{"meta":{"query_hash":"9caf2410bcd6","filters":{"topic":"Grey System Theory Applications"},"cohort_total":136,"direct_labels_cover":1,"predictions_cover":136,"exported":136,"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/9caf2410bcd6","api":"https://metacan.xera.ac/api/v1/cohort?topic=Grey+System+Theory+Applications"},"results":[{"id":"W1479966206","doi":"10.3968/6210","title":"Spurious Relationship of Long Memory Sequences in Presence of Trends Breaks","year":2014,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Spurious relationship; Estimator; Series (stratigraphy); Ordinary least squares; Statistic; Mathematics; Statistics; Feature (linguistics); Regression; Sample size determination; Long memory; Sample (material); Econometrics; Statistical physics; Physics","score_opus":0.02984667377010902,"score_gpt":0.3933495987568521,"score_spread":0.3635029249867431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1479966206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93484443,0.034078985,0.00031810292,0.0007370939,0.003257728,0.00071730855,0.000015812962,0.00005240957,0.025978113],"genre_scores_gemma":[0.9941269,0.00039468106,0.0049191504,0.000054050866,0.00006948217,0.000060069568,0.0000030255085,0.000011307344,0.00036130732],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9883716,0.00089851685,0.0027900264,0.0020169534,0.0046416204,0.0012812882],"domain_scores_gemma":[0.98865426,0.0076735807,0.0017283469,0.001155469,0.0005908805,0.00019748764],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.018697724,0.0004787192,0.0011057308,0.004113336,0.00047452893,0.00020596519,0.006622735,0.00017686687,0.00006895915],"category_scores_gemma":[0.012771498,0.000341188,0.0001888254,0.027241252,0.009624371,0.010990466,0.0005954134,0.0008733416,0.00001695685],"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":[0.00009312331,0.00013864612,0.5399878,0.000047674854,0.0000018554773,0.000009990613,0.0013111125,0.04111042,0.0030473731,0.07426587,0.000013382912,0.33997276],"study_design_scores_gemma":[0.0012384288,0.00032620513,0.63636124,0.0006363667,0.000008212941,0.00006534676,0.003977702,0.023522573,0.009797388,0.32147902,0.0016533071,0.00093421503],"about_ca_topic_score_codex":0.00044248748,"about_ca_topic_score_gemma":0.015421768,"teacher_disagreement_score":0.33903855,"about_ca_system_score_codex":0.00034421598,"about_ca_system_score_gemma":0.00050813315,"threshold_uncertainty_score":0.99990404},"labels":[],"label_agreement":null},{"id":"W1488916133","doi":"10.1108/03684921211243257","title":"Influence factors analysis of online auditing performance assessment","year":2012,"lang":"en","type":"article","venue":"Kybernetes","topic":"Grey System Theory Applications","field":"Decision 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":"University of Toronto","funders":"","keywords":"Audit; Analytic hierarchy process; Computer science; Originality; Operational auditing; Process (computing); Process management; Operations research; Accounting; Internal audit; Mathematics; Business; Psychology","score_opus":0.08896056330268515,"score_gpt":0.41082955503443813,"score_spread":0.321868991731753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488916133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99117804,0.000059156057,0.0002748174,0.00004688786,0.000058233927,0.00010600899,0.00005741075,0.000028509301,0.008190922],"genre_scores_gemma":[0.9982851,0.0000024871308,0.0011155957,0.000028887813,0.00004002717,0.0000118544685,0.000019203402,0.000006397517,0.00049041637],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978295,0.00014992352,0.00068079616,0.00023644259,0.000848611,0.00025470403],"domain_scores_gemma":[0.99667954,0.0016883105,0.0005532931,0.0006921828,0.00027594095,0.00011070173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020976234,0.00011339668,0.0003505113,0.00044701842,0.000095803,0.000042203385,0.00061963545,0.000045170244,0.0004141429],"category_scores_gemma":[0.0009336953,0.000080942606,0.00014144437,0.002303864,0.000093047216,0.00047716367,0.00011924021,0.000082988925,0.00010701896],"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":[7.899702e-7,0.000054921533,0.9719334,0.0000031512745,0.00009665212,3.0831178e-8,0.000497905,0.00489261,0.0006825208,0.020225855,0.00006127915,0.0015508843],"study_design_scores_gemma":[0.00004014826,0.000011904641,0.98734176,0.000007966068,0.00013754751,3.5230758e-7,0.00064497493,0.0066365334,0.0014719655,0.0024940341,0.0011229808,0.00008985117],"about_ca_topic_score_codex":0.00004930475,"about_ca_topic_score_gemma":0.00002876781,"teacher_disagreement_score":0.01773182,"about_ca_system_score_codex":0.000041534324,"about_ca_system_score_gemma":0.000035659752,"threshold_uncertainty_score":0.45345744},"labels":[],"label_agreement":null},{"id":"W1665137144","doi":"10.3968/j.css.1923669720130906.2976","title":"Predication Method for China's Civil Aviation Fuel Consumption","year":2013,"lang":"en","type":"article","venue":"Canadian social science","topic":"Grey System Theory Applications","field":"Decision 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":"Civil aviation; China; Aviation; Fuel efficiency; Artificial neural network; Transportation industry; Gray (unit); Aviation engineering; Consumption (sociology); Network model; System dynamics; Transport engineering; Aeronautics; Computer science; Operations research; Engineering; Automotive engineering; Artificial intelligence; Aerospace engineering; Geography","score_opus":0.06738840093481795,"score_gpt":0.3953794307401045,"score_spread":0.32799102980528655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1665137144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06510225,0.000058734797,0.7966851,0.012248931,0.001088845,0.0036247603,0.00022841638,0.00013033589,0.12083265],"genre_scores_gemma":[0.9939634,7.785895e-7,0.003877775,0.00038139246,0.00017346442,0.0004589967,0.000010739094,0.000007677181,0.0011257584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99750966,0.00017083784,0.00040622908,0.0005569519,0.0009075656,0.00044876366],"domain_scores_gemma":[0.9975842,0.00042568133,0.0002662732,0.00041542752,0.0008229095,0.00048550282],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005926347,0.000099084406,0.00014947969,0.00045073646,0.00125039,0.0005889609,0.001125336,0.00009332215,0.0007543332],"category_scores_gemma":[0.0026218593,0.00009023314,0.00006745197,0.0016103331,0.00045418067,0.00092230906,0.000038055205,0.00007251425,0.0012970468],"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":[0.0000032756338,0.000020198035,0.018098796,0.000012094314,0.0000057372094,2.0030562e-7,0.0039122486,0.000009148003,0.010423651,0.7751762,0.036202766,0.1561357],"study_design_scores_gemma":[0.00026752337,0.000033776545,0.46098658,0.0000080491145,0.000013344444,0.0000045204733,0.0016718599,0.009678474,0.00036397442,0.4706695,0.05600645,0.00029595545],"about_ca_topic_score_codex":0.006811199,"about_ca_topic_score_gemma":0.018195588,"teacher_disagreement_score":0.9288612,"about_ca_system_score_codex":0.0005440088,"about_ca_system_score_gemma":0.00074633403,"threshold_uncertainty_score":0.99980253},"labels":[],"label_agreement":null},{"id":"W1858168038","doi":"10.3968/j.sss.1923018420130401.zr0291","title":"The Index Research on Technical Innovation Ability of the Coal Enterprise Based on SEM","year":2013,"lang":"en","type":"article","venue":"Studies in sociology of science","topic":"Grey System Theory Applications","field":"Decision 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":"Coal; Index (typography); Industrial organization; Countermeasure; Production (economics); Business; Order (exchange); Knowledge management; Computer science; Economics; Engineering; Microeconomics","score_opus":0.32684659163880636,"score_gpt":0.5479791844202044,"score_spread":0.22113259278139807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1858168038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879062,0.000034643188,0.00014198163,0.0073966305,0.0002767078,0.0006519383,0.000003520291,0.000006629159,0.0035817232],"genre_scores_gemma":[0.99951935,0.0000037650955,0.00006567557,0.00015792949,0.000013358113,0.0001740415,6.5632456e-8,0.0000020336322,0.0000637545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9955696,0.0012601999,0.0007676054,0.00045011562,0.0016562673,0.00029623893],"domain_scores_gemma":[0.9787912,0.017270448,0.000388228,0.001334572,0.0021933697,0.00002221536],"candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.036711287,0.00007653521,0.00022974031,0.000334491,0.00092890754,0.000014177616,0.0024720945,0.0000780129,0.000015466969],"category_scores_gemma":[0.03207449,0.000035378886,0.000047155325,0.0035491998,0.088065445,0.0000935693,0.0006420457,0.00038315402,0.000039899864],"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":[0.00017954281,0.000542352,0.43554357,0.000024277548,0.000018365732,3.6042533e-7,0.026340345,0.0022400655,0.03650894,0.48055366,0.011928717,0.006119831],"study_design_scores_gemma":[0.00015674297,0.00017397935,0.60639024,0.00003971689,8.8604787e-7,4.0297425e-7,0.04822255,0.00093485735,0.0015796924,0.34228086,0.00017298952,0.00004707669],"about_ca_topic_score_codex":0.000019257524,"about_ca_topic_score_gemma":0.000022162927,"teacher_disagreement_score":0.17084667,"about_ca_system_score_codex":0.00018170403,"about_ca_system_score_gemma":0.0002494063,"threshold_uncertainty_score":0.99190843},"labels":[],"label_agreement":null},{"id":"W1862108781","doi":"10.3968/j.ccc.1923670020100603.007","title":"Causal Relationship among the National Wealth, the Consumption and Shanghai Composite Index","year":2010,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Grey System Theory Applications","field":"Decision 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":"Cointegration; Granger causality; Composite index; Index (typography); Economics; Value (mathematics); Retail sales; China; Econometrics; Business; Agricultural economics; Composite indicator; Mathematics; Statistics; Geography; Marketing","score_opus":0.11499729337222543,"score_gpt":0.4307466065394889,"score_spread":0.31574931316726346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1862108781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98449296,0.00016508659,0.00029765925,0.009426633,0.00016260521,0.0006597524,0.000030975778,0.00007911878,0.004685198],"genre_scores_gemma":[0.9977001,0.000016926318,0.00022600935,0.00039058737,0.00009396,0.00019136765,0.000062720195,0.000010579527,0.0013077385],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99703896,0.0008256892,0.00067654083,0.00032657254,0.0009414492,0.0001907653],"domain_scores_gemma":[0.99129623,0.0052467426,0.00055353617,0.0014147902,0.0014030791,0.00008560473],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006160126,0.00015671657,0.00014978339,0.000081472455,0.0034506044,0.0018439069,0.0018350536,0.00015658891,0.00011744348],"category_scores_gemma":[0.0031811134,0.00008068019,0.00007255761,0.0005448009,0.002414955,0.0011254047,0.00039785885,0.0006973742,0.00033880642],"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":[0.000020896552,0.000027322792,0.7660375,0.000004034299,0.000013626606,6.818852e-8,0.0023530747,0.00012579255,0.0031898585,0.22278427,0.0032790287,0.0021645231],"study_design_scores_gemma":[0.00020639814,0.0000058488044,0.9092436,0.000009230416,0.000009419414,0.000032891883,0.0005121964,0.003013777,0.00009719804,0.06934477,0.017416865,0.00010780869],"about_ca_topic_score_codex":0.000105288425,"about_ca_topic_score_gemma":0.0013273903,"teacher_disagreement_score":0.15343949,"about_ca_system_score_codex":0.000057820136,"about_ca_system_score_gemma":0.000048863836,"threshold_uncertainty_score":0.9991923},"labels":[],"label_agreement":null},{"id":"W1909293309","doi":"10.3968/j.css.1923669720110703.019","title":"Applying Grey Relational Analysis to Evaluate the Factors Affecting Innovation Capability: Evidence from Chinese High-Tech Industries","year":2011,"lang":"en","type":"article","venue":"Canadian social science","topic":"Grey System Theory Applications","field":"Decision 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":"High tech; Grey relational analysis; Government (linguistics); Order (exchange); Statistical analysis; Technology innovation; Industrial organization; Business; Welfare economics; Computer science; Marketing; Economics; Political science; Mathematics; Statistics; Finance","score_opus":0.2248985819651878,"score_gpt":0.389961002071882,"score_spread":0.1650624201066942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1909293309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883623,0.000010166293,0.0056816917,0.0008839959,0.00029135877,0.00083094696,0.00008539391,0.000036985064,0.0038171557],"genre_scores_gemma":[0.99890846,1.15088554e-7,0.000463638,0.00024979093,0.00011047897,0.00014664039,0.0000082456045,0.0000075050675,0.000105111285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99556166,0.0004338948,0.0006642573,0.0008581124,0.0019921386,0.0004899098],"domain_scores_gemma":[0.9947511,0.0021284567,0.0002982173,0.00082892843,0.0016457831,0.00034751653],"candidate_categories":["metaresearch","bibliometrics","sts"],"consensus_categories":[],"category_scores_codex":[0.012638836,0.00017771038,0.00028369148,0.0013294991,0.0027952562,0.00050445227,0.0019939237,0.00011980391,0.000601822],"category_scores_gemma":[0.025626052,0.000120228244,0.000085609056,0.031565726,0.0008205497,0.001078823,0.00017397065,0.00025748124,0.00020596694],"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":[0.0000039815072,0.000005501851,0.95999366,4.479647e-7,0.00002857029,6.044349e-7,0.014517676,0.0000407218,0.0013218996,0.018635023,0.00016398651,0.0052879276],"study_design_scores_gemma":[0.00003397769,0.000008538036,0.9642486,0.0000067207984,0.000049390306,3.151962e-7,0.008644296,0.00015611923,0.00048832176,0.02587162,0.00033261706,0.00015946885],"about_ca_topic_score_codex":0.1163723,"about_ca_topic_score_gemma":0.12508532,"teacher_disagreement_score":0.030236226,"about_ca_system_score_codex":0.0008297273,"about_ca_system_score_gemma":0.0014143989,"threshold_uncertainty_score":0.99850297},"labels":[],"label_agreement":null},{"id":"W1948565850","doi":"10.3968/j.sms.1923845220110301.4z090","title":"Research on Hysteresis Effects of Authorized Patent on the Development of Regional Economy in Hunan Province","year":2011,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Econometrics; Boosting (machine learning); Econometric model; Regression analysis; Lag; Economics; Duration (music); Business; Industrial organization; Computer science; Mathematics; Statistics; Artificial intelligence","score_opus":0.7611978976266359,"score_gpt":0.5243698947051599,"score_spread":0.236828002921476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948565850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9547516,0.00006636543,0.00028260908,0.00077095634,0.000058301604,0.0009593759,5.3358326e-7,0.000005824843,0.043104425],"genre_scores_gemma":[0.99473774,0.000002590149,0.004807598,0.000019996865,0.000006308373,0.00029063705,3.4042465e-8,0.0000030735678,0.00013204271],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99584603,0.0009564317,0.0010551711,0.00043742702,0.0014168072,0.0002881326],"domain_scores_gemma":[0.9814802,0.017506663,0.00028114568,0.0004769845,0.00021395565,0.00004107082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022477254,0.00011189319,0.00041593474,0.00044338702,0.0002699728,0.000022873128,0.0013869106,0.000037865815,0.000046987887],"category_scores_gemma":[0.005987555,0.000054011325,0.000051198233,0.0015000154,0.0022609218,0.00008388646,0.00033056753,0.00013668776,0.00010932757],"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":[0.000034056382,0.00035134822,0.001524654,0.00014523091,0.000012475927,0.000001447784,0.023390718,0.000007927429,0.00016197935,0.97197795,0.00014315319,0.002249066],"study_design_scores_gemma":[0.00016884923,0.00023281065,0.010299766,0.00070919626,0.0000018861712,4.303163e-7,0.029180558,0.00013344269,0.008629976,0.95048904,0.00007608471,0.00007796719],"about_ca_topic_score_codex":0.00001024087,"about_ca_topic_score_gemma":0.000119620505,"teacher_disagreement_score":0.042972382,"about_ca_system_score_codex":0.00009289295,"about_ca_system_score_gemma":0.00017272195,"threshold_uncertainty_score":0.8330461},"labels":[],"label_agreement":null},{"id":"W1951815371","doi":"10.3968/7480","title":"Friction Coefficient Prediction Method for Extended-Reach Well Based on Grey Prediction","year":2015,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Grey System Theory Applications","field":"Decision 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":"Torque; Drilling; Friction coefficient; Drag coefficient; Drag; Engineering; Drill string; Friction torque; Drilling engineering; Control theory (sociology); Mechanics; Geotechnical engineering; Mechanical engineering; Computer science; Control (management); Materials science; Physics; Artificial intelligence; Aerospace engineering; Thermodynamics","score_opus":0.088763793169328,"score_gpt":0.3760797294458554,"score_spread":0.2873159362765274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951815371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022658485,0.00018696822,0.9914338,0.00061468134,0.000975659,0.00076710235,0.000037343198,0.00012210422,0.00359651],"genre_scores_gemma":[0.9313708,0.000046395966,0.06544891,0.00029513112,0.0001292586,0.0017756532,0.00021785873,0.000025611713,0.0006904063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99649465,0.0003531722,0.0009993323,0.00076074566,0.0011358963,0.00025618105],"domain_scores_gemma":[0.9978434,0.0006888184,0.0003540761,0.00041908404,0.0005086304,0.0001859844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050323484,0.00021916605,0.00027733183,0.00064817735,0.00028137898,0.00016219288,0.00022916951,0.00010466519,0.000021599419],"category_scores_gemma":[0.0008773714,0.00018509364,0.000042681466,0.00063567946,0.000045866353,0.0011731788,0.000040929117,0.000118558586,0.000117653704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039916486,0.00041559912,0.0067480365,0.000021977105,0.000009489429,8.1680207e-7,0.0025295378,0.80094033,0.0001464935,0.007741663,0.0023194884,0.17872737],"study_design_scores_gemma":[0.0017378611,0.0003192319,0.0025452261,0.00004506946,0.0000074947757,0.000004271453,0.0036108398,0.6686188,0.0014671012,0.014620842,0.30682516,0.00019806626],"about_ca_topic_score_codex":0.0000029032647,"about_ca_topic_score_gemma":0.00005377483,"teacher_disagreement_score":0.9291049,"about_ca_system_score_codex":0.000344553,"about_ca_system_score_gemma":0.00020016113,"threshold_uncertainty_score":0.75478995},"labels":[],"label_agreement":null},{"id":"W1967540304","doi":"10.1007/s00500-014-1281-1","title":"A fuzzy-filtered grey network technique for system state forecasting","year":2014,"lang":"en","type":"article","venue":"Soft Computing","topic":"Grey System Theory Applications","field":"Decision 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":"Lakehead University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fuzzy logic; State (computer science); Artificial intelligence; Fuzzy control system; Data mining; Algorithm","score_opus":0.09672367193491371,"score_gpt":0.3398785880649776,"score_spread":0.2431549161300639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967540304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029988326,0.000028133712,0.960228,0.00009922011,0.00055531523,0.0013986101,0.000012518766,0.00036432847,0.007325521],"genre_scores_gemma":[0.9196707,4.129845e-8,0.079167835,0.00008985957,0.0006219066,0.00016075565,0.000004758189,0.000036861686,0.00024728695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639815,0.0005311414,0.0011527998,0.00071454013,0.0005882679,0.0006151181],"domain_scores_gemma":[0.99081296,0.0067091617,0.0008065808,0.00092614896,0.0006002468,0.00014490841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0136444075,0.00021886974,0.00047096758,0.00016635192,0.00078392326,0.00037791868,0.0010423189,0.000087634784,0.000004808852],"category_scores_gemma":[0.0032743206,0.00018460925,0.00018292885,0.0008648255,0.000064009146,0.00014340258,0.00030219724,0.00015006006,0.00014266271],"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":[0.00012913701,0.000082585706,0.01997894,0.0005024587,0.00011012485,0.00000820298,0.0029517186,0.12974793,0.004964243,0.38156736,0.01799507,0.44196224],"study_design_scores_gemma":[0.000564296,0.00011389324,0.0008251997,0.00052243186,0.000021655364,0.00012335504,0.0007185896,0.6665244,0.00093377556,0.3140253,0.015128429,0.00049862906],"about_ca_topic_score_codex":0.000012314478,"about_ca_topic_score_gemma":0.000011748539,"teacher_disagreement_score":0.88968235,"about_ca_system_score_codex":0.00008185449,"about_ca_system_score_gemma":0.0000493402,"threshold_uncertainty_score":0.75281465},"labels":[],"label_agreement":null},{"id":"W1970176118","doi":"10.1016/j.csda.2010.08.011","title":"Applications of the characteristic function-based continuum GMM in finance","year":2010,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Grey System Theory Applications","field":"Decision 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":"Université Laval","funders":"","keywords":"Autoregressive model; Econometrics; Economics; Risk premium; Generalized method of moments; Moment (physics); Variance (accounting); Mathematics; Statistical physics; Computer science; Physics; Panel data; Classical mechanics","score_opus":0.04664258040655074,"score_gpt":0.36758855310519173,"score_spread":0.320945972698641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970176118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023250803,0.000013269511,0.9666327,0.000389972,0.0001314685,0.00033092144,0.009101755,0.000012105526,0.00013698282],"genre_scores_gemma":[0.95127124,6.3888945e-7,0.045856643,0.000116456424,0.000040781233,0.0000891476,0.0023854317,0.000007978167,0.00023166016],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99695456,0.00023685383,0.0010484653,0.00060522323,0.0010014535,0.00015343078],"domain_scores_gemma":[0.99187833,0.0041795867,0.0007871871,0.0022842991,0.0008182518,0.000052334828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212912,0.0001293214,0.0003640097,0.00046083904,0.0002109355,0.00012496645,0.0019864333,0.000056972578,0.000477315],"category_scores_gemma":[0.0021063075,0.00009872472,0.00009925677,0.0042958497,0.00028428662,0.00014651069,0.00026141448,0.00020708653,0.00015792242],"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":[0.000041201434,0.0004770026,0.42578825,0.000023112289,0.0003647369,0.0000017828077,0.00013504413,0.11137388,0.00039909146,0.4114692,0.013820879,0.036105808],"study_design_scores_gemma":[0.000120459095,0.0000039616716,0.5451292,0.0000027360677,0.0001983955,4.7738115e-7,0.000012999032,0.32975435,0.000005520732,0.11425363,0.010441236,0.00007702959],"about_ca_topic_score_codex":0.00015800742,"about_ca_topic_score_gemma":0.003468347,"teacher_disagreement_score":0.9280205,"about_ca_system_score_codex":0.000021560121,"about_ca_system_score_gemma":0.00024047359,"threshold_uncertainty_score":0.52262646},"labels":[],"label_agreement":null},{"id":"W1987629881","doi":"10.5539/jmr.v6n3p51","title":"The Grey Modeling Method of Wave Development Coefficient","year":2014,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Grey System Theory Applications","field":"Decision 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; Sequence (biology); Value (mathematics); Development (topology); Algorithm; Applied mathematics; Statistics; Mathematical analysis","score_opus":0.4980145593102381,"score_gpt":0.5426751772467608,"score_spread":0.04466061793652276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987629881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11364617,0.0000792312,0.88201463,0.00061572937,0.00009397163,0.00020275004,7.412282e-7,0.000002931484,0.0033438138],"genre_scores_gemma":[0.8195779,0.00000764219,0.17955515,0.000006594141,0.00005396313,0.0000080907375,6.7340174e-8,0.0000106039515,0.0007800143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9911404,0.0014987362,0.0019998609,0.0001575556,0.004892537,0.00031086986],"domain_scores_gemma":[0.97798824,0.01562944,0.0008526395,0.00076916954,0.0046141483,0.00014633288],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.11361434,0.00008473277,0.0003639388,0.00047222438,0.00043181106,0.00023407761,0.0015917415,0.000052738742,0.000046541198],"category_scores_gemma":[0.019619234,0.000042603602,0.00012860556,0.00082187203,0.00014177249,0.00009243407,0.00028186687,0.0003829413,0.00012124121],"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":[0.00013636479,0.0010472067,0.000113052745,0.00020417999,0.0002204072,0.000008601665,0.030236337,0.06286017,0.012993515,0.63094175,0.007235608,0.25400278],"study_design_scores_gemma":[0.00020024889,0.00008069365,0.00003528336,0.00011550563,0.000006778912,0.00010200097,0.008161984,0.68285626,0.005454106,0.29319802,0.0097274985,0.000061641644],"about_ca_topic_score_codex":0.0000018292513,"about_ca_topic_score_gemma":0.0000056380545,"teacher_disagreement_score":0.7059317,"about_ca_system_score_codex":0.00008395477,"about_ca_system_score_gemma":0.00032198642,"threshold_uncertainty_score":0.98863894},"labels":[],"label_agreement":null},{"id":"W1994374021","doi":"10.5539/mer.v2n1p71","title":"Identification Method for Evolution of Time Series with Poor Information Using Grey System Theory","year":2012,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Grey System Theory Applications","field":"Decision 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":"Education Department of Henan Province; Henan University; Henan University of Science and Technology; National Natural Science Foundation of China","keywords":"Series (stratigraphy); Sequence (biology); Identification (biology); Time sequence; Computer science; State (computer science); Stability (learning theory); Time series; Function (biology); Data mining; Artificial intelligence; Algorithm; Machine learning; Paleontology","score_opus":0.09404670873599662,"score_gpt":0.4108563152356085,"score_spread":0.3168096064996119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994374021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03447486,0.000042856576,0.96416384,0.000063130385,0.00013405125,0.0008892644,0.00003576367,0.000078286634,0.00011796787],"genre_scores_gemma":[0.95138985,2.1229012e-7,0.04807044,0.0000018161237,0.00008298438,0.00022099323,0.0000064674923,0.000017754013,0.00020949081],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966201,0.00057030644,0.0007521661,0.00021858662,0.0014216618,0.00041718432],"domain_scores_gemma":[0.99474376,0.0027672327,0.00022320803,0.0006347053,0.0014863374,0.00014474915],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.030429732,0.0001137089,0.0002607865,0.0006157431,0.00019222133,0.00013488051,0.00054854574,0.00011461654,0.000032576078],"category_scores_gemma":[0.00555945,0.00008615306,0.000071913106,0.0013878136,0.000046605335,0.0013862405,0.00011325604,0.00016559775,0.00019988784],"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":[0.00013726398,0.00002825081,0.000032807733,0.00016157696,0.000024209688,7.896301e-8,0.00028561472,0.008377817,0.19231972,0.79674494,0.00006783095,0.0018198828],"study_design_scores_gemma":[0.00043751585,0.00016757249,0.00055728486,0.00017741798,0.00003816774,0.000060468195,0.0032099448,0.84801245,0.1248855,0.020501688,0.0017023829,0.000249595],"about_ca_topic_score_codex":0.000017100234,"about_ca_topic_score_gemma":8.2272106e-7,"teacher_disagreement_score":0.916915,"about_ca_system_score_codex":0.0003345267,"about_ca_system_score_gemma":0.00009138426,"threshold_uncertainty_score":0.9983766},"labels":[],"label_agreement":null},{"id":"W2000801471","doi":"10.5539/mas.v6n6p12","title":"An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Grey System Theory Applications","field":"Decision 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":"Spare part; Computer science; Genetic algorithm; Power (physics); Reliability engineering; Forecast error; Mathematical optimization; Operations research; Statistics; Econometrics; Mathematics; Operations management; Economics; Engineering; Machine learning","score_opus":0.25631618706969994,"score_gpt":0.3930832198541933,"score_spread":0.13676703278449337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000801471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15419364,0.000038303355,0.8355206,0.00010626684,0.00024711996,0.0011713697,0.00003986994,0.00013519406,0.008547633],"genre_scores_gemma":[0.9569003,3.0828716e-7,0.041437864,0.00036245774,0.00009607957,0.0006602508,0.0000071762065,0.000034359153,0.0005012085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99465054,0.00009993771,0.0008504295,0.0011899336,0.0020701874,0.0011389466],"domain_scores_gemma":[0.99569476,0.00067611586,0.00048146455,0.0019144759,0.00048379414,0.00074939633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012512921,0.0002918244,0.00042112917,0.00042121593,0.0011695686,0.0005385102,0.0026524367,0.000110602174,0.00009192015],"category_scores_gemma":[0.0010267135,0.00024071897,0.0001252788,0.0013354814,0.00074739277,0.0014272939,0.0002823461,0.00014667682,0.00021603674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032848603,0.0007154644,0.0024591296,0.00002240002,0.000018714223,0.0000010794976,0.030252187,0.58043647,0.20492266,0.15669174,0.003630939,0.02052073],"study_design_scores_gemma":[0.00048865925,0.000020071331,0.00017090447,0.000007122091,0.000010323681,0.0000050859862,0.0009821771,0.92530215,0.0027122046,0.06947279,0.00053180155,0.00029672982],"about_ca_topic_score_codex":0.0000043903483,"about_ca_topic_score_gemma":0.000009890084,"teacher_disagreement_score":0.80270666,"about_ca_system_score_codex":0.00016699094,"about_ca_system_score_gemma":0.00032954235,"threshold_uncertainty_score":0.98162353},"labels":[],"label_agreement":null},{"id":"W2004920507","doi":"10.1115/imece2006-14301","title":"Unsteady Viscous Flows and Stokes's First Problem","year":2006,"lang":"en","type":"article","venue":"Fluids Engineering","topic":"Grey System Theory Applications","field":"Decision 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 Waterloo; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hagen–Poiseuille equation; Flow (mathematics); Couette flow; Unsteady flow; Scaling; Mechanics; Stokes flow; Navier–Stokes equations; Stokes number; Stokes problem; Hele-Shaw flow; Mathematics; Viscous flow; Physics; Mathematical analysis; Classical mechanics; Open-channel flow; Compressibility; Geometry; Turbulence; Reynolds number","score_opus":0.02018699924741574,"score_gpt":0.2648041874839711,"score_spread":0.24461718823655537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004920507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6375499,0.00080469885,0.34556025,0.00059068354,0.00042598124,0.00067889236,0.00003952554,0.00044865438,0.013901434],"genre_scores_gemma":[0.992159,0.000002177098,0.005387315,0.000013713045,0.00016618271,0.00007018257,0.0000044619214,0.00002354166,0.0021734207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984131,0.00002733498,0.00045888065,0.0003665431,0.00047891372,0.00025525436],"domain_scores_gemma":[0.9988819,0.00037013652,0.000052992564,0.000520126,0.000087452936,0.000087408196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010036776,0.00015141614,0.00020355897,0.00020555235,0.00012752794,0.00020038508,0.00037493333,0.000063080784,0.000085867505],"category_scores_gemma":[0.00017295785,0.0001290613,0.00004658148,0.0005103235,0.00002598894,0.00021222082,0.00009427615,0.00009252356,0.0004977872],"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":[0.000022283883,0.00018100874,0.01265174,0.00020699219,0.00005092765,0.00004583732,0.0019033719,0.50951123,0.16929409,0.23438452,0.060406435,0.011341553],"study_design_scores_gemma":[0.0009170887,0.00008605677,0.023659669,0.00012753173,0.000035076824,0.00017210665,0.00027719815,0.42385724,0.0037841045,0.02258265,0.5235409,0.0009603527],"about_ca_topic_score_codex":0.00004943276,"about_ca_topic_score_gemma":0.000046327033,"teacher_disagreement_score":0.46313447,"about_ca_system_score_codex":0.00003898814,"about_ca_system_score_gemma":0.000014346896,"threshold_uncertainty_score":0.6398213},"labels":[],"label_agreement":null},{"id":"W2005588393","doi":"10.1016/j.apm.2010.06.001","title":"An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea","year":2010,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":27,"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":"Estimation; Variance (accounting); Fuzzy inference system; Oil consumption; Fuzzy inference; Regression; Fuzzy logic; Regression analysis; Computer science; Consumption (sociology); Inference; Data mining; Adaptive neuro fuzzy inference system; Algorithm; Econometrics; Engineering; Artificial intelligence; Statistics; Fuzzy control system; Machine learning; Mathematics; Business","score_opus":0.09590459960758634,"score_gpt":0.35801796656157076,"score_spread":0.2621133669539844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005588393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24235258,0.000008394048,0.75694376,0.000036700872,0.00001481617,0.00041043462,0.00012422884,0.000010968844,0.000098111595],"genre_scores_gemma":[0.83535105,5.368237e-7,0.16445757,0.00001707857,0.000017124888,0.00013139813,0.000010173542,0.000009998806,0.0000050616122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765766,0.000092391354,0.0010524421,0.0003597433,0.0006365536,0.00020123503],"domain_scores_gemma":[0.99251765,0.0051249303,0.0011076683,0.0006924908,0.00046418168,0.0000930693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026387752,0.0001703668,0.0006042458,0.0003028853,0.00020139708,0.000046914553,0.00033013715,0.00009420308,0.000005977977],"category_scores_gemma":[0.00046924068,0.00011018943,0.00006036142,0.0009713967,0.00026934565,0.000069299254,0.000062508254,0.000106055995,3.260427e-7],"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":[0.000049655057,0.000040299095,0.000032949378,0.00020931037,0.000090362526,1.7626977e-7,0.00051917863,0.2722692,0.001136269,0.7192443,0.000002621946,0.0064056953],"study_design_scores_gemma":[0.00020278325,0.00003523195,0.00004845385,0.00016001181,0.00030119353,0.0000015706437,0.0006147155,0.9030632,0.0007676958,0.09471263,0.0000020721502,0.000090476635],"about_ca_topic_score_codex":0.0027015903,"about_ca_topic_score_gemma":0.0011959769,"teacher_disagreement_score":0.630794,"about_ca_system_score_codex":0.00003302182,"about_ca_system_score_gemma":0.00021417298,"threshold_uncertainty_score":0.44933945},"labels":[],"label_agreement":null},{"id":"W2015873883","doi":"10.1016/j.ijthermalsci.2009.11.013","title":"Unsteady viscous flows and Stokes's first problem","year":2009,"lang":"en","type":"article","venue":"International Journal of Thermal Sciences","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":30,"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; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hagen–Poiseuille equation; Flow (mathematics); Unsteady flow; Mechanics; Scaling; Couette flow; Stokes number; Stokes flow; Boundary layer; Navier–Stokes equations; Physics; Stokes problem; Mathematics; Classical mechanics; Mathematical analysis; Compressibility; Geometry; Turbulence; Reynolds number; Thermodynamics","score_opus":0.06396829097250538,"score_gpt":0.3863920057852865,"score_spread":0.3224237148127811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015873883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9617048,0.00027242926,0.0028414456,0.017166195,0.00063462893,0.000118010044,0.000008876929,0.000012438396,0.017241167],"genre_scores_gemma":[0.9967001,0.000014208079,0.0022521021,0.00039985334,0.0002966749,0.000001471702,2.060956e-7,0.000002846481,0.0003325],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965233,0.00013727546,0.0007774476,0.00023413237,0.0021584698,0.00016936366],"domain_scores_gemma":[0.9976766,0.0005965809,0.0007373033,0.00015715027,0.000705315,0.00012706323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004830119,0.00010448539,0.00019748193,0.0003795179,0.00022389262,0.0005994943,0.0022280123,0.000037409154,0.00025445802],"category_scores_gemma":[0.00057866244,0.00006490219,0.00010115129,0.00040529287,0.00024463606,0.0009363822,0.00009261357,0.00012141954,0.000102304526],"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":[0.0004996074,0.0008879578,0.056289047,0.0000069556777,0.00025544615,0.0002470935,0.010277162,0.052618172,0.07319208,0.14738667,0.015516244,0.6428236],"study_design_scores_gemma":[0.0022872656,0.0018225833,0.20688248,0.00033556882,0.00005568401,0.003189742,0.004296869,0.015349599,0.0036376375,0.6344615,0.1268924,0.000788677],"about_ca_topic_score_codex":0.000014419381,"about_ca_topic_score_gemma":0.000024696701,"teacher_disagreement_score":0.6420349,"about_ca_system_score_codex":0.0000473897,"about_ca_system_score_gemma":0.00010435786,"threshold_uncertainty_score":0.57809377},"labels":[],"label_agreement":null},{"id":"W2016407678","doi":"10.5539/jsd.v1n3p34","title":"Research on Prediction of Shanghai’s Population Development from 2008 to 2050","year":2009,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Grey System Theory Applications","field":"Decision 