{"meta":{"query_hash":"c1c8528e63a4","filters":{"venue":"Structural Equation Modeling A Multidisciplinary Journal"},"cohort_total":42,"direct_labels_cover":0,"predictions_cover":42,"exported":42,"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/c1c8528e63a4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Structural+Equation+Modeling+A+Multidisciplinary+Journal"},"results":[{"id":"W1971496905","doi":"10.1207/s15328007sem1301_5","title":"Correlates of the Rosenberg Self-Esteem Scale Method Effects","year":2006,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Personality Traits and Psychology","field":"Psychology","cited_by":168,"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":"Psychology; Conscientiousness; Agreeableness; Extraversion and introversion; Hierarchical structure of the Big Five; Personality; Confirmatory factor analysis; Construct validity; Acquiescence; Self-esteem; Social psychology; Scale (ratio); Big Five personality traits; Developmental psychology; Test validity; Psychometrics; Structural equation modeling","score_opus":0.025880436043790717,"score_gpt":0.34137909179749826,"score_spread":0.31549865575370756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971496905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927691,0.0009988588,0.0012728248,0.0001689169,0.000044434277,0.000052114567,0.00035335583,0.000036867288,0.0043036034],"genre_scores_gemma":[0.99734604,0.00016217494,0.0017697439,0.00003656804,0.000014938666,0.0000451048,0.00016091502,0.000010954824,0.00045345706],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942419,0.0021722212,0.0005572966,0.00044904463,0.002446917,0.0001326606],"domain_scores_gemma":[0.9497595,0.026467225,0.0149669135,0.0030148004,0.004596292,0.0011953454],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0070419526,0.00032239442,0.0004245239,0.0013129622,0.00034172498,0.00083163445,0.00040342135,0.00029817387,0.0019938317],"category_scores_gemma":[0.04004517,0.00021017466,0.00044456084,0.00072906516,0.00063235295,0.00029014703,0.0006599585,0.00083005807,0.0003292971],"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":[0.00013185824,0.00011383152,0.9802501,0.00004840364,0.00021500945,0.000053927866,0.000510283,0.00022078058,0.0008019653,0.00044682747,0.0004934766,0.016713507],"study_design_scores_gemma":[0.0000109113325,0.00012275566,0.9978168,0.000025220432,0.00003063771,0.00017759543,0.00014190681,0.0004893059,0.00041868622,0.00024055992,0.0005124135,0.000013163388],"about_ca_topic_score_codex":0.00077124033,"about_ca_topic_score_gemma":0.0014618803,"teacher_disagreement_score":0.99295807,"about_ca_system_score_codex":0.00033763194,"about_ca_system_score_gemma":0.0003891625,"threshold_uncertainty_score":0.037241817},"labels":[],"label_agreement":null},{"id":"W1996072914","doi":"10.1080/10705511.2014.882692","title":"Robust Two-Stage Approach Outperforms Robust Full Information Maximum Likelihood With Incomplete Nonnormal Data","year":2014,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Alabama; Association for Psychological Science","keywords":"Stage (stratigraphy); Maximum likelihood; Computer science; Statistics; Mathematics","score_opus":0.2212540326049161,"score_gpt":0.37844005459102004,"score_spread":0.15718602198610393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996072914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020821149,0.00039203084,0.9759054,0.00025556103,0.00003496189,0.00017138585,0.00021928466,0.0009793153,0.0012208569],"genre_scores_gemma":[0.3404401,0.00033510715,0.65504926,0.00018878309,0.000062794636,0.00042656314,0.0011428407,0.00041680603,0.001937806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98149085,0.012017336,0.0012079307,0.0021187172,0.0027325517,0.00043261354],"domain_scores_gemma":[0.9463329,0.041144077,0.0027200736,0.0053871046,0.0039142957,0.00050148007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019106088,0.0015672365,0.002676014,0.0023242547,0.00085821666,0.0019824565,0.0036723972,0.0019450985,0.0064780572],"category_scores_gemma":[0.0833231,0.0009302447,0.0032933848,0.002537868