],"about_ca_topic_score_codex":0.014148781,"about_ca_topic_score_gemma":0.042824503,"teacher_disagreement_score":0.6493178,"about_ca_system_score_codex":0.0015288556,"about_ca_system_score_gemma":0.001795926,"threshold_uncertainty_score":0.5002056},"labels":[],"label_agreement":null},{"id":"W7028541239","doi":"","title":"Evolution of lipopolysaccharide glycosyltransferase specificity examined by the study of homologous enzymes","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Glycosyltransferase; Enzyme; Substrate specificity; Lipopolysaccharide; Homologous chromosome","score_opus":0.02360419686118303,"score_gpt":0.2593313267162307,"score_spread":0.23572712985504768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028541239","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":[0.99727505,0.0001950059,0.00048114394,0.00008366664,0.00001116645,0.0000085796455,0.00017297288,0.0000072842986,0.0017651143],"genre_scores_gemma":[0.99700683,0.00020365679,0.00074311247,0.00003232112,0.000009679492,0.0000052528408,0.00040100282,0.000014290513,0.0015839683],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974364,0.00005645895,0.000016845623,0.00007967738,0.000063066014,0.00004033008],"domain_scores_gemma":[0.9985483,0.0003993545,0.000374289,0.00015198476,0.00034837733,0.00017763938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005984697,0.00016985444,0.00032253857,0.0009713961,0.00034768425,0.00088116986,0.00033711395,0.0004796025,0.0019276869],"category_scores_gemma":[0.001592017,0.00018137036,0.00027066335,0.0009166311,0.0002996789,0.00035201517,0.00036682954,0.00064993417,0.0007112334],"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":[0.0019080504,0.00025395898,0.12893936,0.00011643357,0.00014577727,0.000733862,0.00072840624,0.0007031969,0.8231864,0.0019548538,0.0004963052,0.040833246],"study_design_scores_gemma":[0.000091696355,0.00084358105,0.7982203,0.000024824036,0.000121100624,0.0021824373,0.0008583131,0.0047157686,0.17963655,0.0010262011,0.012224116,0.00005507963],"about_ca_topic_score_codex":0.0014268468,"about_ca_topic_score_gemma":0.00086344866,"teacher_disagreement_score":0.0019276869,"about_ca_system_score_codex":0.00045929343,"about_ca_system_score_gemma":0.00029227984,"threshold_uncertainty_score":0.006448686},"labels":[],"label_agreement":null},{"id":"W7029719466","doi":"","title":"La historia carnavalesca del 68 en Palinuro de MÃ©xico de Fernando del Paso","year":2010,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Identity (music); Subversion; Sugar industry; Context (archaeology)","score_opus":0.002952077461067618,"score_gpt":0.16714585216392616,"score_spread":0.16419377470285854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029719466","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":[0.1678169,0.027569802,0.0010896794,0.02575949,0.00083663285,0.000034342364,0.00032384275,0.00010813588,0.7764611],"genre_scores_gemma":[0.6481907,0.005939042,0.00041009783,0.0019182381,0.00016250658,0.00005136184,0.00011378236,0.000081594764,0.3431328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983263,0.000040386672,0.00000238234,0.000034238386,0.000026150256,0.000064247986],"domain_scores_gemma":[0.99988794,0.000043432417,0.000018945702,0.000008592646,0.00001936091,0.00002170417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022234321,0.00026237237,0.00012154502,0.00052358623,0.0054221842,0.0017334067,0.0002991568,0.000812247,0.012959676],"category_scores_gemma":[0.00056033867,0.0001468792,0.000085123145,0.0007898314,0.0027656225,0.0008096294,0.001446228,0.001551607,0.0007823167],"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":[0.00019224778,0.00003596904,0.009809837,0.00041802824,0.000010200056,0.0023689845,0.17916873,0.00027628543,0.003676893,0.6144246,0.09350121,0.09611709],"study_design_scores_gemma":[0.000003387347,0.000007360656,0.008149521,0.00017448296,0.0000025391164,0.00015354106,0.0054195286,0.000026865424,0.00033496335,0.0019095114,0.9838125,0.0000058331957],"about_ca_topic_score_codex":0.17204168,"about_ca_topic_score_gemma":0.40161997,"teacher_disagreement_score":0.17204168,"about_ca_system_score_codex":0.007863397,"about_ca_system_score_gemma":0.0029058948,"threshold_uncertainty_score":0.3420806},"labels":[],"label_agreement":null},{"id":"W7030461663","doi":"","title":"Novel strategies for the prevention of cisplatin-induced ototoxicity","year":2013,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Ototoxicity; Cisplatin; Oxidative stress; Inflammation; Inner ear; Hearing loss; Dexamethasone; Regulator","score_opus":0.04190195676015222,"score_gpt":0.3150495211580179,"score_spread":0.27314756439786564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030461663","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":[0.08384969,0.81120956,0.063251525,0.009487266,0.0030749498,0.0008267292,0.0005367909,0.00069096696,0.027072432],"genre_scores_gemma":[0.35264704,0.59364,0.032934684,0.004601771,0.0011891241,0.00056038593,0.0007012069,0.000076028846,0.013649761],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998254,0.00003298838,0.0000150496235,0.00003574744,0.000056281107,0.00003464919],"domain_scores_gemma":[0.9998864,0.000018574448,0.000027324444,0.000008585326,0.00003714895,0.000021908394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028152284,0.00052561087,0.000830963,0.00095259736,0.0003414532,0.0007708846,0.0007325337,0.0008602951,0.004033934],"category_scores_gemma":[0.00030835805,0.00019786341,0.0009691728,0.00033390083,0.0004215781,0.0009977582,0.00069376075,0.0020433597,0.00089762075],"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":[0.00085028546,0.0013455928,0.0012314778,0.01183549,0.00041911256,0.0018655589,0.0003058285,0.0018772085,0.3724527,0.01618112,0.013583573,0.578052],"study_design_scores_gemma":[0.0006340743,0.007036344,0.01069973,0.0028584318,0.0010664192,0.0064059366,0.00059446995,0.005625471,0.2181944,0.020053186,0.7266674,0.00016408967],"about_ca_topic_score_codex":0.00047465245,"about_ca_topic_score_gemma":0.0011776895,"teacher_disagreement_score":0.004033934,"about_ca_system_score_codex":0.00038782018,"about_ca_system_score_gemma":0.000626762,"threshold_uncertainty_score":0.01349479},"labels":[],"label_agreement":null},{"id":"W7034593885","doi":"","title":"A Unique LAD: The Story of No. 131 Light Aid Detachment, RCEME, Attached to 1 Canadian Rocket Battery, RCA, during the Second World War","year":2022,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rocket (weapon); World War II; Government (linguistics); First world war","score_opus":0.0055511708787791365,"score_gpt":0.20507835705504746,"score_spread":0.19952718617626833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034593885","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":[0.007460801,0.008115687,0.000244857,0.5046739,0.017677374,0.00003382086,0.0007598322,0.0001491606,0.46088457],"genre_scores_gemma":[0.02945922,0.003692442,0.0000923405,0.063663535,0.0024613217,0.00002280619,0.0001704345,0.00014647841,0.90029144],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993267,0.00016599568,0.000010702611,0.00006131927,0.00016814744,0.00026722025],"domain_scores_gemma":[0.99887115,0.00019055542,0.00004535153,0.000041926196,0.00020432932,0.00064665725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007173892,0.0005896335,0.00029430908,0.00048473102,0.017493453,0.005471202,0.0007879677,0.0047712103,0.072084405],"category_scores_gemma":[0.0050486154,0.00041573565,0.00021102809,0.0008516812,0.0019682671,0.0025281468,0.0033638363,0.010026181,0.024562413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005592584,0.000003546174,0.00014893993,0.0000056746876,6.118308e-7,0.00021514994,0.0010466822,0.000009430725,0.000014431906,0.0008774054,0.99437153,0.0033010617],"study_design_scores_gemma":[0.0000016115342,0.000004953996,0.0004345238,0.000053631484,0.0000011088332,0.00027357388,0.008090841,0.000011273472,0.000019643856,0.00023759401,0.9908636,0.0000075324247],"about_ca_topic_score_codex":0.17565303,"about_ca_topic_score_gemma":0.45760176,"teacher_disagreement_score":0.82434696,"about_ca_system_score_codex":0.0038962916,"about_ca_system_score_gemma":0.005422583,"threshold_uncertainty_score":0.34926122},"labels":[],"label_agreement":null},{"id":"W7036474869","doi":"","title":"Book Review: <i> âh-âyîtaw isi ê-kî-kiskêyihtahkik maskihkiy . They Knew Both Sides of Medicine: Cree Tales of Curing and Cursing</i>. Told by Alice Ahenakew","year":2002,"lang":"en","type":"article","venue":"Lincoln (University of Nebraska)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Memoir; Alice (programming language); Subarctic climate; Marie curie; Work (physics)","score_opus":0.015153257058508738,"score_gpt":0.24525235370817344,"score_spread":0.2300990966496647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036474869","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":[0.00072315877,0.61833495,0.0007773369,0.0862357,0.10484979,0.00022907875,0.0018323339,0.00046160712,0.18655603],"genre_scores_gemma":[0.0036828164,0.30505487,0.00072570326,0.033615533,0.029736044,0.00016132022,0.0016958299,0.00021407909,0.62511384],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996604,0.00004381283,0.000028032297,0.000052125426,0.00019172044,0.00002400308],"domain_scores_gemma":[0.9976647,0.0010032968,0.00015837737,0.00005958395,0.00085376564,0.00026022966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005163151,0.0010980724,0.0013890192,0.0019786987,0.00072445284,0.0022709537,0.0013933211,0.0019335875,0.13402829],"category_scores_gemma":[0.0040992615,0.000357297,0.00036223576,0.0031149255,0.00072046614,0.0025210632,0.0008732342,0.0027879395,0.08760336],"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":[0.000006822525,0.0000053287076,0.00002355207,0.00027313936,0.0000024832602,0.000023563798,0.00001677663,0.000010430496,0.00003248187,0.0002020531,0.97840595,0.02099744],"study_design_scores_gemma":[0.0000055930036,0.0000101904225,0.00032506313,0.00045738867,0.000003645523,0.000194222,0.000042468637,0.000013107353,0.000027414975,0.00012330264,0.99879164,0.0000059428526],"about_ca_topic_score_codex":0.0056183953,"about_ca_topic_score_gemma":0.016523797,"teacher_disagreement_score":0.13402829,"about_ca_system_score_codex":0.0011584151,"about_ca_system_score_gemma":0.0015727459,"threshold_uncertainty_score":0.44836926},"labels":[],"label_agreement":null},{"id":"W7036714827","doi":"","title":"Cohort Working Life Tables for Older Canadians","year":2010,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Life expectancy; Life table; Cohort; Cohort effect; Cohort study; Table (database)","score_opus":0.1835761142201277,"score_gpt":0.5371482407965803,"score_spread":0.3535721265764526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036714827","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":[0.0044571427,0.0013939807,0.0026225413,0.00036627302,0.0001789107,0.0004809761,0.95835644,0.000577992,0.031565767],"genre_scores_gemma":[0.0315716,0.003685369,0.0110683385,0.00028815746,0.00008746867,0.0010896834,0.9053298,0.00027780843,0.046601705],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99861085,0.00009601054,0.00015421228,0.00014511588,0.0006845724,0.00030924968],"domain_scores_gemma":[0.99254674,0.00059225375,0.0004586872,0.0005534879,0.0054620216,0.0003867886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019397212,0.0006001207,0.0004659267,0.009724852,0.0017033314,0.001601828,0.0012429582,0.00031208724,0.06521333],"category_scores_gemma":[0.008370984,0.0003636003,0.001071248,0.015106402,0.00018640644,0.00063807215,0.00069885177,0.0006935542,0.00854979],"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":[0.00011741308,0.000029425164,0.025991825,0.00070899836,0.00010944771,0.000091291535,0.00051484554,0.0018401984,0.00011851264,0.010021338,0.8593978,0.10105898],"study_design_scores_gemma":[0.000045773937,0.000022281816,0.12624954,0.0003552799,0.000053038166,0.00010016424,0.00045253517,0.0011998344,0.00011543663,0.0015544872,0.86979413,0.00005759139],"about_ca_topic_score_codex":0.9539131,"about_ca_topic_score_gemma":0.9600378,"teacher_disagreement_score":0.06521333,"about_ca_system_score_codex":0.013405507,"about_ca_system_score_gemma":0.030762695,"threshold_uncertainty_score":0.21816027},"labels":[],"label_agreement":null},{"id":"W7044162482","doi":"","title":"Valuation and Risk Management of Some Longevity and P&amp;C Insurance Products","year":2019,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Mitacs","keywords":"Valuation (finance); Longevity risk; Risk management; Annuity; Life insurance; Order (exchange); Interest rate; Risk assessment; Risk management tools","score_opus":0.0820132015119853,"score_gpt":0.3280132749673863,"score_spread":0.246000073455401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7044162482","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":[0.7288286,0.0012041525,0.25021577,0.00059638015,0.00004521076,0.00008503276,0.00020710935,0.00014998537,0.018667838],"genre_scores_gemma":[0.96938825,0.00041912863,0.02650848,0.000010024988,0.000018756098,0.000015964188,0.00008664647,0.000012269526,0.003540557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997069,0.00007249393,0.000016985832,0.000039971776,0.00013788481,0.000025657702],"domain_scores_gemma":[0.9993544,0.00029981873,0.00013511413,0.00007311543,0.000082292485,0.000055271696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001244717,0.000314635,0.00026880583,0.000739666,0.0003024346,0.0016423365,0.0005984811,0.0006598353,0.002147084],"category_scores_gemma":[0.002468011,0.00013057642,0.00053984026,0.0006170186,0.0006316855,0.0018605988,0.0006923157,0.0007783303,0.00013260523],"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":[0.0002643508,0.00020145309,0.017098023,0.00016984058,0.00005134066,0.0013884477,0.00075767207,0.4583786,0.019255407,0.3513862,0.0019692797,0.14907943],"study_design_scores_gemma":[0.000008557609,0.00017310183,0.004359676,0.000032032924,0.000014413854,0.0003420745,0.00015899523,0.94383943,0.0031997997,0.044284713,0.0035621072,0.00002517806],"about_ca_topic_score_codex":0.0010545006,"about_ca_topic_score_gemma":0.0005751092,"teacher_disagreement_score":0.002147084,"about_ca_system_score_codex":0.0007969243,"about_ca_system_score_gemma":0.0004040589,"threshold_uncertainty_score":0.0071827173},"labels":[],"label_agreement":null},{"id":"W7045223611","doi":"","title":"An Actuarial Science Research Study into the Opportunities &amp; Challenges of Self‐quantification Consumer Health Data","year":2017,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Queen's University","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Durvalumab","score_opus":0.31687334985341425,"score_gpt":0.4694292861695174,"score_spread":0.15255593631610315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045223611","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":[0.6999702,0.006811604,0.03942974,0.10398661,0.00059550354,0.0008177819,0.0029923304,0.00012441883,0.14527178],"genre_scores_gemma":[0.9768446,0.0018592161,0.011053349,0.0020719771,0.00024923455,0.00018205287,0.0003574614,0.000054058124,0.0073279794],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9532979,0.04067234,0.00075368164,0.0010007472,0.0035795644,0.0006956765],"domain_scores_gemma":[0.44257927,0.5247539,0.0075159594,0.009769468,0.013523523,0.001857812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07932876,0.00030637128,0.00045278476,0.0034184975,0.0024090183,0.0062765195,0.001259843,0.0015566696,0.012915571],"category_scores_gemma":[0.18423438,0.00039858202,0.00054057344,0.0059082257,0.004711596,0.009007927,0.0026380585,0.0028505952,0.00077149464],"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":[0.0009916696,0.0022777047,0.12935296,0.0009023531,0.00021620051,0.00036058496,0.020491539,0.0059474795,0.00062203634,0.6322941,0.030813048,0.17573038],"study_design_scores_gemma":[0.0005585285,0.0027070437,0.16721143,0.0027718828,0.0005410657,0.000720595,0.118991874,0.100474425,0.005259515,0.35319373,0.24723256,0.00033741596],"about_ca_topic_score_codex":0.024639757,"about_ca_topic_score_gemma":0.01916839,"teacher_disagreement_score":0.07932876,"about_ca_system_score_codex":0.0070966505,"about_ca_system_score_gemma":0.0077314912,"threshold_uncertainty_score":0.41953558},"labels":[],"label_agreement":null},{"id":"W7066589116","doi":"","title":"The impact of the under-reporting of vital events upon epidemiological and demographic measures of the Manitoba Registered Indian population : an exercise in data quality","year":2002,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Epidemiology; Population; Life expectancy; Fertility; Demographic analysis; Epidemiological transition; Data quality; Mortality rate; Life table; Aggregate data","score_opus":0.1323210815349026,"score_gpt":0.34926492715627583,"score_spread":0.21694384562137323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066589116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7353209,0.014322427,0.12883629,0.068205364,0.0019215688,0.002980908,0.009608921,0.00057846366,0.038225185],"genre_scores_gemma":[0.9565965,0.0021232057,0.030078819,0.006389286,0.00073632866,0.00070257956,0.0016169187,0.00022557768,0.0015307899],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.39947376,0.46555042,0.045612194,0.011851116,0.07439325,0.0031191965],"domain_scores_gemma":[0.124714755,0.6378798,0.11286762,0.062412318,0.06021262,0.001912837],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.33674595,0.00064261595,0.0011713231,0.006279765,0.003599573,0.005556197,0.0026566212,0.0011647686,0.0013759454],"category_scores_gemma":[0.6663061,0.0012440438,0.0013944629,0.018754352,0.0086452365,0.005662383,0.0056709917,0.0031496808,0.0003623822],"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":[0.00054410147,0.000097887045,0.86254305,0.0012960057,0.0006647907,0.0003191304,0.0122685395,0.003559415,0.0007753815,0.009762499,0.0049170917,0.10325207],"study_design_scores_gemma":[0.000048107857,0.00051216676,0.9516288,0.0017166315,0.00045230082,0.00081414846,0.006764062,0.0058047012,0.002306822,0.0052844216,0.02446089,0.00020688654],"about_ca_topic_score_codex":0.080895126,"about_ca_topic_score_gemma":0.07902388,"teacher_disagreement_score":0.9191049,"about_ca_system_score_codex":0.01076073,"about_ca_system_score_gemma":0.011077148,"threshold_uncertainty_score":0.8179103},"labels":[],"label_agreement":null},{"id":"W7067181848","doi":"","title":"Life Insurance, Annuities &amp; Pensions: A Canadian Text (3rd Edition)","year":2023,"lang":"en","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Life insurance; Payment; Work (physics); Term (time)","score_opus":0.033592508096064254,"score_gpt":0.26173314771775447,"score_spread":0.2281406396216902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067181848","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":[0.0010858955,0.23542693,0.003901568,0.041081116,0.059934925,0.0005615432,0.09740785,0.0011135746,0.5594866],"genre_scores_gemma":[0.005320691,0.08676472,0.0038328855,0.007580253,0.0050228327,0.000297749,0.018392792,0.0008128334,0.8719753],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986547,0.00005868117,0.00009821793,0.00011208595,0.0008780218,0.00019824547],"domain_scores_gemma":[0.99675876,0.00027319873,0.000116763826,0.000089833135,0.0024866394,0.0002748173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015971586,0.0026166805,0.001417707,0.009649926,0.0041548666,0.005326624,0.0025862788,0.0030901455,0.10349936],"category_scores_gemma":[0.0045329803,0.0009929129,0.0012584554,0.022776822,0.0020371054,0.002238802,0.0013081664,0.0030163513,0.03821366],"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":[0.000007245813,0.0000082695105,0.00009160863,0.000116657786,0.0000017561408,0.000013562041,0.00007247716,0.00010542842,0.000031220297,0.0027178929,0.97871965,0.018114228],"study_design_scores_gemma":[0.000003234491,0.0000021095323,0.00096938584,0.00015292138,0.0000034487075,0.000014190384,0.000056545854,0.000037256345,0.000021970114,0.00058507104,0.9981457,0.000008134247],"about_ca_topic_score_codex":0.9674502,"about_ca_topic_score_gemma":0.9787507,"teacher_disagreement_score":0.10349936,"about_ca_system_score_codex":0.037778154,"about_ca_system_score_gemma":0.088134445,"threshold_uncertainty_score":0.3462398},"labels":[],"label_agreement":null},{"id":"W7071224251","doi":"","title":"Regional disparities in Canadian adult mortality: a comparative study based on smoothed mortality surfaces and age at death distributions","year":2013,"lang":"en","type":"article","venue":"Archined","topic":"Insurance, Mortality, Demography, Risk Management","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":"Mortality rate; Cause of death; Population; Epidemiology; Developed country; Life expectancy; Demographic analysis","score_opus":0.05679726972952702,"score_gpt":0.34170418620100657,"score_spread":0.28490691647147953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071224251","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":[0.99264574,0.0008491012,0.00033574743,0.00010678496,0.0000057946872,0.000019126599,0.0044329762,0.000016844448,0.0015879291],"genre_scores_gemma":[0.9964818,0.00035138105,0.00035050404,0.0000140336715,0.0000036000672,0.0000067245346,0.0025322235,0.0000067001197,0.00025311962],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911195,0.00010221322,0.00005963858,0.0001603557,0.00034655404,0.0002192156],"domain_scores_gemma":[0.99789137,0.00030309486,0.00040565268,0.00020053647,0.0009809529,0.00021847808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014400375,0.00033976906,0.00038541327,0.0043037995,0.0015496919,0.0009947375,0.00087262905,0.00026591826,0.0017604321],"category_scores_gemma":[0.0049429033,0.00019305536,0.00074147765,0.010344845,0.00066288974,0.00045527748,0.0010052171,0.00035479927,0.0001444319],"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":[0.00011278419,0.000017285956,0.9843539,0.000040468512,0.00015346447,0.0000626809,0.0011645377,0.0004993086,0.00015237188,0.00044864483,0.0007544607,0.012240145],"study_design_scores_gemma":[0.0000021117849,0.0000092697,0.99823725,0.000008891582,0.000023683497,0.000029747858,0.0006518585,0.00036051063,0.00002625685,0.000031934,0.00061057624,0.000007866424],"about_ca_topic_score_codex":0.98751533,"about_ca_topic_score_gemma":0.99151784,"teacher_disagreement_score":0.012539116,"about_ca_system_score_codex":0.012539116,"about_ca_system_score_gemma":0.013752787,"threshold_uncertainty_score":0.090978086},"labels":[],"label_agreement":null},{"id":"W7071963512","doi":"","title":"Utilization of outlier-adjusted lee-carter model in mortality estimation on whole life annuities","year":2019,"lang":"en","type":"dissertation","venue":"OpenMETU (Middle East Technical University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Outlier; Annuity; Life annuity; Estimation; Life insurance; Index (typography)","score_opus":0.09640317945958292,"score_gpt":0.30384652511492216,"score_spread":0.20744334565533923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071963512","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":[0.07574785,0.00083381095,0.9179969,0.00062176667,0.00012445051,0.00007182633,0.00035508545,0.0003370591,0.00391123],"genre_scores_gemma":[0.9331498,0.0009041374,0.056953598,0.00022997297,0.00014601447,0.0001956142,0.0007950045,0.00009262463,0.0075331964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994437,0.00015907144,0.00003430405,0.00014384055,0.00010079164,0.000118310076],"domain_scores_gemma":[0.998833,0.00063296914,0.00013514697,0.000055647044,0.00028113322,0.00006208693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013106825,0.00058292336,0.0011102019,0.0006186683,0.00038415013,0.0010307438,0.0022007548,0.0013854792,0.0020108502],"category_scores_gemma":[0.0041582,0.0003677073,0.0010399469,0.0008917892,0.00044464544,0.0011530163,0.0007618995,0.0014246879,0.00030675944],"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":[0.000052464642,0.000027850027,0.0054874723,0.000046091456,0.00004650766,0.00018807904,0.00008834571,0.9674947,0.0006594799,0.0098698,0.00079986517,0.015239322],"study_design_scores_gemma":[0.0000027144322,0.000012467992,0.00031901794,0.0000062078293,0.0000067000105,0.000024919402,0.000009817292,0.9974049,0.00011910954,0.0017600285,0.00032573313,0.000008464681],"about_ca_topic_score_codex":0.019251253,"about_ca_topic_score_gemma":0.010129551,"teacher_disagreement_score":0.019251253,"about_ca_system_score_codex":0.00062325614,"about_ca_system_score_gemma":0.0011374863,"threshold_uncertainty_score":0.0382784},"labels":[],"label_agreement":null},{"id":"W7082653381","doi":"10.22271/ortho.2025.v11.i3d.3814","title":"Corticosteroid injection versus platelet-rich plasma in the management of knee osteoarthritis: A comparative clinical study","year":2025,"lang":"en","type":"article","venue":"International Journal of Orthopaedics Sciences","topic":"Insurance, Mortality, Demography, Risk Management","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":"Corticosteroid; WOMAC; Osteoarthritis; Visual analogue scale; Clinical study; Triamcinolone acetonide; Randomized controlled trial; Prospective cohort study","score_opus":0.07196386275742161,"score_gpt":0.42800858343715215,"score_spread":0.35604472067973053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7082653381","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":[0.98016983,0.017397193,0.00025854018,0.00012737188,0.00016874292,0.0009673761,0.00013328769,0.000012104847,0.000765567],"genre_scores_gemma":[0.9885252,0.0083899675,0.00061770825,0.00026414872,0.0006599626,0.0008976753,0.0002593971,0.0000037666973,0.00038231732],"study_design_codex":"randomized_trial","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99820435,0.001121275,0.00020280917,0.00015476125,0.00020265547,0.000114162336],"domain_scores_gemma":[0.99764556,0.0011049797,0.000492678,0.00014709146,0.00018937617,0.00042034936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028452168,0.0007191813,0.002361809,0.00074442045,0.00030549132,0.00066586287,0.0007036056,0.0013772387,0.003367139],"category_scores_gemma":[0.0023792982,0.0003318571,0.0011710704,0.0009017427,0.0012621027,0.0007282274,0.000356682,0.0009088966,0.00049567793],"study_design_candidate":"nonrandomized_trial","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":[0.91196424,0.030775275,0.0030772625,0.001953965,0.0012940601,0.0002013337,0.00014481111,0.00021490337,0.0066080545,0.00012764153,0.00017659136,0.04346187],"study_design_scores_gemma":[0.29504573,0.693187,0.008620016,0.00010904881,0.0010986804,0.0001868348,0.000096644704,0.00020840247,0.0007854338,0.000060496706,0.0005769623,0.000024852958],"about_ca_topic_score_codex":0.00047498354,"about_ca_topic_score_gemma":0.00076276006,"teacher_disagreement_score":0.003367139,"about_ca_system_score_codex":0.00041855482,"about_ca_system_score_gemma":0.0007906258,"threshold_uncertainty_score":0.015047133},"labels":[],"label_agreement":null},{"id":"W7083307730","doi":"10.1016/j.engstruct.2025.121327","title":"Second order effects on lateral torsional buckling of beam-columns","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Ottawa","funders":"","keywords":"Buckling; Catenary; Nonlinear system; Shell (structure); Beam (structure); Bending; Finite element method; Eigenvalues and eigenvectors; Bending moment","score_opus":0.003103040047893341,"score_gpt":0.24835064882508165,"score_spread":0.24524760877718832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083307730","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":[0.98627067,0.00024400419,0.004663506,0.00016057464,0.000036712307,0.000010771381,0.00017746825,0.00007137869,0.00836497],"genre_scores_gemma":[0.99678683,0.000061270955,0.0001626886,0.000018360824,0.000007090011,0.000004428116,0.000039898947,0.000023337168,0.0028960202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996444,0.0000691278,0.000012265681,0.000035321336,0.000061382125,0.00017744779],"domain_scores_gemma":[0.99755704,0.0015027212,0.00026210208,0.00015634527,0.00024806996,0.0002737447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043644526,0.00032777144,0.0005018579,0.0009038838,0.00038123483,0.0009715771,0.00039643154,0.0006581233,0.010248718],"category_scores_gemma":[0.0035542736,0.0003147865,0.0005930982,0.0004008038,0.0007143292,0.00054721296,0.00074374786,0.000688717,0.00053635566],"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":[0.0010497893,0.00037974262,0.029310249,0.00029885198,0.0001707349,0.0018521877,0.00047116337,0.8225316,0.10604781,0.01774002,0.0012825747,0.018865393],"study_design_scores_gemma":[0.000035867604,0.0002446713,0.087112196,0.000026620908,0.000056911846,0.00023765975,0.00026189137,0.8928594,0.015230873,0.003378395,0.0004708285,0.00008471289],"about_ca_topic_score_codex":0.0068234154,"about_ca_topic_score_gemma":0.0073887454,"teacher_disagreement_score":0.010248718,"about_ca_system_score_codex":0.0005645264,"about_ca_system_score_gemma":0.00057307153,"threshold_uncertainty_score":0.034285367},"labels":[],"label_agreement":null},{"id":"W7083460550","doi":"10.5281/zenodo.17206088","title":"Code of Publication : Communication-Constrained STL Task Decomposition Through Convex Optimization","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"European Commission","keywords":"Decomposition; Task (project management); Code (set theory); Convex optimization; Regular polygon; Upload","score_opus":0.029886213798674507,"score_gpt":0.3088577265623687,"score_spread":0.2789715127636942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083460550","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":[0.0034835206,0.0007537892,0.67896736,0.0021929895,0.0021917257,0.0014845609,0.1533698,0.078692205,0.078864045],"genre_scores_gemma":[0.08494527,0.0014512457,0.5420514,0.0016657048,0.0006572565,0.0067208037,0.20210388,0.065659076,0.09474541],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922514,0.00015439738,0.00006520859,0.00012787303,0.0003356348,0.0000917047],"domain_scores_gemma":[0.9952668,0.0022261133,0.00012971232,0.0005001736,0.0016995498,0.00017762522],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014592356,0.0016938541,0.000947601,0.0009053055,0.00045297408,0.001475192,0.0018396185,0.001316151,0.3601063],"category_scores_gemma":[0.012762719,0.0005382059,0.00083422853,0.0010570337,0.00039620601,0.0010868261,0.001420428,0.001668416,0.10961438],"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":[0.00031474314,0.00017165825,0.0008857442,0.0010633959,0.000053135933,0.00019460631,0.00007541681,0.056321252,0.0017739413,0.025671383,0.8339415,0.0795331],"study_design_scores_gemma":[0.00081733795,0.00013677962,0.0016693437,0.00042925155,0.000031713458,0.00035141467,0.000082901715,0.48754197,0.006733932,0.04770508,0.45438945,0.0001108103],"about_ca_topic_score_codex":0.0072667943,"about_ca_topic_score_gemma":0.008548384,"teacher_disagreement_score":0.3601063,"about_ca_system_score_codex":0.0009441678,"about_ca_system_score_gemma":0.002666357,"threshold_uncertainty_score":0.9127307},"labels":[],"label_agreement":null},{"id":"W7083622154","doi":"10.23952/jnva.10.2026.1.05","title":"Nonhomogeneous, nonautonomous resonant singular equations","year":2025,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Natural Science Foundation of China; Ministerul Cercetării, Inovării şi Digitalizării; National Science Foundation","keywords":"Differential equation; Stability (learning theory); Boundary value problem; Work (physics); Singularity","score_opus":0.012345643849764656,"score_gpt":0.31020935080541534,"score_spread":0.2978637069556507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083622154","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":[0.7276287,0.0003830114,0.2474194,0.0013448574,0.000106858744,0.000084352236,0.000094019284,0.000067454435,0.022871338],"genre_scores_gemma":[0.97816306,0.0001374454,0.011133379,0.000096293355,0.000051163697,0.000053296946,0.000026085598,0.000015376536,0.010324009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995627,0.0001318518,0.000020089177,0.0000995411,0.00011579082,0.0000699817],"domain_scores_gemma":[0.9993591,0.00024083549,0.00014508754,0.000041565858,0.0000742795,0.00013907922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082001725,0.00064395467,0.00096102746,0.00057962444,0.00065237464,0.0013587627,0.0012497844,0.0018664924,0.0014293874],"category_scores_gemma":[0.0023467222,0.00034527015,0.0007386495,0.0002880278,0.0024956462,0.0009878437,0.0023999834,0.0011521818,0.0001709885],"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":[0.00019565367,0.00018692548,0.002448356,0.0001461529,0.00010325033,0.0021031462,0.00078169577,0.5137506,0.039679617,0.4344742,0.00058612676,0.0055443635],"study_design_scores_gemma":[0.0000533897,0.00008026645,0.00062825915,0.000009675332,0.000019381758,0.00021012439,0.00023941504,0.9511221,0.0018114435,0.045219533,0.0005787899,0.000027468303],"about_ca_topic_score_codex":0.0031262243,"about_ca_topic_score_gemma":0.001717643,"teacher_disagreement_score":0.0031262243,"about_ca_system_score_codex":0.0010374734,"about_ca_system_score_gemma":0.00077052036,"threshold_uncertainty_score":0.0075274706},"labels":[],"label_agreement":null},{"id":"W7083852044","doi":"","title":"Alco Locomotives","year":2009,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Tribute; General partnership; Diesel locomotive; World War II; Spanish Civil War; Honor","score_opus":0.015477456080881446,"score_gpt":0.2805036907255906,"score_spread":0.26502623464470915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083852044","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":[0.0026767945,0.001149004,0.00091122393,0.0012564042,0.0011648076,0.000051484178,0.0011914895,0.0011911362,0.99040765],"genre_scores_gemma":[0.009122342,0.00069514534,0.0007724796,0.0007034012,0.00017587615,0.000026762888,0.0010789689,0.00039827713,0.98702675],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993001,0.0000573447,0.000022494403,0.000098235025,0.00039147312,0.00013032496],"domain_scores_gemma":[0.99945503,0.000039981645,0.000024890358,0.00005291244,0.00030221822,0.00012485826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000353287,0.00085828005,0.00030068198,0.0020906844,0.0034220843,0.0050242404,0.00087784545,0.0014579903,0.27182963],"category_scores_gemma":[0.0010296283,0.0004377492,0.00048193167,0.0016602215,0.0008648476,0.0027398798,0.002689305,0.001551262,0.1594419],"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":[0.000090116286,0.000068291076,0.00067814713,0.00021193484,0.0000036857928,0.0003180229,0.00084699696,0.00011534791,0.0010860385,0.050090253,0.82695764,0.11953351],"study_design_scores_gemma":[0.0000015764768,0.000004200324,0.00011334133,0.000012845956,5.922493e-7,0.000060674185,0.000074617,0.000013210913,0.00006865012,0.00018839136,0.9994597,0.000002295905],"about_ca_topic_score_codex":0.017568562,"about_ca_topic_score_gemma":0.06976739,"teacher_disagreement_score":0.27182963,"about_ca_system_score_codex":0.003243641,"about_ca_system_score_gemma":0.0023055035,"threshold_uncertainty_score":0.9093607},"labels":[],"label_agreement":null},{"id":"W7083867943","doi":"","title":"2018 Grammy Awards returning to NYC after 14 years in LA","year":2018,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Square (algebra); Quarter (Canadian coin)","score_opus":0.012054231277151726,"score_gpt":0.27980714854279987,"score_spread":0.26775291726564815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083867943","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":[0.007126818,0.0018701785,0.0004183643,0.029056424,0.030663611,0.00016686079,0.007995378,0.0013464938,0.92135584],"genre_scores_gemma":[0.00420746,0.0004957056,0.00014061328,0.0006362958,0.0014069168,0.000047162022,0.0019066015,0.0002593684,0.9908999],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999054,0.000062915795,0.000026973154,0.000079845755,0.0005043328,0.0002718879],"domain_scores_gemma":[0.99767965,0.00010537032,0.00006852479,0.000119001204,0.0008052508,0.0012221754],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016335788,0.00079123024,0.00046500555,0.0013117534,0.0050874487,0.006301497,0.00066208694,0.0016495812,0.43295917],"category_scores_gemma":[0.0031981007,0.00020432973,0.00042173266,0.0009918951,0.0005739144,0.0022193484,0.0038411876,0.0034702043,0.15627065],"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":[0.000018121633,0.000018068758,0.00018200916,0.000009452453,7.329211e-7,0.00004079814,0.000043693944,0.0000065272475,0.00004814376,0.0009346116,0.99117625,0.0075216456],"study_design_scores_gemma":[0.000004630036,0.000008787689,0.0014241592,0.000020586132,7.520477e-7,0.000019075282,0.00022535639,0.00002411698,0.000056898163,0.00011388161,0.9980981,0.0000038538656],"about_ca_topic_score_codex":0.031586137,"about_ca_topic_score_gemma":0.13590254,"teacher_disagreement_score":0.5670408,"about_ca_system_score_codex":0.0028857025,"about_ca_system_score_gemma":0.003960169,"threshold_uncertainty_score":0.8088149},"labels":[],"label_agreement":null},{"id":"W7084032112","doi":"10.6084/m9.figshare.c.7981551.v2","title":"“It beats the hell out of going to a hospital”: service user experiences of telemedicine-based symptom-triggered alcohol withdrawal management","year":2025,"lang":"en","type":"other","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Toronto; Waypoint Centre for Mental Health Care; Centre for Addiction and Mental Health","funders":"","keywords":"Thematic analysis; Abstinence; Flexibility (engineering); Intervention (counseling); Alcohol use disorder; Qualitative research; Contingency management; Service (business)","score_opus":0.021276468245244647,"score_gpt":0.3074930782111502,"score_spread":0.28621660996590553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084032112","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":[0.9951799,0.00023491797,0.0008415552,0.0013815444,0.000034675755,0.00005384061,0.000032938573,0.000022004708,0.002218754],"genre_scores_gemma":[0.9981628,0.00021406058,0.00037551718,0.0004614678,0.000016717504,0.00004898739,0.000010992283,0.0000111481595,0.00069830136],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9940106,0.0048436765,0.00015673126,0.00012739876,0.00029681585,0.0005647254],"domain_scores_gemma":[0.99432266,0.0038653212,0.0005224734,0.00016338509,0.00038350985,0.0007427509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054851114,0.00035423975,0.00045793428,0.0005427718,0.0036549654,0.002834013,0.0008821746,0.0013861338,0.0038510954],"category_scores_gemma":[0.009823745,0.000404171,0.00044694357,0.00054072135,0.0034839523,0.002547807,0.0040190984,0.0013957183,0.00033950046],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121616955,0.00015685361,0.00863868,0.000285241,0.000012930431,0.0010823905,0.97494304,0.00005020634,0.00140953,0.000506312,0.0009808079,0.011812368],"study_design_scores_gemma":[0.00001760549,0.00058074965,0.006478555,0.0001716136,0.00001668556,0.0007493466,0.98392975,0.0001806206,0.00040916487,0.00022718204,0.007210947,0.000027796634],"about_ca_topic_score_codex":0.0026533965,"about_ca_topic_score_gemma":0.0038222843,"teacher_disagreement_score":0.0054851114,"about_ca_system_score_codex":0.001278942,"about_ca_system_score_gemma":0.0014345953,"threshold_uncertainty_score":0.029008448},"labels":[],"label_agreement":null},{"id":"W7084048085","doi":"10.64628/aam.n6vgvfccv","title":"The importance of international students to Atlantic Canada","year":2018,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of King's College","funders":"","keywords":"Government (linguistics); Agency (philosophy); Work (physics); Population; Indigenous","score_opus":0.011608375482516019,"score_gpt":0.323104851707689,"score_spread":0.31149647622517296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084048085","genre_codex":"commentary","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":[0.29908216,0.0068496205,0.0003412221,0.488822,0.006309258,0.00009291908,0.00066278124,0.00006338005,0.19777665],"genre_scores_gemma":[0.83857286,0.0057244026,0.0004934691,0.053701162,0.00094966794,0.000043502547,0.00023476357,0.00006979215,0.10021035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99663156,0.0003869408,0.00005769919,0.00015459584,0.00063124165,0.0021379103],"domain_scores_gemma":[0.9725753,0.0015010128,0.00075741485,0.00026919556,0.004499426,0.02039765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025773149,0.00023145702,0.00033829792,0.0010463045,0.013625686,0.00785441,0.0012168764,0.0024780969,0.02536591],"category_scores_gemma":[0.00944064,0.00021434283,0.00031732116,0.0015694466,0.0034491052,0.0015862671,0.003980757,0.005732108,0.0010110928],"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":[0.00029498877,0.00070525164,0.3965617,0.00030013153,0.000069889655,0.0015138718,0.037871685,0.00031937685,0.00077670737,0.049280103,0.3003511,0.21195515],"study_design_scores_gemma":[0.000051397874,0.0001226758,0.30112466,0.00069790694,0.000046407633,0.0003831316,0.09897759,0.0002960338,0.00031971073,0.004271192,0.59361374,0.00009563081],"about_ca_topic_score_codex":0.9544032,"about_ca_topic_score_gemma":0.985316,"teacher_disagreement_score":0.04559678,"about_ca_system_score_codex":0.033649396,"about_ca_system_score_gemma":0.1406641,"threshold_uncertainty_score":0.24414462},"labels":[],"label_agreement":null},{"id":"W7084052036","doi":"10.6084/m9.figshare.30091369.v1","title":"Additional file 1 of Understanding the complexities of oral healthcare delivery in correctional settings: a qualitative exploration of barriers, facilitators, and opportunities","year":2025,"lang":"en","type":"article","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Qualitative research; Health care; Healthcare delivery; MEDLINE; Data collection; Context (archaeology)","score_opus":0.21190428829428729,"score_gpt":0.3969868605810997,"score_spread":0.18508257228681244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084052036","genre_codex":"dataset","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":[0.039007492,0.00052664103,0.004211829,0.009076188,0.000302763,0.0077506034,0.8920117,0.0003610742,0.046751704],"genre_scores_gemma":[0.4213152,0.004746533,0.05696427,0.0091279475,0.00053478865,0.14859688,0.19172859,0.0009247671,0.16606098],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.998976,0.0005494849,0.00010559592,0.000082041406,0.0001529326,0.00013395747],"domain_scores_gemma":[0.93509156,0.059458695,0.0012006244,0.0005374545,0.0028536362,0.0008579567],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0042772256,0.00036944344,0.0005937809,0.0018902492,0.0024805642,0.001542065,0.0012846238,0.00084075314,0.59939367],"category_scores_gemma":[0.032794535,0.00036846966,0.00040323756,0.0040291394,0.00063423306,0.002830475,0.0015464188,0.0010763637,0.021993486],"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":[0.00024396226,0.00047171768,0.008001867,0.010330196,0.000015951862,0.00067184516,0.12072214,0.0005125926,0.00021429773,0.0066337297,0.8000494,0.052132342],"study_design_scores_gemma":[0.0010971567,0.00034563526,0.04798395,0.015062372,0.000060533483,0.00078483863,0.43242595,0.0011382877,0.0005159866,0.013393729,0.48698694,0.00020460098],"about_ca_topic_score_codex":0.031356335,"about_ca_topic_score_gemma":0.069630474,"teacher_disagreement_score":0.59939367,"about_ca_system_score_codex":0.0035748682,"about_ca_system_score_gemma":0.006130865,"threshold_uncertainty_score":0.57141626},"labels":[],"label_agreement":null},{"id":"W7084067148","doi":"10.6084/m9.figshare.c.8032684","title":"Support preferences among women with and without postpartum depression and anxiety disorder","year":2025,"lang":"en","type":"other","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Dalhousie University","funders":"","keywords":"Anxiety; Postpartum depression; Edinburgh Postnatal Depression Scale; Mental health; Depression (economics); Postpartum period; Social support; Outpatient clinic","score_opus":0.011953973655620884,"score_gpt":0.2653279009764067,"score_spread":0.2533739273207858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084067148","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":[0.99962795,0.000076122764,0.000010151948,0.000025924746,0.0000016723492,0.000003891585,0.00006411152,3.4678277e-7,0.00018986415],"genre_scores_gemma":[0.9997727,0.000051832158,0.000019191513,0.000021750815,0.0000018230274,0.0000060963953,0.00004990058,1.8335578e-7,0.00007656134],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975044,0.000095580785,0.00002719572,0.000033518176,0.000040334395,0.000052970296],"domain_scores_gemma":[0.999482,0.00018789568,0.00017947779,0.000016354134,0.000047958314,0.00008630405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038789448,0.00012821452,0.00020529915,0.0005014595,0.00036959597,0.00043029964,0.00014291372,0.00021951216,0.001528927],"category_scores_gemma":[0.0023867756,0.000099069555,0.0002250781,0.00042657286,0.00017619121,0.00026600447,0.0003205769,0.00021293078,0.00011855396],"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":[0.00022981106,0.00009001419,0.9902262,0.000028194563,0.00005569591,0.00017304783,0.0017319766,0.000030989453,0.00036281082,0.000025049756,0.00014661021,0.006899614],"study_design_scores_gemma":[0.00001560274,0.00029866892,0.99181855,0.000014196435,0.000024750381,0.00034549608,0.0069303103,0.00013772634,0.00007710695,0.00004511463,0.00028648134,0.000006011206],"about_ca_topic_score_codex":0.004642132,"about_ca_topic_score_gemma":0.0073690163,"teacher_disagreement_score":0.004642132,"about_ca_system_score_codex":0.0002632456,"about_ca_system_score_gemma":0.00021357981,"threshold_uncertainty_score":0.0092301965},"labels":[],"label_agreement":null},{"id":"W7084089631","doi":"10.16016/j.2097-0927.202506048","title":"Development and validation of a prediction model for amputation risk in patients with diabetic foot ulcers based on systematic review and meta-analysis","year":2025,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Amputation; Receiver operating characteristic; Gangrene; Youden's J statistic; Risk assessment; Risk factor; Diabetic foot; Cohort; Confidence interval","score_opus":0.1499845661598874,"score_gpt":0.48616085769257017,"score_spread":0.3361762915326828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084089631","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":[0.032331485,0.91968423,0.032925237,0.0030690099,0.00063916,0.0021894425,0.007353502,0.0005809675,0.0012270948],"genre_scores_gemma":[0.653518,0.28364787,0.045087792,0.0026397211,0.00087531336,0.0059298472,0.007214659,0.00022277351,0.000864085],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9768797,0.012893403,0.0047495184,0.002766573,0.002274196,0.00043647882],"domain_scores_gemma":[0.9462655,0.042731464,0.005442533,0.001719267,0.0034045156,0.00043667323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0457894,0.004613847,0.01629556,0.018386602,0.0009004046,0.004156226,0.0038243488,0.0024428198,0.0032481195],"category_scores_gemma":[0.08238208,0.001853803,0.046900813,0.011541693,0.0007681114,0.0033970287,0.0021011923,0.0019984392,0.0004737897],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014302701,0.00008081277,0.050338864,0.13851158,0.7678964,0.00038107848,0.00015571246,0.0091489935,0.00041272544,0.00056233315,0.0020089748,0.029072247],"study_design_scores_gemma":[0.00050800585,0.00018524461,0.0075899237,0.008288634,0.97300875,0.00023427789,0.00004150145,0.007050034,0.00017649196,0.0012510435,0.0016014677,0.00006457996],"about_ca_topic_score_codex":0.0070576943,"about_ca_topic_score_gemma":0.008768513,"teacher_disagreement_score":0.0457894,"about_ca_system_score_codex":0.0021789644,"about_ca_system_score_gemma":0.005836999,"threshold_uncertainty_score":0.24216044},"labels":[],"label_agreement":null},{"id":"W7084107096","doi":"10.5281/zenodo.17242262","title":"CANADA 🇨🇦, AUSTRALIA 🇦🇺,UK 🇬🇧 WORK VISAS AVAILABLE CALL OR WHATSAPP MR PETER OBENDE ON ☎(08143870581).","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Work (physics)","score_opus":0.04626781247069667,"score_gpt":0.28312094642089985,"score_spread":0.2368531339502032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084107096","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":[0.00050391903,0.00081903965,0.00038342923,0.0038107303,0.0009872346,0.00009330162,0.0126211215,0.0013066385,0.97947454],"genre_scores_gemma":[0.00061123376,0.00021269673,0.0001275463,0.00029133286,0.000030666313,0.000016740441,0.0012924469,0.0003089859,0.9971084],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992118,0.00003943899,0.000021641928,0.00013133048,0.00040383064,0.00019191495],"domain_scores_gemma":[0.9966113,0.00022878453,0.00010652017,0.00014733762,0.0012868461,0.0016190663],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006587499,0.0010587994,0.0008903454,0.0011108817,0.0024032327,0.0031338164,0.0013062708,0.0030773513,0.8671345],"category_scores_gemma":[0.002974955,0.00054353464,0.0006360848,0.0017277506,0.0007076973,0.0011381177,0.0016099717,0.0019088219,0.7514156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044802928,0.000010877831,0.00013506996,0.00004970344,0.0000020149175,0.00001685616,0.00002125288,0.000036296424,0.00007137092,0.0008438464,0.9703837,0.028384136],"study_design_scores_gemma":[0.000014434891,0.000010948703,0.00071595475,0.000058946243,0.000002147684,0.000020537926,0.00004117808,0.00007091259,0.000055703684,0.00019648955,0.9988071,0.0000057203556],"about_ca_topic_score_codex":0.35374105,"about_ca_topic_score_gemma":0.5595935,"teacher_disagreement_score":0.64625895,"about_ca_system_score_codex":0.005535065,"about_ca_system_score_gemma":0.011185892,"threshold_uncertainty_score":0.70336413},"labels":[],"label_agreement":null},{"id":"W7084115527","doi":"10.64628/aam.s35a6egex","title":"How current and future business executives link sustainability and global strife","year":2019,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Calgary","funders":"","keywords":"Sustainability; Current (fluid); Link (geometry); Work (physics)","score_opus":0.008684895187045249,"score_gpt":0.2864025549529362,"score_spread":0.27771765976589097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084115527","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":[0.7193295,0.004043813,0.0022889385,0.15122162,0.000833017,0.000024569408,0.00023632283,0.00003916584,0.12198311],"genre_scores_gemma":[0.9909747,0.00088058866,0.00021106644,0.0031842662,0.000109869274,0.0000064788296,0.000050106766,0.000009060092,0.0045738285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978181,0.0010740084,0.000053198277,0.00021772034,0.0002671838,0.00056979805],"domain_scores_gemma":[0.99196213,0.0031081936,0.0012593083,0.00036679307,0.0012585603,0.002045119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038902408,0.00014748464,0.00019174899,0.0018008419,0.0017645975,0.009709535,0.00061681226,0.002538241,0.010719056],"category_scores_gemma":[0.017406393,0.00021163722,0.00022429977,0.0014467549,0.0035669398,0.00837068,0.0031866857,0.002505119,0.0007628927],"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":[0.00023116072,0.00053047773,0.5158042,0.00014541215,0.0002802298,0.0010750286,0.098054454,0.001533217,0.0005658253,0.21094134,0.034639996,0.13619873],"study_design_scores_gemma":[0.00003318635,0.00018265987,0.29421458,0.00057378475,0.00011731723,0.0005278729,0.360003,0.0023092036,0.00036122446,0.17081575,0.17073737,0.00012406628],"about_ca_topic_score_codex":0.015290215,"about_ca_topic_score_gemma":0.027033912,"teacher_disagreement_score":0.015290215,"about_ca_system_score_codex":0.0022031236,"about_ca_system_score_gemma":0.0038904606,"threshold_uncertainty_score":0.03585881},"labels":[],"label_agreement":null},{"id":"W7084150415","doi":"10.6084/m9.figshare.29999123","title":"Additional file 1 of Pre-diagnostic serum metabolome and breast cancer risk: a nested case-control study","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Alberta Health Services; University of British Columbia","funders":"","keywords":"Breast cancer; Metabolome; Cancer; BRCA2 Protein; MEDLINE","score_opus":0.011710621946954061,"score_gpt":0.28679867118643043,"score_spread":0.2750880492394764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084150415","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":[0.0002613153,0.000024305458,0.000094484574,0.000034632503,0.000011023313,0.00003701534,0.9993038,0.000049884595,0.00018357337],"genre_scores_gemma":[0.0057956125,0.000102664904,0.0014763196,0.00035823762,0.000042862775,0.0014208443,0.9873754,0.00020029054,0.0032277568],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861383,0.00029954882,0.00023991139,0.00043945244,0.00018143309,0.00022585031],"domain_scores_gemma":[0.9851725,0.00900663,0.0015607474,0.001957877,0.0014567612,0.0008454247],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024487774,0.001808159,0.0022414406,0.00275951,0.0011225737,0.0025441428,0.0027652504,0.0024581673,0.539342],"category_scores_gemma":[0.023347432,0.0013087839,0.0027234242,0.0047914824,0.0006021552,0.0012961298,0.0016532707,0.0012835849,0.086729735],"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":[0.0011512395,0.00022862815,0.016884765,0.0031533733,0.00057351775,0.00013977225,0.000094843876,0.001226142,0.00016398403,0.00087343616,0.97060657,0.004903649],"study_design_scores_gemma":[0.023717536,0.00070209935,0.13606262,0.004213878,0.0022499117,0.0012492936,0.0006442826,0.0048309355,0.00091858965,0.012459508,0.8125795,0.0003717853],"about_ca_topic_score_codex":0.025245639,"about_ca_topic_score_gemma":0.036590524,"teacher_disagreement_score":0.539342,"about_ca_system_score_codex":0.001636197,"about_ca_system_score_gemma":0.0029323644,"threshold_uncertainty_score":0.6570727},"labels":[],"label_agreement":null},{"id":"W7084155933","doi":"10.64628/aan.hfu547ep4","title":"Umat Kristen Palestina menuntut gereja Barat untuk memanusiakan anak-anak Gaza","year":2025,"lang":"id","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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 Winnipeg","funders":"","keywords":"Object (grammar); Context (archaeology); Situated; First world war","score_opus":0.016653572455438425,"score_gpt":0.3110776791414965,"score_spread":0.29442410668605806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084155933","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":[0.71558094,0.042936847,0.001694741,0.07456058,0.0032983443,0.00010670738,0.003559154,0.00024503397,0.1580176],"genre_scores_gemma":[0.87877846,0.016236179,0.0025604777,0.00455075,0.00056843937,0.00011756034,0.0017862115,0.000056187557,0.09534583],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996258,0.00008582774,0.000027968043,0.00004326309,0.000071049006,0.00014607952],"domain_scores_gemma":[0.99964523,0.00008843375,0.000056463843,0.000026857757,0.000088615176,0.00009433667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010381631,0.00051277597,0.00044638285,0.00080980913,0.0026423943,0.0029033965,0.00045794298,0.0012831019,0.02390144],"category_scores_gemma":[0.0012368937,0.00025026407,0.00036575872,0.0010445621,0.0009231133,0.0009991861,0.0023897826,0.002335256,0.003123037],"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":[0.0018050281,0.0016414556,0.32265356,0.002088307,0.00045667752,0.011216907,0.018924728,0.0011862372,0.014615511,0.094591886,0.08459238,0.44622737],"study_design_scores_gemma":[0.0001870065,0.00047522364,0.3649916,0.0018922254,0.00025236892,0.0045662248,0.027627708,0.0006017573,0.003133884,0.013635836,0.58249646,0.00013971148],"about_ca_topic_score_codex":0.011705095,"about_ca_topic_score_gemma":0.018786395,"teacher_disagreement_score":0.02390144,"about_ca_system_score_codex":0.0020688628,"about_ca_system_score_gemma":0.005764899,"threshold_uncertainty_score":0.07995826},"labels":[],"label_agreement":null},{"id":"W7084401195","doi":"10.5281/zenodo.17246062","title":"Quebec Parkinson Network Neuroimaging Cohort (QPN-NC)","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"McGill University","funders":"","keywords":"Neuroimaging; Cohort; Raw data; Neuropsychology; Demographics; Cohort study; Functional