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":"Statistic; Population; Geography; Statistics; Econometrics; Demography; Mathematics; Sociology","score_opus":0.13062723036017598,"score_gpt":0.40794680564954067,"score_spread":0.2773195752893647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016407678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892951,0.00009498404,0.005945526,0.0016480352,0.00020206058,0.0006289834,0.0000053417234,0.000013895175,0.0021660363],"genre_scores_gemma":[0.9819675,0.0000035040857,0.013479698,0.00012298221,0.0001217394,0.000025184665,0.000012515278,0.000011012228,0.004255835],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9931713,0.0003981616,0.0020784463,0.00036440205,0.0035140973,0.00047361318],"domain_scores_gemma":[0.99376476,0.00070093287,0.0008436646,0.0004786114,0.0039165113,0.00029549937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012835042,0.0001645327,0.0004264815,0.002027512,0.00040582792,0.00015463441,0.00094421284,0.00010817665,0.00018478894],"category_scores_gemma":[0.0017594602,0.00012749144,0.00007794338,0.0021832408,0.000032072407,0.0004164544,0.00013275103,0.00029125813,0.00019895508],"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":[0.003277571,0.0036225766,0.057402577,0.00012560627,0.0004721067,0.00050239597,0.07772814,0.020347834,0.011511641,0.07737597,0.29526395,0.45236963],"study_design_scores_gemma":[0.00046632614,0.00032892695,0.8157928,0.00015253335,0.000006522697,0.000014181102,0.02079021,0.000028174789,0.006996539,0.025575368,0.12970558,0.00014283136],"about_ca_topic_score_codex":0.000042452204,"about_ca_topic_score_gemma":0.000012355358,"teacher_disagreement_score":0.75839025,"about_ca_system_score_codex":0.001255247,"about_ca_system_score_gemma":0.00088280084,"threshold_uncertainty_score":0.51989496},"labels":[],"label_agreement":null},{"id":"W2019040271","doi":"10.1016/j.apm.2011.05.022","title":"An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China","year":2011,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":102,"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":"China Scholarship Council","keywords":"Simulation software; Mathematical optimization; Software; Series (stratigraphy); Nonlinear system; Computer science; Operations research; China; Government (linguistics); Engineering; Industrial engineering; Mathematics","score_opus":0.4213900783878932,"score_gpt":0.36704626805487117,"score_spread":0.05434381033302205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019040271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2617343,0.0000028094353,0.7331975,0.000081872684,0.000030403362,0.0014394252,0.000019687433,0.000029227613,0.0034647605],"genre_scores_gemma":[0.89378715,4.067355e-7,0.10532051,0.00002480628,0.00004631974,0.00066178216,0.0000051118586,0.000036122587,0.000117790674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656206,0.00022814084,0.0016947266,0.0005718391,0.0005074455,0.0004357893],"domain_scores_gemma":[0.9957886,0.0023120332,0.00053128804,0.0011246733,0.00011625846,0.00012716126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009700075,0.00024561794,0.00066076673,0.00018732986,0.00018664583,0.00009418128,0.001210472,0.00017524412,0.00010988778],"category_scores_gemma":[0.0004989102,0.0001447439,0.0001568095,0.00043856225,0.00015721103,0.00022474119,0.00012306786,0.00023215408,0.00004741953],"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":[0.00024375241,0.0001684012,0.00000527464,0.000019034203,0.000011691907,1.589326e-7,0.009414213,0.4052593,0.0010155288,0.58340734,0.00002084814,0.00043442775],"study_design_scores_gemma":[0.0004994559,0.000013159944,3.7367866e-7,0.000021555354,0.000014181091,7.798488e-7,0.0007622056,0.51898074,0.0020685506,0.47754472,0.0000019720808,0.00009231898],"about_ca_topic_score_codex":0.000012522232,"about_ca_topic_score_gemma":0.0000033111614,"teacher_disagreement_score":0.63205284,"about_ca_system_score_codex":0.000022704839,"about_ca_system_score_gemma":0.00005247372,"threshold_uncertainty_score":0.59024847},"labels":[],"label_agreement":null},{"id":"W2019298140","doi":"10.1007/s11633-006-0131-8","title":"Grey repairable system analysis","year":2006,"lang":"en","type":"article","venue":"International Journal of Automation and Computing","topic":"Grey System Theory Applications","field":"Decision 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":"Simon Fraser University","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Set (abstract data type); Computer science; Sample (material); Foundation (evidence); Reliability theory; Operations research; Mathematics; Engineering","score_opus":0.028007032766891444,"score_gpt":0.3489360422230822,"score_spread":0.3209290094561908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019298140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68355584,0.000072500254,0.30907005,0.00067663344,0.00050635217,0.00004963359,0.000003856469,0.000047322857,0.0060178214],"genre_scores_gemma":[0.99460596,6.308995e-7,0.0047941264,0.000045681176,0.00028798915,5.592933e-7,0.000002758526,0.0000038947333,0.00025838462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972132,0.00018793905,0.00117975,0.00015850096,0.0011751655,0.000085443186],"domain_scores_gemma":[0.9962664,0.00059477874,0.0013188055,0.00016438257,0.0016062002,0.00004941417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034199862,0.000074318734,0.0002395079,0.00084754365,0.00011232733,0.00043991487,0.00053170766,0.000034914767,0.000052691023],"category_scores_gemma":[0.0003720346,0.000057703928,0.00017958511,0.0007231245,0.000030782485,0.000327495,0.00006874294,0.0000750811,0.00003909505],"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":[0.00007642845,0.00021306652,0.2428972,0.000020119163,0.0014955028,0.000096656484,0.0014720925,0.14985088,0.0019837597,0.503905,0.008333234,0.08965605],"study_design_scores_gemma":[0.0005320154,0.0000301672,0.30215675,0.000079119534,0.0001402552,0.0005748139,0.001559638,0.6739561,0.00028621356,0.015727574,0.0048195217,0.00013781675],"about_ca_topic_score_codex":0.000039902578,"about_ca_topic_score_gemma":0.000010603956,"teacher_disagreement_score":0.52410525,"about_ca_system_score_codex":0.00008740216,"about_ca_system_score_gemma":0.00003825377,"threshold_uncertainty_score":0.4242109},"labels":[],"label_agreement":null},{"id":"W2035502230","doi":"10.5539/mas.v4n1p96","title":"Analysis of Covariance in Researching on Influence of the Dormitory Academic Atmosphere on Achievement","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Grey System Theory Applications","field":"Decision 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":"Atmosphere (unit); Covariance; Variance (accounting); Analysis of covariance; Quality (philosophy); Computer science; Statistics; Environmental science; Mathematics; Meteorology; Geography; Business; Accounting; Physics","score_opus":0.0718704327507779,"score_gpt":0.39401607821584433,"score_spread":0.3221456454650664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035502230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983355,0.000012715246,0.008455917,0.0006078222,0.000030288162,0.0004739363,0.000011064671,0.00001131517,0.0070419526],"genre_scores_gemma":[0.9991937,0.0000018826461,0.0002989887,0.0003498815,0.0000084732565,0.00002953434,2.3228219e-7,0.0000039079987,0.000113360125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9943659,0.00020040855,0.0008878855,0.000760467,0.0034280056,0.0003573587],"domain_scores_gemma":[0.9963002,0.0010339004,0.0005559343,0.0017958829,0.00021879429,0.000095283904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009846937,0.00013716896,0.00038475913,0.00036568713,0.0002742973,0.00005433072,0.0038727063,0.00007014507,0.00001521053],"category_scores_gemma":[0.0011784968,0.00008800918,0.0001015839,0.009113865,0.00083584763,0.00020021171,0.00023962786,0.00040561677,0.000046301637],"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":[0.000065899214,0.00010048638,0.009980365,0.0000022467734,0.000009240938,2.5195334e-7,0.0014452279,0.5460951,0.32855722,0.10371123,0.000018739644,0.0100139985],"study_design_scores_gemma":[0.00020950107,0.00005160209,0.7684393,0.000057377834,0.00001947073,2.5503493e-7,0.00031144326,0.112740465,0.02325106,0.09473505,0.000056721296,0.00012777443],"about_ca_topic_score_codex":0.000029038241,"about_ca_topic_score_gemma":0.000033839147,"teacher_disagreement_score":0.7584589,"about_ca_system_score_codex":0.00016151613,"about_ca_system_score_gemma":0.00024206974,"threshold_uncertainty_score":0.71965164},"labels":[],"label_agreement":null},{"id":"W2036242886","doi":"10.1109/icc.2012.6364807","title":"Vertical handover decision making using QoS reputation and GM(1,1) prediction","year":2012,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Concordia University","funders":"","keywords":"Computer science; Handover; Reputation; Vertical handover; Computer network; Quality of service; Voice over IP; Heterogeneous network; Bandwidth (computing); Service provider; Mobile telephony; Heterogeneous wireless network; Service (business); Telecommunications; Mobile radio; Wireless network; Wireless; The Internet; World Wide Web; Business","score_opus":0.14473714034607602,"score_gpt":0.43188206218728753,"score_spread":0.28714492184121154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036242886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5737734,0.000055254248,0.42306587,0.000041207313,0.00036250422,0.00016540315,0.000003383914,0.00004079748,0.0024921661],"genre_scores_gemma":[0.9871887,0.0000010395792,0.012302624,0.00010144219,0.00022003346,0.00001235119,0.0000012154727,0.000009360294,0.00016325267],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99794227,0.00017376518,0.0005418615,0.00031623786,0.0008016224,0.00022425767],"domain_scores_gemma":[0.9975807,0.0015526148,0.00009209959,0.00042589672,0.00022009389,0.00012857771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031024087,0.00009554598,0.00014965716,0.00018359696,0.0002469543,0.00021281397,0.00016155258,0.000083550636,0.0004089717],"category_scores_gemma":[0.0025874947,0.00006924274,0.000038621813,0.00057525083,0.00006627329,0.00093861617,0.00011074903,0.00006724924,0.0002981762],"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":[0.00027416952,0.0002350131,0.56913173,0.000012790458,0.00003770235,0.0000021967821,0.0027262454,0.0011838832,0.024484932,0.14648105,0.0084618125,0.24696846],"study_design_scores_gemma":[0.0011322942,0.00007422624,0.63497484,0.00012275203,0.00008674889,0.00029994122,0.0023206824,0.21814574,0.0029565985,0.126814,0.012641063,0.00043111798],"about_ca_topic_score_codex":0.0000074758655,"about_ca_topic_score_gemma":0.000004298487,"teacher_disagreement_score":0.41341528,"about_ca_system_score_codex":0.000059057704,"about_ca_system_score_gemma":0.000021085518,"threshold_uncertainty_score":0.44779533},"labels":[],"label_agreement":null},{"id":"W2040891219","doi":"10.5539/mer.v2n2p108","title":"Grey Prediction on Rolling Bearing Friction Torque","year":2012,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Grey System Theory Applications","field":"Decision 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":"Education Department of Henan Province; Henan University; Henan University of Science and Technology; National Natural Science Foundation of China","keywords":"Bearing (navigation); Friction torque; Torque; Residual; Reliability (semiconductor); A priori and a posteriori; Test data; Structural engineering; Engineering; Computer science; Artificial intelligence; Algorithm; Physics","score_opus":0.2283160633658574,"score_gpt":0.43050408707203275,"score_spread":0.20218802370617533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040891219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65570724,0.00012192183,0.33628488,0.00055289327,0.0016722564,0.00077468617,0.000016101563,0.00043380322,0.004436201],"genre_scores_gemma":[0.9973036,0.0000037428888,0.0010958104,0.000014228838,0.00071342895,0.00016325615,0.0000031337645,0.00003061848,0.0006721663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956324,0.00034612228,0.0005230752,0.0004502666,0.0023335875,0.0007145095],"domain_scores_gemma":[0.9955754,0.00277095,0.000056313613,0.0008438918,0.00039273535,0.000360684],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.016156925,0.00013693311,0.0002039822,0.0006299665,0.00032024985,0.000213857,0.0006610133,0.00016831867,0.000228248],"category_scores_gemma":[0.008144705,0.00011474357,0.000092030816,0.0015736143,0.000025006426,0.0004167467,0.00020798651,0.0007097896,0.002142762],"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":[0.00009554443,0.00037987132,0.00324871,0.000037684076,0.000049749833,0.0000046909618,0.00087447296,0.05513867,0.18550093,0.7325299,0.0029474716,0.019192299],"study_design_scores_gemma":[0.0009972774,0.0006195054,0.032220025,0.00028672398,0.000022794698,0.00006420987,0.0011616396,0.7200333,0.09811649,0.040089674,0.10558963,0.00079874985],"about_ca_topic_score_codex":0.00001882023,"about_ca_topic_score_gemma":0.000001459788,"teacher_disagreement_score":0.6924402,"about_ca_system_score_codex":0.00025957852,"about_ca_system_score_gemma":0.000027000251,"threshold_uncertainty_score":0.99863416},"labels":[],"label_agreement":null},{"id":"W2045120355","doi":"10.5539/ijms.v1n2p107","title":"Establishment of Index System for Effect Evolution of Poverty-alleviation Fund in Rural Area-- Take the Research Result in Leishan County, Guizhou Province as an Example","year":2009,"lang":"en","type":"article","venue":"International Journal of Marketing Studies","topic":"Grey System Theory Applications","field":"Decision 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":"Analytic hierarchy process; Poverty; Index (typography); Investment (military); Business; Investment management; Economics; Environmental economics; Economic growth; Computer science; Finance; Political science","score_opus":0.16445333212062024,"score_gpt":0.4483200400262062,"score_spread":0.28386670790558594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045120355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99396664,0.000668742,0.0015928906,0.0015996181,0.00039573602,0.0007937563,0.000031116117,0.0000056103854,0.0009459191],"genre_scores_gemma":[0.9994722,0.00003107589,0.0002001061,0.000018689589,0.00013651127,0.00003707563,0.0000023273606,0.000006742579,0.000095267635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9916731,0.0029625986,0.0019861942,0.00027108466,0.0028762468,0.0002307708],"domain_scores_gemma":[0.9781306,0.015131713,0.001696537,0.00034439238,0.0046578506,0.000038955608],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06926166,0.00012883091,0.00046349972,0.0009400987,0.000120625635,0.000114139744,0.0012899346,0.00006098383,0.000005235963],"category_scores_gemma":[0.022574035,0.000082800034,0.00011681363,0.0006960427,0.00014103414,0.0004890764,0.00014347193,0.00023834262,0.0000013527108],"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":[0.026539687,0.0013795795,0.7776733,0.00043387536,0.0007072292,0.00004479196,0.013443251,0.010113485,0.014767304,0.06069891,0.011219668,0.082978934],"study_design_scores_gemma":[0.0033567396,0.0012180577,0.9076389,0.0028508205,0.000028047658,0.000079017795,0.04409832,0.005504156,0.0013280641,0.03222442,0.0014850878,0.00018835806],"about_ca_topic_score_codex":0.00041906044,"about_ca_topic_score_gemma":0.0009830578,"teacher_disagreement_score":0.12996563,"about_ca_system_score_codex":0.0009409876,"about_ca_system_score_gemma":0.00023967357,"threshold_uncertainty_score":0.98565924},"labels":[],"label_agreement":null},{"id":"W2052374316","doi":"10.1049/iet-gtd:20060564","title":"Improved Grey predictor rolling models for wind power prediction","year":2007,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":82,"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":"Wind power; Wind speed; Power (physics); Mathematics; Control theory (sociology); Statistics; Computer science; Artificial intelligence; Meteorology; Engineering; Geography","score_opus":0.07266792943886848,"score_gpt":0.33529896002891146,"score_spread":0.262631030590043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052374316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124600664,0.00013467943,0.8704948,0.00073650386,0.0007430841,0.001518174,0.0012579483,0.00018990628,0.0003242588],"genre_scores_gemma":[0.9925239,0.000009873905,0.0037370364,0.00008915492,0.00052636105,0.00013783382,0.0022042927,0.00003087483,0.00074064935],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956298,0.00021141746,0.0015638412,0.00091658725,0.0011897155,0.0004886516],"domain_scores_gemma":[0.9966156,0.0004983021,0.0004534872,0.00071465434,0.001365239,0.00035275423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006531284,0.00029147184,0.00032960478,0.00020886786,0.00082379417,0.00034432052,0.00046339474,0.0003428934,0.00021089759],"category_scores_gemma":[0.0005445538,0.0002444997,0.00028334602,0.00082013465,0.000075693155,0.0010282323,0.000022767708,0.00017426415,0.000047859627],"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":[0.00086070516,0.0004795803,0.0006639272,0.000033542412,0.00007348361,0.0000016592538,0.0014717892,0.049355436,0.8039843,0.05040319,0.026886284,0.06578609],"study_design_scores_gemma":[0.0017895026,0.00026762023,0.002374599,0.000034425648,0.00006756261,0.000013020714,0.00036171067,0.7931631,0.09854986,0.037120406,0.06587318,0.0003850083],"about_ca_topic_score_codex":0.00000971568,"about_ca_topic_score_gemma":0.000015211526,"teacher_disagreement_score":0.86792326,"about_ca_system_score_codex":0.00022214145,"about_ca_system_score_gemma":0.00014493169,"threshold_uncertainty_score":0.9970409},"labels":[],"label_agreement":null},{"id":"W2112086058","doi":"10.4236/ti.2011.22012","title":"Instant Diffusion Equation of Price Changing and Time-Space Exchanging Description","year":2011,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Grey System Theory Applications","field":"Decision 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":"Diffusion; Instant; Partial differential equation; Diffusion equation; Time derivative; Partial derivative; Order (exchange); Acceleration; Space (punctuation); Differential equation; Mathematics; Derivative (finance); Function (biology); Analogy; Space time; Applied mathematics; Mathematical analysis; Computer science; Economics; Thermodynamics; Physics; Classical mechanics; Chemistry","score_opus":0.09999061544883706,"score_gpt":0.29122982055819563,"score_spread":0.19123920510935857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112086058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96780163,0.00041846847,0.02350263,0.00040776827,0.00003590822,0.00032061938,0.000002255827,0.00007857995,0.0074321656],"genre_scores_gemma":[0.9956341,0.000016768923,0.0036757332,0.000091278926,0.0000052493237,0.000046874666,0.0000011328949,0.0000051989614,0.00052370667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990244,0.000066737186,0.00029553394,0.00026500464,0.00019632833,0.00015198468],"domain_scores_gemma":[0.9992392,0.00008424644,0.00023077437,0.00032529654,0.000080615864,0.000039854247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013042776,0.000085858526,0.00016197046,0.0009648678,0.00015640183,0.00001568447,0.00016413888,0.00011452975,0.00005597187],"category_scores_gemma":[0.0002519046,0.000067674184,0.000015381438,0.00091055915,0.00023518073,0.00017218132,0.00020047209,0.000062534775,0.000039898783],"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":[0.000009506831,0.000034126755,0.0028276083,0.000006957084,0.0000060790544,5.8490474e-7,0.0024988283,5.6067177e-7,0.04624904,0.94287574,0.00003103819,0.0054599573],"study_design_scores_gemma":[0.0003341563,0.00018914291,0.0073728,0.00006822708,0.000019471809,0.00002684687,0.0046427944,0.004443796,0.039115775,0.94151914,0.0021246623,0.00014320218],"about_ca_topic_score_codex":0.0000061968826,"about_ca_topic_score_gemma":0.0000015582666,"teacher_disagreement_score":0.02783245,"about_ca_system_score_codex":0.000023653085,"about_ca_system_score_gemma":0.000011285724,"threshold_uncertainty_score":0.2759673},"labels":[],"label_agreement":null},{"id":"W2119477948","doi":"10.5539/emr.v4n1p1","title":"The New Weakening Buffer Operator with Parameters","year":2015,"lang":"en","type":"article","venue":"Engineering Management Research","topic":"Grey System Theory Applications","field":"Decision 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":"Operator (biology); Buffer (optical fiber); Monotonic function; Mathematics; Applied mathematics; Mathematical optimization; Computer science; Mathematical analysis; Chemistry; Telecommunications","score_opus":0.2821088102235979,"score_gpt":0.43953839920812604,"score_spread":0.15742958898452813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119477948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33896422,0.0010280675,0.52710676,0.020612756,0.0010848896,0.0044509303,0.0000045437732,0.0006079269,0.10613989],"genre_scores_gemma":[0.94847614,0.0000052032015,0.011771649,0.000023809373,0.00008822194,0.00021769636,9.0596126e-7,0.000026954167,0.03938945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961618,0.00022586946,0.00028836654,0.00039079835,0.0024354851,0.0004976508],"domain_scores_gemma":[0.996818,0.0013669332,0.00003550948,0.0011709955,0.00030701415,0.00030152663],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011382731,0.000113808004,0.00012454196,0.00033847074,0.0002887014,0.0009494166,0.0014804009,0.000027520638,0.00003084765],"category_scores_gemma":[0.0011960217,0.00006338347,0.00003215543,0.001743543,0.00008002695,0.00018649847,0.0004032912,0.00025339387,0.001256597],"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":[0.0001250649,0.00005058223,0.0016164448,0.00002517862,0.00021587953,0.00006856786,0.0017700413,0.14580211,0.0003787191,0.42764112,0.35590234,0.06640395],"study_design_scores_gemma":[0.00056924985,0.00011244942,0.0018649065,0.000045054156,0.0000084393105,0.0000088708075,0.003994969,0.025452197,0.0004983152,0.009255723,0.9579713,0.0002185234],"about_ca_topic_score_codex":0.00003159959,"about_ca_topic_score_gemma":0.000013550572,"teacher_disagreement_score":0.6095119,"about_ca_system_score_codex":0.00011152252,"about_ca_system_score_gemma":0.00006592766,"threshold_uncertainty_score":0.999521},"labels":[],"label_agreement":null},{"id":"W2127925682","doi":"10.1109/tpwrs.2006.879246","title":"Grey Predictor for Wind Energy Conversion Systems Output Power Prediction","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":179,"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":"Wind power; Wind speed; Electric power system; Electricity generation; Electricity; Wind power forecasting; Power (physics); Computer science; Meteorology; Engineering; Econometrics; Control theory (sociology); Mathematics; Artificial intelligence; Electrical engineering; Geography","score_opus":0.033744587415876826,"score_gpt":0.27763037153716363,"score_spread":0.2438857841212868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127925682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022884166,0.000319136,0.95024234,0.00017975611,0.015640996,0.002184142,0.0019439588,0.00046297474,0.006142531],"genre_scores_gemma":[0.9648551,0.000003929413,0.000059499194,0.000053745236,0.00017487112,0.00084848155,0.000033278564,0.0000856844,0.033885434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933436,0.00065417367,0.0020021861,0.0012880606,0.0020544864,0.000657508],"domain_scores_gemma":[0.99442905,0.0015769331,0.0007484801,0.0017700818,0.001170603,0.0003048311],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027199704,0.00050815276,0.00077456905,0.00090576743,0.000805955,0.00072058156,0.0009799248,0.00046420543,0.00016772305],"category_scores_gemma":[0.00006967683,0.00042901112,0.00048272818,0.0010792963,0.00017363013,0.0007304663,0.0000051886736,0.00024404343,0.00076859846],"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":[0.0016416603,0.002982567,0.0023744036,0.0003967224,0.00089015125,0.00003453953,0.002424735,0.5093976,0.011490522,0.08700502,0.3804817,0.0008803856],"study_design_scores_gemma":[0.004444161,0.0013769679,0.0017375788,0.0004710971,0.000278458,0.00031755512,0.0049420893,0.08740446,0.0047317515,0.0023482312,0.8905386,0.0014091022],"about_ca_topic_score_codex":0.0007487503,"about_ca_topic_score_gemma":0.000053218628,"teacher_disagreement_score":0.95018286,"about_ca_system_score_codex":0.00037897058,"about_ca_system_score_gemma":0.00015626957,"threshold_uncertainty_score":0.9998162},"labels":[],"label_agreement":null},{"id":"W2133073413","doi":"10.5539/ass.v8n7p256","title":"A Dynamic Analysis of Influencing Factors in Price Fluctuation of Live Pigs --- Based on Statistical Data in Sichuan Province, China","year":2012,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cointegration; Price fluctuation; Granger causality; Economics; Price level; Variance decomposition of forecast errors; Econometrics; Vector autoregression; Production (economics); Impulse response; Monetary economics; Agricultural economics; Macroeconomics; Mathematics","score_opus":0.048259044716560597,"score_gpt":0.38828768593099366,"score_spread":0.34002864121443305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133073413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9737806,0.000004007873,0.018315785,0.000097585864,0.000045112683,0.00034114576,0.0002202374,0.000008031834,0.0071875],"genre_scores_gemma":[0.9987553,1.5536864e-7,0.0011746498,0.000014384893,0.000007281127,0.0000089953655,0.000025603616,0.000004019847,0.000009616913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963245,0.00036008807,0.00077915826,0.0004983951,0.0017040009,0.00033385665],"domain_scores_gemma":[0.9972905,0.0011180594,0.0005390574,0.0007939885,0.00015832034,0.00010005965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007981057,0.00010579526,0.00037594576,0.001279478,0.00013634001,0.000052286607,0.0016187953,0.0000586135,0.00008662386],"category_scores_gemma":[0.005577302,0.00008705525,0.000045894212,0.008924212,0.00063706865,0.0008509325,0.00020640399,0.000119506505,0.000014893565],"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":[0.000032875618,0.0004137317,0.89741766,0.000014877673,0.000018717315,8.117545e-7,0.02703148,0.00045519072,0.009092312,0.04066929,0.000013473097,0.024839554],"study_design_scores_gemma":[0.00009416555,0.000020509773,0.9647995,0.00001431145,0.000025370231,6.1963775e-8,0.004027373,0.02865531,0.00013761334,0.0021400596,0.0000061845712,0.00007953228],"about_ca_topic_score_codex":0.0005677533,"about_ca_topic_score_gemma":0.0013711131,"teacher_disagreement_score":0.06738182,"about_ca_system_score_codex":0.00025884452,"about_ca_system_score_gemma":0.00046703022,"threshold_uncertainty_score":0.66769546},"labels":[],"label_agreement":null},{"id":"W2133197669","doi":"10.3968/7083","title":"Pin on Disc Wear volume Prediction Based on Grey System Theory","year":2015,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Volume (thermodynamics); Lubrication; Mechanics; Materials science; Mathematics; Engineering; Mechanical engineering; Physics; Thermodynamics","score_opus":0.029143317473116673,"score_gpt":0.36722247673833475,"score_spread":0.3380791592652181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133197669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68343484,0.04490382,0.0037480902,0.00519986,0.039079543,0.005725106,0.00020761877,0.0011033445,0.21659775],"genre_scores_gemma":[0.9955917,0.00011987925,0.0025376393,0.00044601905,0.0002920357,0.00019702414,0.000007163712,0.00002352706,0.0007849965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9842557,0.0012886911,0.0019080731,0.003058941,0.007747371,0.0017412402],"domain_scores_gemma":[0.9922983,0.003878402,0.001004784,0.0015455747,0.0007314767,0.0005414641],"candidate_categories":["metaresearch","metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.025802871,0.0007151228,0.00090737175,0.0028792948,0.0012909157,0.00094180467,0.0064899074,0.0002031585,0.00005303526],"category_scores_gemma":[0.011450972,0.00046321453,0.00021498294,0.016774839,0.0055244905,0.01092246,0.00044677092,0.0012001662,0.00047346472],"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":[0.0011273774,0.00046757248,0.06756617,0.000065481974,0.0000058172222,0.000065700246,0.0014681164,0.29042557,0.0014386764,0.43921176,0.00037457293,0.1977832],"study_design_scores_gemma":[0.004165967,0.002353504,0.04592776,0.0018854365,0.000023000617,0.00011435383,0.024464399,0.48816538,0.0048973598,0.3562372,0.06911515,0.0026505163],"about_ca_topic_score_codex":0.000054465887,"about_ca_topic_score_gemma":0.0005932693,"teacher_disagreement_score":0.31215686,"about_ca_system_score_codex":0.0018268225,"about_ca_system_score_gemma":0.0009345006,"threshold_uncertainty_score":0.99978197},"labels":[],"label_agreement":null},{"id":"W2158288838","doi":"10.1016/s2095-3119(12)60055-0","title":"Using Quantile Regression Approach to Analyze Price Movements of Agricultural Products in China","year":2012,"lang":"en","type":"article","venue":"Journal of Integrative Agriculture","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program","keywords":"Quantile; Quantile regression; Econometrics; Heteroscedasticity; Sample (material); Confidence interval; Statistics; Linear regression; Regression; Confidence and prediction bands; Regression analysis; Economics; Mathematics","score_opus":0.07574248865837385,"score_gpt":0.3701716977708142,"score_spread":0.29442920911244036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158288838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869618,0.00042437503,0.007034965,0.00035053462,0.0003072267,0.00053264794,0.00001681714,0.0000054166785,0.0043662474],"genre_scores_gemma":[0.9833955,0.000004531973,0.0153492335,0.00003362759,0.00027488155,0.000009957211,0.000003682163,0.0000066037437,0.00092197204],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9959459,0.000603809,0.001440759,0.00028551824,0.0013889672,0.00033506798],"domain_scores_gemma":[0.9952238,0.00023714599,0.00191758,0.00033009687,0.0020600895,0.00023127827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036678305,0.00026257877,0.0007021723,0.0004396154,0.00009557343,0.00007769228,0.00088852487,0.000119507386,0.000030386393],"category_scores_gemma":[0.0028257628,0.00009949389,0.00021295375,0.0034860687,0.00005231446,0.0009939577,0.00011617667,0.00042672205,0.000018958213],"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":[0.00014032124,0.0017244449,0.059976023,0.00003606734,0.0001367325,0.000002755257,0.032268565,0.0029905764,0.85949683,0.009362908,0.0331949,0.00066985795],"study_design_scores_gemma":[0.00043337836,0.00016628546,0.8615002,0.0004610069,0.00003858546,0.00020626048,0.04692246,0.000066481036,0.087092884,0.00089289737,0.0019428486,0.00027671026],"about_ca_topic_score_codex":0.000034368364,"about_ca_topic_score_gemma":0.000014214245,"teacher_disagreement_score":0.80152416,"about_ca_system_score_codex":0.00021860114,"about_ca_system_score_gemma":0.00007182323,"threshold_uncertainty_score":0.4057243},"labels":[],"label_agreement":null},{"id":"W2166279149","doi":"10.1108/20439371211260162","title":"The optimized GPM(1,1) for forecasting small sample oscillating series","year":2012,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision 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":"Sample (material); Series (stratigraphy); Range (aeronautics); Computer science; Variable (mathematics); Power (physics); Time series; Value (mathematics); Industrial engineering; Operations research; Mathematical optimization; Engineering; Mathematics; Machine learning","score_opus":0.11112063111047962,"score_gpt":0.3379260838433426,"score_spread":0.22680545273286298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166279149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09343809,0.0012011043,0.8994185,0.00040316943,0.00050705264,0.0028223803,0.000090004745,0.0001290891,0.0019906568],"genre_scores_gemma":[0.99040824,0.000008775804,0.00478631,0.000039613962,0.0005789607,0.002421737,0.000020059446,0.000030104255,0.0017061699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967347,0.0009138531,0.0009708032,0.00048201313,0.0004058863,0.00049278745],"domain_scores_gemma":[0.98156226,0.016114688,0.00077418354,0.0009559586,0.0004189745,0.00017393196],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.022906296,0.00021695795,0.0003370919,0.00009042127,0.0018798973,0.0005656346,0.00060601346,0.00011570726,0.0000075197063],"category_scores_gemma":[0.0055242404,0.00013828161,0.000107771964,0.00045040736,0.00021648995,0.0004670039,0.00013009967,0.00009996885,0.00006613767],"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":[0.00014362404,0.000015964506,0.0019962194,0.000030455069,0.00002107702,2.6663301e-8,0.0010204171,0.00029866825,0.0009830517,0.9606404,0.00025745912,0.034592655],"study_design_scores_gemma":[0.00090662116,0.000080514255,0.0013463403,0.00007474227,0.00008082684,0.00013216858,0.021759301,0.03210499,0.000978756,0.70975673,0.23218206,0.0005969787],"about_ca_topic_score_codex":0.000028726761,"about_ca_topic_score_gemma":0.000012103816,"teacher_disagreement_score":0.89697015,"about_ca_system_score_codex":0.000042244013,"about_ca_system_score_gemma":0.000026360625,"threshold_uncertainty_score":0.9994195},"labels":[],"label_agreement":null},{"id":"W2168215921","doi":"10.5539/jsd.v5n2p37","title":"Affecting Factors and Security System of Food Production - A Case Study of Mingshan County","year":2012,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Grey System Theory Applications","field":"Decision 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":"Food security; Production (economics); Per capita; Grain yield; Agricultural economics; Grey relational analysis; Yield (engineering); Unit (ring theory); Business; Food processing; Agricultural science; Agriculture; Geography; Environmental science; Economics; Mathematics; Agronomy; Statistics; Environmental health; Political science","score_opus":0.054477691902206714,"score_gpt":0.32469666834234745,"score_spread":0.27021897644014076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168215921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99849814,0.00018736742,0.0002715623,0.000015442176,0.00021867956,0.000713635,0.0000018554794,0.0000064188266,0.00008689903],"genre_scores_gemma":[0.9995331,5.669314e-7,0.0003102814,0.0000010449029,0.000050276773,0.000011136941,1.6788265e-7,0.000008251476,0.00008514339],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9967341,0.0003531123,0.0014278323,0.00017469589,0.0010475097,0.0002627485],"domain_scores_gemma":[0.99505347,0.00051520177,0.0020348663,0.00026744374,0.001983098,0.00014592241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011080607,0.00013456341,0.0004872309,0.0005345495,0.00023781045,0.000050927058,0.00023968995,0.000048469446,0.0000061313613],"category_scores_gemma":[0.0016647973,0.00009252612,0.00005408428,0.00076951185,0.000044356446,0.000600041,0.000136684,0.0001278299,6.90693e-7],"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":[0.000107280386,0.0014824038,0.6789128,0.00097566046,0.00031397268,0.00026394092,0.31305462,0.00009142965,0.0008753052,0.0025215817,0.0002721972,0.0011287987],"study_design_scores_gemma":[0.00039594882,0.00028842757,0.04634428,0.00008585802,0.000045101428,0.0022155645,0.94226295,0.000006838402,0.0075949584,0.00027478847,0.00038606458,0.00009921297],"about_ca_topic_score_codex":0.00006784974,"about_ca_topic_score_gemma":0.000027543336,"teacher_disagreement_score":0.63256854,"about_ca_system_score_codex":0.00032685307,"about_ca_system_score_gemma":0.00025540096,"threshold_uncertainty_score":0.38403392},"labels":[],"label_agreement":null},{"id":"W2169061612","doi":"10.3968/5723","title":"Grey Prediction of Economy Based on Improved GM (1, 1) Model","year":2014,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Grey System Theory Applications","field":"Decision 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":"Overheating (electricity); Time sequence; Computer science; Econometrics; Economics; Artificial intelligence; Engineering","score_opus":0.0645604184764972,"score_gpt":0.3318068382151693,"score_spread":0.26724641973867214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169061612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037227515,0.000017709357,0.8392186,0.00027926074,0.00008982946,0.0018432688,0.000043546694,0.00013596854,0.121144295],"genre_scores_gemma":[0.8849481,2.8643007e-7,0.11423943,0.000059161397,0.000027144646,0.00062808744,0.000005239495,0.000023753799,0.00006881479],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749273,0.000063742606,0.0011782867,0.00045554488,0.0005723736,0.00023732646],"domain_scores_gemma":[0.9965789,0.001170537,0.00068464765,0.0013178458,0.00016533467,0.00008274435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041007614,0.00019011776,0.00045607003,0.00035791897,0.00006570699,0.000089295645,0.0008031425,0.00012536107,0.000041654457],"category_scores_gemma":[0.00043381684,0.00015180504,0.00008120318,0.0005011546,0.0001757539,0.00007880449,0.00008726083,0.00014016886,0.00013538607],"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":[0.000051332343,0.00077649317,0.0017058875,0.00020760797,0.000011609349,1.7060044e-7,0.0008831333,0.013024848,0.0007632163,0.94388473,0.00030667888,0.038384266],"study_design_scores_gemma":[0.00036891922,0.00003058366,0.000089010035,0.000032859793,0.0000069976895,4.7193126e-7,0.00012061673,0.6539114,0.001866992,0.34327888,0.00021430563,0.00007893346],"about_ca_topic_score_codex":4.5378164e-7,"about_ca_topic_score_gemma":0.0000023407547,"teacher_disagreement_score":0.84772056,"about_ca_system_score_codex":0.00005272949,"about_ca_system_score_gemma":0.000054088585,"threshold_uncertainty_score":0.619043},"labels":[],"label_agreement":null},{"id":"W2171293692","doi":"10.1108/20439371211260234","title":"Grey relational evaluation of innovation competency in an aviation industry cluster","year":2012,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision 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 Waterloo","funders":"","keywords":"Weighting; Aviation; Grey relational analysis; Analytic hierarchy process; Relation (database); Originality; Process (computing); Computer science; Operations research; Engineering; Industrial engineering; Data