,0.0011965756,0.0040142946,0.0034267548,0.0024575803,0.0012270397],"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":[0.0017416672,0.0005781859,0.023006825,0.0013690542,0.0019915176,0.00057488197,0.001357939,0.30927235,0.003973052,0.0657625,0.0052455817,0.58512646],"study_design_scores_gemma":[0.00011932099,0.00043957087,0.0043287817,0.00007979266,0.0002043144,0.00020199819,0.00021216077,0.94817257,0.0028845516,0.04007261,0.003167113,0.00011717102],"about_ca_topic_score_codex":0.0064455583,"about_ca_topic_score_gemma":0.007961788,"teacher_disagreement_score":0.019106088,"about_ca_system_score_codex":0.0011819234,"about_ca_system_score_gemma":0.003555204,"threshold_uncertainty_score":0.10104388},"labels":[],"label_agreement":null},{"id":"W2007186404","doi":"10.1207/s15328007sem0702_1","title":"Point Estimation, Hypothesis Testing, and Interval Estimation Using the RMSEA: Some Comments and a Reply to Hayduk and Glaser","year":2000,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":304,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Interval estimation; Point estimation; Statistics; Statistic; Mathematics; Premise; Structural equation modeling; Econometrics; Statistical hypothesis testing; Test statistic; Sample size determination; Estimation; Sampling distribution; Point (geometry); Confidence interval; Epistemology","score_opus":0.17946195039624302,"score_gpt":0.3933198102466394,"score_spread":0.2138578598503964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007186404","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037220606,0.0044061556,0.003577819,0.98336434,0.0076576737,0.000018932318,0.000054306733,0.00004617294,0.00050249376],"genre_scores_gemma":[0.027845243,0.011855404,0.016988257,0.88952523,0.050200917,0.00054183113,0.000080318365,0.00024995263,0.002712892],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91373837,0.05159785,0.009393743,0.0076713087,0.01650442,0.0010942802],"domain_scores_gemma":[0.39823258,0.535451,0.010039077,0.010598052,0.04338778,0.0022914473],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.120066196,0.0020228252,0.0028595943,0.0033295616,0.0052630757,0.007898712,0.008283846,0.031335168,0.003922678],"category_scores_gemma":[0.46954682,0.0012709947,0.0026544314,0.0057261917,0.026129846,0.016224474,0.0059434804,0.056639083,0.0029291192],"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":[0.00014852235,0.000047865356,0.001343973,0.0005657617,0.00008489732,0.000492277,0.0054206243,0.001356136,0.00031653023,0.16810668,0.7819074,0.040209305],"study_design_scores_gemma":[0.00028800822,0.00020264769,0.004097857,0.004537453,0.00011014045,0.00077737786,0.0082709985,0.0083241705,0.0013329708,0.48648953,0.48481867,0.0007502354],"about_ca_topic_score_codex":0.015123156,"about_ca_topic_score_gemma":0.0065271915,"teacher_disagreement_score":0.87993383,"about_ca_system_score_codex":0.010581571,"about_ca_system_score_gemma":0.0074270014,"threshold_uncertainty_score":0.6349783},"labels":[],"label_agreement":null},{"id":"W2025516546","doi":"10.1080/10705511.2013.742388","title":"A Note on Sample Size and Solution Propriety for Confirmatory Factor Analytic Models","year":2013,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"American Psychological Association","keywords":"Sample size determination; Sample (material); Confirmatory factor analysis; Rule of thumb; Statistic; Statistics; Econometrics; Multivariate statistics; Set (abstract data type); Latent variable; Computer science; Structural equation