neuroimaging","score_opus":0.023119356461478568,"score_gpt":0.28146848508396066,"score_spread":0.2583491286224821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084401195","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":[0.0012077321,0.0000956314,0.0001156048,0.000107938715,0.000016820071,0.0000653844,0.9972548,0.00010252397,0.0010335853],"genre_scores_gemma":[0.0045048464,0.000084452426,0.00046720414,0.00011974206,0.0000157622,0.00028445263,0.9925188,0.00004415318,0.0019605295],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991042,0.00010590489,0.00010099695,0.00028405368,0.0002658541,0.00013899908],"domain_scores_gemma":[0.99601644,0.00040403995,0.00035796827,0.0006072781,0.002263641,0.0003505275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001408637,0.0011834787,0.0011904337,0.002871632,0.001600146,0.0015503656,0.0023660115,0.0011499176,0.032216802],"category_scores_gemma":[0.00871183,0.0004797109,0.00077205425,0.0053594154,0.00045226366,0.0005590112,0.001060658,0.0011393857,0.015674474],"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":[0.00022087354,0.00002559954,0.012448677,0.00023958088,0.00009917135,0.000059331825,0.00003198859,0.00022255174,0.000087391214,0.00045864435,0.9823965,0.0037097107],"study_design_scores_gemma":[0.0010324265,0.000060756895,0.16183765,0.00095077534,0.00023056573,0.00045355884,0.00022229693,0.0015430223,0.0005294994,0.0012100982,0.83181894,0.00011038446],"about_ca_topic_score_codex":0.8304117,"about_ca_topic_score_gemma":0.86360073,"teacher_disagreement_score":0.16958833,"about_ca_system_score_codex":0.008025368,"about_ca_system_score_gemma":0.0116191255,"threshold_uncertainty_score":0.34117413},"labels":[],"label_agreement":null},{"id":"W7091085769","doi":"10.4054/demres.2025.53.22","title":"Online obituaries as a complementary source of data for mortality in Canada","year":2025,"lang":"en","type":"article","venue":"Demographic Research","topic":"Insurance, Mortality, Demography, Risk Management","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":"Representativeness heuristic; Life expectancy; Context (archaeology); Data source; Census; Population; Historical demography","score_opus":0.17880550906889234,"score_gpt":0.4713286579666899,"score_spread":0.2925231488977975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7091085769","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":[0.34574392,0.0028662672,0.0060910997,0.0031631277,0.00017739562,0.00087892864,0.60669774,0.0009247158,0.033456806],"genre_scores_gemma":[0.6037836,0.0034035193,0.024894796,0.0010362073,0.00012679795,0.0012196884,0.3391143,0.0003547117,0.026066314],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99800235,0.00022923367,0.00018545965,0.00027098824,0.00093239726,0.00037954323],"domain_scores_gemma":[0.9814657,0.002296959,0.0018626301,0.0011581625,0.011766759,0.0014497797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015948399,0.00041628315,0.00038012507,0.008091506,0.0025400114,0.0020345915,0.0011264591,0.00031566643,0.008578159],"category_scores_gemma":[0.011755747,0.0001825989,0.00046866387,0.015640689,0.0005224877,0.00077887793,0.0016647419,0.00049517286,0.0014499672],"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":[0.0004828802,0.00015118584,0.5472891,0.0017383911,0.00021715854,0.0007142083,0.008302841,0.0027741075,0.0016672098,0.005270254,0.23446375,0.19692895],"study_design_scores_gemma":[0.000039575283,0.000046652254,0.75339156,0.0010055541,0.00012267623,0.00014939673,0.00785599,0.0052923327,0.0015516353,0.00086004555,0.22955084,0.00013385006],"about_ca_topic_score_codex":0.9909747,"about_ca_topic_score_gemma":0.99407613,"teacher_disagreement_score":0.021883773,"about_ca_system_score_codex":0.021883773,"about_ca_system_score_gemma":0.042880632,"threshold_uncertainty_score":0.15877861},"labels":[],"label_agreement":null},{"id":"W7093566044","doi":"","title":"Page 066","year":2011,"lang":"","type":"article","venue":"Pittsburg State University Digital Commons (Pittsburg State University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Ledger; Period (music); Register (sociolinguistics); Quarter (Canadian coin)","score_opus":0.027349048084439307,"score_gpt":0.21191788488329344,"score_spread":0.18456883679885414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093566044","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":[0.0002341685,0.0006859448,0.00010643805,0.0019336423,0.0034967272,0.00004448972,0.0024034134,0.000421351,0.99067384],"genre_scores_gemma":[0.0007313796,0.00032041306,0.00004074395,0.00072882563,0.0004317513,0.0000114466375,0.0007733319,0.000084525906,0.9968777],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99975735,0.000020101332,0.00001320972,0.000043887427,0.00012766727,0.00003780678],"domain_scores_gemma":[0.99883837,0.00017407576,0.00004200778,0.00010388928,0.0006076594,0.00023407079],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002794064,0.00052183616,0.00043654887,0.0010970487,0.0016021079,0.0041931984,0.00046417944,0.0012304855,0.862426],"category_scores_gemma":[0.0021965841,0.00025822027,0.0002579432,0.0014057062,0.00038162773,0.0013905592,0.001094935,0.0010470314,0.7998918],"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":[0.0000058424507,0.0000070210303,0.000087248605,0.000017589231,4.094071e-7,0.000011125768,0.000016622384,0.000005780389,0.00004250706,0.0005241818,0.9871001,0.012181626],"study_design_scores_gemma":[0.0000029427306,0.000004031268,0.00036529722,0.000044939352,5.9807263e-7,0.000016074204,0.00003768901,0.000009548788,0.000022201915,0.0001309795,0.9993642,0.0000015406363],"about_ca_topic_score_codex":0.007094799,"about_ca_topic_score_gemma":0.014309798,"teacher_disagreement_score":0.13757402,"about_ca_system_score_codex":0.0007859921,"about_ca_system_score_gemma":0.0012748913,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7095790739","doi":"","title":"Overview of the Postcensal Estimates of Population by Age and Sex","year":2013,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Census; Estimation; Natural population growth; Demographic analysis; Population projection; Quarter (Canadian coin)","score_opus":0.024549432782658077,"score_gpt":0.3076503947073348,"score_spread":0.2831009619246767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095790739","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":[0.038385887,0.041348726,0.3383852,0.0055699027,0.002170431,0.0062045273,0.3941958,0.0053687007,0.16837086],"genre_scores_gemma":[0.087869994,0.07328662,0.3505377,0.00076993427,0.0018896655,0.008411423,0.3457775,0.001007492,0.13044962],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979426,0.00067152496,0.00029801042,0.00023479747,0.000764797,0.000088230016],"domain_scores_gemma":[0.9920202,0.001793066,0.00069575926,0.000962917,0.004346161,0.00018185066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059833783,0.0007666832,0.00042126217,0.0103628645,0.00045137407,0.0010614333,0.0013085166,0.00030669683,0.01930888],"category_scores_gemma":[0.010744221,0.00073870167,0.0008568411,0.009805335,0.00019430263,0.0013134123,0.00093098154,0.0011238074,0.009960499],"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":[0.000099786674,0.00017360304,0.04707711,0.0013521211,0.00013086012,0.000079922494,0.00033984767,0.0033711842,0.0005877291,0.012134394,0.18392895,0.7507245],"study_design_scores_gemma":[0.000025329466,0.00018815372,0.23327018,0.0006799368,0.000081236925,0.0003171407,0.00025566085,0.0043713776,0.0012864267,0.0029860379,0.75646377,0.00007476363],"about_ca_topic_score_codex":0.066232726,"about_ca_topic_score_gemma":0.071802296,"teacher_disagreement_score":0.066232726,"about_ca_system_score_codex":0.001326935,"about_ca_system_score_gemma":0.003296205,"threshold_uncertainty_score":0.13169444},"labels":[],"label_agreement":null},{"id":"W7095919315","doi":"","title":"List of Tables Acknowledgements","year":2014,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Group (periodic table); Life insurance; Term (time); Data conversion; Table (database)","score_opus":0.014812982770984818,"score_gpt":0.30091260820439636,"score_spread":0.28609962543341155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095919315","genre_codex":"dataset","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":[0.0007372254,0.005388708,0.0043306337,0.0063208723,0.019068487,0.001895656,0.7709361,0.0037149456,0.18760741],"genre_scores_gemma":[0.007538762,0.0110611385,0.013844241,0.007955999,0.008460278,0.003821508,0.60559607,0.0037344145,0.33798766],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788314,0.0003651998,0.0004525358,0.00035029787,0.0008127363,0.00013609765],"domain_scores_gemma":[0.97033566,0.008359469,0.0019380649,0.0019827425,0.016332623,0.0010514992],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017406448,0.0010154586,0.0014977347,0.007868249,0.0013858626,0.0038609293,0.0018844509,0.0008371111,0.7944959],"category_scores_gemma":[0.039260443,0.00054834597,0.0009145409,0.010153979,0.00040059048,0.0029272859,0.0015410447,0.001473059,0.56577766],"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":[0.000046643985,0.000017005235,0.0002802389,0.0009906577,0.000009257938,0.000029858042,0.000024688048,0.00013507946,0.000060557206,0.0014210697,0.9746052,0.022379875],"study_design_scores_gemma":[0.00002816858,0.0000232748,0.001094677,0.001226552,0.000013283985,0.00007687386,0.0000829805,0.000077740864,0.00007739614,0.0025545587,0.9947279,0.00001665073],"about_ca_topic_score_codex":0.0053498796,"about_ca_topic_score_gemma":0.0041822796,"teacher_disagreement_score":0.20550412,"about_ca_system_score_codex":0.0018591478,"about_ca_system_score_gemma":0.0029226607,"threshold_uncertainty_score":0.29312664},"labels":[],"label_agreement":null},{"id":"W7095922981","doi":"","title":"Study Note on the Actuarial Evaluation of Premium Liabilities Prepared by:","year":2013,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Actuary; Current liability; Contingent liability; Liability; Insurance policy; Constructive; Indemnity","score_opus":0.04896985075814408,"score_gpt":0.35234839366805254,"score_spread":0.30337854290990846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095922981","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":[0.03019447,0.031277627,0.026637241,0.13653333,0.018256823,0.0016440552,0.005874094,0.0011960495,0.7483863],"genre_scores_gemma":[0.2198484,0.024349926,0.035091497,0.030256445,0.008451874,0.00033863186,0.0025538993,0.00055633904,0.67855304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99197185,0.0007056584,0.00053020363,0.00026990517,0.0059910044,0.00053126883],"domain_scores_gemma":[0.9808826,0.0048247976,0.0008120397,0.0006385448,0.012175596,0.00066643476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007534391,0.0006333756,0.00035495896,0.0033346226,0.003126398,0.00511609,0.001543262,0.0019900296,0.009636315],"category_scores_gemma":[0.016194407,0.0003324946,0.00060458144,0.002458172,0.0016898038,0.0016977183,0.0012727663,0.003959074,0.003296177],"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":[0.000058057896,0.00012818164,0.016002728,0.00023343353,0.000021857093,0.0010150166,0.0014464434,0.001703139,0.0022592132,0.03670805,0.79553074,0.1448933],"study_design_scores_gemma":[0.000008921759,0.00009659017,0.038233496,0.0004927113,0.000030772397,0.0004556029,0.0009576056,0.0020450898,0.0020342409,0.0048481724,0.95071024,0.00008658526],"about_ca_topic_score_codex":0.48976746,"about_ca_topic_score_gemma":0.6328308,"teacher_disagreement_score":0.48976746,"about_ca_system_score_codex":0.008794612,"about_ca_system_score_gemma":0.028021816,"threshold_uncertainty_score":0.9738334},"labels":[],"label_agreement":null},{"id":"W7095989377","doi":"","title":"Canadian Population Society,","year":2008,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.023107616432878838,"score_gpt":0.27982096230140796,"score_spread":0.2567133458685291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095989377","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":[0.0037110746,0.034377806,0.0008939387,0.016965056,0.007996463,0.00043696692,0.43921095,0.00080670265,0.49560106],"genre_scores_gemma":[0.011940583,0.030819047,0.0017699287,0.0018798524,0.00047298262,0.00043814423,0.084674105,0.000309526,0.8676958],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982845,0.000086019,0.000140208,0.00021434533,0.00092459924,0.00035030095],"domain_scores_gemma":[0.99736625,0.00012151442,0.00007441528,0.00010923899,0.0020342416,0.0002942418],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010122968,0.0018582889,0.0014879531,0.004951936,0.003284981,0.0038498864,0.0018096906,0.0018115047,0.17512073],"category_scores_gemma":[0.005304093,0.00093717076,0.0013073487,0.012693171,0.00073766895,0.0014993537,0.0013835884,0.0028836054,0.0630139],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027547876,0.000012512885,0.0018024399,0.00024862913,0.000015137488,0.00004923747,0.00006452175,0.00013880445,0.000021014257,0.0024541053,0.95523924,0.039926834],"study_design_scores_gemma":[0.000040395073,0.000008522437,0.022976795,0.0003821929,0.000025997133,0.00008510226,0.00028052792,0.00022168992,0.000038506965,0.0008369535,0.97507733,0.00002607597],"about_ca_topic_score_codex":0.97973025,"about_ca_topic_score_gemma":0.9898751,"teacher_disagreement_score":0.97973025,"about_ca_system_score_codex":0.027513528,"about_ca_system_score_gemma":0.08043118,"threshold_uncertainty_score":0.5858372},"labels":[],"label_agreement":null},{"id":"W7096213841","doi":"","title":"Investment Guarantees in Equity-Linked Insurance: the Canadian Approach","year":2001,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Investment (military); Government (linguistics); Production (economics); Work (physics); Payment","score_opus":0.05326586222836209,"score_gpt":0.33330703468800404,"score_spread":0.28004117245964194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096213841","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":[0.21642534,0.03940039,0.13680193,0.11127937,0.0012290064,0.0003584624,0.0029916016,0.00026735707,0.49124652],"genre_scores_gemma":[0.94657904,0.010347637,0.011003559,0.0013347585,0.00051869504,0.00006684506,0.00029804677,0.000049117607,0.029802274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9979267,0.0004935708,0.000055003937,0.00021389977,0.0006535827,0.0006572981],"domain_scores_gemma":[0.9961339,0.0018937833,0.0003446036,0.00014664872,0.0009984196,0.00048258944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029997916,0.0007888573,0.0013376419,0.0036296612,0.0042288033,0.005955474,0.0029196828,0.0037445612,0.007239752],"category_scores_gemma":[0.015928484,0.0005509174,0.0009649732,0.005092592,0.00458886,0.003277472,0.0020629233,0.004149695,0.00017556253],"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":[0.000031437452,0.000031763317,0.0028757749,0.00006194,0.00003526981,0.00014574469,0.00039866252,0.02230911,0.000037462327,0.94990325,0.0075439746,0.016625592],"study_design_scores_gemma":[0.000082650935,0.000032107222,0.010669253,0.00031490193,0.00016467948,0.00011627983,0.0014058138,0.122401446,0.00011631598,0.8092104,0.055365738,0.00012040846],"about_ca_topic_score_codex":0.9666776,"about_ca_topic_score_gemma":0.96645856,"teacher_disagreement_score":0.06452674,"about_ca_system_score_codex":0.06452674,"about_ca_system_score_gemma":0.055106338,"threshold_uncertainty_score":0.46817642},"labels":[],"label_agreement":null},{"id":"W7096236289","doi":"","title":"U.S. Census Bureau, available on the Census Bureau’s","year":2009,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Population; Net migration rate; Demographic analysis; American Community Survey","score_opus":0.06158454230439917,"score_gpt":0.3320456495053163,"score_spread":0.2704611072009171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096236289","genre_codex":"dataset","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":[0.00084579835,0.0020550087,0.0006469574,0.0010088014,0.0006318778,0.00042635808,0.9505085,0.00035412222,0.043522604],"genre_scores_gemma":[0.009229299,0.009046192,0.0038387312,0.0016957688,0.00034662554,0.0018980848,0.9088635,0.0002915914,0.06479027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887687,0.00015145488,0.00015705187,0.00018319822,0.0005052504,0.00012613878],"domain_scores_gemma":[0.9964796,0.0003624007,0.00025900672,0.00015985912,0.0025801628,0.00015916787],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008451335,0.0010563552,0.0011800302,0.0033677504,0.00051977456,0.0015270567,0.0011898646,0.00073239236,0.097770445],"category_scores_gemma":[0.0046727676,0.000497414,0.0004879078,0.009706354,0.00022780447,0.0012364426,0.0007606289,0.0015018114,0.08801483],"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":[0.00001432501,0.000022983611,0.0014716556,0.00024541558,0.000013185721,0.000015333473,0.00003059078,0.000071357645,0.00002426461,0.00077148183,0.9862912,0.011028232],"study_design_scores_gemma":[0.00005631048,0.000021929172,0.025989363,0.0004809518,0.000028004433,0.00006516966,0.00017940973,0.00018963056,0.000046680758,0.0007309909,0.972191,0.000020589003],"about_ca_topic_score_codex":0.089050345,"about_ca_topic_score_gemma":0.07597451,"teacher_disagreement_score":0.90222955,"about_ca_system_score_codex":0.001718991,"about_ca_system_score_gemma":0.0046477737,"threshold_uncertainty_score":0.32707477},"labels":[],"label_agreement":null},{"id":"W7096566252","doi":"","title":"Abstract Financial Economics and Actuarial Practice","year":2004,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Intuition; Actuarial Analysis; Simple (philosophy); Miller; Cost–benefit analysis","score_opus":0.0134494400247754,"score_gpt":0.2880669750975559,"score_spread":0.2746175350727805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096566252","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":[0.060576525,0.07021973,0.100736305,0.21962902,0.0022222542,0.00007457361,0.00015810979,0.00017848022,0.54620504],"genre_scores_gemma":[0.9363753,0.019454103,0.012904971,0.007584899,0.0019303688,0.000055818196,0.000042643333,0.000032860204,0.021619113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957014,0.0026016613,0.00022212573,0.00040537893,0.00077738654,0.00029215147],"domain_scores_gemma":[0.9887977,0.007616122,0.0010156359,0.0007130299,0.0014285466,0.00042894058],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007232028,0.0004006762,0.00039260896,0.00210973,0.0017809015,0.0076068128,0.00091987394,0.0026595704,0.008555673],"category_scores_gemma":[0.014925358,0.0002086902,0.00022365933,0.0016377659,0.020736631,0.0068404307,0.0030298636,0.0032543903,0.000745007],"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":[0.0000037926138,0.000007476655,0.00033442202,0.000031969237,0.0000023379012,0.000017126316,0.0004926028,0.0006378113,0.000029704886,0.98898065,0.0021895878,0.0072726235],"study_design_scores_gemma":[0.000004256617,0.000013985876,0.0008932141,0.00024203262,0.0000021697383,0.000065480905,0.001224479,0.0013542449,0.00009364942,0.9236915,0.07240357,0.000011361852],"about_ca_topic_score_codex":0.0030593986,"about_ca_topic_score_gemma":0.0015525956,"teacher_disagreement_score":0.99144435,"about_ca_system_score_codex":0.0059936754,"about_ca_system_score_gemma":0.0028755672,"threshold_uncertainty_score":0.04348737},"labels":[],"label_agreement":null},{"id":"W7096844778","doi":"","title":"The Implied Longevity Yield: A Note on Developing an Index for Life Annuities","year":2004,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Life