mining; Mathematics","score_opus":0.12380854314690197,"score_gpt":0.3931378872409777,"score_spread":0.2693293440940757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171293692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9110467,0.00026738935,0.08378092,0.00013054247,0.00025272218,0.0018023557,0.00003068331,0.00003996021,0.0026487196],"genre_scores_gemma":[0.99834657,0.0000013485708,0.00027527116,0.000052522675,0.00024457145,0.00074480474,0.0001528355,0.000018880679,0.00016319993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99236757,0.0033343646,0.0018870349,0.00054164417,0.0015958478,0.00027356244],"domain_scores_gemma":[0.99426806,0.001251468,0.0013529,0.0008804531,0.0021387357,0.000108364904],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.045177825,0.00019835852,0.0003452282,0.0007684362,0.00019300685,0.000102871345,0.00036073622,0.00039085688,0.00007654159],"category_scores_gemma":[0.0022932868,0.00017610299,0.0000353,0.0023138146,0.00013657959,0.0017409254,0.0000665892,0.00027629244,0.000119625314],"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":[0.000054712495,0.00012943137,0.1475017,0.00001846545,0.000007633399,2.1197508e-8,0.0012169179,0.0015117436,0.0036833177,0.83388835,0.000041562922,0.011946153],"study_design_scores_gemma":[0.0010526644,0.000048302907,0.55861014,0.00007310286,0.000057980375,0.000021787575,0.0038012867,0.04016432,0.00055478443,0.39434725,0.0009558555,0.0003125],"about_ca_topic_score_codex":0.00003373806,"about_ca_topic_score_gemma":0.000021474343,"teacher_disagreement_score":0.43954107,"about_ca_system_score_codex":0.00015093578,"about_ca_system_score_gemma":0.00012709276,"threshold_uncertainty_score":0.98319036},"labels":[],"label_agreement":null},{"id":"W2184876889","doi":"10.19026/rjaset.7.629","title":"The Prediction for Shanghai Business Climate Index by Grey Model","year":2014,"lang":"en","type":"article","venue":"Research Journal of Applied Sciences Engineering and Technology","topic":"Grey System Theory Applications","field":"Decision 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":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Index (typography); Quarter (Canadian coin); Business model; Econometrics; Operations research; Computer science; Economics; Mathematics; Geography; Management","score_opus":0.06527438427851293,"score_gpt":0.36848552712780647,"score_spread":0.3032111428492935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184876889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29406354,0.00048020235,0.6969405,0.0064071747,0.00021187469,0.00050724106,0.000018852043,0.00007545348,0.0012951732],"genre_scores_gemma":[0.9970026,0.0000879005,0.0027239246,0.0000071194168,0.000058539496,0.0000614429,2.5349766e-7,0.0000075200724,0.00005068429],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976429,0.00004559895,0.00051834644,0.00026749176,0.0010747692,0.00045089956],"domain_scores_gemma":[0.99625134,0.0021101781,0.00020943928,0.00031020303,0.0010247632,0.00009405458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01841725,0.00008820327,0.00020014733,0.0009084953,0.00087961974,0.00032262373,0.0013452057,0.00011502802,0.0000012934946],"category_scores_gemma":[0.003206409,0.000051817315,0.00002966322,0.0027617454,0.000757417,0.00016525168,0.0001834942,0.00033510962,0.0000047903386],"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":[0.000064580774,0.000034763656,0.00052675663,0.000023589319,0.000018911902,4.160654e-7,0.00012312795,0.081742845,0.049671136,0.7833607,0.004080363,0.08035278],"study_design_scores_gemma":[0.000272512,0.00013831687,0.00014007879,0.000022543947,0.0000035564872,0.00003848408,0.0005874568,0.76232773,0.0019198649,0.21533442,0.01914395,0.000071090035],"about_ca_topic_score_codex":6.9144363e-7,"about_ca_topic_score_gemma":0.0000017514235,"teacher_disagreement_score":0.70293903,"about_ca_system_score_codex":0.00003364505,"about_ca_system_score_gemma":0.00009247289,"threshold_uncertainty_score":0.67654127},"labels":[],"label_agreement":null},{"id":"W2215029138","doi":"10.1016/j.eswa.2015.07.066","title":"An improved grey relational analysis approach for panel data clustering","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for International Governance Innovation; University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Agra; Cluster analysis; Series (stratigraphy); Pairwise comparison; Grey relational analysis; Data mining; Mathematics; Similarity (geometry); Computer science; Mainland China; Statistics; Pattern recognition (psychology); Artificial intelligence; Geography; China","score_opus":0.3202236941888919,"score_gpt":0.41392763083218975,"score_spread":0.09370393664329785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215029138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006130983,0.00032138906,0.9913113,0.0002758784,0.000093245224,0.0036163775,0.0006506524,0.00021280993,0.0029052128],"genre_scores_gemma":[0.8473338,0.0000011045786,0.13723524,0.00007684409,0.00049905886,0.010852571,0.002114862,0.00004769731,0.0018388246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952983,0.0003393293,0.0011775678,0.0016127491,0.0011935815,0.00037844732],"domain_scores_gemma":[0.9907272,0.0008102616,0.0007061623,0.005820993,0.0014212247,0.0005141442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050678146,0.00028690463,0.0005967699,0.00054392125,0.0005425876,0.0005824073,0.0029367274,0.00015452194,0.000017859122],"category_scores_gemma":[0.00040611863,0.00021231949,0.000113133225,0.0028801924,0.00014223203,0.0010118579,0.00024000292,0.000120839446,0.00012893375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083391316,0.002882156,0.037818685,0.00015389532,0.003608144,0.0000024682722,0.014755606,0.45920256,0.0058984286,0.39277694,0.064160496,0.017906703],"study_design_scores_gemma":[0.0005861416,0.000059929203,0.0007265088,0.0000051617862,0.00015146947,0.000026386544,0.006701987,0.8987916,0.000012289834,0.0008399584,0.0917682,0.00033036675],"about_ca_topic_score_codex":0.00028855551,"about_ca_topic_score_gemma":0.0001393239,"teacher_disagreement_score":0.8540761,"about_ca_system_score_codex":0.00013331504,"about_ca_system_score_gemma":0.00026223197,"threshold_uncertainty_score":0.86581373},"labels":[],"label_agreement":null},{"id":"W2284821155","doi":"10.14288/1.0087838","title":"Local linear regression versus backcalculation in forecasting","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Grey System Theory Applications","field":"Decision 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":"Linear regression; Regression; Econometrics; Statistics; Computer science; Mathematics","score_opus":0.0687337502597661,"score_gpt":0.2837936463388803,"score_spread":0.21505989607911424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284821155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9753523,0.000034001587,0.017412798,0.00017988526,0.00011588278,0.00022008973,0.000023085273,0.00003717575,0.00662482],"genre_scores_gemma":[0.99821997,0.0000036851243,0.0012644143,0.000018419378,0.0000208651,2.8013196e-7,0.00000834206,0.000004967476,0.00045903676],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99829733,0.00016983677,0.00028603766,0.00044080173,0.0006089087,0.00019709748],"domain_scores_gemma":[0.9985605,0.00037922556,0.000251159,0.0004325375,0.00027947052,0.000097115786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001208961,0.000038615028,0.00024728384,0.000118725584,0.00020202599,0.00009001497,0.00054690044,0.000113329435,0.00014847811],"category_scores_gemma":[0.00041511428,0.00011347186,0.00010266306,0.0010140422,0.00015976107,0.00044611876,0.000087840555,0.000118332406,0.00012069555],"study_design_candidate":"design_other","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":[0.00004726104,0.00007551025,0.0055346913,0.0000044332846,0.0000037307398,0.000049899147,0.0002090527,0.0005416716,0.00015431993,0.000055092198,0.0012221282,0.9921022],"study_design_scores_gemma":[0.0012195105,0.00008051559,0.9687865,0.00012364617,0.0000074074615,0.000022016036,0.0031566485,0.019539438,0.0000020243874,0.006533889,0.00040596764,0.00012245122],"about_ca_topic_score_codex":0.007903479,"about_ca_topic_score_gemma":0.10012042,"teacher_disagreement_score":0.9919798,"about_ca_system_score_codex":0.0000952732,"about_ca_system_score_gemma":0.000053032425,"threshold_uncertainty_score":0.998703},"labels":[],"label_agreement":null},{"id":"W2314684775","doi":"10.5267/j.msl.2016.3.002","title":"Personnel selection with grey relational analysis","year":2016,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":24,"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":"Grey relational analysis; Selection (genetic algorithm); Multiple-criteria decision analysis; Computer science; Process (computing); Software; Knowledge management; Management science; Operations research; Process management; Artificial intelligence; Business; Mathematics; Engineering; Statistics","score_opus":0.04457238793897265,"score_gpt":0.3062000303912399,"score_spread":0.2616276424522672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314684775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47219497,0.0000011302035,0.50690216,0.013703746,0.000070081456,0.0002272795,0.0000027622782,0.000059311707,0.0068385527],"genre_scores_gemma":[0.98864764,3.8332303e-7,0.0064500673,0.0010920654,0.000036215086,0.000058258098,7.5462054e-7,0.000005540549,0.0037090636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959952,0.00010341652,0.00033414987,0.0008588703,0.002369095,0.00033925855],"domain_scores_gemma":[0.9985193,0.0002902135,0.0002216348,0.000728631,0.00013099489,0.0001092451],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0041652126,0.00011965406,0.00015220119,0.0015717646,0.00064915454,0.00031654583,0.0010897543,0.000017765113,0.00050915405],"category_scores_gemma":[0.00019598595,0.00006470329,0.000086672895,0.009091314,0.0006135151,0.0010771401,0.00013336031,0.00004346153,0.0011167079],"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":[0.00003582431,0.000055044184,0.73047984,0.0000034734667,0.0002567737,0.000009033744,0.00079743663,0.006422877,0.02074057,0.2194717,0.0068247425,0.0149027025],"study_design_scores_gemma":[0.00046588416,0.000035431505,0.97664344,0.000017438011,0.00021471891,0.000008555816,0.0014206333,0.003915699,0.0005032928,0.0053972937,0.011050511,0.00032707385],"about_ca_topic_score_codex":0.000010754053,"about_ca_topic_score_gemma":0.000029499093,"teacher_disagreement_score":0.51645267,"about_ca_system_score_codex":0.00017866492,"about_ca_system_score_gemma":0.000012710984,"threshold_uncertainty_score":0.999661},"labels":[],"label_agreement":null},{"id":"W2337344331","doi":"10.5267/j.dsl.2016.1.002","title":"Application of Grey-TOPSIS approach to evaluate value chain performance of tea processing chains","year":2016,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":36,"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":"TOPSIS; Chain (unit); Value (mathematics); Computer science; Business; Operations management; Industrial organization; Industrial engineering; Environmental economics; Operations research; Mathematics; Engineering; Economics; Machine learning","score_opus":0.06375179917537041,"score_gpt":0.36619918157181974,"score_spread":0.3024473823964493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337344331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5415414,0.0000084729445,0.45550618,0.0015501528,0.0000833839,0.00052597,0.0000097953925,0.000021636377,0.00075301784],"genre_scores_gemma":[0.9726143,0.0000017667487,0.026413191,0.0005951999,0.0000453034,0.00016201193,6.0128946e-7,0.000015807307,0.00015181494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99166155,0.0002155433,0.0015261602,0.0012602117,0.0048310747,0.0005054327],"domain_scores_gemma":[0.99406433,0.0013510021,0.0009816774,0.0020675908,0.0012681497,0.00026723102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018415146,0.00022617733,0.0004994534,0.0014473192,0.0003735251,0.00013885627,0.0036078168,0.00007092223,0.000025003794],"category_scores_gemma":[0.004255742,0.00013501983,0.00013087872,0.0066218055,0.0011436305,0.001081852,0.00043124164,0.00007700789,0.00026665116],"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":[0.000058875103,0.000078103105,0.010936275,0.000012646235,0.0000028449533,1.3510055e-7,0.0009093838,0.0023966513,0.5729676,0.0050450754,0.0004438799,0.40714854],"study_design_scores_gemma":[0.002232712,0.00041789823,0.32237172,0.000712623,0.00005588158,0.000064398046,0.0018578679,0.31442887,0.33057043,0.018409185,0.0075313402,0.0013470624],"about_ca_topic_score_codex":0.000011400557,"about_ca_topic_score_gemma":0.0000014490416,"teacher_disagreement_score":0.43107292,"about_ca_system_score_codex":0.00015100905,"about_ca_system_score_gemma":0.00020058366,"threshold_uncertainty_score":0.6704281},"labels":[],"label_agreement":null},{"id":"W2347421248","doi":"","title":"Analysis and Forecasting on the Quantity of Birth in Canada","year":2001,"lang":"en","type":"article","venue":"Journal of Wenzhou Teachers College","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geodetic datum; Factor (programming language); Term (time); Econometrics; Computer science; Economics; Geography; Geodesy; Physics","score_opus":0.11151371634877943,"score_gpt":0.32553255595786457,"score_spread":0.21401883960908513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347421248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99590397,0.00008859352,0.000595477,0.0016563396,0.000075286225,0.00011092333,0.00002127917,0.0000014284301,0.001546729],"genre_scores_gemma":[0.99929816,0.000007515651,0.00017584242,0.00007838676,0.000026604466,0.000002030259,8.385689e-8,0.0000047267067,0.00040667487],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99704844,0.0005493347,0.0011038153,0.00014744063,0.0010073372,0.00014362214],"domain_scores_gemma":[0.995225,0.0028735558,0.0011996493,0.00035333473,0.00025981778,0.00008864212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073284414,0.000088033594,0.00044352745,0.00056676014,0.0000955372,0.000032857373,0.0005830344,0.000032203036,0.00016731171],"category_scores_gemma":[0.0027616448,0.00005094403,0.00013416968,0.002784454,0.00006117168,0.00012875759,0.000042684333,0.00022945245,0.0000022986337],"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":[0.0000755106,0.000043048905,0.98855126,0.0000018943911,0.00013098509,0.000030428188,0.0007511573,0.0016458886,0.00013572513,0.0040003583,0.0018169183,0.0028168357],"study_design_scores_gemma":[0.0006649748,0.00010622962,0.9483183,0.000047652622,0.00010445216,0.00009544018,0.028869092,0.0074645495,0.00016272938,0.0034738001,0.010554873,0.00013789606],"about_ca_topic_score_codex":0.09206529,"about_ca_topic_score_gemma":0.80935633,"teacher_disagreement_score":0.71729106,"about_ca_system_score_codex":0.00023386456,"about_ca_system_score_gemma":0.00053163955,"threshold_uncertainty_score":0.9139807},"labels":[],"label_agreement":null},{"id":"W2353202697","doi":"","title":"Study on the relationship between foreign TBT and recall of China's consumer products","year":2012,"lang":"en","type":"article","venue":"Shanghai Textile Science & Technology","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Recall; Anticipation (artificial intelligence); China; Positive relationship; Positive correlation; Econometrics; Business; Psychology; Economics; Geography; Computer science; Social psychology; Cognitive psychology; Medicine","score_opus":0.16943656465281115,"score_gpt":0.39152323320722787,"score_spread":0.22208666855441672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2353202697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98406136,0.00010060129,0.00041554074,0.0038102302,0.00012706716,0.0013233337,0.000016215334,0.00010765317,0.0100380145],"genre_scores_gemma":[0.9989511,6.754753e-7,0.000324004,0.000037426318,0.000044079374,0.00016646225,6.0278774e-7,0.000010757059,0.00046492543],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966506,0.0002934439,0.0006528199,0.0007196897,0.0011591051,0.000524362],"domain_scores_gemma":[0.99436736,0.002681194,0.00044509696,0.002021262,0.00037061007,0.000114472285],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.011502903,0.00017195015,0.00032446336,0.0013657712,0.00076664466,0.000090979585,0.0021940586,0.00013240529,0.000052205498],"category_scores_gemma":[0.018613132,0.00010267672,0.00003567087,0.007403722,0.0032857081,0.00045449403,0.0005414273,0.0003166632,0.00037431568],"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":[0.0000028691434,0.00008611318,0.6754749,0.0000014906506,0.0000037741643,2.2621525e-7,0.00100245,0.0000013269572,0.0006823352,0.3189999,0.0002735082,0.0034711435],"study_design_scores_gemma":[0.00013879489,0.00018093338,0.7810578,0.000014081596,0.000016159855,0.000012912131,0.009256601,0.000011413295,0.0041693496,0.20381077,0.0012055901,0.00012564733],"about_ca_topic_score_codex":0.000014157334,"about_ca_topic_score_gemma":0.000007009126,"teacher_disagreement_score":0.11518913,"about_ca_system_score_codex":0.00005472428,"about_ca_system_score_gemma":0.00013130363,"threshold_uncertainty_score":0.9994268},"labels":[],"label_agreement":null},{"id":"W2355563169","doi":"","title":"Reversal Solution to Optimal Box-Cox Transformation Modeling","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Grey System Theory Applications","field":"Decision 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; Heteroscedasticity; Transformation (genetics); Collinearity; Power transform; Autocorrelation; Data transformation; Regression; Algorithm; Data mining; Mathematical optimization; Artificial intelligence; Statistics; Machine learning; Mathematics; Data warehouse; Consistency (knowledge bases)","score_opus":0.05903845196125451,"score_gpt":0.35963337283946323,"score_spread":0.3005949208782087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2355563169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057172917,0.000029284878,0.9358805,0.0019335488,0.00004672316,0.0017591206,0.000032103275,0.00020835953,0.0029374424],"genre_scores_gemma":[0.6981251,0.0000010037174,0.30022016,0.00053014414,0.00024189061,0.00040783006,0.00003431643,0.000021037873,0.00041848267],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968329,0.0000832688,0.0011716842,0.00072219485,0.0007216499,0.00046828555],"domain_scores_gemma":[0.9977273,0.00034807078,0.00018832005,0.00090762274,0.00054120994,0.00028748336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036134631,0.00021234616,0.00025632887,0.0005694201,0.00054942316,0.00026982336,0.0011555323,0.00012748178,0.00006457041],"category_scores_gemma":[0.000012291417,0.00020181658,0.00015531738,0.0017031629,0.000049842714,0.00044786086,0.00014427636,0.00017098278,0.003775418],"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":[0.00008823313,0.0003754091,0.0001975011,0.000024783794,0.000048930633,0.0000017185708,0.012003474,0.085808486,0.12517436,0.14445794,0.014391332,0.6174278],"study_design_scores_gemma":[0.0006632679,0.00006174618,0.0008131648,0.000028593427,0.000038388767,0.00008347519,0.0017730164,0.1572612,0.013420209,0.01804021,0.80710757,0.00070913683],"about_ca_topic_score_codex":0.000026161864,"about_ca_topic_score_gemma":0.000046124424,"teacher_disagreement_score":0.79271626,"about_ca_system_score_codex":0.00016312525,"about_ca_system_score_gemma":0.00005890186,"threshold_uncertainty_score":0.9970003},"labels":[],"label_agreement":null},{"id":"W2356729089","doi":"","title":"Using wavelet transformation and a GM-ARMA model to forecast stock index","year":2011,"lang":"en","type":"article","venue":"Caai Transactions on Intelligent Systems","topic":"Grey System Theory Applications","field":"Decision 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":"","keywords":"Wavelet; Autoregressive–moving-average model; Transformation (genetics); Autoregressive model; Computer science; Mathematics; Mathematical optimization; Algorithm; Applied mathematics; Econometrics; Artificial intelligence","score_opus":0.41209792312177473,"score_gpt":0.39799878686005224,"score_spread":0.014099136261722489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356729089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101459086,0.000041444095,0.89271283,0.00009192494,0.0006372004,0.0015824167,0.00012047856,0.000094447336,0.0032601869],"genre_scores_gemma":[0.99582213,0.0000060743905,0.002277633,0.00008969401,0.00003684409,0.00036799486,0.0000026622472,0.00003615155,0.0013608237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965987,0.00026501005,0.0012357775,0.00062790926,0.0008999706,0.00037258407],"domain_scores_gemma":[0.99786645,0.0002757251,0.0002383913,0.0008502903,0.00040460457,0.00036453953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018079531,0.00029403504,0.0004192375,0.00085698743,0.0004653818,0.00023373443,0.0005514035,0.00017183888,0.0001957776],"category_scores_gemma":[0.000053342366,0.00024817823,0.00016529171,0.00096791267,0.000089054905,0.0005036264,0.00000779484,0.00022481088,0.0004447665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007880727,0.0008891154,0.0008391443,0.00024283835,0.00030294328,0.000009215997,0.09221922,0.7043014,0.0043210946,0.06076,0.0010296555,0.13429727],"study_design_scores_gemma":[0.0002711889,0.00014532216,0.00011267668,0.00012952425,0.00005124138,0.00012383914,0.008178275,0.9779691,0.0051791808,0.0046372227,0.0028033282,0.0003990663],"about_ca_topic_score_codex":0.00027749347,"about_ca_topic_score_gemma":0.000105694206,"teacher_disagreement_score":0.89436305,"about_ca_system_score_codex":0.00019190359,"about_ca_system_score_gemma":0.00007972604,"threshold_uncertainty_score":0.999997},"labels":[],"label_agreement":null},{"id":"W2359591893","doi":"","title":"Optimum Time Response Sequence for GM(1,1)","year":2003,"lang":"en","type":"article","venue":"Zhongguo guanli kexue","topic":"Grey System Theory Applications","field":"Decision 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":"CAE (Canada)","funders":"","keywords":"Sequence (biology); Constant (computer programming); Core (optical fiber); Computer science; Square (algebra); Time sequence; Mathematical optimization; Statistics; Mathematics; Applied mathematics; Econometrics; Algorithm","score_opus":0.1724371303917335,"score_gpt":0.4220657989473739,"score_spread":0.2496286685556404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359591893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7428754,0.00024104628,0.20179254,0.0041487142,0.0010740376,0.0033433537,0.0004090881,0.00042693794,0.04568886],"genre_scores_gemma":[0.9188214,0.0000010968778,0.022474011,0.00043724067,0.00008993859,0.00044496526,0.00000947536,0.000047902828,0.05767398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99568504,0.0010419743,0.000885401,0.00089843926,0.0009427618,0.0005463562],"domain_scores_gemma":[0.9909628,0.0060474337,0.00034874733,0.001845815,0.00054949726,0.0002457294],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0105553,0.0002563468,0.00042146893,0.00032090233,0.00043503568,0.00031507376,0.0013387714,0.00015827667,0.0014467667],"category_scores_gemma":[0.013139844,0.00021257473,0.00024059914,0.0011765456,0.00020000791,0.00038812708,0.00009110983,0.0001356633,0.009672535],"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":[0.001750955,0.0005252713,0.004693494,0.000040727205,0.00013742785,0.000057671226,0.0029395341,0.0022691474,0.21346636,0.5329866,0.2206239,0.020508945],"study_design_scores_gemma":[0.001137463,0.00024788687,0.0018098518,0.00003200092,0.000030846993,0.00019184567,0.00079832826,0.0032271615,0.013246697,0.26179916,0.71685326,0.0006255306],"about_ca_topic_score_codex":0.000005339668,"about_ca_topic_score_gemma":0.0000071614522,"teacher_disagreement_score":0.49622935,"about_ca_system_score_codex":0.00010545422,"about_ca_system_score_gemma":0.00028709092,"threshold_uncertainty_score":0.99946606},"labels":[],"label_agreement":null},{"id":"W2363093906","doi":"10.29173/cais516","title":"Is Grey Literature Ever Used? Using Citation Analysis to Measure the Impact of GESAMP, an International Marine Scientific Advisory Body","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Grey System Theory Applications","field":"Decision 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":"Dalhousie University","funders":"","keywords":"Citation; Measure (data warehouse); Grey literature; Web of science; Library science; Scientific literature; Citation analysis; Political science; Computer science; MEDLINE; Database; Law; Geology","score_opus":0.08727320027593506,"score_gpt":0.3578664483545574,"score_spread":0.27059324807862234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2363093906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953554,0.000028380842,0.00030567017,0.0013492533,0.00015044397,0.00071004935,0.00041423133,0.000018004897,0.0016685601],"genre_scores_gemma":[0.9983394,0.0000031069599,0.0007098011,0.00007966876,0.0000630534,0.00004697919,0.00001408933,0.000015200884,0.0007286619],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960684,0.00013115468,0.0010277064,0.00060511346,0.0018453121,0.00032227935],"domain_scores_gemma":[0.8771666,0.00047199416,0.0015818409,0.00075722067,0.11983429,0.00018800703],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003817184,0.0002759479,0.00057065947,0.0010483356,0.00026354278,0.0075836726,0.004142626,0.0001448466,0.00037761297],"category_scores_gemma":[0.02911861,0.00015982096,0.00049483916,0.0043377304,0.0004982,0.0131027335,0.000720325,0.00023331902,0.000016503614],"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":[0.000119075354,0.00019568914,0.7879527,0.000035158835,0.0006266947,1.8869399e-7,0.06970111,0.00037387104,0.12860978,0.0061537144,0.0026469268,0.0035850434],"study_design_scores_gemma":[0.0002750494,0.00014523404,0.9496822,0.00012813743,0.00023630283,0.00001250411,0.0049232105,0.010163871,0.010305943,0.022505052,0.0013760177,0.00024651535],"about_ca_topic_score_codex":0.00066862593,"about_ca_topic_score_gemma":0.000041218922,"teacher_disagreement_score":0.16172941,"about_ca_system_score_codex":0.00009949465,"about_ca_system_score_gemma":0.00028353688,"threshold_uncertainty_score":0.9934465},"labels":[{"model":"gemma","categories":["bibliometrics","metaresearch"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W2367424470","doi":"","title":"GM(1,1)-based Approach to Image Representation and Retrieval","year":2004,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Computer science; Artificial intelligence; Image retrieval; Pixel; Hilbert curve; Histogram; Image (mathematics); Pattern recognition (psychology); Feature (linguistics); Computer vision; Feature vector; Algorithm","score_opus":0.11844467294771938,"score_gpt":0.404900209958991,"score_spread":0.28645553701127163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367424470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2601671,0.0000071773,0.6344393,0.0030072697,0.000059809026,0.0005369027,0.0000061239934,0.000065730885,0.101710565],"genre_scores_gemma":[0.944899,1.286949e-7,0.053166892,0.0004933549,0.00003103194,0.000026884007,0.0000053239514,0.000006739801,0.0013706011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998147,0.0001167185,0.00036990317,0.0005187175,0.000707073,0.00014057476],"domain_scores_gemma":[0.99838126,0.000411825,0.00008568666,0.00075198757,0.00021786525,0.00015136116],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018825867,0.00008259021,0.00014314428,0.00020134491,0.00012828721,0.00025430624,0.00036989406,0.00004198511,0.00016588275],"category_scores_gemma":[0.0016114893,0.000060786704,0.000042308286,0.0011675352,0.00007448139,0.00021503723,0.00007079057,0.000049497383,0.0010107023],"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":[0.00033036005,0.00056717126,0.005892567,0.000020873893,0.00002553148,0.000008854691,0.003674874,0.0114197135,0.120042786,0.82274956,0.025130574,0.010137157],"study_design_scores_gemma":[0.0036333604,0.00024169049,0.07858829,0.000026434995,0.000035691202,0.000094278286,0.008646322,0.008528464,0.1567389,0.7269335,0.015655013,0.0008780507],"about_ca_topic_score_codex":0.00004698118,"about_ca_topic_score_gemma":0.0000078805415,"teacher_disagreement_score":0.68473196,"about_ca_system_score_codex":0.00003500954,"about_ca_system_score_gemma":0.000049272992,"threshold_uncertainty_score":0.9997671},"labels":[],"label_agreement":null},{"id":"W2372224530","doi":"","title":"Kriging Surface Interpolation and Its Application Based on Self-adaptive Genetic Algorithm","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Grey System Theory Applications","field":"Decision 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":"Kriging; Interpolation (computer graphics); Computer science; Variogram; Genetic algorithm; Algorithm; Function (biology); Convergence (economics); Surface (topology); Mathematical optimization; Mathematics; Image (mathematics); Machine learning; Artificial intelligence","score_opus":0.019803481493562636,"score_gpt":0.30544368753738776,"score_spread":0.2856402060438251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372224530","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039448656,0.00003914727,0.95573443,0.0006190222,0.00005129537,0.0022460737,0.000060438484,0.00028207546,0.0015188605],"genre_scores_gemma":[0.5731275,0.000001380863,0.42556372,0.0002951128,0.00016021701,0.0006863208,0.000019586272,0.00002629177,0.000119832235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971304,0.00016255786,0.00074081204,0.0010867146,0.0005746019,0.00030494132],"domain_scores_gemma":[0.9967253,0.0010512155,0.00040033352,0.0011080503,0.0005025413,0.0002125388],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011396268,0.00028207834,0.00027939424,0.0003254266,0.0005044022,0.00027582565,0.00096223864,0.00016255153,0.000064054824],"category_scores_gemma":[0.000011564739,0.00026154658,0.000094842304,0.0010578888,0.00008600421,0.00021526385,0.00016584937,0.00035059405,0.0011589001],"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":[0.000019651226,0.00053673936,0.0019273353,0.000018584835,0.00003997857,0.0000010116494,0.0007559935,0.0024395487,0.1103285,0.0808133,0.0015352869,0.80158406],"study_design_scores_gemma":[0.00034846563,0.00003880421,0.0060680164,0.000008949804,0.00002246952,0.00002332219,0.00007527588,0.8610842,0.0035606322,0.0115201585,0.11693242,0.00031723166],"about_ca_topic_score_codex":0.000015346268,"about_ca_topic_score_gemma":0.000016471113,"teacher_disagreement_score":0.8586447,"about_ca_system_score_codex":0.00004473908,"about_ca_system_score_gemma":0.00008200355,"threshold_uncertainty_score":0.99998367},"labels":[],"label_agreement":null},{"id":"W2385097625","doi":"","title":"An International Comparative Research on Liabilities Recognization and Provision of Asset Retirement Obligations for Extractive Industry Enterprises","year":2012,"lang":"en","type":"article","venue":"Tongji yu xinxi luntan","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Business; Asset (computer security); Sample (material); Accounting; Finance; Petroleum industry; Coal mining; Current liability; Coal; Engineering; Working capital; Political science; Law","score_opus":0.5466962826657237,"score_gpt":0.6012879281815361,"score_spread":0.054591645515812415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2385097625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98007965,0.00002326936,0.0033846067,0.0006884019,0.00024267733,0.0014461799,0.0003304212,0.000028962499,0.013775824],"genre_scores_gemma":[0.9964569,0.0000029120615,0.0014849326,0.000033152675,0.00016196907,0.0003526176,0.00008494532,0.000013031016,0.001409509],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966627,0.0007269925,0.000677862,0.00045692697,0.0012077409,0.00026781292],"domain_scores_gemma":[0.9939262,0.003388148,0.0003693951,0.0006147431,0.0015322164,0.00016928889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005688616,0.00013974075,0.0002507459,0.0005016325,0.00031404477,0.0001834354,0.00055191445,0.00016171783,0.00054325734],"category_scores_gemma":[0.0018239314,0.00011523125,0.0000509644,0.0005832132,0.00029305278,0.0011697134,0.00010040411,0.0002749432,0.000090476526],"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":[0.0012858011,0.0035103136,0.5362989,0.00008524234,0.00019299389,6.4338354e-7,0.049241934,0.0005526457,0.026387429,0.33625337,0.017476507,0.028714232],"study_design_scores_gemma":[0.0019215445,0.002222867,0.60926044,0.00042964518,0.00007066775,0.000015252866,0.11712106,0.008294205,0.10296064,0.111074656,0.045840483,0.00078854844],"about_ca_topic_score_codex":0.000031234984,"about_ca_topic_score_gemma":0.000023969913,"teacher_disagreement_score":0.2251787,"about_ca_system_score_codex":0.0001723908,"about_ca_system_score_gemma":0.000089280176,"threshold_uncertainty_score":0.5948287},"labels":[],"label_agreement":null},{"id":"W2385568518","doi":"","title":"Forecasting Stock Index Based on the GM(1,1,μ,ν) Model","year":2010,"lang":"en","type":"article","venue":"Computing Technology and Automation","topic":"Grey System Theory Applications","field":"Decision 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":"Concordia University","funders":"","keywords":"Computer science; Index (typography); Dimension (graph theory); Set (abstract data type); Stock (firearms); Econometrics; Statistics; Mathematics","score_opus":0.07953040557058179,"score_gpt":0.3360519602238219,"score_spread":0.2565215546532401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2385568518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6445579,0.0000027865115,0.3493015,0.0044224206,0.00011028108,0.00018986622,0.0000012038305,0.00027955364,0.0011345212],"genre_scores_gemma":[0.9910496,4.1370253e-8,0.008615565,0.00020741858,0.000026470649,0.00002088824,0.0000010723691,0.0000070870756,0.00007184403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882185,0.000080299316,0.00033912787,0.00031507883,0.00028729902,0.00015634582],"domain_scores_gemma":[0.9974425,0.001435549,0.0002684765,0.00068454444,0.00014325762,0.000025693716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025705576,0.00009880612,0.00012664121,0.00046932662,0.0006240454,0.000105522835,0.00058906694,0.00018856536,0.000019225576],"category_scores_gemma":[0.0026857804,0.00006405993,0.000029467345,0.0009430613,0.00020932118,0.0000703711,0.00013557028,0.00036179138,0.00004178879],"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":[0.000008720061,0.000055418575,0.04268511,0.0000062745103,0.0000071157556,0.0000010505274,0.00030900905,0.027725205,0.0031353652,0.5631409,0.0011337186,0.3617921],"study_design_scores_gemma":[0.0000810999,0.0000123808395,0.0027347694,0.000012476178,0.0000020454456,0.000008814947,0.00007055044,0.7766703,0.00038898338,0.21977471,0.00018953638,0.00005436973],"about_ca_topic_score_codex":9.152511e-7,"about_ca_topic_score_gemma":0.0000055922283,"teacher_disagreement_score":0.74894506,"about_ca_system_score_codex":0.000010029067,"about_ca_system_score_gemma":0.000033980737,"threshold_uncertainty_score":0.47997156},"labels":[],"label_agreement":null},{"id":"W2386793968","doi":"","title":"Application of Grey Metabolic Model in the Prediction of the Revenue and Consumption of the Rural Residents","year":2010,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Science North","funders":"","keywords":"Per capita; Consumption (sociology); Gray (unit); Net income; Revenue; Econometrics; MATLAB; Reliability (semiconductor); Economics; Statistics; Agricultural economics; Computer science; Mathematics; Demography; Finance","score_opus":0.05754929722561848,"score_gpt":0.34417428035350833,"score_spread":0.28662498312788987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2386793968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936216,0.00003695295,0.004521368,0.0005587141,0.0000706497,0.000684793,0.00008820231,0.0000029875223,0.0004147563],"genre_scores_gemma":[0.9994793,0.000006473812,0.00014164452,0.000029195731,0.000010989713,0.000059810216,7.5101497e-7,0.0000029381438,0.00026889364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983071,0.00032038,0.0005581939,0.00013436588,0.0006188891,0.00006105738],"domain_scores_gemma":[0.9977997,0.00044185237,0.00051171874,0.0010313219,0.00020278551,0.000012622862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028902455,0.000054382308,0.0001382428,0.000066904686,0.00007575588,0.000015231531,0.00080926844,0.00005576736,0.00001030326],"category_scores_gemma":[0.00062847853,0.000023356299,0.000056416564,0.00050094625,0.00029096578,0.00011655189,0.00010112088,0.00011313627,0.0000045951274],"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":[0.000019196192,0.000068692905,0.5394573,0.000016419348,0.000008125994,6.2460677e-9,0.0024342488,0.0005739582,0.24333315,0.21002713,0.00055864675,0.0035031],"study_design_scores_gemma":[0.00013936136,0.0000037457908,0.88393605,0.00001152007,0.00001574288,0.0000031133823,0.00043152762,0.013087313,0.01269098,0.089552976,0.00010727327,0.000020410367],"about_ca_topic_score_codex":0.00030793107,"about_ca_topic_score_gemma":0.00093365536,"teacher_disagreement_score":0.3444787,"about_ca_system_score_codex":0.0000040262844,"about_ca_system_score_gemma":0.000030887557,"threshold_uncertainty_score":0.15038356},"labels":[],"label_agreement":null},{"id":"W2391639944","doi":"","title":"Forecasting stock indexes based on a revised grey model and the ARMA model","year":2010,"lang":"en","type":"article","venue":"Caai Transactions on Intelligent Systems","topic":"Grey System Theory Applications","field":"Decision 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":"Concordia University","funders":"","keywords":"Autoregressive–moving-average model; Autoregressive model; Mathematics; Computer science; Moving average; Moving-average model; Applied mathematics; Autoregressive integrated moving average; Econometrics; Statistics; Time