modeling; Mathematics; Algorithm","score_opus":0.44235579597820196,"score_gpt":0.4574590563859317,"score_spread":0.015103260407729746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025516546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026303232,0.005566452,0.8272926,0.08186895,0.011419076,0.01163628,0.0018097158,0.0013573626,0.03274636],"genre_scores_gemma":[0.10236723,0.0012348099,0.8575965,0.011260229,0.0015397705,0.021754924,0.0005217488,0.0007080412,0.0030166067],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.502901,0.36039996,0.047766168,0.011599375,0.074879654,0.002453846],"domain_scores_gemma":[0.1444222,0.74315584,0.011538246,0.04133542,0.057405297,0.0021428934],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.41144112,0.0016435644,0.003913511,0.0060929055,0.0060154563,0.009108548,0.007212331,0.0080458205,0.013604873],"category_scores_gemma":[0.7874581,0.0023769848,0.002754903,0.0066630174,0.009229079,0.012882555,0.0073061837,0.014881479,0.0030125782],"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":[0.0028397024,0.00070801517,0.015208191,0.005538281,0.000459629,0.0011787465,0.016291574,0.0045700977,0.0061775832,0.27706468,0.107879214,0.56208426],"study_design_scores_gemma":[0.0028171213,0.004187395,0.030946821,0.01589616,0.00048502162,0.0036270728,0.007203506,0.03815202,0.014039286,0.42981505,0.45203716,0.00079341826],"about_ca_topic_score_codex":0.003006652,"about_ca_topic_score_gemma":0.006080643,"teacher_disagreement_score":0.5885589,"about_ca_system_score_codex":0.003346649,"about_ca_system_score_gemma":0.011706077,"threshold_uncertainty_score":0.7257979},"labels":[],"label_agreement":null},{"id":"W2040384528","doi":"10.1080/10705511.2014.935266","title":"Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models","year":2014,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Western Canada Research Grid","keywords":"Latent variable; Inference; Interval estimation; Latent variable model; Estimation; Econometrics; Interval (graph theory); Statistics; Computer science; Confidence interval; Mathematics; Artificial intelligence; Economics","score_opus":0.10197084436957725,"score_gpt":0.4250064667261869,"score_spread":0.32303562235660965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040384528","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032760387,0.0003201605,0.99502385,0.00014188148,0.00004422142,0.000114089155,0.00011243526,0.00021619082,0.000751132],"genre_scores_gemma":[0.14878905,0.00077254977,0.84679425,0.0001554507,0.00018937328,0.0014945699,0.000709918,0.0002645171,0.0008303171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89728594,0.08928973,0.0021070156,0.0045380755,0.006099358,0.0006799078],"domain_scores_gemma":[0.43958965,0.52306575,0.010449224,0.018545128,0.007695254,0.0006549485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09890927,0.0021689949,0.002571296,0.008024146,0.0013549236,0.0042540194,0.006281473,0.002545433,0.011663983],"category_scores_gemma":[0.425139,0.0012501815,0.004040896,0.0074547953,0.004424902,0.0060658073,0.0056861253,0.007349854,0.001174284],"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":[0.00031225322,0.00024756766,0.013876925,0.00092072244,0.001291001,0.00019116359,0.0016211849,0.10933348,0.00047575918,0.6039735,0.003893429,0.26386306],"study_design_scores_gemma":[0.00011586428,0.0001337297,0.0027006543,0.0006258829,0.00024195814,0.00014079968,0.00029490818,0.46545082,0.0011800188,0.5250509,0.003969162,0.000095273004],"about_ca_topic_score_codex":0.004121858,"about_ca_topic_score_gemma":0.002629756,"teacher_disagreement_score":0.09890927,"about_ca_system_score_codex":0.0021590998,"about_ca_system_score_gemma":0.0026855334,"threshold_uncertainty_score":0.52308846},"labels":[],"label_agreement":null},{"id":"W2058340619","doi":"10.1080/10705510902751010","title":"Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept","year":2009,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Mental Health Research Topics","field":"Psychology","cited_by":1179,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Latent variable; Covariate; Psychology; Predictive power; Set (abstract data type); Variable (mathematics); Social psychology; Sociology; Econometrics; Statistics; Mathematics; Epistemology; Computer