annuity; Deferral; Longevity; Index (typography); Annuity; Present value; Longevity risk","score_opus":0.05236665580733383,"score_gpt":0.35266740977108063,"score_spread":0.3003007539637468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096844778","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":[0.039964598,0.004240162,0.8845475,0.0046097618,0.0011806539,0.00040961948,0.010155907,0.0019734097,0.052918337],"genre_scores_gemma":[0.27976322,0.006641301,0.67268103,0.0003934608,0.0009728868,0.0005329498,0.009013316,0.0007910702,0.02921079],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838376,0.00023404839,0.00019762303,0.00016474759,0.0009373017,0.00008268086],"domain_scores_gemma":[0.99473023,0.0007881362,0.0006435461,0.0008994101,0.0027879835,0.0001507406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003397521,0.0009379833,0.00055757444,0.0026456737,0.0004298341,0.0022028722,0.0013194871,0.0009894398,0.004118301],"category_scores_gemma":[0.019284958,0.00046618836,0.000614001,0.0028461937,0.00046430287,0.0046613356,0.0011052297,0.0023307053,0.0019098006],"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":[0.00015675189,0.000117410134,0.035382304,0.00027942762,0.00007071792,0.00027771172,0.00046934996,0.10045971,0.007921531,0.34080052,0.06497483,0.44908977],"study_design_scores_gemma":[0.000031883137,0.00033660553,0.07613813,0.0004532102,0.00006874691,0.0007196106,0.0002132939,0.28254834,0.015983775,0.18881617,0.43429816,0.0003921351],"about_ca_topic_score_codex":0.011977068,"about_ca_topic_score_gemma":0.0074698403,"teacher_disagreement_score":0.011977068,"about_ca_system_score_codex":0.002348462,"about_ca_system_score_gemma":0.0014976193,"threshold_uncertainty_score":0.023814738},"labels":[],"label_agreement":null},{"id":"W7097131982","doi":"","title":"An Empirical Comparison of Methods for Benchmarking Seasonally Adjusted Series to Annual Totals","year":2015,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Benchmarking; Benchmark (surveying); Seasonal adjustment; Regression; Regression analysis; Census; Time series","score_opus":0.1518808525346703,"score_gpt":0.5187046297286129,"score_spread":0.36682377719394255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097131982","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":[0.6177524,0.00889107,0.3281379,0.0019376999,0.0009606101,0.0012532842,0.006331562,0.0016365014,0.033098932],"genre_scores_gemma":[0.8378409,0.0018713913,0.14822222,0.00026623273,0.00020850722,0.00075944955,0.0065995995,0.0006730828,0.0035585929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9452069,0.03896075,0.0020878545,0.0031686076,0.00994192,0.00063407124],"domain_scores_gemma":[0.74723625,0.18574135,0.012647451,0.01839751,0.03460011,0.0013772805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.070372164,0.00083997444,0.00082159485,0.0053220536,0.00077158504,0.0020833237,0.0021696514,0.0010741199,0.0020626532],"category_scores_gemma":[0.23684703,0.00041584583,0.0014078082,0.0107956985,0.0010770325,0.0027359219,0.0017222032,0.0014279485,0.0005946185],"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":[0.0020633882,0.0006112956,0.30276605,0.0017210799,0.0027841944,0.00013865958,0.004415239,0.106650814,0.0015717518,0.056535896,0.031981338,0.48876026],"study_design_scores_gemma":[0.00041154752,0.001696975,0.59935844,0.0011869202,0.0004768811,0.0003175259,0.0050368686,0.33291474,0.0033567261,0.017560853,0.03730012,0.00038239366],"about_ca_topic_score_codex":0.040142413,"about_ca_topic_score_gemma":0.042324815,"teacher_disagreement_score":0.070372164,"about_ca_system_score_codex":0.0031704952,"about_ca_system_score_gemma":0.002882839,"threshold_uncertainty_score":0.372168},"labels":[],"label_agreement":null},{"id":"W7097160418","doi":"","title":"Research Article Does the recent evolution of Canadian mortality agree with the epidemiologic transition theory?","year":2008,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Research article; Research methodology; Demographic transition; Original research; Epidemiological transition","score_opus":0.10042936935222173,"score_gpt":0.35207655338771804,"score_spread":0.2516471840354963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097160418","genre_codex":"commentary","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":[0.027147176,0.03694759,0.0026659924,0.86507016,0.012110118,0.000046940015,0.0048711956,0.0000967263,0.051044066],"genre_scores_gemma":[0.71930397,0.069778815,0.004787271,0.15125555,0.02453412,0.00005960975,0.0031474459,0.00019309121,0.026940076],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9955687,0.000509601,0.0001927098,0.0007239093,0.002310028,0.00069510867],"domain_scores_gemma":[0.9528731,0.012826588,0.0033300794,0.0021743514,0.025818292,0.0029776087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011260073,0.00051991903,0.00088020944,0.0044880095,0.006884204,0.0062061045,0.0043588853,0.0038225786,0.014218615],"category_scores_gemma":[0.078997724,0.0002929617,0.0009209013,0.011758623,0.0069281026,0.0030404385,0.0015044457,0.0054758196,0.0006698361],"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":[0.0005555694,0.00006777425,0.09641404,0.0014095594,0.00033019666,0.0006022017,0.012164743,0.0016324936,0.00027185932,0.20440227,0.50861466,0.17353465],"study_design_scores_gemma":[0.00011058165,0.00006996034,0.30705714,0.0029754518,0.0004619715,0.00059501344,0.016841533,0.0028757981,0.00042687648,0.06564049,0.60256475,0.00038031937],"about_ca_topic_score_codex":0.9858024,"about_ca_topic_score_gemma":0.9811365,"teacher_disagreement_score":0.063381016,"about_ca_system_score_codex":0.063381016,"about_ca_system_score_gemma":0.09862235,"threshold_uncertainty_score":0.4598636},"labels":[],"label_agreement":null},{"id":"W7097248972","doi":"","title":"and","year":2002,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Underwriting; Payment; Principal (computer security); Life insurance; Hedge fund; Life annuity; Investment (military); Reset (finance)","score_opus":0.0344183898311868,"score_gpt":0.27592041316311666,"score_spread":0.24150202333192985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097248972","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":[0.009999173,0.0012886781,0.03674429,0.0070020664,0.0039076256,0.0004327425,0.006351659,0.001997549,0.9322762],"genre_scores_gemma":[0.08269227,0.0012664414,0.024043903,0.004336617,0.0007028801,0.00029624463,0.009136941,0.0007528878,0.8767718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844617,0.00016187796,0.00008258263,0.00050141354,0.0004760238,0.00033192875],"domain_scores_gemma":[0.9988003,0.00011498267,0.000074398864,0.00026783114,0.00060642225,0.00013602048],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011419393,0.00082525954,0.00049184513,0.0011488737,0.0024812426,0.0044487915,0.0011321213,0.0018753463,0.31535017],"category_scores_gemma":[0.0035445106,0.00025933573,0.0007108898,0.0009283659,0.0010529452,0.004077803,0.002891446,0.0017865365,0.15060021],"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":[0.00029913435,0.00011364281,0.0029934165,0.000265876,0.000041740477,0.0004451044,0.00085622875,0.00066356995,0.002871695,0.48437595,0.29676038,0.21031323],"study_design_scores_gemma":[0.00001661114,0.000031418054,0.0012935674,0.000077174795,0.000012191359,0.0002924021,0.0003452634,0.0005492323,0.0011237732,0.03984203,0.95639503,0.000021307229],"about_ca_topic_score_codex":0.0070458846,"about_ca_topic_score_gemma":0.0065042754,"teacher_disagreement_score":0.6846498,"about_ca_system_score_codex":0.0016124608,"about_ca_system_score_gemma":0.0017382957,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7097351619","doi":"","title":"Presented at The Great Controversy: Current Pension Actuarial Practice in Light of Financial Economics Symposium Sponsored by the Society of Actuaries","year":2003,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Notice; Actuary; Pension; Capitalization; Bankruptcy; Luck; Credibility; Underwriting","score_opus":0.012324475361352437,"score_gpt":0.2790671097412969,"score_spread":0.26674263437994444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097351619","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":[0.0018458278,0.32858646,0.000535625,0.62929946,0.025346257,0.000013873081,0.00005744132,0.000040130686,0.014274965],"genre_scores_gemma":[0.11776865,0.5210315,0.0022634028,0.20779571,0.100649476,0.000092004746,0.00022824063,0.00020024288,0.049970776],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98400927,0.0050636292,0.0012977585,0.0013743803,0.007430429,0.0008246121],"domain_scores_gemma":[0.94986445,0.019579083,0.004279035,0.0013749709,0.021682374,0.003220115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025566282,0.0003472726,0.0007611862,0.003799006,0.0041042343,0.012979082,0.0018659143,0.006357399,0.010982669],"category_scores_gemma":[0.057157777,0.0005916077,0.0005167092,0.004760298,0.0061027687,0.010066533,0.0030487508,0.0087030055,0.0050561638],"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":[0.000042039836,0.000024796644,0.0008456187,0.0009193744,0.000024734787,0.00017581803,0.0035226743,0.000080857666,0.00029283576,0.02654986,0.80532235,0.16219907],"study_design_scores_gemma":[0.000006414142,0.000015771602,0.0028975725,0.001816679,0.000013760266,0.0001574402,0.004475332,0.000058656427,0.00010022159,0.0057891044,0.98463655,0.00003241114],"about_ca_topic_score_codex":0.007520775,"about_ca_topic_score_gemma":0.012066579,"teacher_disagreement_score":0.025566282,"about_ca_system_score_codex":0.0062714205,"about_ca_system_score_gemma":0.010818393,"threshold_uncertainty_score":0.13520902},"labels":[],"label_agreement":null},{"id":"W7097662123","doi":"","title":"Two Age Cohorts in Montréal","year":2011,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.03882509372927116,"score_gpt":0.2946947085719139,"score_spread":0.2558696148426427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097662123","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":[0.9930407,0.00032704693,0.0001344422,0.00035075456,0.000020940935,0.00007773663,0.0035881898,0.000018239543,0.0024418803],"genre_scores_gemma":[0.9913167,0.00017273704,0.00019950193,0.00015616954,0.000011603946,0.000048927035,0.0021317254,0.0000075543903,0.0059551583],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991743,0.00013808235,0.000024719973,0.00013690579,0.00007625057,0.00044965473],"domain_scores_gemma":[0.9989484,0.000046935904,0.00018609756,0.00005395226,0.00030291747,0.0004617134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053663226,0.0006885772,0.0004038552,0.002201,0.0030283332,0.001428035,0.0012336534,0.00077730266,0.0043572467],"category_scores_gemma":[0.0015396022,0.00034855626,0.0008463806,0.0026491296,0.0006041966,0.0007125153,0.001637058,0.00072130875,0.0005030317],"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":[0.00028025886,0.00012734841,0.98666894,0.00001703334,0.000105694824,0.0005562191,0.0034584403,0.00018422115,0.00035959156,0.00052249007,0.0022072305,0.005512502],"study_design_scores_gemma":[0.000015035155,0.000049393293,0.99551386,0.000007646991,0.000022076652,0.00006464249,0.0023129804,0.00018124022,0.000054291708,0.00004322844,0.0017195202,0.000016142569],"about_ca_topic_score_codex":0.98747593,"about_ca_topic_score_gemma":0.9919883,"teacher_disagreement_score":0.01890941,"about_ca_system_score_codex":0.01890941,"about_ca_system_score_gemma":0.009828288,"threshold_uncertainty_score":0.13719803},"labels":[],"label_agreement":null},{"id":"W7097983016","doi":"","title":"Evaluating the performance of the Lee-Carter mortality forecasts","year":2000,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Social security; Population; Pension; Dependency (UML); Public policy; Dependency ratio; Prime (order theory)","score_opus":0.07685878814949458,"score_gpt":0.3786227970840792,"score_spread":0.3017640089345846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097983016","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":[0.90151674,0.0030462698,0.022826388,0.0045902077,0.0013225042,0.00036408342,0.03213187,0.0019546289,0.03224728],"genre_scores_gemma":[0.96428764,0.0005896266,0.01209052,0.0001293997,0.000125851,0.00007025028,0.0198339,0.0001100008,0.0027628525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969932,0.0015881107,0.00018067988,0.0003552109,0.0007158814,0.00016702399],"domain_scores_gemma":[0.9786781,0.012748247,0.00082953286,0.0014315057,0.0054671685,0.0008455104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010467747,0.00061798503,0.00044210846,0.0017193403,0.0005413773,0.0012363378,0.00057289214,0.0008384938,0.0030760043],"category_scores_gemma":[0.043138526,0.00029303096,0.0003404347,0.0016894323,0.0002189818,0.001464366,0.0006591885,0.00072790956,0.0008958943],"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":[0.003471587,0.00046415394,0.23703225,0.00023217339,0.00047166055,0.00020447029,0.00039237912,0.45644975,0.0009086311,0.007201615,0.08308293,0.21008843],"study_design_scores_gemma":[0.00025601886,0.0004863604,0.06287865,0.00015896617,0.00016198066,0.00006791482,0.0007603764,0.9096594,0.002366468,0.0034618857,0.019647902,0.00009406119],"about_ca_topic_score_codex":0.08727958,"about_ca_topic_score_gemma":0.08636683,"teacher_disagreement_score":0.08727958,"about_ca_system_score_codex":0.0016035464,"about_ca_system_score_gemma":0.0017640868,"threshold_uncertainty_score":0.17354316},"labels":[],"label_agreement":null},{"id":"W7098103848","doi":"","title":"Population Change and Lifecourse: Strategic Knowledge Cluster","year":2006,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Perspective (graphical); Pace; Population growth; Context (archaeology); Social policy; Demographic change; Cluster (spacecraft)","score_opus":0.05913345977311642,"score_gpt":0.32596349755704396,"score_spread":0.26683003778392755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098103848","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":[0.19537617,0.018155916,0.1290195,0.33800638,0.0009884222,0.0020549626,0.0020452826,0.00054750184,0.3138058],"genre_scores_gemma":[0.90113634,0.0110195605,0.06530093,0.005584617,0.00027697397,0.0008298327,0.0013789855,0.000055399076,0.014417334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99636203,0.0021209642,0.00023005805,0.00033837828,0.00053365674,0.00041495208],"domain_scores_gemma":[0.9920288,0.0034988828,0.00047083196,0.0010206177,0.0013159558,0.0016649793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008016976,0.00022665663,0.00050849543,0.0050490513,0.0049444493,0.009824831,0.0018808319,0.0025776522,0.0062648747],"category_scores_gemma":[0.0101795355,0.00028686612,0.00046419227,0.008157032,0.0063911574,0.009341692,0.011632462,0.0018616563,0.0006786463],"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":[0.000059538597,0.0001916733,0.029512648,0.0006885652,0.00006483503,0.00075460086,0.04405895,0.0026002245,0.0005268767,0.5296105,0.030746387,0.36118528],"study_design_scores_gemma":[0.000021733405,0.000051153573,0.016650818,0.00088117266,0.000044423417,0.00034699144,0.110822834,0.004350826,0.0005261534,0.50297356,0.363275,0.00005543292],"about_ca_topic_score_codex":0.04757675,"about_ca_topic_score_gemma":0.055741206,"teacher_disagreement_score":0.04757675,"about_ca_system_score_codex":0.010201483,"about_ca_system_score_gemma":0.04050583,"threshold_uncertainty_score":0.094599664},"labels":[],"label_agreement":null},{"id":"W7099462241","doi":"","title":"Western Ontario and McMaster Universities Osteoarthritis Index was used","year":2016,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Osteoarthritis; Knee pain; Index (typography); Physical activity; Body mass index","score_opus":0.015808048483252058,"score_gpt":0.2518138224230307,"score_spread":0.23600577393977862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099462241","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":[0.7333357,0.004697657,0.001813501,0.001918352,0.00060805364,0.0061030746,0.16204698,0.00019618789,0.08928046],"genre_scores_gemma":[0.94619167,0.0014487102,0.0033147363,0.00038913568,0.00014197882,0.0031565384,0.026206909,0.0000271205,0.019123094],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870265,0.00018196284,0.0002698112,0.00015435359,0.0005177686,0.0001734999],"domain_scores_gemma":[0.9981875,0.00010100653,0.0005830496,0.00006879651,0.0007942249,0.0002654028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007167971,0.0006025427,0.00090177025,0.003835452,0.0015013581,0.00081121596,0.0009800937,0.00043032627,0.010217572],"category_scores_gemma":[0.002627231,0.0002522502,0.0009601498,0.0043665594,0.00046857857,0.00039386514,0.00068400183,0.00086980825,0.0010072831],"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":[0.00072447676,0.00026359837,0.96803164,0.0006439683,0.0003507016,0.00034930202,0.00075559266,0.0003089923,0.00043702082,0.0005284892,0.01638646,0.011219728],"study_design_scores_gemma":[0.000096808486,0.0001304477,0.98716474,0.0001408664,0.00009533107,0.00013942378,0.0006811098,0.00043195116,0.00008285081,0.00027124165,0.010737114,0.000028070936],"about_ca_topic_score_codex":0.446385,"about_ca_topic_score_gemma":0.63941205,"teacher_disagreement_score":0.446385,"about_ca_system_score_codex":0.0034221867,"about_ca_system_score_gemma":0.006593178,"threshold_uncertainty_score":0.8875735},"labels":[],"label_agreement":null},{"id":"W7101006589","doi":"","title":"and Services Division of Vital Statistics","year":2011,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Division (mathematics); Health statistics; Table (database); Citation; Public domain; Domain (mathematical analysis); Center (category theory); Quarter (Canadian coin)","score_opus":0.023862396095784767,"score_gpt":0.275239004777445,"score_spread":0.25137660868166023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7101006589","genre_codex":"dataset","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":[0.00094151474,0.0014067031,0.0034627328,0.0040382035,0.0016684729,0.0009618193,0.8409272,0.0026522463,0.1439411],"genre_scores_gemma":[0.011956193,0.0048304866,0.01106335,0.0045310915,0.0018763371,0.004140391,0.80638224,0.0013354484,0.15388452],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99361515,0.0010891064,0.00090834324,0.0010281669,0.0029153754,0.00044385518],"domain_scores_gemma":[0.9816792,0.002727141,0.002036092,0.0026100392,0.0101680355,0.0007793728],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035267852,0.001265326,0.0016239138,0.0051322384,0.0008865842,0.0025995416,0.0024103355,0.0011695174,0.26414528],"category_scores_gemma":[0.030936914,0.00063341693,0.00052853307,0.011766798,0.00036598163,0.0016658274,0.001581707,0.002621122,0.20785531],"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":[0.00006862897,0.0000333515,0.0027826945,0.00014536027,0.00002569795,0.000019647478,0.00003593739,0.0001319981,0.00007217698,0.005230526,0.955745,0.035709146],"study_design_scores_gemma":[0.0002465073,0.0000646543,0.013475988,0.00042430087,0.000042464937,0.0001385821,0.00010178914,0.00095866446,0.00019374133,0.004400293,0.97991586,0.00003714881],"about_ca_topic_score_codex":0.029837811,"about_ca_topic_score_gemma":0.02069267,"teacher_disagreement_score":0.73585474,"about_ca_system_score_codex":0.0032481751,"about_ca_system_score_gemma":0.011495946,"threshold_uncertainty_score":0.883654},"labels":[],"label_agreement":null},{"id":"W7101182474","doi":"","title":"ISSUES