series","score_opus":0.14143847114390767,"score_gpt":0.34586259968178773,"score_spread":0.20442412853788006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2391639944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045888387,0.000052712985,0.94687045,0.000871063,0.0007304155,0.0019771948,0.0001128062,0.00011094515,0.0033860311],"genre_scores_gemma":[0.99456996,0.000005922239,0.00086619327,0.0002867238,0.000075351636,0.0009689058,0.0000029637526,0.000043182452,0.003180808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958707,0.0004913609,0.0012195584,0.0007953824,0.0012538371,0.00036915054],"domain_scores_gemma":[0.9939509,0.0032133881,0.00043708767,0.0016742509,0.00048258237,0.00024179503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050694877,0.0003471523,0.0005391076,0.000495604,0.0008779735,0.0005524759,0.00094460236,0.00020386864,0.00009036208],"category_scores_gemma":[0.0006004734,0.00021652004,0.00028614188,0.00070582365,0.00036681964,0.0002199661,0.000011091996,0.000723433,0.00020099574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021482568,0.000099783036,0.00004963478,0.000027142112,0.000024663554,8.626611e-7,0.0008938041,0.9744183,0.00027516703,0.018519184,0.00032286573,0.0051537827],"study_design_scores_gemma":[0.0006131473,0.0000479877,0.0000062850822,0.00011314114,0.00004355308,0.000026956208,0.0006280796,0.9892982,0.00051643007,0.0076599405,0.0008195741,0.00022666532],"about_ca_topic_score_codex":0.000050251398,"about_ca_topic_score_gemma":0.00007492061,"teacher_disagreement_score":0.94868153,"about_ca_system_score_codex":0.00007603649,"about_ca_system_score_gemma":0.0001429871,"threshold_uncertainty_score":0.88294315},"labels":[],"label_agreement":null},{"id":"W2472633607","doi":"","title":"Apply GM(0,N) and Grey Relational Grade in the Relational Analysis of Business Items-An Example on the Chain Store 7-11","year":2015,"lang":"en","type":"article","venue":"The journal of grey system/Journal of grey system","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Clothing; Business; Purchasing; Marketing; Service (business); Payment; Cash; China; Commerce; Finance","score_opus":0.17801274728716784,"score_gpt":0.3262918610639874,"score_spread":0.14827911377681954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472633607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698475,0.0012059474,0.02256335,0.0027946744,0.0013687395,0.0012389112,0.000094558956,0.00002009433,0.0008662391],"genre_scores_gemma":[0.9983649,0.0000065297418,0.00033799862,0.00011062707,0.00083937275,0.000029041865,0.00000847379,0.00004873632,0.00025430348],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9777723,0.007901329,0.0063092737,0.0005386557,0.0069617326,0.00051669613],"domain_scores_gemma":[0.9702614,0.0094877295,0.010953434,0.0018040896,0.0070433924,0.00044994382],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.070543215,0.00056729786,0.0020442372,0.0028116505,0.0006064573,0.00048287987,0.003681133,0.00031401162,0.00003462516],"category_scores_gemma":[0.0022003995,0.0002743514,0.00076171395,0.0067673153,0.00050646026,0.0013036447,0.00017629187,0.0010280156,0.000041376403],"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":[0.0022800243,0.0009911993,0.3738572,0.00047931814,0.0039596134,0.0003887612,0.044496648,0.19284049,0.0007406409,0.3679035,0.0109551605,0.0011074396],"study_design_scores_gemma":[0.005597729,0.0013323607,0.68568754,0.0031746528,0.0037221268,0.0184038,0.22108674,0.038404837,0.00005248279,0.006720574,0.01473864,0.0010785032],"about_ca_topic_score_codex":0.0004752794,"about_ca_topic_score_gemma":0.00073473045,"teacher_disagreement_score":0.36118293,"about_ca_system_score_codex":0.0006330663,"about_ca_system_score_gemma":0.0007866944,"threshold_uncertainty_score":0.99997085},"labels":[],"label_agreement":null},{"id":"W2523027125","doi":"10.5430/bmr.v5n3p86","title":"A Solution of GM(1,1) based on EXCEL","year":2016,"lang":"en","type":"article","venue":"Business and Management Research","topic":"Grey System Theory Applications","field":"Decision 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":"Visual Basic for Applications; Computer science; Throughput; Extrapolation; Operations research; Microsoft excel; Analogy; Ms excel; Industrial engineering; Software engineering; Statistics; Operating system; Mathematics; Engineering","score_opus":0.2867773153338235,"score_gpt":0.4658006465984868,"score_spread":0.1790233312646633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523027125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24636112,0.0001302528,0.47210574,0.039088864,0.0002670768,0.0027042553,0.00003264506,0.000080343314,0.23922968],"genre_scores_gemma":[0.99000573,0.000038503902,0.00033926032,0.000032941014,0.000022670441,0.0001197748,7.5364176e-7,0.000005663213,0.009434675],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99746567,0.00026667537,0.0002721459,0.00035178693,0.0014262209,0.00021749262],"domain_scores_gemma":[0.9975947,0.00090261974,0.00007353667,0.00070969923,0.00066422374,0.000055200995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006115758,0.000063282154,0.00012337271,0.00065060816,0.0001575741,0.00007733089,0.0004533292,0.00002906205,0.0002728609],"category_scores_gemma":[0.00059624115,0.000034558936,0.000025156292,0.0013770614,0.0002096534,0.000113336246,0.00024613467,0.000034944667,0.0003353887],"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":[0.00016181973,0.00022888897,0.003748044,0.00012309187,0.000014003751,0.0000068356862,0.00004467558,0.000036403548,0.0052038063,0.45368725,0.026322482,0.5104227],"study_design_scores_gemma":[0.0017088178,0.00013212014,0.60897404,0.00039311204,0.000011178342,0.0000014214235,0.00039991547,0.0025898446,0.0011580021,0.13064522,0.25377312,0.00021321961],"about_ca_topic_score_codex":0.000046192163,"about_ca_topic_score_gemma":0.000016228789,"teacher_disagreement_score":0.74364465,"about_ca_system_score_codex":0.000027312655,"about_ca_system_score_gemma":0.000023909277,"threshold_uncertainty_score":0.4310855},"labels":[],"label_agreement":null},{"id":"W2571543094","doi":"10.5430/air.v6n1p91","title":"Factor analysis of teacher professional development and evaluation based on math methods of RaschGSP curve, ISM, GSM and MSM","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Grey System Theory Applications","field":"Decision 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 education; Teamwork; Rasch model; Psychology; Pedagogy; Computer science","score_opus":0.6924952795520097,"score_gpt":0.6536971404244697,"score_spread":0.03879813912754004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571543094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95128536,0.00005323625,0.046321426,0.00050952827,0.000090956135,0.0006266076,0.000018273557,0.0000057397524,0.0010888438],"genre_scores_gemma":[0.99219245,0.0000033395434,0.007270598,0.000006453458,0.000017124217,0.000092252354,0.0000040772916,0.0000070795763,0.00040662507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932241,0.0024787013,0.00091965194,0.0005471895,0.0025810732,0.0002493048],"domain_scores_gemma":[0.991809,0.004826548,0.00046398633,0.0010781554,0.0016936826,0.00012859133],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.039732233,0.000112486254,0.00038402667,0.0010637757,0.0006089192,0.00019605631,0.0007857323,0.00010376326,0.000462312],"category_scores_gemma":[0.012790997,0.00008080514,0.00007357453,0.001084216,0.0006858976,0.00018602505,0.0002762406,0.00022213132,0.00004917002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015277251,0.00033541486,0.0302901,0.000023742386,0.00013479534,5.5717595e-7,0.0035846357,0.00044711717,0.018736182,0.070043765,0.000053723146,0.8761972],"study_design_scores_gemma":[0.00006729922,0.00011265558,0.28056106,0.00007934487,0.00007658891,3.4725352e-7,0.0038879006,0.42706233,0.20763123,0.07956688,0.00078557123,0.00016877438],"about_ca_topic_score_codex":0.00012889152,"about_ca_topic_score_gemma":0.00022080958,"teacher_disagreement_score":0.8760284,"about_ca_system_score_codex":0.00005462341,"about_ca_system_score_gemma":0.00036736482,"threshold_uncertainty_score":0.9955247},"labels":[],"label_agreement":null},{"id":"W2607022806","doi":"10.6000/1927-5129.2017.13.16","title":"ARIMA Forecasting Chinese Macroeconomic Variables Based on Factor and Principal Component Backdating","year":2017,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Autoregressive integrated moving average; Principal component analysis; Econometrics; Factor analysis; Principal (computer security); Computer science; China; Key (lock); Time series; Statistics; Economics; Mathematics; Geography","score_opus":0.1366044752220683,"score_gpt":0.3708814026981382,"score_spread":0.23427692747606987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607022806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9727096,0.00001999882,0.0060338555,0.0008827039,0.0004058733,0.0002334267,0.00001457681,0.000010233665,0.01968974],"genre_scores_gemma":[0.9906638,0.000001226695,0.008881189,0.00015594628,0.00022672399,0.000008302158,3.0143934e-7,0.000009235273,0.000053236367],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967048,0.00012838999,0.001145704,0.00048908324,0.00120492,0.0003271083],"domain_scores_gemma":[0.99349517,0.0026771333,0.0027194547,0.00069031864,0.00019672197,0.00022121062],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009478547,0.00020898685,0.000511631,0.00038524732,0.0018144882,0.0015743339,0.002196583,0.000060774193,0.00018076209],"category_scores_gemma":[0.0020934648,0.00013158334,0.00011625324,0.0002546581,0.0007247284,0.00062110124,0.00024678485,0.00024332303,0.000055468005],"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":[0.00056983315,0.0005862644,0.5590776,0.00007836901,0.000114865536,0.00005229212,0.0033960538,0.034193352,0.07890621,0.104348615,0.0010240952,0.21765243],"study_design_scores_gemma":[0.003152347,0.0006571235,0.6034181,0.0003160609,0.0000526039,0.000301257,0.002961431,0.24465637,0.004762672,0.1346825,0.0041479287,0.0008915831],"about_ca_topic_score_codex":0.000012204738,"about_ca_topic_score_gemma":0.000018949739,"teacher_disagreement_score":0.21676084,"about_ca_system_score_codex":0.0000660361,"about_ca_system_score_gemma":0.00023984666,"threshold_uncertainty_score":0.999485},"labels":[],"label_agreement":null},{"id":"W2626687215","doi":"10.1016/j.jclepro.2017.06.167","title":"Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model","year":2017,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":156,"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":"Graduate Research and Innovation Projects of Jiangsu Province; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Multivariable calculus; Adaptability; Benchmark (surveying); Combustion; Greenhouse gas; Fuel efficiency; Engineering; Computer science; Control engineering; Automotive engineering; Economics","score_opus":0.24638760257703235,"score_gpt":0.40754020767580873,"score_spread":0.16115260509877638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626687215","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8179443,0.000041101343,0.17846201,0.0012593021,0.0013839275,0.0002106994,0.00001661909,0.000014852236,0.0006672258],"genre_scores_gemma":[0.958226,0.0000035641588,0.039437324,0.000022983215,0.0012578921,0.000003302657,0.0000022668564,0.000022001383,0.0010246172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996859,0.00015600264,0.0012341856,0.00041672986,0.0011229627,0.00021112505],"domain_scores_gemma":[0.9933506,0.00035615676,0.0032210185,0.0011876549,0.0017111491,0.00017341864],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0050703916,0.00017920478,0.0004161367,0.00029120923,0.001055285,0.00054370315,0.0009790545,0.00011321346,0.00007199304],"category_scores_gemma":[0.01603519,0.00012478705,0.00017567528,0.0002474762,0.00012598357,0.0020547307,0.00016214595,0.00035687338,0.000020769106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002541391,0.00031871424,0.018863391,0.000015056789,0.00006686326,0.000004734976,0.0018082472,0.7188068,0.24842057,0.00045909316,0.001774937,0.009207489],"study_design_scores_gemma":[0.0009820486,0.00004712865,0.012579182,0.00022434606,0.00009496049,0.0004739941,0.00095955795,0.87582225,0.0035748514,0.103621475,0.0013685968,0.00025158972],"about_ca_topic_score_codex":0.000093900235,"about_ca_topic_score_gemma":0.000025340622,"teacher_disagreement_score":0.2448457,"about_ca_system_score_codex":0.00012131117,"about_ca_system_score_gemma":0.00016502567,"threshold_uncertainty_score":0.9922532},"labels":[],"label_agreement":null},{"id":"W2733172450","doi":"10.1108/gs-05-2017-0011","title":"Forecasting the total energy consumption in China using a new-structure grey system model","year":2017,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision 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":"University of Ottawa","funders":"","keywords":"Energy consumption; Smoothness; China; Computer science; Consumption (sociology); Econometrics; Economics; Mathematics; Engineering; Geography","score_opus":0.08134494366514301,"score_gpt":0.33028316533792373,"score_spread":0.24893822167278074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733172450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65373665,0.0002644466,0.3437841,0.00010304236,0.00022788475,0.00083016284,0.000037759466,0.000054586253,0.0009613852],"genre_scores_gemma":[0.99833244,0.0000028852505,0.0002517185,0.000018086565,0.000305135,0.00020522728,0.000008469761,0.000028995795,0.0008470349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632955,0.0008578411,0.0010111629,0.0008098046,0.00064513326,0.00034650398],"domain_scores_gemma":[0.9955138,0.0007432838,0.0013445219,0.0020667356,0.00017753697,0.00015415005],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006300703,0.00028847123,0.00044951934,0.00020190673,0.0016526377,0.0011304918,0.0011747467,0.00021376138,0.000008450422],"category_scores_gemma":[0.0006735573,0.00019578602,0.00008702412,0.00026564868,0.00027540227,0.0006889853,0.00025531388,0.00020146753,0.000024699515],"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":[0.00009702015,0.000015427135,0.0067999405,0.00006525309,0.000019400008,0.0000015822064,0.0012218223,0.03403473,0.0067055277,0.93688047,0.000072017785,0.014086829],"study_design_scores_gemma":[0.00041793103,0.0000106760135,0.009716931,0.00015562822,0.000030590774,0.0003123162,0.0013743332,0.8564958,0.00023677283,0.13081783,0.00018061737,0.00025057019],"about_ca_topic_score_codex":0.00067422644,"about_ca_topic_score_gemma":0.000100329074,"teacher_disagreement_score":0.82246107,"about_ca_system_score_codex":0.00013553971,"about_ca_system_score_gemma":0.00007623898,"threshold_uncertainty_score":0.9999064},"labels":[],"label_agreement":null},{"id":"W2747590284","doi":"10.5539/ijsp.v6n5p110","title":"Applying Kolmogorov-Zurbenko Adaptive R-Software","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Grey System Theory Applications","field":"Decision 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":"Smoothing; R package; Mean squared error; Window (computing); Noise (video); Algorithm; Mathematics; Computer science; Statistics; Software; Software package; Applied mathematics; Artificial intelligence","score_opus":0.10940207839551737,"score_gpt":0.3976836116263705,"score_spread":0.28828153323085315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747590284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1118245,0.00010841882,0.88168967,0.0015341991,0.0014126558,0.00041491463,0.0007138039,0.000009562311,0.0022922822],"genre_scores_gemma":[0.9188921,0.000011069431,0.08068763,0.000058668127,0.00017156976,0.000016119422,0.0000018660788,0.000005593384,0.00015538473],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99746233,0.00013810475,0.0008461032,0.00022568463,0.0012187138,0.00010905671],"domain_scores_gemma":[0.99351436,0.00166219,0.001445447,0.0004792885,0.002772542,0.00012615489],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003732505,0.000100089244,0.00024191689,0.00011055612,0.00030519222,0.00069684174,0.0014507363,0.000044375043,0.00016215804],"category_scores_gemma":[0.010730318,0.00007386061,0.00006967253,0.000042128784,0.00028862938,0.00044142804,0.0002423323,0.00016730737,0.00003125004],"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":[0.00023688006,0.00014076802,0.12568079,0.000009858244,0.00014380661,0.0000532427,0.00052403327,0.000134248,0.00012576598,0.40602311,0.0042982036,0.4626293],"study_design_scores_gemma":[0.00030254072,0.00006602574,0.1253879,0.000026807209,0.0000126004625,0.000089596084,0.00012684293,0.0005510007,0.000045052166,0.8643166,0.008996639,0.00007840458],"about_ca_topic_score_codex":0.000031378415,"about_ca_topic_score_gemma":0.000043409807,"teacher_disagreement_score":0.80706763,"about_ca_system_score_codex":0.00006840275,"about_ca_system_score_gemma":0.000117362346,"threshold_uncertainty_score":0.9976027},"labels":[],"label_agreement":null},{"id":"W2756020741","doi":"10.5539/hes.v7n4p15","title":"A Multilevel Comprehensive Assessment of International Accreditation for Business Programmes-Based on AMBA Accreditation of GDUFS","year":2017,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Grey System Theory Applications","field":"Decision 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":"Division of Undergraduate Education; Division of Graduate Education; Guangdong University of Foreign Studies","keywords":"Accreditation; Computer science; Management science; Higher education; Engineering management; Operations research; Engineering; Medical education; Political science","score_opus":0.3297255762436835,"score_gpt":0.5592675240511833,"score_spread":0.2295419478074998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756020741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92801464,0.0002984029,0.03844308,0.0130691305,0.0127131725,0.0031494424,0.000396086,0.000068664536,0.0038473606],"genre_scores_gemma":[0.97823846,0.000009411236,0.019001974,0.000110473404,0.0003170202,0.0011701396,0.00008565229,0.000015138743,0.0010517031],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99734825,0.00014085816,0.0009129306,0.00043920055,0.001016897,0.00014188346],"domain_scores_gemma":[0.98434114,0.0030289288,0.0020358486,0.0009573924,0.009589843,0.000046836394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011194504,0.00017564272,0.00042663314,0.00040344032,0.0003642475,0.0001446918,0.00084122637,0.00006534417,0.0000552023],"category_scores_gemma":[0.0020801933,0.00014223455,0.0001284871,0.00028814303,0.00031784066,0.00038968428,0.00009606612,0.0000586169,0.000016140288],"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":[0.0009612749,0.006567724,0.2884904,0.0010353953,0.0012821731,8.831241e-7,0.0062655504,0.007970316,0.013756645,0.3697869,0.13442099,0.16946176],"study_design_scores_gemma":[0.0008767496,0.00010347291,0.9539665,0.00015219308,0.000049318416,4.3322655e-7,0.0043562986,0.001602918,0.0007237582,0.011574659,0.026451537,0.00014216473],"about_ca_topic_score_codex":0.00003141967,"about_ca_topic_score_gemma":0.000010017669,"teacher_disagreement_score":0.6654761,"about_ca_system_score_codex":0.000121695855,"about_ca_system_score_gemma":0.0004910241,"threshold_uncertainty_score":0.58001566},"labels":[],"label_agreement":null},{"id":"W2765510136","doi":"10.5539/emr.v6n2p47","title":"The Grey Clustering Analysis for Photovoltaic Equipment Importance Classification","year":2017,"lang":"en","type":"article","venue":"Engineering Management Research","topic":"Grey System Theory Applications","field":"Decision 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":"Cluster analysis; Analytic hierarchy process; Key (lock); Photovoltaic system; Reliability engineering; Hierarchy; Computer science; Process (computing); Function (biology); Operations research; Data mining; Engineering; Artificial intelligence; Electrical engineering","score_opus":0.31998186601866446,"score_gpt":0.48217699207126175,"score_spread":0.16219512605259728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765510136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08000023,0.00018134643,0.8889649,0.0035828808,0.00052645727,0.0035061021,0.00001797278,0.00014480478,0.023075307],"genre_scores_gemma":[0.9839812,0.000029712815,0.0026640915,0.000007666974,0.00008519721,0.0015281395,0.0000059325107,0.000018048402,0.011680023],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99650276,0.00011379796,0.00055892725,0.0005992061,0.00166604,0.0005592432],"domain_scores_gemma":[0.9943993,0.001560096,0.00023387553,0.0032225456,0.00047558363,0.00010857709],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.017501336,0.00012847454,0.00020012257,0.00063011365,0.0022182378,0.0021633725,0.0030390478,0.00004241908,0.000046917532],"category_scores_gemma":[0.002305427,0.00009049368,0.00017333242,0.0010629435,0.00012269258,0.00027778666,0.000715842,0.00016997391,0.00018003756],"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":[0.00016580524,0.00016791382,0.08272697,0.00024625324,0.002442019,0.000020717564,0.0007790469,0.07761051,0.012726367,0.6986782,0.024846932,0.09958925],"study_design_scores_gemma":[0.00025255204,0.000023792287,0.29549548,0.000017624132,0.000059147176,5.5824495e-7,0.00073646865,0.5343967,0.0002769083,0.009629408,0.15894239,0.00016897667],"about_ca_topic_score_codex":0.000021335569,"about_ca_topic_score_gemma":0.00013468177,"teacher_disagreement_score":0.903981,"about_ca_system_score_codex":0.00018072063,"about_ca_system_score_gemma":0.000017194026,"threshold_uncertainty_score":0.9990807},"labels":[],"label_agreement":null},{"id":"W2771773343","doi":"10.24900/ijss/01036173.2017.1201","title":"A Quantitative Method for Dynamic Risk Prediction Using AHP and Grey Modeling: Case Study of a Mud-Flow Hazard","year":2017,"lang":"en","type":"article","venue":"International Journal of Safety Science","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":2,"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":"Analytic hierarchy process; Hazard; Computer science; Hazard analysis; Flow (mathematics); Risk analysis (engineering); Operations research; Reliability engineering; Engineering; Mathematics; Business; Chemistry","score_opus":0.19672059502777509,"score_gpt":0.5119146166872954,"score_spread":0.3151940216595204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771773343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51740396,0.00001679855,0.48162714,0.00017733406,0.00045828888,0.00020416078,0.00007910473,0.0000022751199,0.000030938136],"genre_scores_gemma":[0.87213403,0.0000071931736,0.12777874,0.00000851987,0.000048107606,0.0000049172527,2.2014433e-7,0.00000537767,0.00001290575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615395,0.00023364779,0.001221856,0.00036888215,0.0018782573,0.0001434275],"domain_scores_gemma":[0.99056315,0.0013051465,0.0025325539,0.00048214028,0.0049924105,0.00012461697],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015182238,0.000108495784,0.00029757048,0.0006487537,0.00087852794,0.00047114358,0.0018781951,0.0000345383,0.0000070707565],"category_scores_gemma":[0.010118295,0.00008161237,0.000107098065,0.00027884785,0.00040301905,0.0017746652,0.00026988838,0.00014403307,0.0000014928966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021668589,0.001019066,0.067892104,0.000014125639,0.00047680584,0.00034310727,0.03126062,0.74552226,0.024125837,0.013382296,0.000027563698,0.11376933],"study_design_scores_gemma":[0.000979578,0.00034574553,0.006968588,0.000047645794,0.000046298257,0.0020510997,0.012933121,0.9460954,0.00008082224,0.030369245,0.0000147223955,0.000067710185],"about_ca_topic_score_codex":0.0002965295,"about_ca_topic_score_gemma":0.00026997583,"teacher_disagreement_score":0.35473007,"about_ca_system_score_codex":0.00014966026,"about_ca_system_score_gemma":0.00031257528,"threshold_uncertainty_score":0.9982199},"labels":[],"label_agreement":null},{"id":"W2793266273","doi":"10.1016/j.energy.2018.01.169","title":"Forecasting China's electricity consumption using a new grey prediction model","year":2018,"lang":"en","type":"article","venue":"Energy","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":264,"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":"Nanjing University of Aeronautics and Astronautics; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Electricity; China; Consumption (sociology); Econometrics; Operations research; Economics; Engineering; Computer science; Geography; Electrical engineering","score_opus":0.18249523556768843,"score_gpt":0.3621006321507219,"score_spread":0.17960539658303348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793266273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41747242,0.000026845073,0.57891583,0.00005474445,0.000165144,0.000061387596,0.0000064086566,0.00006392719,0.0032332886],"genre_scores_gemma":[0.99007285,0.0000017702275,0.006419383,0.000081016646,0.00047875816,0.000011069458,0.0000042763286,0.000014145308,0.0029167389],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807656,0.0001557887,0.000482023,0.00043403642,0.00060676533,0.0002448238],"domain_scores_gemma":[0.9985963,0.00020560218,0.0002791507,0.0005272815,0.0002587889,0.00013288081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012382431,0.00011885943,0.00016540811,0.00024155217,0.00038040165,0.00015082877,0.00038227724,0.00008765442,0.00019393036],"category_scores_gemma":[0.0007903966,0.000101014084,0.00006649796,0.0007364835,0.00008057691,0.00036029372,0.00007844868,0.00006877617,0.00012713489],"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":[0.00023817012,0.0002084209,0.026055466,0.000011851209,0.00008969293,0.0000052431446,0.0033176579,0.20371784,0.08687594,0.3670283,0.033031177,0.27942026],"study_design_scores_gemma":[0.00014429835,0.00003057701,0.0010820288,0.0000105727795,0.0000095251025,0.00003102495,0.000016821554,0.8790577,0.0035585337,0.11390253,0.002069307,0.00008706101],"about_ca_topic_score_codex":0.00034299117,"about_ca_topic_score_gemma":0.00018717453,"teacher_disagreement_score":0.6753399,"about_ca_system_score_codex":0.00009508261,"about_ca_system_score_gemma":0.00012633484,"threshold_uncertainty_score":0.4119235},"labels":[],"label_agreement":null},{"id":"W2898898090","doi":"10.28924/2291-8639-16-2018-921","title":"An Application of Grey System Theory and DEA in Strategic Alliance in Vietnamese Agricultural Industry","year":2018,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Grey System Theory Applications","field":"Decision 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":"Vietnamese; Strategic alliance; Order (exchange); General partnership; Agriculture; Business; Alliance; Marketing; Industrial organization; Strategic business unit; Stock (firearms); Operations research; Finance; Engineering","score_opus":0.03462643853065326,"score_gpt":0.3781126839694991,"score_spread":0.3434862454388458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898898090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9426762,0.00017033941,0.055842947,0.0003245843,0.000028982446,0.0002146617,0.000030214394,0.0000055223454,0.0007065652],"genre_scores_gemma":[0.99892974,0.000024599743,0.00075846707,0.000028940975,0.00015002157,0.00005622593,0.000008305793,0.0000047778885,0.000038924896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.997327,0.000323935,0.0012660983,0.0003140544,0.00065834133,0.000110520705],"domain_scores_gemma":[0.99671566,0.00043957838,0.0010702757,0.00034457294,0.0013202368,0.00010967401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003633133,0.00011565577,0.00038115954,0.0007991394,0.000061333056,0.00014442399,0.0008170642,0.00012055924,0.000026213369],"category_scores_gemma":[0.00011787253,0.00008464696,0.00009815478,0.001883488,0.00020691362,0.0003920503,0.000057448207,0.00021265021,0.000008067606],"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":[0.000111301066,0.0003398053,0.32763848,0.000011213503,0.00033878387,0.0000042793818,0.0011172108,0.0014974383,0.011237953,0.6373488,0.00001638344,0.020338371],"study_design_scores_gemma":[0.00068839296,0.000095409625,0.8902375,0.000074939795,0.00020150386,0.00012244511,0.017699033,0.011538534,0.00086942135,0.077893436,0.00037100734,0.00020838501],"about_ca_topic_score_codex":0.00007688196,"about_ca_topic_score_gemma":0.00055740494,"teacher_disagreement_score":0.562599,"about_ca_system_score_codex":0.00007823487,"about_ca_system_score_gemma":0.000055696182,"threshold_uncertainty_score":0.3451803},"labels":[],"label_agreement":null},{"id":"W2900040644","doi":"10.5539/ijef.v10n12p43","title":"Time-Series Forecasting Models for Gasoline Prices in China","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Exponential smoothing; Gasoline; Econometrics; Series (stratigraphy); Time series; Computer science; Autoregressive conditional heteroskedasticity; Inflation (cosmology); Artificial neural network; Economics; Machine learning; Engineering","score_opus":0.07769504445817906,"score_gpt":0.3286050961702249,"score_spread":0.25091005171204583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900040644","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767225,0.00010352919,0.019268574,0.0018149177,0.000406236,0.000083080165,0.00003571611,0.0000011959027,0.0015642497],"genre_scores_gemma":[0.98573893,0.00012113564,0.013346442,0.000059944843,0.00035842074,0.000005139643,7.84855e-7,0.000004824901,0.00036438805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990566,0.000010601524,0.0006345017,0.00012973383,0.00008949954,0.0000791132],"domain_scores_gemma":[0.998514,0.00028375286,0.0006559789,0.00009364862,0.00043240885,0.00002020505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014493772,0.000056066336,0.00016336513,0.0001777215,0.000044880075,0.00011854542,0.00048387292,0.000026215934,0.00001629949],"category_scores_gemma":[0.0003510271,0.000046296806,0.000050888746,0.00005992461,0.00008803211,0.0006286903,0.00005413424,0.000043415403,0.0000098976325],"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":[0.0005908326,0.000090477755,0.0015637678,0.0000033015708,0.0000584847,0.0000058006317,0.001937568,0.0743193,0.000110458044,0.84750295,0.001497545,0.07231951],"study_design_scores_gemma":[0.00035572177,0.00008639451,0.0015174666,0.000026378577,0.0000018439221,0.000103460494,0.00005974472,0.39931193,0.00015835799,0.57859373,0.019728616,0.00005633632],"about_ca_topic_score_codex":0.0000035430025,"about_ca_topic_score_gemma":0.0000405785,"teacher_disagreement_score":0.32499263,"about_ca_system_score_codex":0.00003300948,"about_ca_system_score_gemma":0.000047814217,"threshold_uncertainty_score":0.1887929},"labels":[],"label_agreement":null},{"id":"W2903100097","doi":"10.30564/ret.v1i4.110","title":"Research based on the development trend of world language","year":2018,"lang":"en","type":"article","venue":"Review of Educational Theory","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thriving; Construct (python library); Normalization (sociology); Population; Phenomenon; Immigration; Computer science; Linguistics; Geography; History; Sociology; Demography; Social science; Epistemology","score_opus":0.2461681887604181,"score_gpt":0.5325979753652851,"score_spread":0.28642978660486695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903100097","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051131558,0.030538773,0.0008288178,0.042292584,0.0005240689,0.002146908,0.000077664736,0.000015804379,0.8724438],"genre_scores_gemma":[0.98662543,0.000051278468,0.0010789535,0.0007914684,0.00013602249,0.00017359052,0.000009245449,0.00000786327,0.011126156],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949572,0.00251178,0.00077877595,0.00024467977,0.0013695395,0.0001379977],"domain_scores_gemma":[0.9748469,0.02282452,0.00035853576,0.0010914762,0.00082574633,0.00005280583],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.03374767,0.00007984169,0.00022930537,0.00035677903,0.0001994899,0.000016131322,0.0010957646,0.000018682047,0.014936668],"category_scores_gemma":[0.010674405,0.000043697913,0.00009155648,0.0019357129,0.00063774205,0.000039561794,0.00006804072,0.00012050873,0.00137126],"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":[0.000013466415,0.00013943539,0.00021455018,0.00019625282,0.000009072631,2.1181819e-8,0.0007002392,8.3696295e-7,0.00013916614,0.9560904,0.027766144,0.014730421],"study_design_scores_gemma":[0.0001468353,0.00008467766,0.018228,0.007938064,0.000020828093,0.0000020837892,0.003464563,0.000031926393,0.0075818887,0.5743914,0.38791716,0.00019252805],"about_ca_topic_score_codex":0.0000019034806,"about_ca_topic_score_gemma":0.000019888821,"teacher_disagreement_score":0.9354939,"about_ca_system_score_codex":0.00004756511,"about_ca_system_score_gemma":0.0006387974,"threshold_uncertainty_score":0.9994063},"labels":[],"label_agreement":null},{"id":"W2909845760","doi":"10.5430/bmr.v8n1p1","title":"Simplified Machine Diagnosis Techniques with Absolute Deterioration Factor by Utilizing the Second Order Autocorrelation Function","year":2019,"lang":"en","type":"article","venue":"Business and Management Research","topic":"Grey System Theory Applications","field":"Decision 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":"Bicoherence; Autocorrelation; Kurtosis; Statistics; Lag; Mathematics; Index (typography); Function (biology); Bispectrum; Amplitude; Applied mathematics; Computer science; Physics; Spectral density","score_opus":0.10761026975733007,"score_gpt":0.3888652113588609,"score_spread":0.2812549416015308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909845760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86102694,0.00027959258,0.09161704,0.0047754077,0.00019736002,0.007292322,0.000050939838,0.00018259847,0.034577794],"genre_scores_gemma":[0.9859128,0.000048572932,0.00026006778,0.00009451317,0.000025508796,0.0010464162,0.000021821124,0.000015713525,0.012574559],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99756104,0.0002882909,0.00031922953,0.0004903522,0.0010889057,0.00025215658],"domain_scores_gemma":[0.9979193,0.0005672221,0.0001337789,0.0006681825,0.0006645605,0.000046921352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003009417,0.00012500277,0.00014734604,0.00032410357,0.00047227833,0.0006594677,0.00038844097,0.000056672852,0.0008452301],"category_scores_gemma":[0.000103399485,0.0000707635,0.000019171548,0.0016591154,0.00010339497,0.00049440283,0.00025894077,0.00014684034,0.0002165215],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005630072,0.00026874433,0.07528375,0.00044001138,0.00014896243,0.000005212508,0.00066815695,0.00015080831,0.009401243,0.10597248,0.047072668,0.76002496],"study_design_scores_gemma":[0.0004669579,0.00014974797,0.40328735,0.00006686099,0.00001595507,0.0000031593147,0.0011662247,0.0028127297,0.00032080302,0.008845108,0.5826512,0.00021392059],"about_ca_topic_score_codex":0.00008639871,"about_ca_topic_score_gemma":0.00024100245,"teacher_disagreement_score":0.75981104,"about_ca_system_score_codex":0.00004252861,"about_ca_system_score_gemma":0.000021200716,"threshold_uncertainty_score":0.9254677},"labels":[],"label_agreement":null},{"id":"W2910986166","doi":"10.5539/cis.v12n1p49","title":"Research and Improvement Method Based on k-mean Clustering Algorithm","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Grey System Theory Applications","field":"Decision 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":"Cluster analysis; Computer science; Outlier; Sensitivity (control systems); Noise (video); Algorithm; Filter (signal processing); CURE data clustering algorithm; Correlation clustering; Artificial intelligence; Computer vision","score_opus":0.0925961158579673,"score_gpt":0.4322403759090277,"score_spread":0.3396442600510604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910986166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053885527,0.0000027707606,0.93493617,0.0004145664,0.00024407079,0.00041120412,0.000005082998,0.000022725348,0.010077882],"genre_scores_gemma":[0.90787363,0.0000013264988,0.09104464,0.0009051698,0.0000315342,0.000018754883,0.0000013952535,0.0000022112658,0.00012131795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974284,0.00009520548,0.0004045029,0.00031209952,0.0015317632,0.00022799481],"domain_scores_gemma":[0.9976996,0.0008488472,0.00011713175,0.00054880534,0.00064640597,0.00013918626],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.013528169,0.00007461753,0.00011599666,0.0008003381,0.0004211607,0.0011529919,0.0006198911,0.000026924256,0.00003895786],"category_scores_gemma":[0.00012056887,0.0000536631,0.000014899664,0.0014007529,0.00025375298,0.0031521039,0.0004207985,0.00010775113,0.00043354047],"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":[0.000006743204,0.000011613653,0.00032419086,0.000009605336,8.627485e-7,8.71906e-8,0.0014093621,0.002658915,0.00031267453,0.017981015,0.00026768545,0.9770172],"study_design_scores_gemma":[0.0002196308,0.00020102947,0.0068017133,0.000014714505,3.7784537e-7,0.000004427686,0.00051293185,0.9795757,0.00044662075,0.0026769585,0.009476781,0.00006913563],"about_ca_topic_score_codex":0.000007810324,"about_ca_topic_score_gemma":6.1884845e-7,"teacher_disagreement_score":0.9769481,"about_ca_system_score_codex":0.00004138269,"about_ca_system_score_gemma":0.000081681064,"threshold_uncertainty_score":0.9998839},"labels":[],"label_agreement":null},{"id":"W2913759468","doi":"10.14505//jemt.v9.6(30).01","title":"Forecasting the Foreign Tourist Arrivals to Vietnam Using the Autoregressive Integrated Moving Average Method","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Management and Tourism","topic":"Grey System Theory Applications","field":"Decision 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":"Autoregressive integrated moving average; Tourism; Vietnamese; Government (linguistics); Business; Tertiary sector of the economy; Quarter (Canadian coin); Work (physics); Moving average; Service (business); Order (exchange); Computer science; Operations research; Time