science","score_opus":0.17479769374811469,"score_gpt":0.37888111477693015,"score_spread":0.20408342102881546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058340619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1792056,0.0010178728,0.80971974,0.0010288658,0.000071048096,0.00040618415,0.0007327202,0.00014893191,0.007669057],"genre_scores_gemma":[0.87125546,0.0005159942,0.12602696,0.00009462294,0.000048374088,0.0006235227,0.0006323939,0.000034390836,0.00076823804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99319357,0.0047237547,0.000295127,0.00062617374,0.0009895002,0.00017196212],"domain_scores_gemma":[0.98142654,0.01311375,0.0013412518,0.0019715726,0.0017206519,0.00042628738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00790455,0.00066196016,0.0007461395,0.0034721405,0.00069185795,0.0032491446,0.00094188744,0.0007159625,0.0020502713],"category_scores_gemma":[0.031383786,0.0003009249,0.0015822554,0.003477211,0.0018856585,0.0035364055,0.0021742987,0.0018463597,0.00037250982],"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":[0.00017146609,0.00041764922,0.10124725,0.0005571304,0.00076999754,0.00009124742,0.008288641,0.013059072,0.0012523027,0.5633028,0.0028879382,0.30795446],"study_design_scores_gemma":[0.000033379987,0.00014719798,0.06480765,0.00036636213,0.00019912695,0.0001860739,0.0032792669,0.16408466,0.0007660591,0.7617795,0.004243007,0.000107747204],"about_ca_topic_score_codex":0.001700699,"about_ca_topic_score_gemma":0.0017715945,"teacher_disagreement_score":0.00790455,"about_ca_system_score_codex":0.0016186206,"about_ca_system_score_gemma":0.0021928707,"threshold_uncertainty_score":0.041803718},"labels":[],"label_agreement":null},{"id":"W2069664813","doi":"10.1207/s15328007sem1003_7","title":"Structure of Perceptions of Service Quality in Libraries: A LibQUAL+(tm) Study","year":2003,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Texas A and M University; U.S. Department of Education","keywords":"Confirmatory factor analysis; Service quality; Psychology; Library science; Perception; Scale (ratio); Graduate students; Service (business); Medical education; Computer science; Geography; Business; Pedagogy; Medicine; Marketing; Cartography","score_opus":0.06609132315408978,"score_gpt":0.3257763473969722,"score_spread":0.2596850242428824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069664813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995241,0.0000104295,0.00007701553,0.000035156558,4.956876e-7,0.000011676422,0.000024117857,0.0000010990516,0.0003159031],"genre_scores_gemma":[0.9996037,0.000013734885,0.000117624026,0.00003609695,0.0000018699377,0.000018479135,0.000042408014,0.0000013344013,0.00016478756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99799985,0.0010116177,0.00015534699,0.00010097349,0.00044079355,0.00029134925],"domain_scores_gemma":[0.98913634,0.003979236,0.0029100135,0.00068424665,0.002058034,0.0012321562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053901607,0.00014469396,0.00025284087,0.0014768527,0.001191877,0.0018463017,0.00038660056,0.0003657746,0.002164291],"category_scores_gemma":[0.013909291,0.00021253788,0.0003114654,0.0024717976,0.0011504535,0.001724771,0.0016518475,0.00084644795,0.0002703067],"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":[0.00009677473,0.0004949149,0.96401966,0.000028643437,0.00002154874,0.000036408976,0.023480864,0.00007820094,0.0002925043,0.0004116801,0.0002204271,0.010818398],"study_design_scores_gemma":[0.000011483434,0.00043811454,0.9532755,0.000026405993,0.000015812131,0.00007453685,0.04430976,0.0006572865,0.0002954446,0.00019118845,0.0006883536,0.000016102698],"about_ca_topic_score_codex":0.024986794,"about_ca_topic_score_gemma":0.033110935,"teacher_disagreement_score":0.024986794,"about_ca_system_score_codex":0.0020999967,"about_ca_system_score_gemma":0.0031694376,"threshold_uncertainty_score":0.049682736},"labels":[],"label_agreement":null},{"id":"W2070018731","doi":"10.1207/s15328007sem1002_3","title":"Testing Recursive Path Models With Correlated Errors Using D-Separation","year":2003,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Conditional independence; Mathematics; Directed acyclic graph; Path (computing); Independence (probability theory); Markov chain; Path analysis (statistics); Statistic; Graph; Statistics; Test statistic; Algorithm; Combinatorics; Statistical hypothesis testing; Discrete mathematics; Applied mathematics; Computer