IN ESTIMATING THE POPULATION OF CANADIAN MUNICIPALITIES","year":2013,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Census; Metropolitan area; American Community Survey; Population; Context (archaeology); Estimation; Strengths and weaknesses; Small area estimation","score_opus":0.029433649074984778,"score_gpt":0.31149346068875994,"score_spread":0.28205981161377514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7101182474","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":[0.1529945,0.0783086,0.37585825,0.12363096,0.0035743543,0.0033156592,0.043728154,0.0017911321,0.21679844],"genre_scores_gemma":[0.54512596,0.036335852,0.38074845,0.005656459,0.0010920811,0.0016770229,0.01037139,0.00047283483,0.01851995],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97937757,0.008159591,0.0017914425,0.0016629888,0.007675581,0.0013329018],"domain_scores_gemma":[0.9475535,0.020217663,0.0018046419,0.0029474047,0.026663255,0.0008136036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034532707,0.0010186436,0.0014408613,0.009933089,0.00555931,0.007846273,0.006490963,0.001067591,0.0027470787],"category_scores_gemma":[0.13353486,0.0008193006,0.0014954666,0.027277539,0.0024636725,0.0036822297,0.003831832,0.0022782637,0.00066050916],"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":[0.00009640622,0.00006229899,0.18121187,0.0017533486,0.00053386006,0.00048276255,0.010995277,0.038414523,0.00029389662,0.16743933,0.071710825,0.5270056],"study_design_scores_gemma":[0.00009950927,0.00009251503,0.36112934,0.0055527417,0.0005157407,0.000676225,0.03585309,0.09799091,0.0013675892,0.07573533,0.42052552,0.00046142295],"about_ca_topic_score_codex":0.99074495,"about_ca_topic_score_gemma":0.9880796,"teacher_disagreement_score":0.05385854,"about_ca_system_score_codex":0.05385854,"about_ca_system_score_gemma":0.07777812,"threshold_uncertainty_score":0.39077288},"labels":[],"label_agreement":null},{"id":"W7104433647","doi":"10.71781/12899","title":"Analyse de la distribution des décès aux grands âges selon le niveau de scolarité à partir d’un suivi de la mortalité sur 20 ans au Canada","year":2018,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Distribution (mathematics); Moorland; General interest","score_opus":0.005701902771095305,"score_gpt":0.2090495154741442,"score_spread":0.20334761270304888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104433647","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":[0.97950464,0.00077270035,0.004080558,0.000559477,0.00001595982,0.00003236362,0.009759966,0.00008300255,0.0051912754],"genre_scores_gemma":[0.9867229,0.00051464594,0.001650056,0.000040188657,0.000005460653,0.000018309334,0.0042926483,0.000020042185,0.0067358017],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99952805,0.00005740944,0.000020068224,0.00009216762,0.00015407744,0.00014829512],"domain_scores_gemma":[0.99797183,0.0003122067,0.00018487585,0.0001082212,0.0012340199,0.00018875785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009971325,0.00027074415,0.0004626673,0.0016252387,0.0009190999,0.0010518242,0.0007908018,0.00026754988,0.0030136781],"category_scores_gemma":[0.0031713876,0.00018316144,0.0007340309,0.0041589923,0.00048729175,0.00029166244,0.00059259357,0.0005845017,0.00030621505],"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":[0.00012695327,0.000017625474,0.95017827,0.00008725446,0.00023735553,0.000118317104,0.0028452463,0.007751335,0.0006338361,0.002448585,0.0028850322,0.03267024],"study_design_scores_gemma":[0.000003594471,0.000016668286,0.9849642,0.000049552047,0.00006521114,0.00003266138,0.0021348523,0.0065203924,0.00021175227,0.00020413405,0.005778607,0.000018465953],"about_ca_topic_score_codex":0.9902327,"about_ca_topic_score_gemma":0.99079764,"teacher_disagreement_score":0.009767294,"about_ca_system_score_codex":0.008164546,"about_ca_system_score_gemma":0.014006518,"threshold_uncertainty_score":0.059238136},"labels":[],"label_agreement":null},{"id":"W7104444658","doi":"10.71781/13042","title":"Changements épidémiologiques au Canada : un regard sur les causes de décès des personnes âgées de 65 ans et plus, 1979-2007","year":2012,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Ile de france; Context (archaeology); Western europe","score_opus":0.06852528206684445,"score_gpt":0.34188045957807234,"score_spread":0.2733551775112279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104444658","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":[0.9000856,0.017947871,0.0015232838,0.01471565,0.00031926006,0.00017784307,0.03883695,0.00010172901,0.02629183],"genre_scores_gemma":[0.9663924,0.009682119,0.001273227,0.0013921603,0.000072782204,0.00007862987,0.0075802775,0.000027216392,0.013501197],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986304,0.00011033081,0.000069437076,0.0001703989,0.00058329746,0.00043614753],"domain_scores_gemma":[0.99579924,0.0002654388,0.0005033969,0.000102237835,0.0027584336,0.0005712256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011043855,0.000378565,0.00046130386,0.0024659575,0.0030899253,0.0020094938,0.00089064846,0.00045989448,0.0029420462],"category_scores_gemma":[0.0039728717,0.0002609143,0.00083915156,0.0055055497,0.00091458234,0.0006143039,0.00104095,0.0011993375,0.00024408197],"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":[0.00017875484,0.0000368533,0.947644,0.00031501756,0.00019630243,0.00021706725,0.0054710116,0.00070035615,0.00048545623,0.0031182903,0.008388677,0.033248264],"study_design_scores_gemma":[0.000009198269,0.000023949819,0.9806718,0.00022142686,0.00008368122,0.000054134336,0.0041206977,0.00028305157,0.00017635598,0.00022146995,0.014111702,0.000022533923],"about_ca_topic_score_codex":0.99772173,"about_ca_topic_score_gemma":0.99886286,"teacher_disagreement_score":0.04922017,"about_ca_system_score_codex":0.04922017,"about_ca_system_score_gemma":0.117890306,"threshold_uncertainty_score":0.35711902},"labels":[],"label_agreement":null},{"id":"W7110490468","doi":"","title":"Uç değer için düzeltilmiş lee-carter modelinin tam hayat anüite hesaplamalarindaki ölüm tahmininde kullanımı","year":2019,"lang":"","type":"dissertation","venue":"OpenMETU (Middle East Technical University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Outlier; Annuity; Life annuity; Index (typography); Life insurance; Stochastic modelling","score_opus":0.03841244223550114,"score_gpt":0.26287246849625123,"score_spread":0.2244600262607501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110490468","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":[0.18724398,0.008775541,0.7607136,0.0022869008,0.0006444634,0.00021767168,0.0015466422,0.00153429,0.037036948],"genre_scores_gemma":[0.92710066,0.0028013678,0.03590639,0.00034853062,0.00013886548,0.0001896671,0.000948683,0.00014851689,0.03241732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969995,0.000048705027,0.000018592036,0.000100167905,0.000057148456,0.000075418924],"domain_scores_gemma":[0.99943703,0.00029332447,0.0000554687,0.000018711575,0.00017092015,0.000024416737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006271268,0.0011067954,0.0014756458,0.00047006115,0.0005688646,0.002075176,0.0012076946,0.0013773121,0.0104323095],"category_scores_gemma":[0.001592496,0.00035214308,0.00082215125,0.00047453822,0.0005793623,0.0010664804,0.0006103843,0.0016561253,0.0010867987],"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":[0.0004197238,0.00013027701,0.0059404103,0.00048994436,0.00016542617,0.00080579624,0.00035594514,0.8581585,0.0054660346,0.032794613,0.010661718,0.08461154],"study_design_scores_gemma":[0.00001868244,0.00006197312,0.0012420965,0.00003530927,0.000035945814,0.00012274155,0.000059357786,0.9885794,0.00080734974,0.00651337,0.0024936686,0.000030086368],"about_ca_topic_score_codex":0.018030433,"about_ca_topic_score_gemma":0.011224523,"teacher_disagreement_score":0.018030433,"about_ca_system_score_codex":0.00081423594,"about_ca_system_score_gemma":0.00097531785,"threshold_uncertainty_score":0.035851},"labels":[],"label_agreement":null},{"id":"W7110599283","doi":"","title":"Impact of Outliers in Mortality Rates on the Valuation of Life Annuities","year":2021,"lang":"","type":"article","venue":"OpenMETU (Middle East Technical University)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Outlier; Annuity; Life annuity; Longevity risk; Valuation (finance)","score_opus":0.10300711804599504,"score_gpt":0.3082688506585639,"score_spread":0.20526173261256886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110599283","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":[0.98516166,0.00016794223,0.012921005,0.00017610435,0.000021636277,0.000016655275,0.00019047451,0.000035726218,0.0013088019],"genre_scores_gemma":[0.9988238,0.000048199414,0.00087280234,0.000012276504,0.0000048032316,0.0000040766517,0.0000965029,0.000005421565,0.00013200955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816763,0.0009894494,0.00009694039,0.00022280784,0.00028334794,0.00023981449],"domain_scores_gemma":[0.9872067,0.008060631,0.0024802468,0.0010887535,0.00079987606,0.00036389288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00410509,0.00041724773,0.00050071016,0.00065472344,0.00027654812,0.0014279705,0.00070547353,0.00080625765,0.00087764097],"category_scores_gemma":[0.020050444,0.000191087,0.0006352666,0.00074145925,0.00058156176,0.0014439964,0.0008331755,0.0010188819,0.000087761386],"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":[0.00042898595,0.0001435632,0.1649016,0.00004984227,0.0001925266,0.0008451118,0.0002523956,0.80630195,0.0017109101,0.011082618,0.0007514395,0.013339096],"study_design_scores_gemma":[0.000015376221,0.00022026796,0.04601466,0.000019827312,0.000045937366,0.0002250605,0.00032791594,0.94569457,0.0014054643,0.0055900575,0.0004018655,0.00003889481],"about_ca_topic_score_codex":0.0051095947,"about_ca_topic_score_gemma":0.0025667555,"teacher_disagreement_score":0.0051095947,"about_ca_system_score_codex":0.00068811735,"about_ca_system_score_gemma":0.0004534556,"threshold_uncertainty_score":0.021710038},"labels":[],"label_agreement":null},{"id":"W7112040612","doi":"","title":"РОЗРАХУНОК ФУНКЦІЇ ЧИСЕЛЬНОСТІ НАСЕЛЕННЯ ДЛЯ ДИНАМІЧНОЇ МОДЕЛІ З НЕПЕРЕРВНИМ ЧАСОМ","year":2008,"lang":"en","type":"article","venue":"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Identification (biology); Process (computing); Function (biology); Term (time)","score_opus":0.11224341347093074,"score_gpt":0.36749109401562763,"score_spread":0.2552476805446969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112040612","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":[0.102973685,0.00962413,0.6738931,0.0025863019,0.00082848483,0.00017162683,0.0007311095,0.00046046957,0.20873106],"genre_scores_gemma":[0.7273904,0.008562382,0.2108963,0.00013718074,0.00030780924,0.00027639588,0.00036711156,0.00026056723,0.051801816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894935,0.00019424342,0.00006559445,0.00020950507,0.00046071367,0.000120591685],"domain_scores_gemma":[0.9989943,0.0002679013,0.00012577363,0.00017419293,0.00037744094,0.000060500883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012246694,0.00033932264,0.0003967376,0.001651757,0.000875766,0.0025980684,0.000472662,0.0006137596,0.009757547],"category_scores_gemma":[0.003152873,0.00050585804,0.00045651972,0.0016961553,0.0013551429,0.0015286468,0.0010359397,0.001024915,0.0039210333],"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":[0.00011794555,0.00006312629,0.0077807633,0.00047909975,0.00006171933,0.0011317817,0.0034932585,0.011264801,0.018260233,0.56506723,0.00595194,0.38632813],"study_design_scores_gemma":[0.000047875685,0.00026300657,0.016863346,0.00039919946,0.00018890199,0.003936787,0.003635146,0.027948821,0.027998403,0.38711286,0.53136325,0.00024236673],"about_ca_topic_score_codex":0.0042511118,"about_ca_topic_score_gemma":0.0040372647,"teacher_disagreement_score":0.009757547,"about_ca_system_score_codex":0.0010523755,"about_ca_system_score_gemma":0.002212382,"threshold_uncertainty_score":0.032642245},"labels":[],"label_agreement":null},{"id":"W7113026731","doi":"","title":"The End of International Migration? The Case of North America","year":2025,"lang":"en","type":"article","venue":"Journals & Books Hosting (International Knowledge Sharing Platform)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Population; Projections of population growth; Population ageing; Fertility; Working population; World population; Demographic transition; Population projection; Developed country","score_opus":0.030901599399788,"score_gpt":0.3478400837289761,"score_spread":0.31693848432918814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113026731","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":[0.29820463,0.019685704,0.0010344209,0.16437724,0.0013258739,0.000033173757,0.00016375324,0.000022605154,0.51515263],"genre_scores_gemma":[0.95798236,0.011470653,0.0007734304,0.00925175,0.000672808,0.00006670925,0.00007092392,0.000029356848,0.019681998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99844944,0.0006398899,0.000028072267,0.00010662213,0.00012186585,0.000654041],"domain_scores_gemma":[0.9989064,0.00033663175,0.0001821917,0.00009165814,0.00021354163,0.00026969105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001786063,0.00019839557,0.00026513927,0.0008898561,0.006197144,0.0050059953,0.0008496714,0.0021483267,0.004946378],"category_scores_gemma":[0.0034653167,0.00015536041,0.0004748468,0.0018468738,0.005457266,0.0067653186,0.0032640812,0.0030498102,0.00026341682],"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":[0.00009095461,0.000069542926,0.037835203,0.00031153308,0.00006824911,0.008289619,0.10601827,0.001052593,0.00029905778,0.7146311,0.06348615,0.06784783],"study_design_scores_gemma":[0.00002078232,0.000050957227,0.0767506,0.0012168331,0.000058022302,0.0029788555,0.25309265,0.0008611112,0.00018787953,0.104956605,0.55977106,0.0000546267],"about_ca_topic_score_codex":0.19463155,"about_ca_topic_score_gemma":0.25738305,"teacher_disagreement_score":0.8053684,"about_ca_system_score_codex":0.006041325,"about_ca_system_score_gemma":0.0034778721,"threshold_uncertainty_score":0.38699734},"labels":[],"label_agreement":null},{"id":"W7114991794","doi":"10.71781/32646","title":"Érosion des gains en matière d’espérance de vie à la naissance dans les provinces canadiennes : étude des causes de décès à la hausse aux âges adultes","year":2025,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Research methodology; Context (archaeology); Population; Rural population","score_opus":0.039291995900023434,"score_gpt":0.35064945544967513,"score_spread":0.3113574595496517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114991794","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":[0.97749203,0.004885011,0.0009943018,0.0006734853,0.00004525986,0.0000643759,0.007901809,0.000042955144,0.007900766],"genre_scores_gemma":[0.9935486,0.001348694,0.0007033836,0.00008891844,0.0000093352455,0.00003699404,0.0017080662,0.0000089503565,0.0025470667],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978398,0.00036885598,0.00014819227,0.00031744712,0.000752309,0.0005733585],"domain_scores_gemma":[0.99386287,0.0010587996,0.0012356425,0.00043396166,0.0029960794,0.000412726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023884817,0.0004475065,0.00047427794,0.0026783543,0.0013610311,0.0016785893,0.0009256948,0.0003984147,0.0021127518],"category_scores_gemma":[0.008020427,0.00029055728,0.0008893743,0.0072824387,0.0010134107,0.0005575101,0.0012770096,0.00074775424,0.00018583429],"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":[0.000101434576,0.000009390954,0.97907525,0.00019990146,0.00020980663,0.00007132487,0.0037297234,0.00035086126,0.00021345618,0.00042614317,0.00059299526,0.0150196375],"study_design_scores_gemma":[0.0000022449608,0.000025639763,0.9935343,0.0000791715,0.00006126563,0.000045223627,0.003173472,0.00016551062,0.00011258119,0.000058280104,0.0027314713,0.00001090296],"about_ca_topic_score_codex":0.95260787,"about_ca_topic_score_gemma":0.97445583,"teacher_disagreement_score":0.04739213,"about_ca_system_score_codex":0.011496297,"about_ca_system_score_gemma":0.019773806,"threshold_uncertainty_score":0.0953424},"labels":[],"label_agreement":null},{"id":"W7116400892","doi":"10.5287/ora-zrpnrz6nw","title":"Mortality inequality and its implications for retirees","year":2023,"lang":"en","type":"dissertation","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Insurance, Mortality, Demography, Risk Management","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":"H2020 European Research Council","keywords":"Life expectancy; Longevity; Inequality; Pension; Earnings; Quarter (Canadian coin); Mortality rate; Social inequality; Distribution (mathematics); Population","score_opus":0.07593073285281596,"score_gpt":0.35726235541590823,"score_spread":0.2813316225630923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116400892","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":[0.8228389,0.023680666,0.005559626,0.060935006,0.00052232656,0.000034438883,0.0014626228,0.000030642997,0.084935755],"genre_scores_gemma":[0.9921657,0.0037062764,0.00024590193,0.00078283873,0.00031895176,0.000012490598,0.00015501892,0.000004333472,0.0026084296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99932826,0.00023776483,0.000029022423,0.00008537701,0.000111940455,0.00020762776],"domain_scores_gemma":[0.9980274,0.00081540045,0.0005530593,0.00009109649,0.00024223086,0.00027072255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009164613,0.000147648,0.00020194681,0.0009001087,0.0010832658,0.0011255167,0.0004187362,0.00057529315,0.0055473493],"category_scores_gemma":[0.0058550932,0.00006816718,0.00025822618,0.0010271823,0.0011713621,0.0013532599,0.002269298,0.0011548959,0.00024912917],"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":[0.00020621532,0.00013458634,0.36324304,0.0002483613,0.0001221444,0.0007687614,0.0121334465,0.0028951587,0.0003494715,0.42125562,0.013310961,0.18533222],"study_design_scores_gemma":[0.000016459357,0.00018973993,0.7153903,0.0007379869,0.00008734544,0.00062732917,0.012119379,0.0030420513,0.00031798167,0.20731284,0.060108803,0.000049706458],"about_ca_topic_score_codex":0.0075250473,"about_ca_topic_score_gemma":0.008572487,"teacher_disagreement_score":0.0075250473,"about_ca_system_score_codex":0.0010402373,"about_ca_system_score_gemma":0.0005844095,"threshold_uncertainty_score":0.018557727},"labels":[],"label_agreement":null},{"id":"W7118876806","doi":"10.3917/riges.322.0092a","title":"Les