series; Marketing; Finance; Geography; Engineering","score_opus":0.045530943399961256,"score_gpt":0.3146699184769343,"score_spread":0.26913897507697304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913759468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88393295,0.0002462474,0.10525445,0.0012030542,0.00036689674,0.0010030435,0.000012056356,0.0000074120476,0.007973876],"genre_scores_gemma":[0.9847155,0.000017924196,0.01141097,0.00035163978,0.00017525585,0.000010492417,5.514608e-7,0.000017193044,0.0033004428],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99726224,0.0004380958,0.00080279633,0.0002826602,0.00097527384,0.0002389297],"domain_scores_gemma":[0.99762416,0.000808824,0.0008907963,0.00053374615,0.000032257434,0.000110236906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004693979,0.00018625174,0.0003033229,0.00020217871,0.00040843626,0.0003209575,0.0009382006,0.000043668362,0.00034953989],"category_scores_gemma":[0.00012522915,0.00009013992,0.00016695738,0.0002585764,0.00008530579,0.00035611624,0.00047641617,0.00024663925,0.00006226814],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086923636,0.00074236235,0.10698141,0.00012559294,0.0023993081,0.00087559066,0.019671826,0.11814208,0.03678507,0.04213147,0.037707947,0.6335681],"study_design_scores_gemma":[0.0046517714,0.0010546682,0.1638881,0.0009977439,0.001003091,0.0019957926,0.13618012,0.21239011,0.0034495373,0.17387249,0.29883164,0.0016849234],"about_ca_topic_score_codex":0.000005964313,"about_ca_topic_score_gemma":0.0000013151603,"teacher_disagreement_score":0.63188314,"about_ca_system_score_codex":0.00011204337,"about_ca_system_score_gemma":0.00000950533,"threshold_uncertainty_score":0.38272166},"labels":[],"label_agreement":null},{"id":"W2922218518","doi":"10.1007/s11356-019-04715-z","title":"Using the seasonal FGM(1,1) model to predict the air quality indicators in Xingtai and Handan","year":2019,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":43,"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 Natural Science Foundation of China","keywords":"Environmental science; Air quality index; Air pollution; Pollution; Air temperature; Seasonality; Quarter (Canadian coin); Meteorology; Environmental engineering; Statistics; Geography; Mathematics; Ecology; Biology","score_opus":0.20385419909689567,"score_gpt":0.473268159105173,"score_spread":0.26941396000827733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922218518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923378,0.00007312464,0.0005319477,0.005226094,0.000034450488,0.0007144358,0.000032379092,0.0000046308537,0.0010450977],"genre_scores_gemma":[0.9987226,0.000008084982,0.00009968948,0.0003506372,0.000018720868,0.000033387605,3.8968696e-7,0.0000036444496,0.0007628351],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9951371,0.00066659803,0.00028622482,0.00053383026,0.0029745079,0.00040172503],"domain_scores_gemma":[0.99861807,0.00052510976,0.0000681885,0.0005667441,0.000027345168,0.00019454252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027522834,0.00008025363,0.00010628229,0.00042915414,0.0012511538,0.00025012917,0.0009490425,0.000039530245,0.00008739446],"category_scores_gemma":[0.000600602,0.00004271202,0.000020167856,0.002273675,0.0023120937,0.0003660151,0.0007595795,0.00024817756,0.00013886452],"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":[0.000085625994,0.00011780216,0.7638131,0.000005848819,0.0000060089496,8.756175e-7,0.010999957,0.010152017,0.14537387,0.042313486,0.00066206965,0.02646934],"study_design_scores_gemma":[0.00021743902,0.000052074374,0.8949158,0.000013114272,0.0000016479371,0.000008529708,0.010016838,0.08055927,0.0013515232,0.00900995,0.0037428513,0.00011094657],"about_ca_topic_score_codex":0.00010092767,"about_ca_topic_score_gemma":0.000038924754,"teacher_disagreement_score":0.14402235,"about_ca_system_score_codex":0.00022959002,"about_ca_system_score_gemma":0.00017009024,"threshold_uncertainty_score":0.962299},"labels":[],"label_agreement":null},{"id":"W2922375149","doi":"10.17159/2411-9717/2019/v119n1a1","title":"A comparison of indirect lognormal and discrete Gaussian change of support methods for various variogram estimators","year":2019,"lang":"en","type":"article","venue":"Journal of the Southern African Institute of Mining and Metallurgy","topic":"Grey System Theory Applications","field":"Decision 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":"Polytechnique Montréal","funders":"","keywords":"Variogram; Log-normal distribution; Estimator; Statistics; Gaussian; Mathematics; Econometrics; Geostatistics; Applied mathematics; Statistical physics; Geology; Kriging; Physics; Spatial variability","score_opus":0.12492575156877095,"score_gpt":0.42952780531022944,"score_spread":0.3046020537414585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922375149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9441979,0.00036985977,0.05314188,0.00020583693,0.00047632123,0.00040732103,0.000048936166,0.0000046260043,0.0011473321],"genre_scores_gemma":[0.9465006,0.000004249023,0.053142615,0.000012051407,0.00003258954,0.000008240572,6.020299e-7,0.000011809578,0.0002872668],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99742144,0.0003592092,0.0013350907,0.00019587454,0.0005174657,0.00017092629],"domain_scores_gemma":[0.9952173,0.0010022434,0.0029309115,0.00042052375,0.00031377428,0.00011522315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005957513,0.00015311563,0.0010165212,0.00037030634,0.00008643257,0.000033281394,0.00066704175,0.000081420076,0.000031126376],"category_scores_gemma":[0.0010964756,0.00008896754,0.00029959794,0.000477674,0.00044090228,0.00019081097,0.00016143707,0.00011954696,0.0000011133862],"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":[0.0016774073,0.000859069,0.34054604,0.0006778868,0.0025213577,0.000006095876,0.2477172,0.002178105,0.026230432,0.019501857,0.0002860405,0.3577985],"study_design_scores_gemma":[0.019051015,0.014182238,0.20707265,0.0038849197,0.008030171,0.0020115497,0.3899353,0.055359416,0.036605593,0.060894232,0.19981186,0.003161062],"about_ca_topic_score_codex":0.000072262985,"about_ca_topic_score_gemma":0.000036314046,"teacher_disagreement_score":0.35463744,"about_ca_system_score_codex":0.0000133701515,"about_ca_system_score_gemma":0.00014585443,"threshold_uncertainty_score":0.3627991},"labels":[],"label_agreement":null},{"id":"W2946961151","doi":"10.33423/jabe.v20i4.355","title":"Predicting the Talents Needed in Coal Industry in China","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Grey System Theory Applications","field":"Decision 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":"Coal; China; Production (economics); Business; Industry of China; Economics; Industrial organization; Economy; Natural resource economics; Environmental economics; Engineering; Geography; Waste management; Macroeconomics","score_opus":0.043104069209937916,"score_gpt":0.29813304695991855,"score_spread":0.2550289777499806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946961151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933103,0.000017516684,0.00016864388,0.0012705073,0.00025122703,0.00012777469,0.0000031893599,0.0000021037524,0.004848751],"genre_scores_gemma":[0.9994129,0.0000121746325,0.00009158288,0.00014406086,0.0002990147,0.0000053522026,2.4193162e-7,0.0000066680454,0.000027998236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99866235,0.00003190273,0.0008780538,0.00015204393,0.0001340964,0.00014152794],"domain_scores_gemma":[0.9987095,0.00025337015,0.00065906957,0.00022047598,0.00010583411,0.000051746196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031029116,0.000085046115,0.0002613277,0.00026063033,0.00009645344,0.00017400348,0.00046787484,0.00015573112,0.00004162726],"category_scores_gemma":[0.0001728833,0.000054887845,0.000025651396,0.00044247412,0.0001396255,0.00024535484,0.00010365405,0.0003889073,0.0000188797],"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":[0.00070680183,0.00024753748,0.90951955,0.000019957677,0.000042363212,0.000009068681,0.009410586,0.017381044,0.00058625266,0.023706999,0.0013375893,0.037032265],"study_design_scores_gemma":[0.00097203744,0.000021651435,0.9384104,0.000038277034,0.0000065692934,0.00007389808,0.008910038,0.003819918,0.00014192951,0.045097888,0.0024006637,0.000106707324],"about_ca_topic_score_codex":0.000047412188,"about_ca_topic_score_gemma":0.00025091035,"teacher_disagreement_score":0.036925558,"about_ca_system_score_codex":0.00004948463,"about_ca_system_score_gemma":0.0000815264,"threshold_uncertainty_score":0.22382614},"labels":[],"label_agreement":null},{"id":"W2947034755","doi":"10.5220/0007708201580166","title":"Generalized Dirichlet Regression and other Compositional Models with Application to Market-share Data Mining of Information Technology Companies","year":2019,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":10,"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":"Latent Dirichlet allocation; Computer science; Data modeling; Regression analysis; Regression; Topic model; Data mining; Econometrics; Data science; Artificial intelligence; Statistics; Machine learning; Mathematics; Database","score_opus":0.07290700291791297,"score_gpt":0.3502497025460311,"score_spread":0.27734269962811814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947034755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53038514,0.000034033328,0.45349398,0.0012340739,0.00002509814,0.0010066339,0.00039387986,0.00007425057,0.0133529045],"genre_scores_gemma":[0.9215238,7.2779517e-7,0.07762355,0.00026586614,0.000008201057,0.00007288714,0.000109509005,0.0000069556663,0.00038846704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983178,0.00008393838,0.00053089636,0.00034900653,0.00060104224,0.00011734166],"domain_scores_gemma":[0.9976346,0.00029664466,0.00035807161,0.0013114238,0.00034255683,0.000056721925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010447418,0.00011284121,0.00025404638,0.00042916753,0.00008595435,0.00008483632,0.00088097295,0.00007261876,0.00031841788],"category_scores_gemma":[0.00008576104,0.00007233271,0.000013665413,0.00080332666,0.000077467506,0.0008325924,0.000416447,0.00004692008,0.00016543327],"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":[0.0006978358,0.00012962808,0.071286105,0.000085069856,0.000075985095,2.6897973e-7,0.002088984,0.009314367,0.01581945,0.8328675,0.035661854,0.031972956],"study_design_scores_gemma":[0.0017779648,0.00019319051,0.014326054,0.00024567734,0.000031006177,0.000064194945,0.006846402,0.8577075,0.002452613,0.057983294,0.057864655,0.00050747173],"about_ca_topic_score_codex":0.000022228416,"about_ca_topic_score_gemma":0.000024321893,"teacher_disagreement_score":0.8483931,"about_ca_system_score_codex":0.000015893635,"about_ca_system_score_gemma":0.000030529296,"threshold_uncertainty_score":0.34864527},"labels":[],"label_agreement":null},{"id":"W2947858844","doi":"10.1139/cjce-2018-0778","title":"Identification of unbalanced bids based on grey-fuzzy evaluation method","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bidding; Identification (biology); Grey relational analysis; Fuzzy logic; Closeness; Data mining; Rank (graph theory); Fuzzy set; Mathematics; Relation (database); Computer science; Set (abstract data type); Ranking (information retrieval); Mathematical optimization; Artificial intelligence; Statistics; Economics","score_opus":0.04202163030911668,"score_gpt":0.33408666016117106,"score_spread":0.2920650298520544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947858844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40115872,0.00023010872,0.58621883,0.00075544586,0.0021493745,0.0006728137,0.000045334324,0.000016239133,0.00875316],"genre_scores_gemma":[0.99881035,3.6836695e-7,0.0009425201,0.000031061452,0.000063238615,0.000006870553,0.0000016230472,0.000014017495,0.00012995918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975526,0.00019959954,0.00092989294,0.00017800088,0.0009671128,0.00017281373],"domain_scores_gemma":[0.9966463,0.0007641069,0.000645402,0.000559307,0.001118841,0.00026605578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0092578735,0.000100787554,0.0002753846,0.0010956058,0.000039569313,0.00008783818,0.00057292054,0.000063763124,0.0005238949],"category_scores_gemma":[0.0032609939,0.00008935969,0.000122009114,0.0008901163,0.000017805261,0.00023264802,0.00000576456,0.00014265644,0.000085474676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007968701,0.000007823412,0.0029699167,0.000015853959,0.000017417327,0.00000191755,0.00023361511,0.9555536,0.032603186,0.0045789885,0.0005670468,0.003442658],"study_design_scores_gemma":[0.00095903466,0.00013896816,0.060473148,0.00025845162,0.000060725662,0.00003819988,0.00030676235,0.9104268,0.009598673,0.01146297,0.0060149003,0.00026136165],"about_ca_topic_score_codex":0.00008025876,"about_ca_topic_score_gemma":0.0027231653,"teacher_disagreement_score":0.5976516,"about_ca_system_score_codex":0.00021764464,"about_ca_system_score_gemma":0.00074210006,"threshold_uncertainty_score":0.5736282},"labels":[],"label_agreement":null},{"id":"W2961984786","doi":"10.1111/rssa.12491","title":"UK Regional Nowcasting Using a Mixed Frequency Vector Auto-Regressive Model with Entropic Tilting","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Grey System Theory Applications","field":"Decision 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":"Blackberry (Canada)","funders":"","keywords":"Nowcasting; Autoregressive model; Aggregate (composite); Econometrics; Computer science; Exploit; Vector autoregression; Economics; Geography; Meteorology","score_opus":0.05528175492662648,"score_gpt":0.3289328123172136,"score_spread":0.27365105739058715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961984786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1750944,0.00013850676,0.82222646,0.0008423423,0.0004995257,0.00047385803,0.00047016327,0.000017072856,0.00023768141],"genre_scores_gemma":[0.5599629,0.000008102297,0.4388,0.00021432256,0.00014898505,0.000009245169,0.000004610702,0.000036766098,0.0008150306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99405307,0.00046347117,0.0018179024,0.0005350136,0.0024791674,0.0006514048],"domain_scores_gemma":[0.99129397,0.0044573178,0.0020937964,0.0006490577,0.0012603655,0.0002454669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002685034,0.0003822162,0.0008383667,0.000053633506,0.0005527498,0.000362858,0.0013403934,0.00020938291,0.00028882897],"category_scores_gemma":[0.0023178135,0.00023962386,0.0004842405,0.00094308465,0.0007670087,0.00029167256,0.00031485572,0.0010239321,0.000026788788],"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":[0.00039916098,0.00040345732,0.09187,0.00029404645,0.0007431883,0.00008069176,0.012548263,0.31724507,0.003736849,0.51553226,0.054828804,0.0023182193],"study_design_scores_gemma":[0.0013437956,0.00020735612,0.011830362,0.00036682555,0.00014791968,0.0001478845,0.007932046,0.79873645,0.000051911513,0.17829478,0.00050911494,0.0004315564],"about_ca_topic_score_codex":0.00005302062,"about_ca_topic_score_gemma":0.0000619851,"teacher_disagreement_score":0.4814914,"about_ca_system_score_codex":0.0006577341,"about_ca_system_score_gemma":0.00082909886,"threshold_uncertainty_score":0.9771577},"labels":[],"label_agreement":null},{"id":"W2980991470","doi":"10.5539/ijel.v9n6p49","title":"Applying Student-Problem Chart, Grey Student-Problem Chart and Grey Structure Modeling to Analyze the Effect of an Elementary School English Remedial Instruction","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Grey System Theory Applications","field":"Decision 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":"Remedial education; Chart; Mathematics education; Test (biology); Bar chart; Psychology; Mathematics; Statistics","score_opus":0.019028514618474685,"score_gpt":0.3405782251773302,"score_spread":0.3215497105588555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980991470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98391145,0.00007451331,0.0007853915,0.00004936706,0.012423896,0.0011443436,0.00010467273,0.000027014457,0.0014793363],"genre_scores_gemma":[0.98781484,0.000014584762,0.0028840248,0.00012816433,0.009024722,0.000034315457,0.000023396276,0.000034435878,0.00004152313],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99332625,0.00052535406,0.0020473949,0.00052195456,0.0032781768,0.00030089106],"domain_scores_gemma":[0.97503185,0.0010630785,0.0014863341,0.0006230241,0.021471577,0.00032414414],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0052201175,0.00031758912,0.00063330564,0.0006905724,0.00017735454,0.00063110335,0.0023335645,0.0001327002,0.00021579255],"category_scores_gemma":[0.030875005,0.00022414017,0.00020070546,0.0005071749,0.00008680151,0.00047246675,0.00043860375,0.0006434861,0.000017822002],"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":[0.0016574787,0.00029875632,0.7682794,0.000096119315,0.0017244461,0.00004222362,0.022993013,0.17093629,0.0032178957,0.012698537,0.0026928708,0.015362979],"study_design_scores_gemma":[0.054605547,0.01471712,0.1572658,0.0043344824,0.003533687,0.00043091385,0.21588784,0.09194979,0.029693738,0.13697198,0.28419036,0.0064187613],"about_ca_topic_score_codex":0.000026858193,"about_ca_topic_score_gemma":0.00009223023,"teacher_disagreement_score":0.6110136,"about_ca_system_score_codex":0.00020237097,"about_ca_system_score_gemma":0.00012597571,"threshold_uncertainty_score":0.97728837},"labels":[],"label_agreement":null},{"id":"W3011767151","doi":"10.1016/j.scitotenv.2020.137964","title":"Using grey clustering to evaluate nitrogen pollution in estuaries with limited data","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Grey System Theory Applications","field":"Decision 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":"École de Technologie Supérieure","funders":"European Commission; National Water Commission; United States Agency for International Development","keywords":"Pollution; Cluster analysis; Environmental science; Water quality; Nutrient pollution; Computer science; Environmental resource management; Water resource management; Data mining; Ecology; Machine learning","score_opus":0.19536491464705072,"score_gpt":0.3524820746439333,"score_spread":0.15711715999688255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011767151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98151934,0.000022061946,0.006548072,0.010943857,0.000057102294,0.00064618606,0.000022914019,0.000008466729,0.0002320056],"genre_scores_gemma":[0.9973693,7.2173003e-7,0.0023878657,0.00015313158,0.000020135514,0.000011526851,4.4540596e-7,0.0000065022455,0.000050404255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996693,0.0002585595,0.00040490043,0.0005406748,0.0018535695,0.00024929427],"domain_scores_gemma":[0.9975273,0.000151578,0.00024287484,0.0019365008,0.000033450626,0.00010828484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005087375,0.00011091883,0.00016432203,0.000088208755,0.0003784642,0.00012297691,0.0036004528,0.000017506092,0.00005076446],"category_scores_gemma":[0.0009601171,0.00005397952,0.000030183435,0.0015583469,0.00099109,0.0004167583,0.0025907042,0.0000908135,0.00012923841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000612569,0.000027062508,0.00050610286,0.000002105625,0.000005943931,4.134087e-7,0.0027364441,0.8974346,0.097548,0.00054612994,0.000034443794,0.0010974971],"study_design_scores_gemma":[0.00047095516,0.00016709807,0.058738507,0.000065539585,0.000054678436,0.000038830818,0.0052020433,0.9026291,0.024166483,0.007959769,0.00022253979,0.00028450668],"about_ca_topic_score_codex":0.0000827282,"about_ca_topic_score_gemma":0.0000102831855,"teacher_disagreement_score":0.07338152,"about_ca_system_score_codex":0.000120286255,"about_ca_system_score_gemma":0.00010288413,"threshold_uncertainty_score":0.6690597},"labels":[],"label_agreement":null},{"id":"W3016134273","doi":"10.1108/gs-11-2019-0055","title":"Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method","year":2020,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision 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":"Quarter (Canadian coin); Per capita; Economics; Econometrics; China; Originality; Grey relational analysis; Value (mathematics); Statistics; Mathematics; Geography","score_opus":0.26679653729639496,"score_gpt":0.40645180712489837,"score_spread":0.1396552698285034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016134273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2649437,0.0009188436,0.7325084,0.00011735378,0.000055253717,0.0009865408,0.00023004357,0.000036474266,0.00020338195],"genre_scores_gemma":[0.9939202,0.000004594937,0.005746215,0.000036334626,0.000065772845,0.00008203734,0.000102882426,0.000023474628,0.000018462319],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99429715,0.0024578348,0.0012798199,0.000854415,0.00090065313,0.00021012055],"domain_scores_gemma":[0.9932986,0.0029799477,0.00149395,0.00179423,0.0003017721,0.00013152395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013135311,0.00023408825,0.0006036984,0.00012650939,0.00035560847,0.0001707969,0.0017089096,0.00012040228,0.000012504912],"category_scores_gemma":[0.0016078305,0.00015442452,0.00011422273,0.0010930288,0.00039682759,0.0004013868,0.00035605955,0.0001733269,0.000010136261],"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":[0.0003280407,0.00019096851,0.05499361,0.0011337921,0.00028573256,6.163314e-7,0.008544229,0.0038294476,0.11015049,0.8052054,0.00022796913,0.015109722],"study_design_scores_gemma":[0.0011462476,0.00008125379,0.035042826,0.00034608506,0.00055018044,0.000054554137,0.036926642,0.8537202,0.007807193,0.059430495,0.004111566,0.0007827682],"about_ca_topic_score_codex":0.000100060584,"about_ca_topic_score_gemma":0.0000027408255,"teacher_disagreement_score":0.84989077,"about_ca_system_score_codex":0.000020266927,"about_ca_system_score_gemma":0.000080935126,"threshold_uncertainty_score":0.6297249},"labels":[],"label_agreement":null},{"id":"W3041248092","doi":"10.5539/ijef.v12n8p91","title":"Analysis and Forecasting the Agriculture Production Sector in Rwanda","year":2020,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Grey System Theory Applications","field":"Decision 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":"Agriculture; Production (economics); Investment (military); Workforce; Economics; Economic sector; Sector model; Agricultural productivity; Primary sector of the economy; Agricultural economics; Business; Economic growth; Economy; Geography; Macroeconomics; Political science","score_opus":0.0704806777385682,"score_gpt":0.2914785560847182,"score_spread":0.22099787834614998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041248092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98449796,0.0001890077,0.00074834086,0.014167991,0.0001681085,0.000043717973,0.0000094875695,6.394331e-7,0.0001747168],"genre_scores_gemma":[0.99901026,0.00025426713,0.0002930242,0.0001682818,0.00021261394,0.0000016627318,4.3503763e-7,0.0000015520841,0.0000578829],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992372,0.000033819342,0.00045650097,0.00013018577,0.000100933066,0.00004138036],"domain_scores_gemma":[0.99896634,0.00021769833,0.0005149438,0.00006108809,0.00021930001,0.000020611003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009753567,0.000042266227,0.0001405556,0.00010612881,0.000038954902,0.00013820759,0.00027749696,0.000018454473,0.000007303593],"category_scores_gemma":[0.0005758091,0.000025285757,0.00005090115,0.00022866146,0.000035794656,0.0002094096,0.000044784545,0.00008202336,0.0000018743458],"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":[0.00036645072,0.00008073454,0.39565483,0.000006109637,0.00074121176,0.000020981084,0.008074575,0.24323863,0.0010063498,0.12144202,0.0019099839,0.22745812],"study_design_scores_gemma":[0.0008972533,0.000098244716,0.697248,0.00003899117,0.00008141262,0.0003240811,0.0019890517,0.12417132,0.0008003681,0.084233984,0.08988614,0.0002311892],"about_ca_topic_score_codex":0.0000073529163,"about_ca_topic_score_gemma":0.00012506447,"teacher_disagreement_score":0.30159312,"about_ca_system_score_codex":0.000018237646,"about_ca_system_score_gemma":0.000018140443,"threshold_uncertainty_score":0.1332739},"labels":[],"label_agreement":null},{"id":"W3048056907","doi":"","title":"The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada","year":2020,"lang":"en","type":"article","venue":"DR-NTU (Nanyang Technological University)","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Latent class model; Class (philosophy); Geography; Computer science; Artificial intelligence","score_opus":0.07867150772803225,"score_gpt":0.29975174544680416,"score_spread":0.22108023771877192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048056907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89801615,0.0000340544,0.09699543,0.0033086364,0.000036172583,0.0010827316,0.000116306495,0.00010288358,0.00030761174],"genre_scores_gemma":[0.9988316,0.0000033423464,0.0009191114,0.00007482239,0.000011203338,0.000032786706,0.000022569782,0.0000064637125,0.00009805674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99816835,0.00012051657,0.00053463,0.0005053628,0.00041320903,0.0002579162],"domain_scores_gemma":[0.9963453,0.0022744606,0.00046404792,0.0005207367,0.00028591658,0.00010955149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007659903,0.00014538626,0.00037872122,0.00018045139,0.0003088971,0.000048790822,0.0014816512,0.00018240386,0.000010148076],"category_scores_gemma":[0.003407428,0.00010355718,0.00019958004,0.0024581016,0.00030052432,0.00015281231,0.00024227792,0.00011431289,0.0000015487695],"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":[0.00010201037,0.000067961795,0.39252657,0.000025659761,0.00021637681,0.0000019749061,0.00028844317,0.0022711281,0.008811775,0.58800083,0.0006498427,0.0070374166],"study_design_scores_gemma":[0.0026168956,0.0003382763,0.42659563,0.000040609993,0.0006717414,5.6961306e-7,0.040513497,0.11883552,0.01841838,0.09294025,0.29790735,0.0011212648],"about_ca_topic_score_codex":0.031277746,"about_ca_topic_score_gemma":0.5604573,"teacher_disagreement_score":0.5291795,"about_ca_system_score_codex":0.00040325377,"about_ca_system_score_gemma":0.00014159278,"threshold_uncertainty_score":0.97517306},"labels":[],"label_agreement":null},{"id":"W3072980033","doi":"10.1016/j.engappai.2020.103863","title":"A novel grey Riccati–Bernoulli model and its application for the clean energy consumption prediction","year":2020,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":61,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Bernoulli's principle; Energy consumption; Consumption (sociology); Bernoulli trial; Mathematical optimization; Statistics; Mathematics; Electrical engineering","score_opus":0.1321349813117746,"score_gpt":0.33976245501329777,"score_spread":0.20762747370152318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3072980033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057661464,0.00024279515,0.9906503,0.0015880206,0.00004697012,0.0013575287,0.00018572743,0.000121026336,0.00004147229],"genre_scores_gemma":[0.9865229,0.000039453436,0.010679126,0.00008522522,0.0001553993,0.0024381438,0.000020560066,0.000025468218,0.000033738663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978385,0.000024675244,0.0009313278,0.00056455703,0.0004476152,0.00019334287],"domain_scores_gemma":[0.9968315,0.001465662,0.00035349283,0.00065775926,0.0005521609,0.00013943613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010372471,0.00017348089,0.00022609554,0.00014473166,0.00026974556,0.00008826506,0.0008021484,0.00009598326,0.000014471051],"category_scores_gemma":[0.0008359644,0.00014612469,0.00009101326,0.00088065973,0.00010724381,0.00020125219,0.00009810142,0.000110889196,0.00006529838],"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":[0.000021571908,0.000050809722,0.000033786997,0.000028082339,0.000018279006,8.767423e-9,0.0004433146,0.3837195,0.08635473,0.417884,0.00006560328,0.11138032],"study_design_scores_gemma":[0.000031080435,0.000026721846,0.00014178692,0.0000072814237,0.000031313444,0.0000024869698,0.00024609032,0.93145394,0.05018916,0.015375462,0.002374968,0.000119679826],"about_ca_topic_score_codex":0.000019085868,"about_ca_topic_score_gemma":0.000015785317,"teacher_disagreement_score":0.98075676,"about_ca_system_score_codex":0.00003052634,"about_ca_system_score_gemma":0.00004392254,"threshold_uncertainty_score":0.5958792},"labels":[],"label_agreement":null},{"id":"W3083112951","doi":"","title":"Prévision de l’activité économique au Québec et au Canada à l’aide des méthodes Machine Learning","year":2020,"lang":"fr","type":"article","venue":"CIRANO Project Reports","topic":"Grey System Theory Applications","field":"Decision 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":"Humanities; Political science; Forestry; Geography; Philosophy","score_opus":0.17984238908079297,"score_gpt":0.35260471379145836,"score_spread":0.17276232471066538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083112951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9176987,0.0021740235,0.04068293,0.0133862635,0.0010174117,0.0014914048,0.000057660036,0.00020237143,0.02328928],"genre_scores_gemma":[0.98526144,0.00004334916,0.0036702126,0.00094158645,0.00046013188,0.00018727995,0.00002103123,0.000084881176,0.009330114],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920173,0.0029012081,0.0018136094,0.0014885864,0.0007598722,0.0010194214],"domain_scores_gemma":[0.9929016,0.0035559405,0.0014149778,0.0011098755,0.0003692837,0.0006483008],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009345257,0.00052245497,0.000844811,0.00020683308,0.0010278308,0.00037032686,0.0006740949,0.0002814807,0.00034349764],"category_scores_gemma":[0.015121113,0.0004958588,0.00027262466,0.0011939302,0.00040752062,0.00072348956,0.0003679192,0.00079628016,0.00008908371],"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":[0.00010290393,0.00014130006,0.86594725,0.00035747435,0.00011912441,0.0015628964,0.016491083,0.005508823,0.004203912,0.005211759,0.0021755465,0.09817793],"study_design_scores_gemma":[0.0007209242,0.00037242717,0.10726864,0.000805711,0.00022852517,0.005813781,0.006277348,0.018899862,0.011891019,0.011260291,0.8348108,0.0016506544],"about_ca_topic_score_codex":0.99052775,"about_ca_topic_score_gemma":0.9904188,"teacher_disagreement_score":0.8326353,"about_ca_system_score_codex":0.0033977886,"about_ca_system_score_gemma":0.042162206,"threshold_uncertainty_score":0.9997493},"labels":[],"label_agreement":null},{"id":"W3083676348","doi":"10.1016/j.aej.2020.08.026","title":"Evaluating the effect of sample length on forecasting validity of FGM(1,1)","year":2020,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Grey System Theory Applications","field":"Decision 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":"National Natural Science Foundation of China","keywords":"Statistics; Sample (material); Mathematics; Sample size determination; Forecast error; Mean squared error; Econometrics; Population; Environmental science; Demography; Chemistry","score_opus":0.3160212566664375,"score_gpt":0.4150021868139443,"score_spread":0.09898093014750681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083676348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90835315,0.000047459725,0.09057769,0.00030900483,0.00028316854,0.0002179553,0.000015095127,0.00001758202,0.00017891364],"genre_scores_gemma":[0.9959336,0.0000015383687,0.003747026,0.000019286537,0.00026172295,0.000008775813,6.001358e-7,0.000015302538,0.000012156644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971556,0.0004821665,0.00086960784,0.00019964731,0.0011014533,0.00019153222],"domain_scores_gemma":[0.98536927,0.013281156,0.00061749533,0.00039207522,0.0002169668,0.00012303276],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008567206,0.00013856185,0.00037549544,0.00011153552,0.00014498839,0.00007370679,0.0007639088,0.000043558233,0.00015755363],"category_scores_gemma":[0.024622813,0.00008086608,0.000199809,0.00057576736,0.000039816565,0.00011252724,0.00008151039,0.0003107613,0.000020827789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025366395,0.000028755661,0.035122655,0.00010666797,0.00012747762,0.000005011088,0.0024009836,0.8727389,0.052199684,0.0029660524,0.0010291227,0.03302104],"study_design_scores_gemma":[0.0015189314,0.0033124574,0.009955886,0.00028557403,0.00012066788,0.00015710214,0.00027364466,0.92018205,0.060501028,0.0020466656,0.0013756728,0.00027031594],"about_ca_topic_score_codex":0.0000036989975,"about_ca_topic_score_gemma":4.0026478e-7,"teacher_disagreement_score":0.08758046,"about_ca_system_score_codex":0.000023192133,"about_ca_system_score_gemma":0.00003649489,"threshold_uncertainty_score":0.9835932},"labels":[],"label_agreement":null},{"id":"W3104422417","doi":"10.1016/j.scitotenv.2020.143576","title":"A grey spatiotemporal incidence model with application to factors causing air pollution","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Grey System Theory Applications","field":"Decision 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":"University of Waterloo","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Air pollution; Pollution; Incidence (geometry); Statistics; Environmental science; Computer science; Geography; Data mining; Mathematics; Ecology","score_opus":0.05049642751420131,"score_gpt":0.28993575015263723,"score_spread":0.23943932263843593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104422417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8636311,0.0000056387385,0.11937615,0.01590258,0.000031932173,0.00076514995,0.000014714253,0.000016484375,0.00025622122],"genre_scores_gemma":[0.9981276,3.591064e-7,0.0013577086,0.00023170689,0.000019395024,0.000040763265,3.8034986e-7,0.000007873953,0.000214235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996476,0.00012030548,0.00041427446,0.0005336526,0.0022193368,0.0002364264],"domain_scores_gemma":[0.99802417,0.00011256425,0.00040256532,0.0012199203,0.000068188594,0.00017261865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165534,0.00013759464,0.00016484634,0.00007194,0.00059396005,0.00007175582,0.0016898384,0.000026205542,0.000022324815],"category_scores_gemma":[0.00043658374,0.00006556374,0.00006403663,0.0013699083,0.0010548347,0.00037928484,0.0006531491,0.00010281753,0.00015208292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016879247,0.000016985423,0.00019735318,0.0000012140509,0.0000022583151,4.8987896e-8,0.002515471,0.91994077,0.07419708,0.0026508672,0.0000437923,0.00041726857],"study_design_scores_gemma":[0.0002486311,0.00020994655,0.17928097,0.000033584445,0.000041720155,0.000017000148,0.0034937242,0.66191244,0.13653645,0.017703265,0.0001416674,0.0003805776],"about_ca_topic_score_codex":0.000084947766,"about_ca_topic_score_gemma":0.0000043834702,"teacher_disagreement_score":0.2580283,"about_ca_system_score_codex":0.0001681441,"about_ca_system_score_gemma":0.00013102581,"threshold_uncertainty_score":0.45683205},"labels":[],"label_agreement":null},{"id":"W3108792264","doi":"10.3846/tede.2020.13742","title":"EVALUATION OF THE COORDINATION BETWEEN CHINA’S TECHNOLOGY AND ECONOMY USING A GREY MULTIVARIATE COUPLING MODEL","year":2020,"lang":"en","type":"article","venue":"Technological and Economic Development of Economy","topic":"Grey System Theory Applications","field":"Decision 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 Manitoba","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Interpretability; Computer science; Convergence (economics); Heuristic; China; Process (computing); Fuzzy logic; Multivariate statistics; Coupling (piping); Investment (military); Mathematical optimization; Artificial intelligence; Economics; Mathematics; Machine learning; Engineering; Geography; Economic growth; Political science","score_opus":0.21522110957723395,"score_gpt":0.3533190470349844,"score_spread":0.13809793745775045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108792264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96793234,0.000060230308,0.027205564,0.0024721546,0.00001698746,0.0005744988,0.000009970153,0.00004136509,0.0016868784],"genre_scores_gemma":[0.9915888,0.0000010838068,0.008304144,0.000021826268,0.000009060932,0.000055644716,0.0000014604545,0.0000063107905,0.000011700191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981943,0.00004677184,0.0010047886,0.00047928357,0.00012639757,0.0001484545],"domain_scores_gemma":[0.9985944,0.0001619172,0.0007190672,0.00030208696,0.0001676162,0.00005490105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035827393,0.00014413129,0.00042781836,0.00027180856,0.00017606835,0.000044350072,0.00053914014,0.00018626775,0.000045731318],"category_scores_gemma":[0.0005441263,0.00010453905,0.000042253585,0.00027549823,0.00039259784,0.00023067214,0.00048770625,0.00012072566,0.000012946427],"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":[0.0000355678,0.000100691585,0.26002336,0.00008109037,0.00031924152,2.2009169e-7,0.0017265647,0.043981817,0.0071546417,0.46421593,0.00005393367,0.22230692],"study_design_scores_gemma":[0.00046642715,0.000017422406,0.014532998,0.000016520715,0.000039552873,0.0000024383785,0.00047907647,0.6764378,0.0056017824,0.30180672,0.00045824968,0.00014102038],"about_ca_topic_score_codex":0.000004124906,"about_ca_topic_score_gemma":0.000004074081,"teacher_disagreement_score":0.63245595,"about_ca_system_score_codex":0.000113026064,"about_ca_system_score_gemma":0.00022342447,"threshold_uncertainty_score":0.42629787},"labels":[],"label_agreement":null},{"id":"W3122941014","doi":"","title":"Labor market and natural rate of unemployment in US and Canadian time series analysis","year":2011,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Natural rate of unemployment; Economics; Disequilibrium; Cointegration; Unemployment; Unemployment rate; Productivity; Full employment; Real wages; Labour economics; Series (stratigraphy); Time series; Econometrics; Keynesian economics; Macroeconomics; Mathematics; Statistics","score_opus":0.026295180735653706,"score_gpt":0.2523152399153032,"score_spread":0.22602005917964949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122941014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9797235,0.00076291524,0.000052818326,0.0009885175,0.00006833876,0.000667302,0.0023718255,0.000022860346,0.015341923],"genre_scores_gemma":[0.98505044,0.0005067673,0.0032910667,0.000054810756,0.000011428818,0.0000028710513,0.000120976,0.000023255465,0.010938368],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99553865,0.0015028641,0.00075889006,0.0011241011,0.00062469445,0.00045081574],"domain_scores_gemma":[0.9955302,0.0010802706,0.0008023097,0.0016692351,0.00046521705,0.00045279987],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028988707,0.00042155958,0.0012780745,0.002177162,0.0002989606,0.00006070105,0.0019318865,0.0002594583,0.00093433465],"category_scores_gemma":[0.0005464143,0.000449207,0.00035563158,0.0011456031,0.001459613,0.0002306696,0.0022700597,0.00067652843,0.000017590448],"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":[0.0037102015,0.00065042626,0.79475033,0.00082654186,0.0059232865,0.00054752076,0.14804016,0.0009453615,0.0018746703,0.020955166,0.0064080413,0.015368299],"study_design_scores_gemma":[0.0009054322,0.00012732996,0.9341942,0.0002963145,0.00069437665,0.000023818515,0.014611687,0.011716222,0.000051016945,0.025685774,0.010983612,0.0007102249],"about_ca_topic_score_codex":0.18073875,"about_ca_topic_score_gemma":0.5267758,"teacher_disagreement_score":0.34603703,"about_ca_system_score_codex":0.00019460049,"about_ca_system_score_gemma":0.0006518155,"threshold_uncertainty_score":0.99997896},"labels":[],"label_agreement":null},{"id":"W3125342563","doi":"10.1016/j.energy.2021.119952","title":"Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach","year":2021,"lang":"en","type":"article","venue":"Energy","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":73,"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 