défis d’une population mondiale en déséquilibre , par Jacques Henripin, Éditions Varia, collection «Entretiens», 2006, 103 pages. Recension faite par Yves-Marie Abraham, professeur à HEC Montréal","year":2007,"lang":"","type":"article","venue":"Gestion","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Context (archaeology); Subject (documents)","score_opus":0.014164343500186492,"score_gpt":0.2681638394298766,"score_spread":0.2539994959296901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118876806","genre_codex":"review","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013184502,0.90285695,0.0012239598,0.07708514,0.008246241,0.000014866899,0.0010850758,0.00005883557,0.008110526],"genre_scores_gemma":[0.07025014,0.8187681,0.008491163,0.022479555,0.032454938,0.0002601059,0.003284773,0.00023571309,0.043775514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99823487,0.00064110855,0.00020277554,0.00028379002,0.0005087165,0.00012877303],"domain_scores_gemma":[0.9981988,0.00080311036,0.00019417227,0.000080784645,0.0005764541,0.00014666512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004426701,0.0020337396,0.0014290968,0.0037980299,0.0015903853,0.0057745823,0.0018015656,0.0022570824,0.005685368],"category_scores_gemma":[0.008153209,0.00079539145,0.0007891445,0.0051694475,0.0039106943,0.005465401,0.0018065254,0.0036335897,0.0013436122],"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":[0.00010260023,0.000029815232,0.0037502362,0.0010501614,0.0000828773,0.00008410796,0.0029020319,0.00039009808,0.00022795053,0.060110055,0.81118834,0.12008177],"study_design_scores_gemma":[0.000018949922,0.000016453649,0.013594305,0.0011280117,0.000023848042,0.0003299587,0.001054168,0.00027099365,0.00011681932,0.012326707,0.97108924,0.000030589443],"about_ca_topic_score_codex":0.25439236,"about_ca_topic_score_gemma":0.20160557,"teacher_disagreement_score":0.25439236,"about_ca_system_score_codex":0.008090694,"about_ca_system_score_gemma":0.0059243343,"threshold_uncertainty_score":0.50582325},"labels":[],"label_agreement":null},{"id":"W7119249317","doi":"","title":"Estimaciones de esperanza de vida al nacer en áreas menores de la región pampeana","year":2021,"lang":"es","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Context (archaeology); Population; Quarter (Canadian coin)","score_opus":0.016678963298112085,"score_gpt":0.2868751878393571,"score_spread":0.27019622454124503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7119249317","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":[0.9895006,0.0009892494,0.0047458503,0.00037231427,0.000014408895,0.00003438176,0.0019901684,0.000041214815,0.002311839],"genre_scores_gemma":[0.9924816,0.00053356163,0.004666314,0.000042074756,0.000008432696,0.00005381171,0.0010116447,0.000004137765,0.0011985159],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994993,0.0002658525,0.000015760532,0.00011726612,0.00005518632,0.00004664328],"domain_scores_gemma":[0.9988846,0.00062109006,0.00021353556,0.00007683338,0.00014209215,0.00006190939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014439478,0.00036638795,0.0002709272,0.0007654914,0.0002919916,0.0004907484,0.00040884406,0.00026622874,0.0019534735],"category_scores_gemma":[0.002976685,0.00018155047,0.0005111042,0.001082622,0.00020168684,0.000254915,0.00054398744,0.00030502016,0.0001858754],"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":[0.00009083522,0.000022035018,0.97363746,0.00007692048,0.000276846,0.000079296224,0.00037326445,0.0055794315,0.00034279894,0.0004942539,0.0005426314,0.018484317],"study_design_scores_gemma":[0.0000070498027,0.00009678483,0.98437804,0.00008121702,0.00012231588,0.00009207668,0.00061087,0.011857784,0.00023762374,0.0003489979,0.0021590993,0.000008126633],"about_ca_topic_score_codex":0.12594023,"about_ca_topic_score_gemma":0.1709016,"teacher_disagreement_score":0.12594023,"about_ca_system_score_codex":0.0010372531,"about_ca_system_score_gemma":0.0011083542,"threshold_uncertainty_score":0.25041437},"labels":[],"label_agreement":null},{"id":"W7119502417","doi":"10.63575/cia.2024.20212","title":"Machine Learning-Enhanced Dynamic Asset Allocation in Target-Date Investment Strategies for Pension Funds","year":2024,"lang":"","type":"article","venue":"Journal of Computing Innovations and Applications","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"Asset allocation; Capital allocation line; Investment strategy; Probabilistic logic; Gradient boosting; Investment (military); Volatility (finance); Boosting (machine learning); Pension; Investment performance","score_opus":0.018762255486968134,"score_gpt":0.3274653535051968,"score_spread":0.30870309801822865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7119502417","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":[0.43329456,0.00036584717,0.5615203,0.0004940372,0.000035570167,0.000060901708,0.0000773782,0.00041307174,0.003738302],"genre_scores_gemma":[0.9759932,0.000051848045,0.023291573,0.00003132438,0.000005985065,0.000021593147,0.000031193715,0.000012086648,0.00056123215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973315,0.00010864818,0.000012786332,0.000052546446,0.000051694897,0.000041161187],"domain_scores_gemma":[0.9994462,0.00030669587,0.00008594754,0.000047003206,0.00008621393,0.000027925176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013884082,0.00034705419,0.00038712184,0.00032597024,0.00016852078,0.00063108106,0.0005695153,0.0004255127,0.0009848844],"category_scores_gemma":[0.0034965351,0.00020405912,0.0002662074,0.0002139979,0.000316395,0.0010591949,0.00064820144,0.0005935834,0.00020098843],"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":[0.000089653484,0.000084700834,0.003623729,0.00002371183,0.000026187445,0.000044796758,0.000053721873,0.8881738,0.0023936762,0.0066735013,0.0004712527,0.098341286],"study_design_scores_gemma":[0.0000040773702,0.00004467565,0.00042806406,0.000003698466,0.0000049739638,0.000009416771,0.000008053409,0.9952362,0.0008144696,0.003240362,0.00020279012,0.0000033392917],"about_ca_topic_score_codex":0.0016297386,"about_ca_topic_score_gemma":0.0022093693,"teacher_disagreement_score":0.0016297386,"about_ca_system_score_codex":0.0005700054,"about_ca_system_score_gemma":0.0006974964,"threshold_uncertainty_score":0.007342756},"labels":[],"label_agreement":null},{"id":"W7132901972","doi":"","title":"Comparison of standardization methods of mortality rates: A study of Canada and Sweden","year":2024,"lang":"en","type":"dissertation","venue":"Trepo - Institutional Repository of Tampere University","topic":"Insurance, Mortality, Demography, Risk Management","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":"Standardization; Population; Epidemiology; MEDLINE; Public health","score_opus":0.03561870550053976,"score_gpt":0.3893662560266575,"score_spread":0.3537475505261177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132901972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854913,0.0028852336,0.001835597,0.0012035879,0.0000635878,0.0002058391,0.0009422424,0.000030755476,0.0073420624],"genre_scores_gemma":[0.9928692,0.0018621008,0.002330316,0.0002370199,0.000017920975,0.0001101646,0.001331396,0.00004488855,0.0011971256],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.981635,0.0056942306,0.0009026601,0.0011616107,0.008802927,0.0018035115],"domain_scores_gemma":[0.9683544,0.0072151227,0.0036569731,0.0014549188,0.017965468,0.0013532563],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018547488,0.00049101154,0.0009643101,0.0065915263,0.0050225984,0.0029366896,0.0013982261,0.0004966872,0.0009006011],"category_scores_gemma":[0.061529566,0.00035161304,0.0010359416,0.012961482,0.002454529,0.0009232497,0.0023383808,0.0009773169,0.00013601844],"study_design_candidate":"simulation_or_modeling","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":[0.0003307793,0.00012318307,0.88564163,0.00020360517,0.00048632905,0.00022650743,0.02672978,0.00088890654,0.00015325383,0.0046115527,0.0047667776,0.075837635],"study_design_scores_gemma":[0.000028521776,0.00006353289,0.97223747,0.00020348797,0.00009747411,0.0001254659,0.019081656,0.001083134,0.00019014381,0.0005522411,0.0062749074,0.00006190782],"about_ca_topic_score_codex":0.9816508,"about_ca_topic_score_gemma":0.9778697,"teacher_disagreement_score":0.9814525,"about_ca_system_score_codex":0.043769028,"about_ca_system_score_gemma":0.06780431,"threshold_uncertainty_score":0.31756806},"labels":[],"label_agreement":null},{"id":"W7133039775","doi":"","title":"Equity-linked annuities and insurances","year":2006,"lang":"","type":"dissertation","venue":"TSpace","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; University of Toronto; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Martingale (probability theory); Martingale pricing; Life insurance; Local martingale; Life annuity; Risk-neutral measure; Interest rate; Valuation (finance); Longevity risk","score_opus":0.03034903952630779,"score_gpt":0.39076157798199196,"score_spread":0.36041253845568416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133039775","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":[0.01977682,0.0023278154,0.9545732,0.00040162914,0.00013673524,0.000064127955,0.00007715269,0.0000766667,0.02256586],"genre_scores_gemma":[0.5970573,0.0036005592,0.36213085,0.0002839523,0.00043285856,0.00016825527,0.00021470836,0.00014688929,0.03596455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999099,0.00027212713,0.000047222267,0.00015839617,0.0003754975,0.000047837908],"domain_scores_gemma":[0.99900633,0.00044746438,0.00018650583,0.00015297167,0.00015651478,0.000050246308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015033187,0.0004898578,0.00036385638,0.0007888001,0.00040658028,0.001706697,0.0011024021,0.0011477923,0.0071027144],"category_scores_gemma":[0.0047256434,0.0003505161,0.0006986001,0.00090646814,0.0014184166,0.0031178272,0.0011838587,0.0016079395,0.0009966239],"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":[0.0000070736423,0.000015415608,0.00024577344,0.000029791001,0.0000071969616,0.000048527934,0.00006914709,0.01648866,0.00081689574,0.959953,0.0005187583,0.021799725],"study_design_scores_gemma":[0.0000127176145,0.000051533414,0.00061843346,0.00006732477,0.000010703394,0.00022284813,0.000056201705,0.22032101,0.001398677,0.75517046,0.022047382,0.000022822107],"about_ca_topic_score_codex":0.0006763638,"about_ca_topic_score_gemma":0.00044481692,"teacher_disagreement_score":0.0071027144,"about_ca_system_score_codex":0.0008364147,"about_ca_system_score_gemma":0.0005467844,"threshold_uncertainty_score":0.023760974},"labels":[],"label_agreement":null},{"id":"W7135522609","doi":"","title":"Comparison of population development in the US and Canada","year":2011,"lang":"cs","type":"dissertation","venue":"Digital Repository (National Repository of Grey Literature)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Fertility; Population; Life expectancy; Baby boom; Population growth; Total fertility rate; Sub-replacement fertility; Birth rate","score_opus":0.014707743338177636,"score_gpt":0.28135079511101,"score_spread":0.2666430517728324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135522609","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":[0.87577635,0.007784436,0.0002471773,0.0035763422,0.00012977567,0.00014718165,0.06341211,0.00010250868,0.048824124],"genre_scores_gemma":[0.9749825,0.004168532,0.0002304938,0.0002990516,0.000013597921,0.00004192123,0.015418504,0.000015960379,0.004829432],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990344,0.00005141332,0.000067090405,0.00008145757,0.00035077444,0.0004150204],"domain_scores_gemma":[0.99730253,0.00012163071,0.00020240115,0.000047950492,0.0018024453,0.0005231025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054557686,0.00022612391,0.0003595868,0.0049383296,0.00194706,0.0017112446,0.00089824444,0.00028333394,0.004741238],"category_scores_gemma":[0.003002961,0.00014810034,0.000725688,0.01386758,0.0004996022,0.00051521015,0.0012349931,0.00053019583,0.00035232425],"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":[0.00030369277,0.00007147389,0.90078914,0.0003650259,0.0003150759,0.0003974907,0.0036651609,0.0011509763,0.00018853502,0.0056645824,0.033261262,0.05382749],"study_design_scores_gemma":[0.000008334846,0.000013002845,0.9879831,0.00008357607,0.000033661454,0.00005346466,0.003553377,0.0002651944,0.000047913392,0.00012834718,0.007814008,0.000016136437],"about_ca_topic_score_codex":0.9971704,"about_ca_topic_score_gemma":0.99816245,"teacher_disagreement_score":0.03684767,"about_ca_system_score_codex":0.03684767,"about_ca_system_score_gemma":0.047974702,"threshold_uncertainty_score":0.26734978},"labels":[],"label_agreement":null},{"id":"W7135606364","doi":"","title":"Comparison of official life tables construction in selected countries","year":2015,"lang":"cs","type":"dissertation","venue":"Digital Repository (National Repository of Grey Literature)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Estimation; Czech; Work (physics); Smoothing; Statistical analysis; Table (database)","score_opus":0.014831556590131393,"score_gpt":0.3092501644188489,"score_spread":0.2944186078287175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135606364","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":[0.67137766,0.010990769,0.09273323,0.0019606357,0.0012956224,0.0012779947,0.101623364,0.0027256308,0.11601503],"genre_scores_gemma":[0.8685792,0.0038758928,0.044822924,0.00016879098,0.00014207473,0.0011347875,0.07497773,0.0005315848,0.0057669426],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9794422,0.011330541,0.00330653,0.001170675,0.003737004,0.0010131088],"domain_scores_gemma":[0.92486215,0.035499852,0.0062016193,0.008226018,0.02436717,0.00084327016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017984146,0.00040171266,0.00071888836,0.012612841,0.000451153,0.0036569075,0.0009475658,0.00035163516,0.008120645],"category_scores_gemma":[0.08201439,0.00031112085,0.0010291586,0.019644536,0.0006176588,0.0015583038,0.0016718165,0.0007181536,0.001127792],"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":[0.0020742395,0.00028185145,0.24703132,0.005758662,0.0011482656,0.00039130935,0.007321391,0.040718075,0.0009077848,0.15808436,0.08330921,0.45297354],"study_design_scores_gemma":[0.00023052153,0.00074008765,0.5644193,0.0036448494,0.0005316707,0.0005974318,0.019209307,0.025964826,0.0050991154,0.017108943,0.3622235,0.00023038604],"about_ca_topic_score_codex":0.0085339155,"about_ca_topic_score_gemma":0.004216691,"teacher_disagreement_score":0.017984146,"about_ca_system_score_codex":0.0025941704,"about_ca_system_score_gemma":0.0031534682,"threshold_uncertainty_score":0.09511042},"labels":[],"label_agreement":null},{"id":"W7135727878","doi":"","title":"Seasonal profile of nuptiality, natality and mortality in the Czech republic and international comparison","year":2014,"lang":"cs","type":"dissertation","venue":"Digital Repository (National Repository of Grey Literature)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Seasonality; Czech; Quarter (Canadian coin); Period (music); Seasonal adjustment","score_opus":0.0186182001401023,"score_gpt":0.31441993204293006,"score_spread":0.29580173190282777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135727878","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":[0.9918922,0.0013129123,0.00023955706,0.00014701523,0.000018212977,0.00001553262,0.0021867063,0.00001627041,0.004171573],"genre_scores_gemma":[0.99836296,0.00034657525,0.0000985195,0.0000089427685,0.0000044162093,0.0000056702543,0.00082654617,0.000004037265,0.00034229748],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996983,0.00003903116,0.000048484537,0.000082327315,0.000062283405,0.000069549256],"domain_scores_gemma":[0.99912494,0.00013425038,0.00038808107,0.000060471353,0.00018942922,0.00010293687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006319239,0.000108809734,0.00019194324,0.002158845,0.00024422325,0.0007863561,0.0002134986,0.00013015674,0.0015175599],"category_scores_gemma":[0.0017383272,0.00008718217,0.00035314253,0.002684429,0.00031428633,0.00041065962,0.00069265097,0.00026457084,0.0001496565],"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":[0.00017194901,0.000025663134,0.9623242,0.00023094002,0.00021542344,0.00021600873,0.0019992008,0.0014332891,0.0010196192,0.0024629938,0.0023267309,0.027573934],"study_design_scores_gemma":[0.000001098581,0.000009819751,0.99784875,0.000025691399,0.000010283456,0.000048558126,0.0006176824,0.000158498,0.00003747346,0.00007224239,0.0011643517,0.0000055788687],"about_ca_topic_score_codex":0.025759721,"about_ca_topic_score_gemma":0.027169038,"teacher_disagreement_score":0.025759721,"about_ca_system_score_codex":0.00069021614,"about_ca_system_score_gemma":0.00072243664,"threshold_uncertainty_score":0.051219583},"labels":[],"label_agreement":null},{"id":"W7139453736","doi":"","title":"Contribution des causes de décès aux gains en espérance de vie à 65 ans au Canada et l’influence de leur profil par âge, 1979-2007","year":2013,"lang":"en","type":"article","venue":"University of Southern Denmark Research Portal (University of Southern Denmark)","topic":"Insurance, Mortality, Demography, Risk Management","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":"Research methodology; Statistical analysis; Ile de france","score_opus":0.02189680598741535,"score_gpt":0.28354355991039204,"score_spread":0.2616467539229767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139453736","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":[0.95975804,0.0066899294,0.000760237,0.0021669285,0.00008676662,0.00006300112,0.01840576,0.00005619882,0.0120131],"genre_scores_gemma":[0.9806354,0.0035011559,0.00045032238,0.0001922145,0.000024413574,0.000026787147,0.0046033915,0.000015224912,0.01055108],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999121,0.00007374464,0.000040272153,0.00012202809,0.00028973175,0.00035323558],"domain_scores_gemma":[0.9978522,0.00015511736,0.0003031243,0.00006753382,0.0012365106,0.00038557837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007082455,0.00042165068,0.00042477893,0.001332692,0.0017241464,0.0012036373,0.0007558232,0.00035526534,0.0034049954],"category_scores_gemma":[0.0025379937,0.0002451513,0.0009944229,0.0019893185,0.00064624485,0.0003396285,0.0010823961,0.0010796881,0.00026529023],"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":[0.00016757987,0.000021164844,0.9757656,0.00013557138,0.00027354353,0.00014699358,0.001695588,0.0005754824,0.00027513792,0.001009868,0.0025961096,0.017337231],"study_design_scores_gemma":[0.0000050332183,0.000021032813,0.9926226,0.00009291013,0.000105854524,0.000055384284,0.0017594025,0.00034162748,0.00013256028,0.00009828561,0.00474929,0.000015956788],"about_ca_topic_score_codex":0.9926494,"about_ca_topic_score_gemma":0.99609286,"teacher_disagreement_score":0.016852189,"about_ca_system_score_codex":0.016852189,"about_ca_system_score_gemma":0.036199637,"threshold_uncertainty_score":0.12227172},"labels":[],"label_agreement":null},{"id":"W71456638","doi":"10.7202/600490ar","title":"Vérification