Natural Science Foundation of China","keywords":"Electricity; Consumption (sociology); Quarter (Canadian coin); Econometrics; Mains electricity; Environmental economics; Value (mathematics); Secondary sector of the economy; Environmental science; Seasonality; Economics; Agricultural economics; Statistics; Engineering; Mathematics; Geography; Economy","score_opus":0.21383195071315064,"score_gpt":0.33518658108357957,"score_spread":0.12135463037042893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125342563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6023104,0.00011090575,0.39702287,0.000011357693,0.000047922247,0.00008309487,0.000008830952,0.000011562615,0.00039301117],"genre_scores_gemma":[0.9964701,0.0000046655446,0.0033540982,0.000016332178,0.00007281725,0.00001941781,0.000009847753,0.000010518045,0.000042236283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705017,0.00067797507,0.00076024473,0.00053918944,0.00068481616,0.0002876363],"domain_scores_gemma":[0.9978067,0.0010916225,0.0003273945,0.0003292834,0.0003593835,0.00008561754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026223457,0.00013831035,0.00032837974,0.00042294952,0.00021157782,0.00010948716,0.00025490506,0.00014870403,0.000038600865],"category_scores_gemma":[0.0029866702,0.00013166547,0.00006873901,0.0026430192,0.00005310183,0.00023194044,0.00010747353,0.00018746198,0.0000012569484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011343309,0.00072971196,0.2002879,0.000029114004,0.0000645799,0.000013248224,0.0016050778,0.5949189,0.042181995,0.13359259,0.000039106413,0.026424337],"study_design_scores_gemma":[0.0004045032,0.000015718013,0.00075756,0.00002009437,0.000014590417,0.000042257077,0.00011058439,0.9863262,0.0028461854,0.009320412,0.000018269797,0.00012360016],"about_ca_topic_score_codex":0.0004311459,"about_ca_topic_score_gemma":0.00017490025,"teacher_disagreement_score":0.39415962,"about_ca_system_score_codex":0.000119963915,"about_ca_system_score_gemma":0.00033287038,"threshold_uncertainty_score":0.5369162},"labels":[],"label_agreement":null},{"id":"W3132127288","doi":"10.1093/jcde/qwab009","title":"A novel particle swarm optimization-based grey model for the prediction of warehouse performance","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":79,"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; École de Technologie Supérieure","funders":"","keywords":"Performance indicator; Particle swarm optimization; Warehouse; Supply chain; Data mining; Genetic algorithm; Taguchi methods; Computer science; Key (lock); Engineering; Operations research; Machine learning","score_opus":0.09769746644814277,"score_gpt":0.29296198254697847,"score_spread":0.19526451609883572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132127288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035181247,0.00010921718,0.9641799,0.00031670046,0.00008212761,0.00010985803,0.000011292937,0.000006718161,0.0000029636253],"genre_scores_gemma":[0.74816686,0.0000040172104,0.2517492,0.000023380555,0.000027005772,0.0000075181033,9.643661e-7,0.0000051246243,0.000015934616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989486,0.000025821668,0.0004875521,0.00007840128,0.0003929066,0.00006672063],"domain_scores_gemma":[0.99642855,0.0022647984,0.00022377781,0.00009251592,0.00094638776,0.00004397914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012543133,0.00005411185,0.00012410263,0.00007408899,0.00007993388,0.000057380737,0.00013332668,0.000021316944,0.0000044016074],"category_scores_gemma":[0.00061118545,0.00003779404,0.000056863144,0.00025754288,0.000019707259,0.00018147165,0.000011069466,0.00005050117,4.6024041e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025459569,0.000025389943,0.00010137964,0.000010793649,0.000017670798,1.8901272e-7,0.0001790537,0.99675226,0.0012629947,0.0010890921,0.00006663259,0.00046906873],"study_design_scores_gemma":[0.00043220996,0.000035992867,0.0014933377,0.000028008662,0.000018702009,0.00003381489,0.000043357366,0.99528426,0.0012893591,0.0012777514,0.00003072264,0.00003251256],"about_ca_topic_score_codex":1.6689425e-7,"about_ca_topic_score_gemma":6.05036e-8,"teacher_disagreement_score":0.71298563,"about_ca_system_score_codex":0.000016580863,"about_ca_system_score_gemma":0.00014439096,"threshold_uncertainty_score":0.15411963},"labels":[],"label_agreement":null},{"id":"W3151344568","doi":"10.1142/9789812775702_0057","title":"FUZZY LOGIC STRATEGIES FOR THE TREATMENT OF THE MOLECULAR RECOGNITION PROBLEM","year":2002,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Grey System Theory Applications","field":"Decision 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 Saskatchewan","funders":"Deutsche Forschungsgemeinschaft","keywords":"Fuzzy logic; Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.18368940824968316,"score_gpt":0.33529640886775547,"score_spread":0.1516070006180723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151344568","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020630356,0.0006291249,0.0014069035,0.00051425846,0.0011448103,0.0038553358,0.0003894286,0.000045718072,0.9918081],"genre_scores_gemma":[0.036171716,0.000002015036,0.0005118978,0.000059018974,0.00008695204,0.0005585214,0.000017118735,0.000035598427,0.96255714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962244,0.00013952979,0.0010140992,0.00093057845,0.0013980936,0.00029328666],"domain_scores_gemma":[0.9943431,0.0013316281,0.0010569385,0.002447539,0.00075039477,0.00007040785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023808146,0.00035038395,0.00045807846,0.0003995325,0.00091041607,0.00079188414,0.0016439322,0.00013791228,0.00045174567],"category_scores_gemma":[0.000048626385,0.00016986068,0.00067346066,0.00023049531,0.0012168665,0.00006488952,0.000099979065,0.0001393881,0.0005383355],"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":[0.000010974499,0.000037564518,0.0000013032713,0.000013519564,0.000061169754,9.876858e-7,0.0007890634,0.000052432282,0.00058180594,0.95597976,0.01019624,0.032275192],"study_design_scores_gemma":[0.00011305971,0.000036784226,0.0000025112765,0.00004928095,0.000081378756,0.0000026623075,0.00012314128,0.000031464766,0.00051350234,0.54801565,0.45092,0.00011056458],"about_ca_topic_score_codex":0.0000043326845,"about_ca_topic_score_gemma":0.00073856453,"teacher_disagreement_score":0.44072375,"about_ca_system_score_codex":0.000125824,"about_ca_system_score_gemma":0.00020846717,"threshold_uncertainty_score":0.7636157},"labels":[],"label_agreement":null},{"id":"W3157737197","doi":"10.23977/jeis.2021.61004","title":"Analysis of Hornet Forecast Model based on Fuzzy Theory","year":2021,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Grey System Theory Applications","field":"Decision 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":"Statistics; Range (aeronautics); Sample (material); Class (philosophy); Mathematics; Encoding (memory); Fuzzy logic; Computer science; Artificial intelligence; Econometrics; Ecology; Biology; Engineering","score_opus":0.03510496882100831,"score_gpt":0.33580496674242516,"score_spread":0.30069999792141683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157737197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5513335,0.00016872653,0.42570817,0.000747249,0.00008014244,0.00009173315,0.000024029101,0.0000042890288,0.021842146],"genre_scores_gemma":[0.9982495,0.00003427078,0.0012573003,0.00039836686,0.0000069867456,0.0000012328853,0.0000015431128,0.0000014084774,0.00004939956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969951,0.00009638124,0.0010146233,0.000116284304,0.0016047101,0.00017291463],"domain_scores_gemma":[0.99535716,0.00062757626,0.0011693839,0.00038679992,0.0023501085,0.00010896568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0113718035,0.00007000694,0.000273067,0.0014746969,0.00016365442,0.0002685236,0.0005950677,0.000032451906,0.00004742161],"category_scores_gemma":[0.0025040405,0.000048081005,0.00014090934,0.0049258885,0.00019250189,0.0027447047,0.000048966613,0.00012810275,0.000006877648],"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":[0.000060292426,0.000040844643,0.0006944455,0.0000034700954,0.000037748927,3.431103e-7,0.0006955003,0.3205793,0.0021296628,0.65172875,0.0001722787,0.023857364],"study_design_scores_gemma":[0.00021444481,0.00013467905,0.0041265604,0.00001015389,0.000079357254,0.000014115523,0.0005513289,0.9286991,0.003128884,0.06070211,0.0022725337,0.00006674425],"about_ca_topic_score_codex":3.510174e-7,"about_ca_topic_score_gemma":0.0000026978946,"teacher_disagreement_score":0.6081198,"about_ca_system_score_codex":0.00007003186,"about_ca_system_score_gemma":0.0010760378,"threshold_uncertainty_score":0.39412627},"labels":[],"label_agreement":null},{"id":"W3162148190","doi":"10.31829/2692-4242/biogen2020-3(1)-109","title":"The Forecast of The Number of Inbound Tourists and The Analysis of The Source Market During The Epidemic of Coronavirus Disease","year":2020,"lang":"en","type":"article","venue":"International Journal of Biology and Genetics","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Hubei University; Hubei University of Technology","keywords":"Tourism; Markov chain; Econometrics; Order (exchange); Business; Computer science; Operations research; Economics; Statistics; Geography; Mathematics","score_opus":0.05548839037648869,"score_gpt":0.392103972528415,"score_spread":0.33661558215192633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162148190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903638,0.0013681113,0.0006556457,0.007108745,0.00015418587,0.00011915455,0.00010383021,5.0089164e-7,0.00012604981],"genre_scores_gemma":[0.9993557,0.00027206852,0.0000333829,0.00015895693,0.00005845629,0.000002193661,3.3714585e-7,0.0000027722886,0.0001161468],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767685,0.00081832526,0.00089112105,0.00010282009,0.00044177414,0.000069114576],"domain_scores_gemma":[0.9937406,0.0037090564,0.0016384923,0.00032137215,0.0005526751,0.00003780521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024940167,0.0000709878,0.0002640466,0.000046403737,0.00013684724,0.000020094201,0.0014009612,0.000039819406,0.000047011665],"category_scores_gemma":[0.0018342204,0.000024535622,0.00024874552,0.00032303037,0.0015234229,0.000024394141,0.0003430058,0.00013240395,3.7705775e-7],"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":[0.0007303976,0.000026463753,0.9804395,0.0000064748297,0.0009756533,3.7345777e-7,0.0013888148,0.0012932913,0.0017880927,0.006282347,0.00016527924,0.0069033564],"study_design_scores_gemma":[0.0004601271,0.000018404773,0.9612432,0.000022416902,0.00029444887,0.000029689278,0.0007273997,0.0030625009,0.0011857586,0.030698236,0.002224464,0.000033363664],"about_ca_topic_score_codex":0.000017790719,"about_ca_topic_score_gemma":0.00004607173,"teacher_disagreement_score":0.024415888,"about_ca_system_score_codex":0.0000080966975,"about_ca_system_score_gemma":0.000059663635,"threshold_uncertainty_score":0.56131154},"labels":[],"label_agreement":null},{"id":"W3163861059","doi":"10.23977/aetp.2021.52006","title":"Research on the Model and Application Progress Based on Grey Relational Analysis Theory","year":2021,"lang":"en","type":"article","venue":"Advances in Educational Technology and Psychology","topic":"Grey System Theory Applications","field":"Decision 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":"Grey relational analysis; Gray (unit); Relational model; Computer science; Data mining; Set (abstract data type); Artificial intelligence; Relational database; Mathematics; Statistics","score_opus":0.10319154227531893,"score_gpt":0.5089122119414126,"score_spread":0.40572066966609366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163861059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4622639,0.0058492008,0.14219849,0.34752557,0.00027904846,0.0010033799,0.00005830394,0.00007535934,0.040746745],"genre_scores_gemma":[0.9942425,0.00008761117,0.0028630972,0.0013574159,0.00003072626,0.000822556,0.00002206987,0.000006932873,0.0005670786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972437,0.00082022155,0.000405096,0.00082563696,0.00048751585,0.00021782107],"domain_scores_gemma":[0.9909699,0.0073999576,0.00015309078,0.0009897652,0.0004436936,0.000043575776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047331587,0.000114887735,0.00019972274,0.001710793,0.00040568065,0.00003602845,0.00048709745,0.00021914471,0.00015794422],"category_scores_gemma":[0.0017828972,0.00008235707,0.00003798296,0.0051781447,0.0013756659,0.00012597075,0.00006977246,0.00048895035,0.00007128443],"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":[0.000049220547,0.000187822,0.04660701,0.0000016001726,0.000016389013,5.53246e-7,0.000066976165,0.005122469,0.000026057483,0.9335345,0.00020112084,0.014186296],"study_design_scores_gemma":[0.00014800238,0.000037700214,0.07021422,0.000007842045,0.00001133363,0.000010521956,0.0005096629,0.012697142,0.000030742165,0.90923166,0.007027507,0.00007367037],"about_ca_topic_score_codex":4.3554618e-7,"about_ca_topic_score_gemma":0.000026291571,"teacher_disagreement_score":0.5319786,"about_ca_system_score_codex":0.000034440818,"about_ca_system_score_gemma":0.00013262875,"threshold_uncertainty_score":0.50686985},"labels":[],"label_agreement":null},{"id":"W3171619937","doi":"10.1007/s00500-021-05878-z","title":"RETRACTED ARTICLE: Coopetition analysis in industry upgrade and urban expansion based on fractional derivative gray Lotka–Volterra model","year":2021,"lang":"en","type":"article","venue":"Soft Computing","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":14,"is_retracted":true,"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":"National Natural Science Foundation of China","keywords":"Gray (unit); Upgrade; Volterra equations; Structural equation modeling; Urban expansion; Fractional calculus; Computer science; Mathematical optimization; Econometrics; Mathematics; Applied mathematics; Engineering; Urban planning; Civil engineering; Nonlinear system; Machine learning; Physics","score_opus":0.06264919667399797,"score_gpt":0.3533600226681286,"score_spread":0.2907108259941306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171619937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61058533,0.000011691396,0.38817835,0.00062500086,0.00004718052,0.000098710225,0.0000113425485,0.000037607442,0.00040480462],"genre_scores_gemma":[0.99623805,2.9519595e-7,0.0030405715,0.0004913436,0.00006631966,0.000009751026,0.00003482281,0.000011413868,0.00010741681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705184,0.0005201647,0.0006644815,0.00067864393,0.0008492667,0.00023559002],"domain_scores_gemma":[0.9964573,0.002181812,0.000312914,0.000516032,0.00041394983,0.00011799629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019943684,0.00015482648,0.0003162524,0.0005400693,0.00027825896,0.0002063721,0.00021538258,0.0002453481,0.00013490951],"category_scores_gemma":[0.002261993,0.000143961,0.00009052752,0.0037344932,0.00007940299,0.00022666082,0.00009566175,0.00063248066,0.000033899494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050818897,0.00029485548,0.21494515,0.000008005127,0.00006209852,0.000021517826,0.0013483642,0.75857013,0.011358582,0.008014889,0.00065827527,0.0046672844],"study_design_scores_gemma":[0.00027322763,0.00001298715,0.18221675,0.00004429221,0.000026660893,0.000004097637,0.0006036725,0.81209445,0.0013444064,0.003212331,0.000043522217,0.00012361951],"about_ca_topic_score_codex":0.00001534633,"about_ca_topic_score_gemma":0.00003686278,"teacher_disagreement_score":0.38565275,"about_ca_system_score_codex":0.0000970335,"about_ca_system_score_gemma":0.00014660685,"threshold_uncertainty_score":0.5870559},"labels":[],"label_agreement":null},{"id":"W3192925020","doi":"10.1088/1742-6596/1982/1/012050","title":"Assessment and Information System Establishment of the COVID-19 Impacts and countermeasures: Gray Prediction Model Applied in Analysis and Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Tourism; Tertiary sector of the economy; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Economic impact analysis; Gray (unit); Business; Economic growth; Geography; Economics; Infectious disease (medical specialty); Marketing; Medicine","score_opus":0.05050825503498543,"score_gpt":0.3272831815937721,"score_spread":0.27677492655878666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192925020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7085333,0.00004263219,0.29020122,0.00046365213,0.00007000196,0.00020507946,0.00014082651,0.000006003867,0.00033730795],"genre_scores_gemma":[0.9993817,0.00007964918,0.00045005698,0.00004449543,0.000018117247,0.000011270151,0.000004897499,0.0000025370841,0.000007237642],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978556,0.00020805745,0.00086141424,0.00014461306,0.0008406955,0.000089632624],"domain_scores_gemma":[0.9975519,0.00022485637,0.0010213504,0.00026750908,0.0008236499,0.00011068902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022916372,0.00010219186,0.00035633688,0.00022753338,0.00013697066,0.00034056418,0.00016145314,0.00005078654,0.0000028535774],"category_scores_gemma":[0.00029987842,0.000067654226,0.000054862252,0.00082683493,0.00015641167,0.0016633094,0.00008950251,0.00013313544,1.6194288e-7],"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":[0.00021183584,0.000114201415,0.582317,0.0004883413,0.00043287405,0.0000019917152,0.018653573,0.04503783,0.017986435,0.32393247,0.00014623831,0.010677218],"study_design_scores_gemma":[0.0013163663,0.00012799911,0.8263449,0.00021809938,0.0004787443,0.00012494146,0.035162218,0.07234652,0.0068025826,0.056785963,0.00013442273,0.00015725428],"about_ca_topic_score_codex":0.000013578046,"about_ca_topic_score_gemma":0.000066503955,"teacher_disagreement_score":0.29084846,"about_ca_system_score_codex":0.00013373524,"about_ca_system_score_gemma":0.0005782375,"threshold_uncertainty_score":0.3284068},"labels":[],"label_agreement":null},{"id":"W3196087597","doi":"10.1016/j.apenergy.2021.117540","title":"Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods","year":2021,"lang":"en","type":"article","venue":"Applied Energy","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":72,"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 Natural Science Foundation of China","keywords":"Electricity; Quarter (Canadian coin); Renewable energy; Electricity generation; Coronavirus disease 2019 (COVID-19); Business; Economics; Engineering; Geography; Power (physics); Medicine","score_opus":0.24225964463317962,"score_gpt":0.38588536650179783,"score_spread":0.14362572186861822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196087597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19947155,0.00017361657,0.7966324,0.00021826952,0.00008522995,0.00010223017,0.000013473278,0.000058101763,0.0032451267],"genre_scores_gemma":[0.9845227,0.000016916778,0.014266175,0.00056682935,0.00021812778,0.000039753453,0.00009911031,0.000028683446,0.00024170676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618995,0.0013663927,0.00068213174,0.0007497637,0.000729732,0.00028201158],"domain_scores_gemma":[0.9974583,0.0014151629,0.00034112658,0.00031157924,0.0002867653,0.00018707018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046485267,0.00019764839,0.00028467772,0.00028822158,0.0006586549,0.00031361348,0.00019662501,0.00011184537,0.00012170207],"category_scores_gemma":[0.0010425316,0.000192069,0.000050653314,0.0011003191,0.000060922026,0.00037096205,0.00015025615,0.00025930846,0.000007154864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003217478,0.00003810351,0.0009158595,0.000005236597,0.00002271965,0.0000066409043,0.0002659494,0.789881,0.039996136,0.15536265,0.00007121063,0.013402343],"study_design_scores_gemma":[0.0003943655,0.000014366422,0.00032455154,0.0000049622486,0.000016950435,0.000096308264,0.00028080936,0.915586,0.005755064,0.073504396,0.0038530813,0.00016918103],"about_ca_topic_score_codex":0.00024330926,"about_ca_topic_score_gemma":0.0003872305,"teacher_disagreement_score":0.78505117,"about_ca_system_score_codex":0.00028994435,"about_ca_system_score_gemma":0.0003190904,"threshold_uncertainty_score":0.7832346},"labels":[],"label_agreement":null},{"id":"W321160435","doi":"","title":"Changepoint Detection in Multinomial Logistic Regression with Application to Sky-Cloudiness Conditions in Canada","year":2009,"lang":"en","type":"article","venue":"EGUGA","topic":"Grey System Theory Applications","field":"Decision 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":"Multinomial logistic regression; Cloud cover; Multinomial distribution; Logistic regression; Sky; Statistics; Geography; Mathematics; Remote sensing; Computer science; Econometrics; Environmental science; Meteorology; Cloud computing","score_opus":0.05454024695938879,"score_gpt":0.3505045801121125,"score_spread":0.2959643331527237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W321160435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97245735,0.000011246645,0.023196826,0.0022221103,0.00014989612,0.0010255548,0.000023877812,0.000030454094,0.00088269706],"genre_scores_gemma":[0.9988823,3.9902886e-7,0.00025982858,0.00027996692,0.00006811191,0.00035263874,0.000010580341,0.000007993529,0.0001381766],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99796236,0.00017162894,0.00054596324,0.0005062158,0.0005642173,0.00024960362],"domain_scores_gemma":[0.9985243,0.00039255756,0.00020596127,0.0006089232,0.00014774562,0.000120526354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092649506,0.00013845235,0.00023832567,0.0003742554,0.00011141982,0.000047382942,0.00038697405,0.000054743057,0.000049620146],"category_scores_gemma":[0.00056374347,0.000104507446,0.000020845493,0.0016883198,0.000028840095,0.0001247875,0.000033885837,0.00013325844,0.00010886382],"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":[0.001121874,0.0009103184,0.44889528,0.0000398867,0.000019439085,0.00016568766,0.0054719625,0.08990122,0.10767905,0.021734826,0.0070769293,0.31698352],"study_design_scores_gemma":[0.000889314,0.00009934064,0.9694238,0.000092764574,0.0000053090594,0.000030639963,0.0015072196,0.0074085924,0.0058383113,0.0120979445,0.0022948894,0.00031190642],"about_ca_topic_score_codex":0.23232156,"about_ca_topic_score_gemma":0.9387718,"teacher_disagreement_score":0.7064502,"about_ca_system_score_codex":0.00070719654,"about_ca_system_score_gemma":0.00027790663,"threshold_uncertainty_score":0.7727905},"labels":[],"label_agreement":null},{"id":"W3217440393","doi":"10.1155/2021/2383473","title":"Performance Prediction of the Ferrous Metal Smelting and Rolling Processing Industry in Supply-Side Structural Reform in China","year":2021,"lang":"en","type":"article","venue":"Journal of Mathematics","topic":"Grey System Theory Applications","field":"Decision 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":"Humanities and Social Science Fund of Ministry of Education of China","keywords":"Smelting; Order (exchange); Supply side; China; Nonferrous metal; Business; Economics; Engineering; Metallurgy; Finance; Commerce; Materials science","score_opus":0.05937481019351308,"score_gpt":0.334302296594028,"score_spread":0.2749274864005149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217440393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99852926,0.0001629671,0.00026172848,0.0002643349,0.00008569856,0.000094777046,0.0000033684032,0.0000022610845,0.0005956097],"genre_scores_gemma":[0.99562585,0.0000063392963,0.004249771,0.0000075890084,0.000038093283,0.0000017722969,1.5111101e-7,0.000006208475,0.000064229855],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976971,0.000118831755,0.0012775988,0.00010273399,0.0006889686,0.000114754366],"domain_scores_gemma":[0.9981388,0.00023905073,0.0011281277,0.00022104342,0.000238803,0.000034210887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036608689,0.000080175116,0.00031300503,0.00018112264,0.00008173866,0.00007342549,0.00032169482,0.000120895944,0.00001186074],"category_scores_gemma":[0.0012709057,0.00004603269,0.000055854995,0.0007272647,0.00005332804,0.00044299837,0.000090996116,0.0006062233,5.6357345e-7],"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":[0.000037793652,0.00022340113,0.90527725,0.00052205945,0.000038222133,0.000026031914,0.03546528,0.012762594,0.016482016,0.002734522,0.000012190746,0.026418632],"study_design_scores_gemma":[0.0007481205,0.000049719183,0.7549254,0.0014825426,0.000035658486,0.0016279673,0.031814132,0.15569444,0.009807214,0.0436942,0.000013556355,0.000107057625],"about_ca_topic_score_codex":0.0000055123987,"about_ca_topic_score_gemma":0.000033720757,"teacher_disagreement_score":0.15035187,"about_ca_system_score_codex":0.000104985396,"about_ca_system_score_gemma":0.00014415868,"threshold_uncertainty_score":0.26337722},"labels":[],"label_agreement":null},{"id":"W4210259930","doi":"10.4095/219888","title":"BRDF Normalization of Hyperspectral Image Data","year":2002,"lang":"en","type":"report","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Natural Resources Canada","funders":"","keywords":"Normalization (sociology); Hyperspectral imaging; Bidirectional reflectance distribution function; Computer science; Artificial intelligence; Remote sensing; Computer vision; Geology; Reflectivity; Optics; Physics","score_opus":0.4060748186976839,"score_gpt":0.4714361202166111,"score_spread":0.06536130151892716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210259930","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040373811,0.0004553408,0.044185266,0.00029033108,0.0007011958,0.000554124,0.0010024123,0.000081961756,0.95232564],"genre_scores_gemma":[0.32769868,0.0007836477,0.025580298,0.00010503413,0.0014036121,0.00007892753,0.0021644991,0.00015115702,0.6420342],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99292666,0.00024368278,0.0017009436,0.0010143401,0.0038804617,0.00023389663],"domain_scores_gemma":[0.98962647,0.0008203646,0.0013618674,0.0058213114,0.002266695,0.00010329831],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007665866,0.00023624177,0.0006789445,0.00050061115,0.00008889297,0.00018541647,0.0040752515,0.00026358376,0.010928394],"category_scores_gemma":[0.0064044795,0.00017455353,0.00015298791,0.0011737164,0.00017600677,0.00052469596,0.0007231728,0.00018120628,0.002676761],"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":[0.0000023329005,0.00009754325,0.001200494,0.000046576257,0.000048650832,0.0000052719506,0.00012382376,0.000010197622,0.0005063964,0.008825598,0.9829816,0.006151497],"study_design_scores_gemma":[0.00018039091,0.000031231895,0.002527389,0.00006897542,0.000116437695,0.00018722714,0.0006180565,0.0019230994,0.00064851803,0.010447416,0.98279905,0.0004522106],"about_ca_topic_score_codex":0.00028195602,"about_ca_topic_score_gemma":0.00010259849,"teacher_disagreement_score":0.32729495,"about_ca_system_score_codex":0.000105766245,"about_ca_system_score_gemma":0.0005225186,"threshold_uncertainty_score":0.99809974},"labels":[],"label_agreement":null},{"id":"W4210507036","doi":"10.21203/rs.3.rs-1334110/v1","title":"Short-time traffic flow prediction based on seasonal gray Fourier model","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fourier transform; Fourier series; Gray (unit); Fourier analysis; Nonlinear system; Time series; Statistics; Mathematics; Meteorology; Econometrics; Environmental science; Applied mathematics; Computer science; Geography; Mathematical analysis; Physics","score_opus":0.20367518111449462,"score_gpt":0.4623568796485951,"score_spread":0.2586816985341005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210507036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6889783,0.0005734221,0.16136128,0.013754077,0.0024207102,0.019094389,0.027188394,0.001841933,0.08478749],"genre_scores_gemma":[0.9819756,0.0000070285123,0.0025205049,0.00006336571,0.0003731154,0.0033759496,0.0008091025,0.00009471738,0.010780602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9782602,0.00454185,0.0010950213,0.0022644051,0.012873914,0.0009645857],"domain_scores_gemma":[0.98822075,0.0049849018,0.00021616291,0.004255913,0.0017492536,0.0005730111],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.027082223,0.00044597595,0.0006637787,0.0018153923,0.0014031667,0.00085738086,0.0035449804,0.0004832265,0.0069860597],"category_scores_gemma":[0.0042850547,0.00038224735,0.0005886559,0.0022172742,0.0003316015,0.00017928764,0.0018982183,0.0031056034,0.0024590788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017615649,0.0003041985,0.00054599147,0.000067980516,0.000026269872,0.0000149983,0.00031717418,0.92033535,0.000041634994,0.0009952362,0.054021567,0.023153424],"study_design_scores_gemma":[0.00022917343,0.00016619518,0.0017646549,0.00014599845,0.000013589581,0.0000028255754,0.00034021493,0.96261555,0.000017174254,0.02109922,0.0133201815,0.00028521992],"about_ca_topic_score_codex":0.000011128777,"about_ca_topic_score_gemma":0.000017062055,"teacher_disagreement_score":0.2929973,"about_ca_system_score_codex":0.00098354,"about_ca_system_score_gemma":0.0016946528,"threshold_uncertainty_score":0.9998969},"labels":[],"label_agreement":null},{"id":"W4213024052","doi":"10.55365/1923.x2021.19.24","title":"Combining Grey Theory and Data Envelopment Analysis to Evaluate the Business Performance of the Vietnamese Seafood Industry","year":2021,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Grey System Theory Applications","field":"Decision 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":"Vietnamese; Data envelopment analysis; Order (exchange); Process (computing); Business; Econometrics; Computer science; Empirical research; Industrial organization; Operations management; Operations research; Environmental economics; Economics; Marketing; Statistics; Mathematics; Finance","score_opus":0.10662973031721959,"score_gpt":0.3620249374847295,"score_spread":0.25539520716750996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213024052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708959,0.02509035,0.0001092543,0.0030335097,0.000054753236,0.00031938183,0.000085112435,0.0000011546178,0.00041060918],"genre_scores_gemma":[0.92977023,0.06921759,0.00025175058,0.00051566696,0.000008353772,0.000025393354,0.000005035357,0.0000039742367,0.0002019783],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99843115,0.00028961457,0.0006892669,0.00037006542,0.00012867815,0.000091226626],"domain_scores_gemma":[0.9969774,0.00054176856,0.00042224198,0.0017395845,0.00029396897,0.000025043066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00800663,0.000089553876,0.00045503187,0.000042949898,0.00013595597,0.000045243272,0.0009514516,0.000038310343,0.000039686136],"category_scores_gemma":[0.00119363,0.000049906816,0.000054633387,0.0010513488,0.00013727903,0.00013358305,0.00080353214,0.00009361118,0.000007821549],"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":[0.00005356696,0.00013589031,0.07588139,0.0016084302,0.0007111993,7.7218186e-7,0.0018080863,0.003011135,0.00010438398,0.31083363,0.0015586257,0.60429287],"study_design_scores_gemma":[0.00046992608,0.000044492714,0.77185416,0.0036870278,0.000898023,0.000038458,0.0009859931,0.017178057,0.00068194646,0.024414435,0.1793098,0.00043769387],"about_ca_topic_score_codex":0.0000026272378,"about_ca_topic_score_gemma":0.000020511356,"teacher_disagreement_score":0.69597274,"about_ca_system_score_codex":0.000010665638,"about_ca_system_score_gemma":0.00015908697,"threshold_uncertainty_score":0.27749544},"labels":[],"label_agreement":null},{"id":"W4213296590","doi":"10.1007/s11356-022-19178-y","title":"Yard waste prediction from estimated municipal solid waste using the grey theory to achieve a zero-waste strategy","year":2022,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":27,"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 Regina","funders":"University of Regina","keywords":"Yard; Municipal solid waste; Zero waste; Waste management; Engineering; Environmental science","score_opus":0.1940928670818819,"score_gpt":0.43376220150442135,"score_spread":0.23966933442253946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213296590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932089,0.00015106096,0.0029365253,0.0011302477,0.00021809977,0.00094773626,0.0005601143,0.000025052412,0.000822262],"genre_scores_gemma":[0.9981808,0.000011120788,0.00015744357,0.00020029713,0.00009899949,0.00016868731,0.000012277327,0.000014093308,0.0011562797],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990627,0.002222824,0.0005452636,0.0009516887,0.004917597,0.00073561544],"domain_scores_gemma":[0.9976071,0.0007010533,0.0001603205,0.0010815397,0.00008023924,0.000369707],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.022736847,0.00017347733,0.00019615707,0.0005612948,0.0054191807,0.00058233883,0.0019228371,0.000052155865,0.0009390349],"category_scores_gemma":[0.000605968,0.00012414916,0.000058918835,0.002775411,0.0021503437,0.0006487409,0.0021423209,0.0005307961,0.00025402164],"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":[0.00040792962,0.00020200087,0.0020446023,0.0000021838237,0.000023542249,0.000008395576,0.00964583,0.14528489,0.80966485,0.0075083924,0.0014624377,0.023744954],"study_design_scores_gemma":[0.0012024383,0.0015323269,0.02442331,0.000054412933,0.000044650136,0.00012960365,0.34137675,0.46210977,0.03588667,0.12341735,0.009104566,0.00071814866],"about_ca_topic_score_codex":0.00038582704,"about_ca_topic_score_gemma":0.000029578612,"teacher_disagreement_score":0.77377814,"about_ca_system_score_codex":0.00065623136,"about_ca_system_score_gemma":0.00021871747,"threshold_uncertainty_score":0.99997425},"labels":[],"label_agreement":null},{"id":"W4234064007","doi":"10.1190/1.1817690","title":"3D AVO Crossplotting — an effective visualization technique","year":2003,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Visualization; Computer science; Data visualization; Artificial intelligence","score_opus":0.06332324925174425,"score_gpt":0.43774783268995887,"score_spread":0.3744245834382146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234064007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113001876,0.000008806697,0.80121106,0.000025923151,0.00009103325,0.0010538945,0.000002703529,0.00022669583,0.08437802],"genre_scores_gemma":[0.98797363,1.863695e-7,0.0095559945,0.00012601777,0.00002700868,0.000423106,0.0000021939518,0.000016508013,0.0018753656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972304,0.0010328564,0.00047045667,0.00048735432,0.00058989704,0.00018899882],"domain_scores_gemma":[0.9973658,0.0011552369,0.00019232597,0.00077040424,0.0004150255,0.00010121016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0068746987,0.00011410094,0.00017083701,0.00019936572,0.00031832446,0.0002896296,0.00041976475,0.00008746883,0.00078961276],"category_scores_gemma":[0.0037609946,0.000085136744,0.00004929901,0.00114021,0.000074572294,0.00046453744,0.000038531893,0.0000682468,0.0010661855],"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":[0.000005861147,0.00009858904,0.009674773,0.0000036303943,0.0000057155194,0.0000010736547,0.0006795696,0.00018392935,0.029419253,0.9483363,0.0006561537,0.010935135],"study_design_scores_gemma":[0.0005287443,0.00023992242,0.0155338645,0.00003102535,0.000017203687,0.00009135455,0.0027580857,0.0067193354,0.34613752,0.55554575,0.07177661,0.00062058243],"about_ca_topic_score_codex":0.000014655866,"about_ca_topic_score_gemma":0.000023506625,"teacher_disagreement_score":0.87497175,"about_ca_system_score_codex":0.000046951664,"about_ca_system_score_gemma":0.00003327375,"threshold_uncertainty_score":0.9997116},"labels":[],"label_agreement":null},{"id":"W4239111643","doi":"10.1007/978-1-4939-7131-2_101247","title":"Spatiotemporal Outlier","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Computer science; Artificial intelligence","score_opus":0.18770476389089646,"score_gpt":0.3885851172241107,"score_spread":0.20088035333321425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239111643","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004456124,0.000040530947,0.007899837,0.0005839252,0.0005398235,0.0004185663,0.000062853695,0.00011663579,0.99029326],"genre_scores_gemma":[0.012009885,0.0000015170142,0.0011335348,0.00032552474,0.000540319,0.0000161173,0.00001734678,0.00004696173,0.9859088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964282,0.000051254032,0.0009770565,0.0008039171,0.001555419,0.00018415046],"domain_scores_gemma":[0.99574804,0.00063070335,0.0005898219,0.0021774874,0.0007022011,0.0001517298],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002394522,0.0002829955,0.0004656695,0.0003555529,0.00013528737,0.00024209527,0.0013769404,0.00037092352,0.119498685],"category_scores_gemma":[0.00037423853,0.0001982763,0.00024351686,0.000082036226,0.00027281814,0.00012581813,0.0002504674,0.00018015002,0.19290495],"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":[0.0000030440046,0.00000355179,0.00002359083,0.000001166474,0.0000130943345,0.0000015347823,0.00007505138,2.0467273e-7,0.000002714924,0.6627757,0.3323334,0.0047669997],"study_design_scores_gemma":[0.000034128825,0.000013940104,0.000023898965,0.000009092668,0.0000071342747,0.0000042305883,0.00002030395,0.000012880954,0.000013040743,0.4609417,0.53878796,0.00013169537],"about_ca_topic_score_codex":0.000007754997,"about_ca_topic_score_gemma":0.000091069196,"teacher_disagreement_score":0.20645459,"about_ca_system_score_codex":0.000051532406,"about_ca_system_score_gemma":0.000109538705,"threshold_uncertainty_score":0.88130623},"labels":[],"label_agreement":null},{"id":"W4239936846","doi":"10.5539/jsd.v1n2p55","title":"Research on Prediction of China’s Population Development from 2008 to 2050","year":2009,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Grey System Theory Applications","field":"Decision 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":"China; Population; Statistics; Econometrics; Geography; Mathematics; Demography; Archaeology","score_opus":0.1058648676847015,"score_gpt":0.40260871420692984,"score_spread":0.29674384652222835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239936846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889383,0.00007807056,0.005858251,0.0017665836,0.00019829597,0.00059386884,0.0000046528703,0.000012951223,0.0025490525],"genre_scores_gemma":[0.98243886,0.0000035179544,0.013355345,0.0000836439,0.00011432597,0.000020410394,0.000011237816,0.000010675829,0.0039619654],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99314,0.00042133883,0.0020999212,0.00035412493,0.003519889,0.00046474353],"domain_scores_gemma":[0.9947802,0.0004934046,0.0008514118,0.00047509535,0.0031103378,0.00028954237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012948479,0.00016659638,0.0004333888,0.0019316409,0.00043303863,0.00015380986,0.00091178284,0.000104437226,0.00017833432],"category_scores_gemma":[0.0017755517,0.00012741493,0.000077950084,0.0020674074,0.00003158842,0.00042470152,0.00012812884,0.00031790536,0.00015508835],"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":[0.0034393666,0.003830664,0.057452444,0.0001266487,0.00047780416,0.00053183443,0.09077564,0.032714117,0.009789109,0.086232364,0.2399202,0.4747098],"study_design_scores_gemma":[0.00038504784,0.000278438,0.88098407,0.000116803916,0.00000506396,0.000013007757,0.0119700385,0.000024665287,0.004619352,0.023912637,0.077576235,0.00011463522],"about_ca_topic_score_codex":0.000059830316,"about_ca_topic_score_gemma":0.000010457736,"teacher_disagreement_score":0.8235316,"about_ca_system_score_codex":0.0011754823,"about_ca_system_score_gemma":0.00089653616,"threshold_uncertainty_score":0.519583},"labels":[],"label_agreement":null},{"id":"W4255235439","doi":"10.1007/978-1-4614-6170-8_110002","title":"Spatiotemporal Outlier","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.1495182461640933,"score_gpt":0.3689327607088389,"score_spread":0.21941451454474561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255235439","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000106589005,0.000035970243,0.049272694,0.00088259747,0.00040676107,0.00037130248,0.00003278595,0.00011253673,0.9488747],"genre_scores_gemma":[0.03985153,0.0000013382393,0.00081733847,0.00044582988,0.00032702828,0.000017479068,0.000017745519,0.00004672924,0.958475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964242,0.00007115725,0.0010142457,0.00076569413,0.001547333,0.00017739412],"domain_scores_gemma":[0.9955822,0.0010119708,0.00062778714,0.0021722568,0.0004485636,0.00015719794],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0028829407,0.00028703624,0.00054386887,0.0003479928,0.000116981275,0.00022499982,0.0013240641,0.00036301688,0.02560092],"category_scores_gemma":[0.0004398044,0.00020186679,0.0002682167,0.00006472792,0.0001459614,0.00006668478,0.00020247919,0.00022171758,0.09671542],"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":[0.0000019531647,0.00000203758,0.000029528619,0.0000016159786,0.000009844046,9.2885875e-7,0.00002325612,0.0000017514266,0.000002237175,0.8470946,0.13491572,0.017916508],"study_design_scores_gemma":[0.0000402556,0.000009523455,0.000031093507,0.000008998188,0.0000074530803,0.0000037673012,0.0000070371534,0.000035724213,0.0000064978285,0.4280552,0.57165277,0.00014165844],"about_ca_topic_score_codex":0.00000951492,"about_ca_topic_score_gemma":0.00006697876,"teacher_disagreement_score":0.43673706,"about_ca_system_score_codex":0.00004346752,"about_ca_system_score_gemma":0.00007651579,"threshold_uncertainty_score":0.9752898},"labels":[],"label_agreement":null},{"id":"W4285705591","doi":"10.46855/energy-proceedings-2191","title":"Forecasting Annual Electricity Consumption of Office Buildings Using A Modified Grey Interval Model","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"National Key Research and Development Program of China","keywords":"Interval (graph theory); Consumption (sociology); Electricity; Environmental science; Architectural engineering; Operations research; Econometrics; Business; Economics; Engineering; Mathematics; Electrical engineering; Art","score_opus":0.3568751125242841,"score_gpt":0.41025747971527227,"score_spread":0.053382367190988156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285705591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48140535,0.00002848866,0.51608205,0.000105532454,0.0001242469,0.00056598504,0.0001918005,0.00007884471,0.0014177152],"genre_scores_gemma":[0.9605329,0.0000021375195,0.03892744,0.00009291572,0.000081527236,0.000047170986,0.000016257905,0.000034337103,0.00026533275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994299,0.00043787138,0.0020315568,0.0013480888,0.0015165005,0.0003669875],"domain_scores_gemma":[0.99365133,0.0014166633,0.0019211124,0.0012189928,0.0015951057,0.00019678078],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0041561993,0.0003900493,0.0009936882,0.00056393445,0.00016421314,0.00025633664,0.0019364222,0.00038640935,0.00012365148],"category_scores_gemma":[0.0043662703,0.00034134055,0.00039803886,0.0008480466,0.00016839433,0.00031949003,0.0018999405,0.00067779445,0.0000737086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045165588,0.00021619484,0.0020332695,0.0004050289,0.0001910332,0.0000059176145,0.004161898,0.885036,0.029211152,0.06676763,0.0028414563,0.008678765],"study_design_scores_gemma":[0.00019282376,0.000024199811,0.000107167914,0.00011356233,0.000059367503,0.000017503433,0.00030893806,0.90141696,0.0027501672,0.094710484,0.000024347944,0.00027445226],"about_ca_topic_score_codex":0.00026160924,"about_ca_topic_score_gemma":0.00003469686,"teacher_disagreement_score":0.47912753,"about_ca_system_score_codex":0.00014801884,"about_ca_system_score_gemma":0.00039788315,"threshold_uncertainty_score":0.99990386},"labels":[],"label_agreement":null},{"id":"W4294704757","doi":"10.1111/anzs.12373","title":"Penalised, post‐pretest, and post‐shrinkage strategies in nonlinear growth models","year":2022,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Grey System Theory Applications","field":"Decision 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":"Brock University","funders":"","keywords":"Lasso (programming language); Shrinkage; Subspace topology; Mathematics; Estimator; Nonlinear system; Estimation; Shrinkage estimator; Statistics; Econometrics; Applied mathematics; Computer science; Mathematical analysis; Engineering","score_opus":0.05821540037106006,"score_gpt":0.3370547767390908,"score_spread":0.27883937636803074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294704757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9742163,0.00014644803,0.012484977,0.008845759,0.00043308098,0.0004419674,0.0024956563,0.000019262507,0.00091659476],"genre_scores_gemma":[0.9760626,0.000025951344,0.017809391,0.0001862108,0.00012211027,0.0000048409584,0.000024300229,0.000021254586,0.005743359],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99639934,0.00041806532,0.001316944,0.00028733382,0.0012612198,0.00031712046],"domain_scores_gemma":[0.9966963,0.0011383157,0.000857139,0.00032319888,0.00066194875,0.0003230895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024746547,0.00019189589,0.00045533775,0.00048223912,0.00018971636,0.0003757574,0.00079936755,0.000053624604,0.0006146062],"category_scores_gemma":[0.00052860007,0.0001575895,0.00007574856,0.00057930104,0.00011121176,0.00063276704,0.0001484637,0.00051933056,0.000021287673],"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":[0.0016547645,0.0010038791,0.062447175,0.00011101557,0.00029098138,0.002603915,0.025121888,0.054985862,0.009678972,0.41099814,0.41688582,0.014217602],"study_design_scores_gemma":[0.0027486705,0.0014523356,0.06561114,0.00006132819,0.00008662612,0.0019443932,0.01722937,0.0027042977,0.00010592441,0.89177716,0.015806738,0.0004720255],"about_ca_topic_score_codex":0.0004755123,"about_ca_topic_score_gemma":0.00013880196,"teacher_disagreement_score":0.48077902,"about_ca_system_score_codex":0.00007031072,"about_ca_system_score_gemma":0.0005045518,"threshold_uncertainty_score":0.6729507},"labels":[],"label_agreement":null},{"id":"W4310230915","doi":"10.5539/ies.v15n6p109","title":"A Comparative Study on the Forecast Models of the Enrollment Proportion of General Education and Vocational Education","year":2022,"lang":"en","type":"article","venue":"International Education Studies","topic":"Grey System Theory Applications","field":"Decision 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":"Vocational education; Exponential smoothing; Autoregressive integrated moving average; Econometrics; Mathematics education; General education; Higher education; China; Sample (material); Demographic economics; Statistics; Psychology; Economic growth; Geography; Economics; Mathematics; Time series","score_opus":0.30167297083723726,"score_gpt":0.49579017787537744,"score_spread":0.19411720703814017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310230915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97663635,0.0005139447,0.0002309616,0.0080059515,0.0033360217,0.0017250075,0.000056284003,0.000009370157,0.009486082],"genre_scores_gemma":[0.9906205,0.000007871352,0.00028033613,0.0004027162,0.00014416856,0.0028689713,0.000020362959,0.000008370588,0.005646734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959812,0.0006821255,0.00096734345,0.00040176284,0.001871621,0.00009595552],"domain_scores_gemma":[0.9931764,0.0008444348,0.0012535827,0.00062789913,0.0040632337,0.000034449462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022497596,0.00015414886,0.00026901579,0.00033940325,0.0005655278,0.000054474047,0.00088133634,0.00001992763,0.00012379684],"category_scores_gemma":[0.0010225105,0.00009385961,0.00009696693,0.00076564605,0.0002504306,0.00022976633,0.00036251495,0.00014660058,0.000009062835],"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":[0.00012925586,0.008229567,0.034253146,0.000017022525,0.00042684443,3.0052092e-8,0.052469146,0.0037556656,0.001135106,0.8287482,0.03947481,0.03136116],"study_design_scores_gemma":[0.0003446163,0.00026074017,0.2165999,0.00006731151,0.00007960457,0.00002072875,0.36261716,0.0012722882,0.0012712763,0.4041529,0.013103098,0.00021039801],"about_ca_topic_score_codex":0.0001005724,"about_ca_topic_score_gemma":0.000029958861,"teacher_disagreement_score":0.42459536,"about_ca_system_score_codex":0.0002934162,"about_ca_system_score_gemma":0.0014744463,"threshold_uncertainty_score":0.434964},"labels":[],"label_agreement":null},{"id":"W4313368301","doi":"10.1016/j.apenergy.2022.120189","title":"A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system","year":2022,"lang":"en","type":"article","venue":"Applied Energy","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multivariable calculus; Benchmark (surveying); Generalizability theory; Nonlinear system; Compensation (psychology); Computer science; Benchmarking; Energy (signal processing); Mathematical optimization; Artificial intelligence; Engineering; Control engineering; Mathematics; Statistics; Economics","score_opus":0.04812053865554027,"score_gpt":0.2793617637733157,"score_spread":0.2312412251177754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313368301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050799306,0.000006932734,0.9652302,0.0003459645,0.00015921517,0.0006486436,0.00017252931,0.0000778187,0.028278716],"genre_scores_gemma":[0.98812175,1.9065588e-7,0.007243243,0.00064806954,0.000058852136,0.0031676197,0.00014695598,0.00003188665,0.00058140885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733955,0.00028066194,0.000709258,0.00074653886,0.0006147037,0.00030930468],"domain_scores_gemma":[0.9951285,0.0030480693,0.000606351,0.001046563,0.00009097308,0.000079543206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020710137,0.00023294849,0.0003167303,0.00021040434,0.0011081072,0.00015299124,0.0009496359,0.00005493482,0.00012340727],"category_scores_gemma":[0.00008523327,0.00017763511,0.00014611713,0.0003389531,0.00006129586,0.00010651279,0.00021185643,0.00015387779,0.00003796327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012579926,0.000072525654,0.000003836597,0.0000035433877,0.000016572045,3.408407e-7,0.00011806077,0.75196177,0.00023239915,0.23894851,0.00018838038,0.008328271],"study_design_scores_gemma":[0.0005704748,0.000069298374,0.000008076281,0.0000069476137,0.000018523631,0.0000036757158,0.0020640935,0.9375664,0.0002608593,0.03960773,0.01964422,0.00017967499],"about_ca_topic_score_codex":0.000097766126,"about_ca_topic_score_gemma":0.000043568332,"teacher_disagreement_score":0.9830418,"about_ca_system_score_codex":0.00056193693,"about_ca_system_score_gemma":0.00009386008,"threshold_uncertainty_score":0.85227764},"labels":[],"label_agreement":null},{"id":"W4317735665","doi":"10.1007/s10668-023-02935-5","title":"A novel method for forecasting renewable energy consumption structure based on compositional data: evidence from China, the USA, and Canada","year":2023,"lang":"en","type":"article","venue":"Environment Development and Sustainability","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":6,"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":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"China; Renewable energy; Consumption (sociology); Economics; Environmental science; Natural resource economics; Engineering; Geography; Sociology; Social science","score_opus":0.11956848632492136,"score_gpt":0.33976281775429695,"score_spread":0.2201943314293756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317735665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54850894,0.000037607213,0.4477178,0.0027681435,0.000056256602,0.00055428914,0.0003332846,0.0000107509595,0.000012925094],"genre_scores_gemma":[0.966446,0.0000034482223,0.032535367,0.00015383933,0.000027568896,0.00015821871,0.0002347935,0.000007581749,0.0004332248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976217,0.00025373095,0.0004262477,0.0007502798,0.00071413844,0.00023386601],"domain_scores_gemma":[0.993475,0.0054803304,0.00016636885,0.0007334938,0.000053929645,0.000090853086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034628946,0.00015697842,0.00019208055,0.000061084,0.00075768627,0.0001266828,0.00045561919,0.000049485057,0.00012058379],"category_scores_gemma":[0.001186711,0.00010696236,0.000018532246,0.00014001415,0.00012910679,0.00016981992,0.0003400176,0.000069715614,0.0000017011538],"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":[0.0004443012,0.00013151254,0.7780882,0.00015026322,0.000098365526,0.000008483984,0.002236268,0.12594551,0.0023236515,0.0024810934,0.009224253,0.07886807],"study_design_scores_gemma":[0.0003300983,0.000019272145,0.77175117,0.000022191683,0.000015489946,0.000003168553,0.0006380988,0.1917162,0.0005391482,0.015014967,0.01976361,0.00018661695],"about_ca_topic_score_codex":0.07505878,"about_ca_topic_score_gemma":0.17259447,"teacher_disagreement_score":0.417937,"about_ca_system_score_codex":0.0003609625,"about_ca_system_score_gemma":0.00044166012,"threshold_uncertainty_score":0.9311005},"labels":[],"label_agreement":null},{"id":"W4320501062","doi":"10.2991/978-94-6463-102-9_127","title":"Research on the Stock Price Forecasting of Netflix Based on Linear Regression, Decision Tree, and Gradient Boosting Models","year":2023,"lang":"en","type":"book-chapter","venue":"Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Gradient boosting; Decision tree; Boosting (machine learning); Econometrics; Linear regression; Computer science; Stock (firearms); Regression; Artificial intelligence; Machine learning; Mathematics; Statistics; Geography; Random forest","score_opus":0.26625508698025124,"score_gpt":0.38758323441779385,"score_spread":0.12132814743754261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320501062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22263888,0.0017175574,0.44715855,0.04836697,0.03523125,0.0175069,0.00037753538,0.0013615804,0.22564079],"genre_scores_gemma":[0.8703713,0.001195157,0.106018774,0.0010273602,0.0020472659,0.00029739304,0.0000416534,0.00027437674,0.018726729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9766512,0.0017602514,0.004105918,0.005302548,0.010118383,0.002061709],"domain_scores_gemma":[0.9533212,0.039925743,0.0022612803,0.0029700403,0.001063037,0.00045866473],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.0323146,0.0014154592,0.0022255685,0.0077534844,0.0030172993,0.0017959157,0.010128847,0.0007786755,0.000056649704],"category_scores_gemma":[0.0006165717,0.0008132983,0.00044319913,0.007624112,0.0047447355,0.001069888,0.0030582466,0.0016168429,0.00026158465],"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":[0.00008709875,0.00019803391,0.0004914719,0.000058088695,0.000025456942,0.00029951154,0.0008192024,0.22793467,0.00000933122,0.72123486,0.007400256,0.041442025],"study_design_scores_gemma":[0.00071838795,0.0013929157,0.0024744626,0.006203023,0.000014968097,0.00008728148,0.00003358247,0.87409985,0.00009982769,0.101170994,0.0127195185,0.0009851631],"about_ca_topic_score_codex":0.0003370366,"about_ca_topic_score_gemma":0.0010310606,"teacher_disagreement_score":0.64773244,"about_ca_system_score_codex":0.00031718923,"about_ca_system_score_gemma":0.00081951823,"threshold_uncertainty_score":0.9998596},"labels":[],"label_agreement":null},{"id":"W4380480931","doi":"10.1057/s41599-023-01834-4","title":"Measurement of the coupling coordination relationship between the structures of secondary vocational school programs and industries in China","year":2023,"lang":"en","type":"article","venue":"Humanities and Social Sciences Communications","topic":"Grey System Theory Applications","field":"Decision 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":"McGill University","funders":"National Social Science Fund of China","keywords":"Vocational education; China; Secondary sector of the economy; Sustainable development; Business; Quality (philosophy); Psychology; Mathematics education; Political science; Pedagogy; Economics; Physics; Economy","score_opus":0.43085945419764865,"score_gpt":0.40223219372889446,"score_spread":0.02862726046875419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380480931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98735416,0.00035668784,0.000046251334,0.00869299,0.000035869925,0.00044805443,0.000023624782,0.000014532156,0.0030278463],"genre_scores_gemma":[0.9997236,0.000010758996,0.00005876844,0.000022644905,0.000020984628,0.00006057144,0.000006491394,0.0000028430584,0.000093312585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99809635,0.0003689184,0.00050522876,0.00014407546,0.00077817484,0.00010724509],"domain_scores_gemma":[0.9973947,0.001464611,0.00038452205,0.00044487984,0.00029825102,0.000013063508],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0055006566,0.00006589347,0.00014207803,0.00016933841,0.0022365428,0.00017295261,0.0011960447,0.00005018462,0.000009886314],"category_scores_gemma":[0.0012742516,0.000040372826,0.000032220356,0.0014488795,0.0026834474,0.00022078944,0.0004184607,0.00019315226,0.0000013741555],"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":[8.216005e-7,0.000009262602,0.47765225,0.0000063200496,0.000004822024,4.0917687e-9,0.005989057,0.00002107888,0.000024936273,0.5138946,0.00016774159,0.0022291043],"study_design_scores_gemma":[0.000065248765,0.000009953283,0.8233218,0.00001972862,0.000007335143,2.603208e-7,0.019151213,0.0003082397,0.000007896089,0.15632823,0.00074373395,0.00003633452],"about_ca_topic_score_codex":0.00023801021,"about_ca_topic_score_gemma":0.001262718,"teacher_disagreement_score":0.3575664,"about_ca_system_score_codex":0.000032425956,"about_ca_system_score_gemma":0.00021982985,"threshold_uncertainty_score":0.9990624},"labels":[],"label_agreement":null},{"id":"W4380536670","doi":"10.5267/j.ijdns.2023.6.002","title":"Global gold prices forecasting using Bayesian nonparametric quantile generalized additive model","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Grey System Theory Applications","field":"Decision 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":"Universitas Padjadjaran","keywords":"Econometrics; Outlier; Volatility (finance); Index (typography); Economics; Gold as an investment; Nonparametric statistics; Quantile; Sharpe ratio; Bayesian probability; Statistics; Mathematics; Financial economics; Computer science","score_opus":0.25273497463981237,"score_gpt":0.45227647696647455,"score_spread":0.19954150232666218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380536670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5897438,0.00015298268,0.40706456,0.00045981514,0.0010987219,0.00011443316,0.0004359084,0.000017900511,0.000911872],"genre_scores_gemma":[0.9527548,0.000047220245,0.046569537,0.000108768014,0.00044406415,0.0000013885676,0.000012165415,0.000004749507,0.00005732671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959811,0.000089377696,0.0008276541,0.00045280354,0.002355407,0.00029368038],"domain_scores_gemma":[0.9961468,0.0010073514,0.0009633295,0.00048729847,0.0012126538,0.00018256817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008814989,0.00010803474,0.00023073447,0.0005802732,0.00026526317,0.0007733086,0.0043951957,0.000034887908,0.000026762198],"category_scores_gemma":[0.0028042498,0.000080550584,0.000054572392,0.0044158148,0.0003468523,0.0024323387,0.0011824771,0.0000989612,0.000018460265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019203425,0.00009449894,0.03649037,0.000004264373,0.00013711608,0.00011325265,0.00057883805,0.6587208,0.0011897532,0.05143512,0.044268973,0.206775],"study_design_scores_gemma":[0.00020126233,0.000018460136,0.0027370723,0.000046238973,0.000011856864,0.0002455442,0.00021279561,0.9510314,0.00001985655,0.043886285,0.0015046087,0.000084609965],"about_ca_topic_score_codex":0.00001673127,"about_ca_topic_score_gemma":0.00002118277,"teacher_disagreement_score":0.36301097,"about_ca_system_score_codex":0.000093518116,"about_ca_system_score_gemma":0.000372358,"threshold_uncertainty_score":0.816744},"labels":[],"label_agreement":null},{"id":"W4382239615","doi":"10.1007/978-981-19-7826-5_130","title":"Tourism Prediction in Canada and the US Based on a Modified GM (1, 1) Model Considering COVID-19 Effect","year":2023,"lang":"en","type":"book-chapter","venue":"Applied economics and policy studies","topic":"Grey System Theory Applications","field":"Decision 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":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Tourism; Coronavirus disease 2019 (COVID-19); Econometrics; Value (mathematics); Harmony (color); Harmony search; Sample (material); Operations research; Mathematical optimization; Computer science; Economics; Geography; Mathematics; Machine learning","score_opus":0.10714709288407546,"score_gpt":0.3301787518718052,"score_spread":0.2230316589877297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382239615","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2859818,0.001380476,0.0008506804,0.031906422,0.00079685263,0.008165621,0.0030569318,0.00019968562,0.66766155],"genre_scores_gemma":[0.9785386,0.0008450566,0.00002208325,0.0033193151,0.00014966367,0.0004924004,0.000009396015,0.00004528628,0.016578242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980677,0.000064614556,0.0007553614,0.0006689521,0.00020297707,0.00024044621],"domain_scores_gemma":[0.99169415,0.007124418,0.00040026178,0.0006014171,0.000033755798,0.00014597038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023269136,0.00034083717,0.00085436093,0.0003813178,0.00036211047,0.00010798161,0.0002656066,0.00013016429,0.0000046663113],"category_scores_gemma":[0.0005718845,0.00024079306,0.00006667282,0.000074295625,0.0003556592,0.000029537106,0.0002455226,0.0002272554,0.000023656983],"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":[0.0001386752,0.0000017873997,0.00009180346,0.000037128095,0.00010129857,0.0000017987801,0.00064401963,0.20350127,5.3598046e-7,0.79085594,0.0032278798,0.0013978394],"study_design_scores_gemma":[0.0019050053,0.000023332448,0.00022761086,0.00003598233,0.000052407104,0.0000045081815,0.000379589,0.14067484,0.0000033912452,0.8465789,0.0098108575,0.0003035934],"about_ca_topic_score_codex":0.15380253,"about_ca_topic_score_gemma":0.6807334,"teacher_disagreement_score":0.69255674,"about_ca_system_score_codex":0.00062096067,"about_ca_system_score_gemma":0.0009639918,"threshold_uncertainty_score":0.9819256},"labels":[],"label_agreement":null},{"id":"W4383343184","doi":"10.21203/rs.3.rs-3098016/v1","title":"Exploring the mechanism of grey forecasting models: A perspective from dynamic system modelling","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Grey System Theory Applications","field":"Decision 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; Nanjing University of Aeronautics and Astronautics; Fundamental Research Funds for the Central Universities; Nanjing University; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Mechanism (biology); Perspective (graphical); Operations research; Artificial intelligence; Engineering","score_opus":0.7578104377828704,"score_gpt":0.4871047573199071,"score_spread":0.2707056804629633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383343184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25906956,0.0005500915,0.7299211,0.00080204784,0.0007882805,0.0033950394,0.0013723245,0.00031286027,0.0037886868],"genre_scores_gemma":[0.9942931,0.00007302263,0.0021435462,0.0000021444848,0.00022105055,0.0025945397,0.0000322731,0.00011185853,0.0005284622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98639244,0.003219428,0.0015425775,0.0018893222,0.006102,0.00085422554],"domain_scores_gemma":[0.9761323,0.013349598,0.00075429404,0.003874661,0.0056670196,0.00022213657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.024840651,0.00039480513,0.00089261355,0.0014205842,0.00093125104,0.000751238,0.004443427,0.00027369746,0.000028418182],"category_scores_gemma":[0.0041964687,0.0002741665,0.0005036694,0.0024527395,0.0002918888,0.00047365265,0.0044589215,0.0020041873,0.000469034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004110998,0.000029534565,0.000013975011,0.0002521701,0.000108735716,0.000017337774,0.016686745,0.5436094,0.00008450409,0.43820465,0.00004663648,0.00090524706],"study_design_scores_gemma":[0.00005819577,0.00001626577,0.000018427656,0.0010090405,0.000012256442,0.000002189922,0.09234431,0.48269778,0.00006437411,0.42365754,0.000006906279,0.00011271083],"about_ca_topic_score_codex":0.0053249463,"about_ca_topic_score_gemma":0.0006817031,"teacher_disagreement_score":0.73522353,"about_ca_system_score_codex":0.0012531386,"about_ca_system_score_gemma":0.00059703673,"threshold_uncertainty_score":0.99997103},"labels":[],"label_agreement":null},{"id":"W4385863815","doi":"10.18687/laccei2023.1.1.156","title":"Applying the Grey Systems Theory to Assess Air Quality in La Oroya - Peru","year":2023,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Computer science; Philosophy; Epistemology","score_opus":0.2902415485768638,"score_gpt":0.4729803751991961,"score_spread":0.18273882662233232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385863815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69197094,0.00007362769,0.08006706,0.0050863237,0.0009770168,0.005752415,0.000051098126,0.0006862779,0.21533522],"genre_scores_gemma":[0.9720094,8.8214233e-7,0.00010931285,0.00039851104,0.000067490604,0.0032627007,0.0000021504857,0.0000164614,0.024133064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99274135,0.003936575,0.0009851365,0.00062283734,0.0013553479,0.00035878617],"domain_scores_gemma":[0.982011,0.01581376,0.00018431086,0.001621944,0.00024301665,0.0001259197],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.04450335,0.00015242767,0.00033299532,0.00035773363,0.00028804314,0.0004339097,0.0017455549,0.000090534624,0.00021184153],"category_scores_gemma":[0.0052088397,0.00008453526,0.00009502318,0.0032780662,0.00010108898,0.0002002892,0.00038143317,0.00017573673,0.008245441],"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":[0.000014896343,0.00002597779,0.010320931,0.000011049693,0.000008746036,0.0000042141346,0.0016625517,0.002851077,0.00061339326,0.9682737,0.009165942,0.0070475205],"study_design_scores_gemma":[0.00038976307,0.000020979063,0.19055624,0.000072987576,0.000010076588,0.000024190136,0.10998336,0.004203565,0.00022132942,0.30290246,0.39111358,0.0005014576],"about_ca_topic_score_codex":0.00027434455,"about_ca_topic_score_gemma":0.0004013928,"teacher_disagreement_score":0.66537124,"about_ca_system_score_codex":0.000062799896,"about_ca_system_score_gemma":0.0000806453,"threshold_uncertainty_score":0.99252677},"labels":[],"label_agreement":null},{"id":"W4389371296","doi":"10.1109/tii.2023.3330299","title":"A Novel Intelligent Forecasting Framework for Quarterly or Monthly Energy Consumption","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Grey System Theory Applications","field":"Decision 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":"Queen's University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province","keywords":"Univariate; Computer science; Multivariate statistics; Support vector machine; Energy consumption; Grey relational analysis; Data mining; Artificial intelligence; Machine learning; Predictive modelling; Nonlinear system; Econometrics; Statistics; Engineering; Mathematics","score_opus":0.41495320041755995,"score_gpt":0.3999424788526085,"score_spread":0.01501072156495148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389371296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0181002,0.0000018532223,0.977164,0.00028884155,0.0022716818,0.0009177678,0.0006418493,0.0002738076,0.00034005157],"genre_scores_gemma":[0.9803991,0.0000051746974,0.016281823,0.00024900748,0.00032368387,0.0009770568,0.000030279323,0.000040560215,0.0016933588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962754,0.00009825187,0.0019040317,0.00027639675,0.000943548,0.00050233415],"domain_scores_gemma":[0.99063873,0.007341796,0.00064599974,0.0007864513,0.0003648528,0.00022216835],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027064176,0.00026384057,0.00040659364,0.00083454064,0.0006212525,0.00042126066,0.00070285844,0.00044410164,0.00030911405],"category_scores_gemma":[0.00077486306,0.00020581727,0.00025829158,0.001951776,0.000112611684,0.00058291364,0.000003620904,0.00040395718,0.0008890889],"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":[0.0010618616,0.0003380292,0.000017687391,0.000053323412,0.00019839242,0.0000021396447,0.012029445,0.19449899,0.00016607568,0.026178446,0.012014417,0.7534412],"study_design_scores_gemma":[0.003286008,0.0016319187,0.00001423527,0.00043691875,0.00017141973,0.000054532382,0.028539302,0.8339683,0.011867774,0.06115919,0.057874244,0.0009961824],"about_ca_topic_score_codex":0.000031874795,"about_ca_topic_score_gemma":0.0000835003,"teacher_disagreement_score":0.96229887,"about_ca_system_score_codex":0.00014811914,"about_ca_system_score_gemma":0.00020363327,"threshold_uncertainty_score":0.99988884},"labels":[],"label_agreement":null},{"id":"W4390722369","doi":"10.23977/acss.2023.071105","title":"Application of a Modified Grey Model Based on Least Squares in Energy Prediction","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Grey System Theory Applications","field":"Decision 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":"Constant (computer programming); Series (stratigraphy); Per capita; Mathematical optimization; Term (time); Computer science; Least-squares function approximation; Energy consumption; Energy (signal processing); Econometrics; Mathematics; Statistics; Engineering","score_opus":0.05907655046651157,"score_gpt":0.33991107579739865,"score_spread":0.2808345253308871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390722369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06327131,0.00037951636,0.93503314,0.00010000475,0.00020680102,0.00041306685,0.000044935077,0.000050633244,0.00050062005],"genre_scores_gemma":[0.99907094,0.00003398951,0.00039173313,0.00003213285,0.00006156094,0.0003388305,0.00001239256,0.0000100700345,0.00004836744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974621,0.00033927977,0.00084289827,0.0005300905,0.0006426298,0.0001829791],"domain_scores_gemma":[0.9978003,0.0011492955,0.00032096205,0.0005240944,0.0001511698,0.000054197208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022302447,0.0001352686,0.00036987037,0.00067687064,0.000055231383,0.00007358458,0.000378523,0.000076845194,0.0000012238106],"category_scores_gemma":[0.00006557029,0.0001105096,0.000045241086,0.0012955645,0.000057275294,0.00030137025,0.000055885077,0.00006684768,0.000013217447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028105354,0.00004215804,0.006424944,0.00003163138,0.0000017264115,7.660045e-7,0.00017379738,0.9411758,0.0003107074,0.023028843,0.00007221548,0.028709315],"study_design_scores_gemma":[0.0003591321,0.000065450775,0.0026448593,0.00013328869,0.00000144706,0.0000014464466,0.0001139519,0.9736146,0.00006654419,0.022364656,0.0005449604,0.000089689405],"about_ca_topic_score_codex":0.00006965675,"about_ca_topic_score_gemma":0.000084043786,"teacher_disagreement_score":0.9357996,"about_ca_system_score_codex":0.00003300773,"about_ca_system_score_gemma":0.00002765723,"threshold_uncertainty_score":0.45064506},"labels":[],"label_agreement":null},{"id":"W4391131551","doi":"10.1016/j.cnsns.2024.107871","title":"A non-linear grey Fourier model based on kernel method for seasonal traffic speed forecasting","year":2024,"lang":"en","type":"article","venue":"Communications in Nonlinear Science and Numerical Simulation","topic":"Grey System Theory Applications","field":"Decision 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":"Simon Fraser University","funders":"","keywords":"Kernel (algebra); Applied mathematics; Mathematical optimization; Linear model; Computer science; Spectral density; Fourier transform; Algorithm; Mathematics; Statistics; Machine learning; Mathematical analysis","score_opus":0.20654460850259077,"score_gpt":0.46626756853972345,"score_spread":0.2597229600371327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391131551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058569547,0.000072038165,0.9363272,0.0030377547,0.00009756845,0.0008962954,0.000054034994,0.000077227276,0.0008682881],"genre_scores_gemma":[0.7081138,0.0000017848108,0.2914227,0.00021662342,0.000045213408,0.00007635374,0.000017131124,0.000014279578,0.00009214171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969012,0.00020163434,0.0007106958,0.00074577227,0.0011152579,0.00032544232],"domain_scores_gemma":[0.98985565,0.0074701943,0.00015648639,0.0015861092,0.00076513743,0.0001664412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009028214,0.0001748299,0.0002649947,0.0006508681,0.0010248704,0.00038859315,0.0015909768,0.00009038321,0.000014241165],"category_scores_gemma":[0.005472659,0.00014231159,0.00010079844,0.0036779712,0.00044176204,0.0006156739,0.00035008357,0.00026821234,0.00004220135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029872148,0.000092521805,0.0001948952,0.000007966217,0.0000022045508,2.9863477e-7,0.00045226197,0.8804348,0.0001447437,0.0027766551,0.000030497442,0.11583327],"study_design_scores_gemma":[0.00028859015,0.000058939913,0.0003053417,0.000059752947,0.0000110609,0.0000024073872,0.00011983974,0.98748624,0.000036194815,0.007483972,0.0039862543,0.00016139654],"about_ca_topic_score_codex":0.00000820838,"about_ca_topic_score_gemma":0.000008809049,"teacher_disagreement_score":0.64954424,"about_ca_system_score_codex":0.00016439325,"about_ca_system_score_gemma":0.0004749028,"threshold_uncertainty_score":0.78825784},"labels":[],"label_agreement":null},{"id":"W4393089530","doi":"10.5267/j.dsl.2024.1.001","title":"Grey comprehensive evaluation of development performance of provinces in China based on spatiotemporal probability function and variable weight strategy","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Grey System Theory Applications","field":"Decision 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":"China; Variable (mathematics); Function (biology); Statistics; Regional science; Computer science; Mathematics; Geography","score_opus":0.08165912520417365,"score_gpt":0.35214041022082837,"score_spread":0.2704812850166547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393089530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97308105,0.000045383287,0.024909973,0.0003762231,0.0002804318,0.00085751154,0.000006970175,0.00001740682,0.0004250377],"genre_scores_gemma":[0.99314576,7.3090445e-7,0.006681914,0.000080046826,0.000011718078,0.000064415915,0.0000027008678,0.000005394946,0.00000729239],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9936963,0.00034048405,0.0010767588,0.0008271901,0.0038454419,0.00021386365],"domain_scores_gemma":[0.99692285,0.0012717338,0.00033470982,0.00064311957,0.0007543405,0.00007323778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017547384,0.00014862716,0.00028174027,0.00105932,0.00016427475,0.00019954417,0.0006233493,0.00005153137,0.00008760613],"category_scores_gemma":[0.0011873401,0.000105220184,0.000036486912,0.0034253802,0.0005845465,0.00091229804,0.000095360156,0.0001245947,0.000026334757],"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":[0.00044601536,0.00029547658,0.16919771,0.00017492782,0.000011549461,0.0000014908933,0.0015245652,0.18647715,0.08535659,0.010197752,0.00034716522,0.5459696],"study_design_scores_gemma":[0.0003043398,0.00012308989,0.6127577,0.00021773862,0.000007765132,0.0000014882868,0.000097257274,0.3648634,0.005490837,0.01581825,0.0002119247,0.000106227766],"about_ca_topic_score_codex":0.000033347915,"about_ca_topic_score_gemma":0.000021465703,"teacher_disagreement_score":0.5458634,"about_ca_system_score_codex":0.00021843915,"about_ca_system_score_gemma":0.00096091814,"threshold_uncertainty_score":0.6081608},"labels":[],"label_agreement":null},{"id":"W4393121437","doi":"10.5267/j.ijiec.2024.2.004","title":"A