de deux méthodes d’estimation indirecte de la mortalité","year":2008,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Medicine","score_opus":0.018122790718418554,"score_gpt":0.2943601455419886,"score_spread":0.27623735482357004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W71456638","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":[0.044140197,0.0013803563,0.9501208,0.00025326404,0.00022127232,0.00028434535,0.0006341209,0.00047403658,0.0024915608],"genre_scores_gemma":[0.19500387,0.0009445865,0.7965365,0.0001411891,0.000064260676,0.0010687894,0.0010428365,0.00021261479,0.0049853935],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.96698195,0.019292368,0.0021987162,0.004143371,0.006723639,0.0006599342],"domain_scores_gemma":[0.8981649,0.078222275,0.00423549,0.0074466513,0.011566743,0.00036392018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0397537,0.0025457214,0.001980645,0.0041873646,0.0010394963,0.002737481,0.0018730422,0.0022022726,0.0035884364],"category_scores_gemma":[0.089726575,0.001153304,0.0030892908,0.0021632286,0.0017914488,0.0022157994,0.0025752247,0.002442722,0.0017030962],"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":[0.0017117346,0.00041247372,0.19967683,0.0024333212,0.0025998768,0.00032669245,0.0037608903,0.040937502,0.030619346,0.021149023,0.002246499,0.6941259],"study_design_scores_gemma":[0.00061450887,0.0032692237,0.26300347,0.0016017349,0.0019590466,0.0022277974,0.003031456,0.46036798,0.16823176,0.020951992,0.073734574,0.0010065793],"about_ca_topic_score_codex":0.014958647,"about_ca_topic_score_gemma":0.014808158,"teacher_disagreement_score":0.0397537,"about_ca_system_score_codex":0.001048698,"about_ca_system_score_gemma":0.00297723,"threshold_uncertainty_score":0.21024019},"labels":[],"label_agreement":null},{"id":"W7151512537","doi":"10.70675/7dd5984azcf77z4495za30fz8f58f2d0cb06","title":"Risques de décès aux très grands âges de la vie","year":2022,"lang":"","type":"dissertation","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","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":"Chinatown; Identity (music); Diseconomies of scale","score_opus":0.014606085143242192,"score_gpt":0.3654369997555643,"score_spread":0.35083091461232213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151512537","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":[0.89268875,0.014511477,0.06234499,0.004427408,0.00024807264,0.0001036517,0.009315954,0.00042182746,0.015937876],"genre_scores_gemma":[0.97655666,0.0048847827,0.0072123837,0.00026410763,0.00010673295,0.000076959455,0.003905762,0.000058288944,0.00693421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981036,0.00062984665,0.000088431276,0.0005680531,0.0004128666,0.00019715293],"domain_scores_gemma":[0.9826874,0.011551263,0.0023165767,0.001219022,0.0017552049,0.00047050242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005102192,0.000660259,0.0010007727,0.0013093625,0.0007174757,0.0020629182,0.0010186508,0.001173041,0.0066141584],"category_scores_gemma":[0.025397938,0.0005002009,0.002199192,0.0011116152,0.0008919813,0.0014247572,0.0010686549,0.0018354635,0.0009998579],"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":[0.0005707204,0.00007223515,0.80992335,0.00092058454,0.0015947026,0.000640493,0.0035096512,0.057156164,0.0025553047,0.016477035,0.00415726,0.10242252],"study_design_scores_gemma":[0.000027107379,0.0006759411,0.88472635,0.0008571349,0.0008882308,0.0017577044,0.0027543637,0.057249777,0.0021908388,0.019553602,0.029087855,0.00023113085],"about_ca_topic_score_codex":0.07656176,"about_ca_topic_score_gemma":0.084118076,"teacher_disagreement_score":0.07656176,"about_ca_system_score_codex":0.0016946502,"about_ca_system_score_gemma":0.001748971,"threshold_uncertainty_score":0.15223223},"labels":[],"label_agreement":null},{"id":"W7165143815","doi":"10.18356/9789211575620c046","title":"Rejoinder of Mr. Willis (Canada)","year":2000,"lang":"en","type":"book-chapter","venue":"Pleadings, oral arguments, documents","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.01926570013072642,"score_gpt":0.2693470857956518,"score_spread":0.2500813856649254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7165143815","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":[0.001674207,0.012991585,0.00030900433,0.8410371,0.029302057,0.00006149356,0.00025649628,0.00011766988,0.114250414],"genre_scores_gemma":[0.010591394,0.0033911082,0.0004845848,0.38898787,0.0036569538,0.000053444335,0.000067398316,0.00011423149,0.592653],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979144,0.00016833309,0.00005592391,0.00027594657,0.0010274716,0.0005579971],"domain_scores_gemma":[0.997398,0.00038791032,0.000064097476,0.000083295585,0.0013230195,0.00074375543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020065974,0.00054657727,0.0007023069,0.0014205012,0.011803233,0.0061530443,0.0020489409,0.017625257,0.02457858],"category_scores_gemma":[0.0057001747,0.0006273677,0.00071751984,0.0013431599,0.004868644,0.002412594,0.0022416764,0.017219279,0.0070286547],"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":[0.0000068694244,0.000005181962,0.00011227426,0.000015644828,0.0000021121366,0.0001244675,0.00026665986,0.000026770393,0.000042898217,0.008684236,0.9864696,0.0042432058],"study_design_scores_gemma":[0.0000040061645,0.0000017218669,0.00021661489,0.000048034264,0.0000022669674,0.000041091684,0.00027774437,0.00001833478,0.00003361109,0.00091242563,0.9984363,0.000007775676],"about_ca_topic_score_codex":0.73075247,"about_ca_topic_score_gemma":0.91149056,"teacher_disagreement_score":0.26924753,"about_ca_system_score_codex":0.019603517,"about_ca_system_score_gemma":0.036697168,"threshold_uncertainty_score":0.5416664},"labels":[],"label_agreement":null},{"id":"W7165188969","doi":"10.18356/9789211575620c044","title":"Rejoinder of Mr. Hankey (Canada)","year":2000,"lang":"en","type":"book-chapter","venue":"Pleadings, oral arguments, documents","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.02104846815670349,"score_gpt":0.27249167757608866,"score_spread":0.2514432094193852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7165188969","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":[0.0015282524,0.010725756,0.00029619643,0.84946054,0.02957161,0.0000629863,0.00026478647,0.00012559455,0.107964315],"genre_scores_gemma":[0.008938958,0.002679126,0.0004362266,0.40473905,0.003304366,0.000055474964,0.00006947252,0.000120190096,0.5796571],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976241,0.00019554203,0.000063274616,0.00031499093,0.0011195196,0.0006825797],"domain_scores_gemma":[0.9969319,0.00042077276,0.00007077446,0.000091263035,0.0015786099,0.00090659154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021208085,0.00061167224,0.0007768054,0.0014261975,0.012453412,0.006709092,0.0022546065,0.018166622,0.026294775],"category_scores_gemma":[0.005760402,0.00066679413,0.0008011578,0.0013097373,0.0047242064,0.0025574032,0.0023240976,0.018010853,0.008061431],"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":[0.000006302987,0.0000044868084,0.000090243295,0.000013560973,0.0000019874526,0.00010197624,0.00021390176,0.00002203854,0.00004080997,0.0061705606,0.9899783,0.0033557268],"study_design_scores_gemma":[0.0000039922897,0.0000019455586,0.00022909464,0.000043211534,0.0000025392249,0.00004156088,0.0003079362,0.000018580122,0.000036003734,0.0007435753,0.998563,0.0000085879565],"about_ca_topic_score_codex":0.7633713,"about_ca_topic_score_gemma":0.9226646,"teacher_disagreement_score":0.23662871,"about_ca_system_score_codex":0.023791322,"about_ca_system_score_gemma":0.040831942,"threshold_uncertainty_score":0.47604448},"labels":[],"label_agreement":null},{"id":"W75262094","doi":"10.1007/978-1-4614-6507-2_3","title":"Analytical Long-Term Care Capacity Planning","year":2013,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Long-term care; Term (time); Capacity planning; Operations research; Computer science; Event (particle physics); Discharge planning; Operations management; Engineering; Medicine","score_opus":0.10119730057560608,"score_gpt":0.45327848131546034,"score_spread":0.3520811807398543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75262094","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":[0.021174025,0.005402617,0.84240353,0.0032753784,0.0005297967,0.00016840525,0.0008759613,0.00034850015,0.12582175],"genre_scores_gemma":[0.7497574,0.008366269,0.167894,0.000364888,0.0007677294,0.0004271021,0.0014632215,0.00026265986,0.07069678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994062,0.00022262504,0.000018637125,0.000075510885,0.00017755969,0.00009960206],"domain_scores_gemma":[0.998376,0.0011342362,0.00006889414,0.000077847,0.00027431297,0.00006868421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014535042,0.0012671931,0.00083763135,0.0015708549,0.00065319956,0.0021331375,0.0018973262,0.0010101661,0.012964381],"category_scores_gemma":[0.005881286,0.00063356454,0.0011265681,0.0024860257,0.0009010839,0.0019377015,0.0010862817,0.0017426992,0.0009770101],"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":[0.000015138569,0.000039272116,0.0005927143,0.00007949741,0.00003976359,0.00007787427,0.00007288841,0.7834492,0.00014227712,0.15563092,0.01096942,0.048891038],"study_design_scores_gemma":[0.0000027204558,0.00001030171,0.00025657396,0.000054266147,0.000012001154,0.0000249351,0.00009207524,0.7891132,0.00014334319,0.20458545,0.005693295,0.000011885182],"about_ca_topic_score_codex":0.021180758,"about_ca_topic_score_gemma":0.02202279,"teacher_disagreement_score":0.021180758,"about_ca_system_score_codex":0.0033001974,"about_ca_system_score_gemma":0.0032627564,"threshold_uncertainty_score":0.043370128},"labels":[],"label_agreement":null},{"id":"W758726884","doi":"10.71781/15576","title":"Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles","year":2009,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Insurance, Mortality, Demography, Risk Management","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":"Political science; Art","score_opus":0.03924953548006557,"score_gpt":0.36416834620741667,"score_spread":0.32491881072735107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W758726884","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":[0.9503795,0.0029070224,0.023243146,0.0018505492,0.00013023661,0.0001230342,0.016689144,0.00066327845,0.004013964],"genre_scores_gemma":[0.97208494,0.0014943986,0.010124354,0.00012885891,0.00003652925,0.00010638819,0.009215476,0.00013924684,0.0066697854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918324,0.00032870797,0.000037387268,0.00015284051,0.000105322106,0.000192454],"domain_scores_gemma":[0.99597746,0.0023013083,0.00022404446,0.00019195436,0.0010419079,0.00026346123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004645262,0.0012657697,0.0016708497,0.0015433348,0.0010719002,0.0026813543,0.0028069909,0.0009986365,0.0035893125],"category_scores_gemma":[0.00779641,0.0008587914,0.0029948358,0.0025617396,0.0008744947,0.0009448801,0.001064018,0.0016285906,0.0003392929],"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":[0.00088788435,0.00009397109,0.06798794,0.00013407222,0.00095634937,0.00007120339,0.00033644703,0.9045003,0.00022224247,0.0038080094,0.0051892875,0.015812205],"study_design_scores_gemma":[0.00018604538,0.000093027615,0.061908014,0.00009522998,0.0004258812,0.000031216503,0.00060271577,0.9307458,0.00035613347,0.0023764102,0.0030851925,0.000094228286],"about_ca_topic_score_codex":0.98538,"about_ca_topic_score_gemma":0.9580761,"teacher_disagreement_score":0.019749226,"about_ca_system_score_codex":0.019749226,"about_ca_system_score_gemma":0.021336798,"threshold_uncertainty_score":0.1432913},"labels":[],"label_agreement":null},{"id":"W8544633","doi":"10.1097/00005537-199601000-00015","title":"Fertility in Nepal 1981-2000 : levels, trends, and components of change","year":2003,"lang":"en","type":"article","venue":"The Laryngoscope","topic":"Insurance, Mortality, Demography, Risk Management","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":"United States Agency for International Development","keywords":"Fertility; Total fertility rate; Residence; Demography; Geography; Birth rate; Population; Marital status; Quarter (Canadian coin); Socioeconomics; Family planning; Economics; Research methodology; Sociology","score_opus":0.10329123035478086,"score_gpt":0.3259599069141515,"score_spread":0.22266867655937064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W8544633","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":[0.99289775,0.0013027218,0.00008662751,0.00044998113,0.0000107006135,0.0000135296295,0.0030272296,0.000017759618,0.0021937755],"genre_scores_gemma":[0.9973079,0.0007899965,0.000079405756,0.00006320957,0.000010408868,0.000015309532,0.0012360387,0.0000025951388,0.00049501145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982125,0.0000388814,0.000019794728,0.000028311677,0.00003357532,0.000058160032],"domain_scores_gemma":[0.99920064,0.00016449067,0.00030770028,0.000033752633,0.00018664317,0.00010682148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028768313,0.00015627294,0.00017189463,0.0020667,0.00043366812,0.00050913735,0.00047255048,0.00037974172,0.0013136682],"category_scores_gemma":[0.0017693916,0.00024661017,0.00024245073,0.002867145,0.00046630413,0.00064656034,0.00050992076,0.0006460031,0.00030269855],"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":[0.0000352385,0.000021557713,0.9928114,0.000035772096,0.00003233438,0.00012032932,0.00073119556,0.000067661946,0.00009660216,0.00011275223,0.00040362994,0.00553164],"study_design_scores_gemma":[0.0000016560858,0.00002191911,0.998434,0.000010886952,0.00000875185,0.00017339269,0.00046432437,0.00010678961,0.000023481507,0.000023772101,0.00072827487,0.0000026558205],"about_ca_topic_score_codex":0.100814484,"about_ca_topic_score_gemma":0.123732485,"teacher_disagreement_score":0.100814484,"about_ca_system_score_codex":0.0014015043,"about_ca_system_score_gemma":0.0009497504,"threshold_uncertainty_score":0.20045537},"labels":[],"label_agreement":null},{"id":"W936501996","doi":"10.1016/j.socscimed.2015.07.013","title":"Clarifying hierarchical age–period–cohort models: A rejoinder to Bell and Jones","year":2015,"lang":"en","type":"letter","venue":"Social Science & Medicine","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":46,"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 British Columbia","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Institute on Aging; National Institute on Alcohol Abuse and Alcoholism","keywords":"Period (music); Demography; Cohort; Gerontology; Medicine; Sociology; Philosophy; Internal medicine; Aesthetics","score_opus":0.06703587144779964,"score_gpt":0.35317491006335516,"score_spread":0.2861390386155555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W936501996","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027579055,0.0025897885,0.0038817406,0.98596096,0.0069376,0.000008425929,0.000059757076,0.000023157,0.00026275215],"genre_scores_gemma":[0.009464103,0.003437647,0.0132710235,0.9253976,0.047119156,0.00009483884,0.000061594095,0.000106093285,0.0010479118],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9394885,0.043546982,0.0048929933,0.00473893,0.006470089,0.00086250616],"domain_scores_gemma":[0.483112,0.46764213,0.008372831,0.014181728,0.022203777,0.004487568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14522637,0.0016339102,0.0045464053,0.0026129072,0.005082848,0.007900461,0.0088536125,0.03744349,0.0045410045],"category_scores_gemma":[0.4032888,0.0017764618,0.0036057949,0.0023112348,0.016460825,0.019021733,0.006650724,0.08456567,0.002525732],"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":[0.00017635396,0.00009059537,0.0022924806,0.00029613802,0.0003777883,0.00046498832,0.0025171086,0.0013605575,0.00009769545,0.09887407,0.85670006,0.036752187],"study_design_scores_gemma":[0.00033059824,0.00007416117,0.0012979843,0.0012065475,0.00021935959,0.0005638966,0.0013413936,0.0058370624,0.00018705489,0.66649306,0.32223362,0.0002153927],"about_ca_topic_score_codex":0.024826327,"about_ca_topic_score_gemma":0.027033716,"teacher_disagreement_score":0.14522637,"about_ca_system_score_codex":0.007780879,"about_ca_system_score_gemma":0.0102827335,"threshold_uncertainty_score":0.76803964},"labels":[],"label_agreement":null},{"id":"W98088619","doi":"","title":"Efficient Hedging Methodology Applied to Equity-Linked Life Insurance","year":2005,"lang":"en","type":"article","venue":"Spectrum Research Repository (Concordia University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","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":"","keywords":"Life insurance; Actuarial science; Equity (law); Insurance policy; Imperfect; Black–Scholes model; Profit (economics); Economics; Business; Auto insurance risk selection; Key person insurance; Microeconomics; Financial economics; Volatility (finance)","score_opus":0.09532174268850789,"score_gpt":0.368580496253746,"score_spread":0.2732587535652381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W98088619","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":[0.010482256,0.00029302944,0.9883189,0.0000613006,0.0000175014,0.000019415138,0.000008406102,0.000024738549,0.00077445485],"genre_scores_gemma":[0.66247445,0.0011528062,0.3292474,0.000098623794,0.00019023017,0.00016418743,0.00012377217,0.00009290657,0.006455539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898463,0.00051137345,0.00005891041,0.00010601791,0.0002803405,0.000058723348],"domain_scores_gemma":[0.9980471,0.0012442117,0.00018834627,0.00023005517,0.00021021726,0.000080024336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035773406,0.00072056946,0.0009780411,0.0009971671,0.00034308323,0.0012425291,0.001086292,0.0009622506,0.0017694025],"category_scores_gemma":[0.0076434175,0.00049473287,0.00086184003,0.000881922,0.0011768327,0.0018872423,0.0013525416,0.0013665446,0.00019406823],"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":[0.000041011706,0.00008048831,0.0011065432,0.00010753098,0.000095755844,0.00018043499,0.00018762554,0.44283375,0.003635089,0.48857433,0.00038078806,0.062776625],"study_design_scores_gemma":[0.000008524389,0.000037712543,0.00018618666,0.000007848026,0.000011675796,0.000038255977,0.000008974659,0.93326473,0.0005208132,0.065181956,0.00072457024,0.000008750265],"about_ca_topic_score_codex":0.00067358394,"about_ca_topic_score_gemma":0.00045896453,"teacher_disagreement_score":0.0035773406,"about_ca_system_score_codex":0.0007191645,"about_ca_system_score_gemma":0.0006681672,"threshold_uncertainty_score":0.018918991},"labels":[],"label_agreement":null}]}