study on the nonlinear relationship between market, subsidy, and income of photovoltaic enterprises based on chaos theory","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Grey System Theory Applications","field":"Decision 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":"Huaiyin Normal University; Government of Jiangsu Province","keywords":"Subsidy; Nonlinear system; Photovoltaic system; CHAOS (operating system); Chaos theory; Economics; Control theory (sociology); Econometrics; Business; Industrial organization; Computer science; Physics; Market economy; Engineering; Chaotic; Electrical engineering; Management","score_opus":0.12199054192735206,"score_gpt":0.3699762213888851,"score_spread":0.24798567946153302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393121437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92857534,0.000035985347,0.06749883,0.0016460756,0.0012888771,0.00043034452,0.00011455342,0.00003272497,0.0003772755],"genre_scores_gemma":[0.99894714,5.2517316e-7,0.0003839069,0.000031907108,0.00053711806,0.000015543026,0.0000032121452,0.000017231467,0.0000634135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9968688,0.00043062904,0.001075489,0.00020453932,0.0013178822,0.000102653335],"domain_scores_gemma":[0.9739237,0.02482468,0.0004041561,0.00025380868,0.0005144288,0.00007923861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004825235,0.0001486014,0.00025980407,0.001145559,0.000084752464,0.0002699936,0.0007836787,0.00007294782,0.000066308676],"category_scores_gemma":[0.0072303773,0.000098811935,0.0001510716,0.0007182821,0.00006553764,0.00017453253,0.00006702367,0.0005142499,0.00002089461],"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":[0.00063761504,0.00081756106,0.40808955,0.00002100882,0.0011572392,0.0001327962,0.00414599,0.492864,0.00039898077,0.079055,0.002759812,0.009920438],"study_design_scores_gemma":[0.0036424696,0.0016014935,0.5968105,0.002256113,0.0002853881,0.00014686928,0.0034754977,0.3451421,0.0007994671,0.04199329,0.0033117943,0.0005350212],"about_ca_topic_score_codex":0.00000477212,"about_ca_topic_score_gemma":9.686194e-7,"teacher_disagreement_score":0.18872094,"about_ca_system_score_codex":0.00009612861,"about_ca_system_score_gemma":0.0001710884,"threshold_uncertainty_score":0.86559594},"labels":[],"label_agreement":null},{"id":"W4396568524","doi":"10.1080/03155986.2024.2346708","title":"Stability analysis and enhancement of super-efficiency model based on space distance","year":2024,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Grey System Theory Applications","field":"Decision 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":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China; National Natural Science Foundation of China","keywords":"Stability (learning theory); Space (punctuation); Computer science; Mathematics; Machine learning","score_opus":0.13856985666607624,"score_gpt":0.43540835902144487,"score_spread":0.2968385023553686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396568524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2000686,0.00019557172,0.78096265,0.0008840833,0.00008224496,0.00095955335,0.00034710375,0.000022689961,0.016477507],"genre_scores_gemma":[0.9986367,0.000009427488,0.00041909126,0.000038773655,0.000016410075,0.00020135115,0.00004476011,0.0000032386408,0.00063022424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583876,0.00023504178,0.0010878196,0.0002699438,0.0023703342,0.00019810082],"domain_scores_gemma":[0.99569356,0.0019990283,0.00010960303,0.00048634846,0.0015892914,0.00012214563],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011913578,0.00010902612,0.0002571561,0.0010685332,0.00033494947,0.001205726,0.00027583155,0.0000663184,0.00010764975],"category_scores_gemma":[0.0013518749,0.00007630187,0.00006375793,0.0022853815,0.00022848744,0.0013909136,0.00008296976,0.0001537266,0.00010992756],"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":[0.00006351437,0.000043751574,0.002947296,0.00022780252,0.00005114558,2.1365132e-7,0.0036846069,0.08979227,0.00045853973,0.89795935,0.0009491306,0.0038223902],"study_design_scores_gemma":[0.0001223414,0.00006483229,0.0011569659,0.00004730985,0.000009062648,9.166326e-7,0.0017463217,0.98422086,0.00054452225,0.00094700576,0.011061246,0.00007863047],"about_ca_topic_score_codex":0.00011092422,"about_ca_topic_score_gemma":0.000026985574,"teacher_disagreement_score":0.89701235,"about_ca_system_score_codex":0.000101736085,"about_ca_system_score_gemma":0.00038528457,"threshold_uncertainty_score":0.99983114},"labels":[],"label_agreement":null},{"id":"W4396691630","doi":"10.1016/j.ins.2024.120711","title":"A novel intervention effect-based quadratic time-varying nonlinear discrete grey model for forecasting carbon emissions intensity","year":2024,"lang":"en","type":"article","venue":"Information Sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Toronto Metropolitan University","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Intensity (physics); Nonlinear system; Quadratic equation; Discrete time and continuous time; Carbon fibers; Intervention (counseling); Applied mathematics; Mathematics; Econometrics; Computer science; Environmental science; Statistics; Mathematical optimization; Algorithm; Physics; Psychology; Geometry","score_opus":0.12240096505059837,"score_gpt":0.3916220335033118,"score_spread":0.26922106845271343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396691630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13337699,0.000028605877,0.860297,0.0008753864,0.00045316163,0.00096500263,0.00010428218,0.00020654582,0.0036929855],"genre_scores_gemma":[0.9829459,1.8668472e-7,0.0161667,0.00015063319,0.00005690889,0.00020981561,0.000040395436,0.000008730654,0.00042072468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967189,0.00010037211,0.0012223156,0.00041819148,0.001210563,0.00032968193],"domain_scores_gemma":[0.9958536,0.0025584155,0.00043636898,0.00041734238,0.0005985064,0.00013577884],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009438827,0.00020187529,0.00030209444,0.000765319,0.0006895143,0.0016436097,0.0008396677,0.000090198475,0.00003784754],"category_scores_gemma":[0.005506336,0.00013802938,0.0002525942,0.0017808055,0.0002473676,0.0028224022,0.00013423349,0.00014356362,0.0002516182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037220228,0.00017218689,0.0015860929,0.00096236373,0.000096,0.0000024546005,0.01855591,0.7088062,0.021048982,0.01682185,0.0050747376,0.22650103],"study_design_scores_gemma":[0.00026886293,0.00010954956,0.000037379938,0.0002757636,0.000020732115,0.000014550845,0.00045639335,0.9908119,0.0014602258,0.005609611,0.00076535315,0.0001696905],"about_ca_topic_score_codex":0.00005027857,"about_ca_topic_score_gemma":0.000018765855,"teacher_disagreement_score":0.8495689,"about_ca_system_score_codex":0.00009267876,"about_ca_system_score_gemma":0.00025504592,"threshold_uncertainty_score":0.9993928},"labels":[],"label_agreement":null},{"id":"W4399457656","doi":"10.1016/j.apenergy.2024.123531","title":"Mixed-frequency data Sampling Grey system Model: Forecasting annual CO<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si10.svg\" display=\"inline\" id=\"d1e3562\"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> emissions in China with quarterly and monthly economic-energy indicators","year":2024,"lang":"lv","type":"article","venue":"Applied Energy","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":21,"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":"Queen's University; Queen's University Belfast","keywords":"Scalable Vector Graphics; Sampling (signal processing); Statistics; Computer science; Computer graphics (images); Algorithm; Mathematics; Database; World Wide Web; Telecommunications","score_opus":0.03161857880955433,"score_gpt":0.26414232863314524,"score_spread":0.23252374982359092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399457656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79218227,0.0013667244,0.017384967,0.0002942695,0.0011875449,0.00008722392,0.0022375525,0.0003205679,0.1849389],"genre_scores_gemma":[0.9908847,0.0001851794,0.0040205372,0.00023706682,0.0008629785,0.0012478861,0.0019074026,0.00045188234,0.00020236486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9916043,0.00033034067,0.0022434357,0.002520571,0.0016705536,0.0016307628],"domain_scores_gemma":[0.9921349,0.0020477527,0.0016289586,0.0031407266,0.000091068105,0.00095658115],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002924915,0.00082985515,0.00050802727,0.0008368009,0.001300548,0.0017139858,0.002899913,0.001272698,0.006833514],"category_scores_gemma":[0.00038295778,0.0011640014,0.00047127984,0.0013847736,0.0009497907,0.0015880166,0.0014335858,0.0009337608,0.0005815618],"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":[0.00035144246,0.00013800144,0.000014370363,0.0005023353,0.0005269469,0.00031756796,0.0028432482,0.018049633,0.0007336065,0.9514598,0.017011944,0.008051055],"study_design_scores_gemma":[0.0009481842,0.0005344344,0.00005110057,0.0013089898,0.0005481904,0.00072516204,0.0073745875,0.93041486,0.05151674,0.0010648723,0.00425979,0.001253093],"about_ca_topic_score_codex":0.0033705584,"about_ca_topic_score_gemma":0.0044397623,"teacher_disagreement_score":0.950395,"about_ca_system_score_codex":0.00005103109,"about_ca_system_score_gemma":0.0015468284,"threshold_uncertainty_score":0.99999964},"labels":[],"label_agreement":null},{"id":"W4405371371","doi":"10.3390/jrfm17120558","title":"The Predictive Grey Forecasting Approach for Measuring Tax Collection","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Grey System Theory Applications","field":"Decision 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":"Ad valorem tax; Tax credit; Tax reform; Indirect tax; Public economics; Direct tax; Tax revenue; Business; Value-added tax; Revenue; Economics; Finance","score_opus":0.06923782806356489,"score_gpt":0.2971312367525173,"score_spread":0.22789340868895241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405371371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019631712,0.0011779664,0.97518814,0.00013275402,0.00068830384,0.0004710346,0.000013769755,0.000010333016,0.0026859893],"genre_scores_gemma":[0.9928051,0.00021791505,0.0056235804,0.000009233594,0.0003423173,0.000062539235,2.595723e-7,0.000007135532,0.0009318986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99854475,0.00010867318,0.00053963443,0.00017551027,0.000501009,0.00013044344],"domain_scores_gemma":[0.99821615,0.0009875314,0.00033027577,0.00014065401,0.00028049352,0.000044902135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006672562,0.00007504872,0.00014830197,0.00022439858,0.000639663,0.0004135318,0.0002700193,0.000030533207,0.0000016683326],"category_scores_gemma":[0.0013687615,0.000042990814,0.000120028395,0.00055094925,0.000047299618,0.00019705444,0.0000674375,0.00013032964,0.0000026822213],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002279355,0.00003241763,0.0011975755,0.000048672362,0.00005623093,0.0000071001296,0.0018307434,0.0016010702,0.000006958075,0.055559848,0.012719948,0.9267115],"study_design_scores_gemma":[0.0007760751,0.00036775324,0.026011763,0.0001812356,0.00026669365,0.00012286796,0.0074586878,0.0990513,0.000053864547,0.42747912,0.43804187,0.00018878201],"about_ca_topic_score_codex":0.0000036450278,"about_ca_topic_score_gemma":0.000011515024,"teacher_disagreement_score":0.9731734,"about_ca_system_score_codex":0.000056070265,"about_ca_system_score_gemma":0.00003371858,"threshold_uncertainty_score":0.49198356},"labels":[],"label_agreement":null},{"id":"W4405793102","doi":"10.54254/2754-1169/2024.ga18931","title":"SSEC Forecast Based on ARIMA and ETS Models","year":2024,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Grey System Theory Applications","field":"Decision 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":"Autoregressive integrated moving average; Econometrics; Meteorology; Environmental science; Computer science; Statistics; Geography; Mathematics; Time series","score_opus":0.06917824494184087,"score_gpt":0.3701179098258013,"score_spread":0.3009396648839604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405793102","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24873169,0.001704185,0.019083634,0.0070255157,0.0005837846,0.0005535002,0.00003497731,0.00006480756,0.7222179],"genre_scores_gemma":[0.9968619,0.00023424094,0.0017059023,0.00062606274,0.000037257545,0.000040306833,8.470953e-7,0.000004964926,0.00048856385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998311,0.00006177,0.00037440177,0.0006686362,0.00021848729,0.00036570767],"domain_scores_gemma":[0.99819374,0.0013794795,0.000043722557,0.00023392893,0.000013212695,0.00013589673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019990716,0.000121581936,0.00017007903,0.00038980026,0.0001655504,0.0006077172,0.00040932064,0.000031751824,0.000034829573],"category_scores_gemma":[0.00007004406,0.000090158625,0.000031655192,0.00038199237,0.00060729345,0.00097310904,0.0001402663,0.00006864804,0.000054914493],"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":[0.0000042190445,0.000011774324,0.0016649079,0.000013250147,0.000001780429,0.0000021217616,0.000036309204,0.004732194,2.2187042e-7,0.93590754,0.000032211534,0.05759344],"study_design_scores_gemma":[0.00006971166,0.000037317597,0.00092729554,0.000025030668,0.0000031567083,0.0000017271792,0.00035952983,0.3098252,0.0000054616044,0.6656202,0.023041332,0.00008402131],"about_ca_topic_score_codex":0.00001097612,"about_ca_topic_score_gemma":0.00007250146,"teacher_disagreement_score":0.74813014,"about_ca_system_score_codex":0.000044899032,"about_ca_system_score_gemma":0.000021776863,"threshold_uncertainty_score":0.5860231},"labels":[],"label_agreement":null},{"id":"W4407074595","doi":"10.1080/03155986.2025.2455879","title":"A new super-efficiency directional distance function model in the presence of negative data","year":2025,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Grey System Theory Applications","field":"Decision 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":"Function (biology); Computer science; Econometrics; Mathematics; Biology; Evolutionary biology","score_opus":0.22249417217922313,"score_gpt":0.45976757748582575,"score_spread":0.23727340530660262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407074595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023324668,0.00031686638,0.90095866,0.0028112184,0.00033144795,0.0023289206,0.00043810008,0.000020623593,0.06946953],"genre_scores_gemma":[0.9961429,0.000011078706,0.0005066987,0.00011576165,0.000039078906,0.00020249022,0.000077745084,0.0000021328851,0.0029020864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99532145,0.0004583557,0.0012074815,0.00025911973,0.002546119,0.00020745498],"domain_scores_gemma":[0.9933112,0.0038329796,0.0001795116,0.0009036343,0.001713427,0.000059242426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013687749,0.00009786817,0.0001852326,0.00071605324,0.00047282252,0.0008414736,0.0013327735,0.00007547112,0.00003381356],"category_scores_gemma":[0.0058841337,0.00006245195,0.000026666507,0.0023433738,0.00019440138,0.0035240557,0.00031008304,0.00024058257,0.00007395074],"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":[0.000089803,0.00002408219,0.0019765722,0.0000334626,0.000008986202,7.853304e-8,0.0032284393,0.03687732,0.000043336142,0.92606413,0.025513735,0.0061400463],"study_design_scores_gemma":[0.0005461847,0.000044128534,0.0116779255,0.00009950747,0.0000036514336,0.0000063790867,0.012621554,0.85251117,0.000034529796,0.025399279,0.09695484,0.00010085302],"about_ca_topic_score_codex":0.00071752904,"about_ca_topic_score_gemma":0.00020187898,"teacher_disagreement_score":0.97281826,"about_ca_system_score_codex":0.000067682784,"about_ca_system_score_gemma":0.0009904177,"threshold_uncertainty_score":0.81143486},"labels":[],"label_agreement":null},{"id":"W4407560820","doi":"10.4171/owr/2024/38","title":"Mathematics, Statistics, and Geometry of Extreme Events in High Dimensions","year":2025,"lang":"en","type":"article","venue":"Oberwolfach Reports","topic":"Grey System Theory Applications","field":"Decision 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","funders":"","keywords":"Leverage (statistics); Extreme value theory; Inference; Series (stratigraphy); Probabilistic logic; Statistical inference; Multivariate statistics; Computer science; Mathematical statistics; Data science; Mathematics; Statistics; Artificial intelligence; Machine learning; Geology","score_opus":0.058997811383636085,"score_gpt":0.3478108612545966,"score_spread":0.2888130498709605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407560820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97166944,0.00018653231,0.023982843,0.00021238936,0.00031163095,0.0004503976,0.000043585598,0.000021885708,0.003121281],"genre_scores_gemma":[0.98376536,0.0000065970025,0.010949184,0.000029502236,0.000007088591,0.000034296365,0.000008107374,0.000008645432,0.0051912083],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968744,0.00014339473,0.0015899622,0.00048997416,0.00070940267,0.00019289316],"domain_scores_gemma":[0.99640816,0.0014173068,0.0006638002,0.0011309658,0.0003015471,0.000078227225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035785488,0.0001327033,0.00044084273,0.0005911193,0.0000796447,0.000027635935,0.00024083005,0.000080899765,0.00017970557],"category_scores_gemma":[0.004564553,0.00010840833,0.0000456996,0.0010746245,0.000113487426,0.000109019704,0.00021859308,0.000103565915,0.000033359778],"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":[0.0000076126107,0.0003516087,0.7958221,0.00007617981,0.00003711874,0.0000767914,0.00043704567,0.0000799746,0.0020014567,0.18948112,0.005186643,0.006442378],"study_design_scores_gemma":[0.00015330115,0.000011578421,0.38271955,0.00008967728,0.000018281273,0.000051431587,0.00036508567,0.00023139431,0.00063049875,0.6143391,0.0012899064,0.00010019551],"about_ca_topic_score_codex":0.00019436052,"about_ca_topic_score_gemma":0.00015494539,"teacher_disagreement_score":0.42485797,"about_ca_system_score_codex":0.00004072123,"about_ca_system_score_gemma":0.00009420202,"threshold_uncertainty_score":0.54645264},"labels":[],"label_agreement":null},{"id":"W4409794966","doi":"10.61091/jcmcc127b-441","title":"Research on the Construction and Application of Macroeconomic Forecasting Model Based on Time Series Cluster Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Grey System Theory Applications","field":"Decision 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":"Series (stratigraphy); Cluster (spacecraft); Time series; Computer science; Econometrics; Data mining; Industrial engineering; Machine learning; Economics; Engineering; Geology","score_opus":0.05287863939163315,"score_gpt":0.3536100654717357,"score_spread":0.3007314260801025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409794966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8560409,0.000026924039,0.13701062,0.0015066231,0.0012956213,0.00055855664,0.0000064830474,0.0000119319675,0.003542329],"genre_scores_gemma":[0.9962033,0.0000018056265,0.0035707324,0.00003530684,0.0001484249,0.00000742325,6.132883e-7,0.000010166762,0.0000222478],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99659073,0.0005209689,0.001443435,0.00027995024,0.0009698683,0.00019507024],"domain_scores_gemma":[0.9877556,0.009059489,0.0013787275,0.00051841076,0.0012112404,0.00007654498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013193976,0.00016684596,0.0006747626,0.00094657566,0.00052861165,0.0003066352,0.00058914337,0.00011633888,0.0000060799375],"category_scores_gemma":[0.00200518,0.000114362985,0.00017060559,0.0014551631,0.00031013315,0.00013307668,0.00019433997,0.00039571317,0.0000037708965],"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":[0.00017710704,0.00013782944,0.0003079639,0.00003545324,0.00011262534,3.3821055e-7,0.00028400525,0.01556985,0.00020704654,0.9807783,0.00018142135,0.0022080874],"study_design_scores_gemma":[0.00058598927,0.00014926778,0.000055574765,0.00008293518,0.00007547349,0.000004305835,0.00044814835,0.4703457,0.00036488933,0.5277726,0.00006144097,0.0000536736],"about_ca_topic_score_codex":0.0000038131477,"about_ca_topic_score_gemma":5.5824717e-7,"teacher_disagreement_score":0.45477587,"about_ca_system_score_codex":0.000084076535,"about_ca_system_score_gemma":0.00014772631,"threshold_uncertainty_score":0.46635872},"labels":[],"label_agreement":null},{"id":"W4411450856","doi":"10.1002/2688-8319.70031","title":"Grey matters: Ensuring management information is a part of the permanent evidence base by creating open grey literature principles","year":2025,"lang":"en","type":"article","venue":"Ecological Solutions and Evidence","topic":"Grey System Theory Applications","field":"Decision 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":"Grey literature; Base (topology); Computer science; Knowledge management; Data science; Political science; MEDLINE; Mathematics; Law","score_opus":0.14617589889948826,"score_gpt":0.3679204727167603,"score_spread":0.22174457381727206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411450856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8816729,0.0063416664,0.011586082,0.08449675,0.0006202897,0.0045916294,0.00016683378,0.00009846083,0.010425378],"genre_scores_gemma":[0.9934365,0.0003025187,0.0009054712,0.002186,0.000013218926,0.0002172656,0.0000024491708,0.0000024078695,0.0029341618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9975156,0.00045188106,0.00085558475,0.0004051168,0.00051278184,0.00025900133],"domain_scores_gemma":[0.9961935,0.0021372824,0.0004183057,0.0007851817,0.00038938233,0.00007629673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004151181,0.0001441764,0.00024582038,0.00011292578,0.0009206727,0.0009611368,0.0014630876,0.0000871896,0.00024013709],"category_scores_gemma":[0.0040330254,0.000085685584,0.0000911421,0.0010844667,0.0002123467,0.001920625,0.001841186,0.00015869187,0.00007992831],"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":[0.0003101891,0.0007344274,0.21868703,0.0012186737,0.0002335222,0.000010933418,0.009958223,0.0026542586,0.002805015,0.42331964,0.30957913,0.030488938],"study_design_scores_gemma":[0.00046791945,0.00013287942,0.828501,0.007885716,0.00012316114,0.000026567477,0.0036862264,0.007333276,0.00068834424,0.038421128,0.11229762,0.0004361761],"about_ca_topic_score_codex":0.0000387781,"about_ca_topic_score_gemma":0.000025813666,"teacher_disagreement_score":0.609814,"about_ca_system_score_codex":0.00008923178,"about_ca_system_score_gemma":0.000050001338,"threshold_uncertainty_score":0.9268264},"labels":[],"label_agreement":null},{"id":"W4412845518","doi":"10.1080/14765284.2025.2538934","title":"Price predictions of scrap steel for north China via machine learning","year":2025,"lang":"en","type":"article","venue":"Journal of Chinese Economic and Business Studies","topic":"Grey System Theory Applications","field":"Decision 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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Scrap; China; Economics; Materials science; Metallurgy; Geography","score_opus":0.04001446240897147,"score_gpt":0.34715090675193605,"score_spread":0.3071364443429646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412845518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9531068,0.0046979073,0.0381894,0.0020221944,0.00078134984,0.00025214933,0.000047190468,0.000009666672,0.00089330436],"genre_scores_gemma":[0.99759746,0.0005079276,0.0006478142,0.00002920408,0.00017202013,0.000016603075,0.0000015455321,0.0000071882255,0.0010202547],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982658,0.000077453995,0.0011729383,0.00019848118,0.0001649886,0.00012032569],"domain_scores_gemma":[0.99636275,0.0013280223,0.0011789043,0.00020689415,0.000880216,0.00004323183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017819991,0.00014775468,0.0006921347,0.0004516652,0.00032418303,0.00005597028,0.000342952,0.0000358383,0.000015684107],"category_scores_gemma":[0.0017688188,0.00009207317,0.00013873872,0.0005171628,0.00016580246,0.00036062568,0.00014302589,0.000116608826,0.0000037981722],"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":[0.0002561199,0.00013223887,0.959593,0.00021542043,0.0007514745,0.0000011694037,0.0018560453,0.019844526,0.00024985595,0.0047505363,0.0028960297,0.009453604],"study_design_scores_gemma":[0.001027084,0.00008432737,0.91553676,0.00008992118,0.000091368915,0.000040158786,0.00066842703,0.006401116,0.000016872356,0.065959595,0.009977551,0.00010681677],"about_ca_topic_score_codex":0.000015383584,"about_ca_topic_score_gemma":0.00012835441,"teacher_disagreement_score":0.061209057,"about_ca_system_score_codex":0.00005212153,"about_ca_system_score_gemma":0.00010979321,"threshold_uncertainty_score":0.3754635},"labels":[],"label_agreement":null},{"id":"W4414200013","doi":"10.31764/jtam.v9i1.27513","title":"Study of Economic Growth in IKN based on Autoregressive and Distributed Lag Approach","year":2025,"lang":"en","type":"article","venue":"JTAM (Jurnal Teori dan Aplikasi Matematika)","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Distributed lag; Quarter (Canadian coin); Government (linguistics); Government spending; Value (mathematics); Lag; Poverty; Capital (architecture); Economic interventionism","score_opus":0.0469685644298061,"score_gpt":0.35497644413776464,"score_spread":0.3080078797079585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414200013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98325604,0.000034197074,0.0040701083,0.00041232855,0.00021752935,0.0017072952,0.00014400497,0.000055635857,0.010102872],"genre_scores_gemma":[0.9986654,0.0000013676802,0.00053359295,0.00006258138,0.00002971022,0.00029953523,0.000014334849,0.000018043842,0.00037542614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99556404,0.0008655783,0.0016599578,0.0007964132,0.00080206315,0.00031194396],"domain_scores_gemma":[0.9957369,0.001979285,0.00088843744,0.0010911664,0.00017309646,0.0001311001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039521474,0.000309953,0.00088052574,0.0009091725,0.00018141193,0.0002558714,0.0010805618,0.00013018659,0.000030958785],"category_scores_gemma":[0.0010864898,0.00023515863,0.00010902497,0.000990086,0.00015859354,0.00022950185,0.00021029843,0.00025845124,0.00006689179],"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":[0.00068977027,0.0046061403,0.65150064,0.0007287378,0.0002453046,0.00005762176,0.0038070395,0.01234881,0.0005945012,0.31266266,0.009881431,0.0028773625],"study_design_scores_gemma":[0.0075057703,0.00082444877,0.79064363,0.00096504635,0.00015187831,0.000037188664,0.026652602,0.059566084,0.0021597212,0.1097489,0.0008113335,0.0009333908],"about_ca_topic_score_codex":0.000056889952,"about_ca_topic_score_gemma":0.000053763786,"teacher_disagreement_score":0.20291376,"about_ca_system_score_codex":0.00020348703,"about_ca_system_score_gemma":0.00016258183,"threshold_uncertainty_score":0.958949},"labels":[],"label_agreement":null},{"id":"W4414616723","doi":"10.47191/jefms/v8-i9-45","title":"Forecasting Gold Price Trends in Vietnam in the Fourth Quarter of 2025 Using the Arima Model","year":2025,"lang":"en","type":"article","venue":"Journal of Economics Finance and Management Studies","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Context (archaeology); Quarter (Canadian coin); Profitability index; Time series; Predictability; Box–Jenkins","score_opus":0.17986036590495139,"score_gpt":0.37860212375263075,"score_spread":0.19874175784767936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414616723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97623366,0.0013739857,0.0057292283,0.005630538,0.00014413988,0.00021866421,0.0000025515842,8.6449904e-7,0.010666337],"genre_scores_gemma":[0.9965033,0.0011348543,0.0015181263,0.00024175801,0.000015186263,0.00001296603,5.291108e-8,0.0000020910943,0.0005716666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987069,0.00007755118,0.00086836226,0.00013867294,0.00009476765,0.000113763694],"domain_scores_gemma":[0.99849325,0.00050379976,0.0006713546,0.0002462274,0.00007938248,0.000005970277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039056004,0.00008016044,0.0003065544,0.00041575017,0.00009306309,0.00005527253,0.00047735687,0.000017131335,0.0000010619888],"category_scores_gemma":[0.00008829071,0.000044590437,0.000069622685,0.0005060343,0.00007904177,0.00020941594,0.00016175419,0.00009231358,4.274865e-7],"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":[0.00011321961,0.00015912633,0.026764821,0.00009183238,0.00027263683,0.000008847718,0.011439933,0.41852003,0.000007483771,0.4745944,0.0111782905,0.05684936],"study_design_scores_gemma":[0.0013020401,0.000078049976,0.051352248,0.00042684775,0.000104651066,0.000012276352,0.05548084,0.47862244,0.000009459206,0.40372643,0.008717005,0.00016770448],"about_ca_topic_score_codex":0.0000046394352,"about_ca_topic_score_gemma":0.00021664942,"teacher_disagreement_score":0.07086798,"about_ca_system_score_codex":0.000042505497,"about_ca_system_score_gemma":0.000016973523,"threshold_uncertainty_score":0.18183452},"labels":[],"label_agreement":null},{"id":"W4414819000","doi":"10.1108/gs-05-2025-0057","title":"Novel method for flood-affected area prediction based on non-equigap multivariable grey model","year":2025,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision 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 on Governance","funders":"","keywords":"Multivariable calculus; Flood myth; Warning system; Population; Equidistant; Nonlinear system; Flood warning; Flood forecasting","score_opus":0.04790912013401627,"score_gpt":0.36190119939928,"score_spread":0.31399207926526373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414819000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009145026,0.000052687807,0.9783567,0.00029485972,0.00036209676,0.005870197,0.00037006408,0.00022097222,0.005327387],"genre_scores_gemma":[0.9716353,0.0000012640754,0.015913311,0.0002778935,0.0001292221,0.0078012845,0.00010717922,0.00003491429,0.0040996214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959093,0.00071166223,0.001117237,0.0012640327,0.00062698405,0.00037082683],"domain_scores_gemma":[0.9916783,0.0050839847,0.00060135673,0.0015943848,0.0008855089,0.00015644109],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.013342305,0.00035575934,0.0005842573,0.000536373,0.0007139434,0.00035485704,0.0007286885,0.0003198705,0.000010189237],"category_scores_gemma":[0.001983475,0.00029139683,0.00015810727,0.0012041073,0.00010916609,0.00031825312,0.00008709201,0.00018581172,0.0000593381],"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":[0.00046075298,0.000305945,0.0003688445,0.00015283725,0.000056274894,1.0759516e-7,0.000222882,0.1300857,0.10149184,0.75456774,0.0018370737,0.010450014],"study_design_scores_gemma":[0.0012684728,0.00006497342,0.00081271835,0.00014452137,0.000077154116,0.000004425418,0.00035624127,0.8748517,0.0026485233,0.11594737,0.0036029855,0.00022091618],"about_ca_topic_score_codex":0.00004023321,"about_ca_topic_score_gemma":0.00000532102,"teacher_disagreement_score":0.96249026,"about_ca_system_score_codex":0.00012600105,"about_ca_system_score_gemma":0.00014278154,"threshold_uncertainty_score":0.9999538},"labels":[],"label_agreement":null},{"id":"W649599463","doi":"","title":"Stock index prediction based on Grey theory, ARIMA model and Wavelet method","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Grey System Theory Applications","field":"Decision 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":"Autoregressive integrated moving average; Autoregressive–moving-average model; Wavelet; Mathematics; Moving average; Statistics; Time series; Algorithm; Computer science; Econometrics; Artificial intelligence; Autoregressive model","score_opus":0.06275104524583514,"score_gpt":0.3991202540350165,"score_spread":0.33636920878918136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649599463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06370881,0.000020243238,0.7901211,0.00025590803,0.0011320313,0.0015705182,0.00030269034,0.00022078062,0.14266796],"genre_scores_gemma":[0.8610859,0.0000019042087,0.026508251,0.00039577173,0.00014199817,0.00046991184,0.00050910347,0.00008099539,0.110806145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948464,0.0007685928,0.0010278273,0.0012806506,0.0017822499,0.0002942661],"domain_scores_gemma":[0.99329764,0.003279851,0.00070640456,0.0017302979,0.0007664622,0.00021934319],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0080758175,0.00043739358,0.0005973763,0.000869461,0.0003649759,0.0004301157,0.000955654,0.00086216413,0.00089063984],"category_scores_gemma":[0.002369397,0.0003201056,0.0001847491,0.000595971,0.000073770185,0.00024695453,0.000046912355,0.0007999067,0.000254957],"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":[0.0017885263,0.0005317383,0.0016073313,0.00013806579,0.00013749595,0.000004428186,0.004043896,0.013819629,0.0085977195,0.6460944,0.023878371,0.29935846],"study_design_scores_gemma":[0.00037098632,0.00006625384,0.0066416524,0.00003988489,0.00005313025,0.000004243302,0.0009770726,0.6371051,0.0008401927,0.35183635,0.0017630071,0.0003020842],"about_ca_topic_score_codex":0.000033412995,"about_ca_topic_score_gemma":0.00050023425,"teacher_disagreement_score":0.7973771,"about_ca_system_score_codex":0.000054678836,"about_ca_system_score_gemma":0.00029828952,"threshold_uncertainty_score":0.9999251},"labels":[],"label_agreement":null},{"id":"W6949848958","doi":"10.5281/zenodo.3432745","title":"VariantEffect/rapimave: v0.1 : First official release","year":2019,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Grey System Theory Applications","field":"Decision 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":"Documentation; Offset (computer science); License; Parsing; Point (geometry)","score_opus":0.06422829124360732,"score_gpt":0.30852264179154126,"score_spread":0.24429435054793394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949848958","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034009707,0.00015329846,0.022779832,0.00093080977,0.00049638294,0.0016184028,0.0011293365,0.0015149846,0.9713429],"genre_scores_gemma":[0.030283323,0.00005423713,0.000330543,0.0002059333,0.00096691545,3.4625606e-7,0.0015361652,0.012955211,0.95366734],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944767,0.0013258286,0.0007094298,0.001262247,0.0016805228,0.0005453225],"domain_scores_gemma":[0.99530447,0.00034011208,0.000666725,0.0025522,0.0008230034,0.00031347483],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003364663,0.0003702404,0.0005293364,0.001108027,0.0017627188,0.0018398783,0.004405209,0.00031834634,0.13920893],"category_scores_gemma":[0.0052876407,0.00034289845,0.00019102081,0.0016783113,0.0002856398,0.00017502042,0.002035631,0.0004966797,0.36480367],"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":[0.000038110167,0.000105340994,0.0000010610098,0.00005833817,0.00005121462,0.000012277722,0.00026829998,0.000028304394,0.00010308783,0.021564512,0.950856,0.026913512],"study_design_scores_gemma":[0.00045476283,0.00011097141,0.000051709467,0.00010859553,0.000027200924,0.00008494757,0.00016181932,0.00027726268,0.000019223948,0.0011929774,0.99715847,0.0003520732],"about_ca_topic_score_codex":0.00006161061,"about_ca_topic_score_gemma":0.000004930851,"teacher_disagreement_score":0.22559474,"about_ca_system_score_codex":0.00017778938,"about_ca_system_score_gemma":0.000016504568,"threshold_uncertainty_score":0.9999023},"labels":[],"label_agreement":null},{"id":"W7006019021","doi":"","title":"Stock index prediction based on Grey theory, ARIMA model and Wavelet methods","year":2010,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Wavelet; Stock market index; Wavelet transform; Time series; Dimension (graph theory); Stock (firearms); Moving average; Rank (graph theory); Autoregressive–moving-average model","score_opus":0.07996139937358693,"score_gpt":0.393375341015495,"score_spread":0.31341394164190806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006019021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66808766,0.000050939594,0.030810881,0.00039271364,0.0017397704,0.0019510408,0.0001441557,0.00022277846,0.29660007],"genre_scores_gemma":[0.86064905,0.000010492833,0.00083069946,0.00001784129,0.00024695543,0.000032454216,0.00009898477,0.00007310578,0.13804044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884038,0.0050964076,0.0007809447,0.0019504079,0.0029392012,0.0008292122],"domain_scores_gemma":[0.98946357,0.005403432,0.00064828637,0.0025180785,0.0013368826,0.0006297606],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.011733707,0.00051885954,0.00076278957,0.004117869,0.0017634798,0.0007422793,0.002325655,0.0010549775,0.00015960496],"category_scores_gemma":[0.0024652309,0.0004960716,0.00032814516,0.0025639837,0.0006427426,0.0005356876,0.00027242827,0.0029244898,0.00009395842],"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":[0.019980527,0.0019760192,0.087260105,0.00071779557,0.0010940146,0.0011163799,0.008060738,0.0063123642,0.13109165,0.6131701,0.02057672,0.10864357],"study_design_scores_gemma":[0.0030346434,0.001443771,0.21186604,0.00040036542,0.00031572257,0.00007122873,0.0117137125,0.3608906,0.032060675,0.32088587,0.055373676,0.0019436959],"about_ca_topic_score_codex":0.0010622572,"about_ca_topic_score_gemma":0.004875343,"teacher_disagreement_score":0.35457826,"about_ca_system_score_codex":0.0006186243,"about_ca_system_score_gemma":0.0018271997,"threshold_uncertainty_score":0.99974906},"labels":[],"label_agreement":null}]}