science","score_opus":0.1075245674385468,"score_gpt":0.3212577342572336,"score_spread":0.2137331668186868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070018731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26020688,0.0000940907,0.7344696,0.0005932766,0.000044500222,0.00014051543,0.00039781796,0.00041180302,0.0036414359],"genre_scores_gemma":[0.9066535,0.000058123398,0.092005804,0.00012113448,0.000023561865,0.00015341424,0.00048224308,0.000052278116,0.0004498787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95446044,0.034967612,0.0011979386,0.0055589005,0.0029158308,0.00089916756],"domain_scores_gemma":[0.5594969,0.40721995,0.010507131,0.015651291,0.005264311,0.0018604408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031419173,0.0010501972,0.0018927339,0.0025125723,0.0011653356,0.0025686156,0.0027508617,0.001993811,0.0069149695],"category_scores_gemma":[0.21799254,0.0008314759,0.0025320482,0.003250229,0.004158613,0.004669775,0.0052995994,0.0032200436,0.00054104615],"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":[0.0014162802,0.00081602903,0.11990652,0.00033993882,0.0017700598,0.00097334536,0.0016268174,0.16662125,0.0016862364,0.5596062,0.0026165214,0.14262073],"study_design_scores_gemma":[0.00020553979,0.00031534638,0.00909397,0.000052249125,0.00015043336,0.00016799812,0.00035398838,0.62052476,0.0012859347,0.36647376,0.0013141789,0.000061903214],"about_ca_topic_score_codex":0.0058733067,"about_ca_topic_score_gemma":0.003156535,"teacher_disagreement_score":0.031419173,"about_ca_system_score_codex":0.0017465567,"about_ca_system_score_gemma":0.0035886,"threshold_uncertainty_score":0.16616243},"labels":[],"label_agreement":null},{"id":"W2089037674","doi":"10.1080/10705511003659375","title":"Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison","year":2010,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute on Drug Abuse","keywords":"Statistics; Statistic; Monte Carlo method; Sample size determination; Missing data; Mathematics; Type I and type II errors; Chi-square test; Econometrics","score_opus":0.4244016015080558,"score_gpt":0.4533223356727255,"score_spread":0.028920734164669726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089037674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021383506,0.00032454033,0.9956678,0.00034153103,0.00008795379,0.00018127727,0.000044358792,0.000106281186,0.0011079106],"genre_scores_gemma":[0.10836015,0.0011218678,0.88477284,0.00052336603,0.00037385718,0.0025889468,0.00022755511,0.00041168358,0.0016198063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90106493,0.08032203,0.002703264,0.004604759,0.010750314,0.000554805],"domain_scores_gemma":[0.42575917,0.52719826,0.01189047,0.02491615,0.009240804,0.0009951855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13642573,0.0019068706,0.004029995,0.0052258116,0.0014593843,0.004181698,0.005253162,0.0034701924,0.009985706],"category_scores_gemma":[0.5066421,0.001291406,0.003139763,0.0062809996,0.007880005,0.013777678,0.0047730426,0.0071674935,0.0013014283],"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":[0.000101438476,0.000082072205,0.002966687,0.00036959467,0.00022615827,0.0002672079,0.0008507798,0.049967263,0.00017867984,0.87592185,0.0030706103,0.06599765],"study_design_scores_gemma":[0.00005459232,0.00020132169,0.0009811907,0.00029120417,0.0000826046,0.00027412432,0.00015307554,0.26232108,0.0003810611,0.7292975,0.0058840904,0.00007813332],"about_ca_topic_score_codex":0.0017916475,"about_ca_topic_score_gemma":0.0015116481,"teacher_disagreement_score":0.13642573,"about_ca_system_score_codex":0.0021467328,"about_ca_system_score_gemma":0.003968487,"threshold_uncertainty_score":0.7214968},"labels":[],"label_agreement":null},{"id":"W2093729006","doi":"10.1080/10705511.2013.742385","title":"Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies","year":2013,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Bonferroni correction; Mathematics; Statistics; Type I and type II errors; Multiplicity (mathematics); Multiple comparisons problem; Structural equation modeling; Econometrics; Mathematical analysis","score_opus":0.3600477337163394,"score_gpt":0.4263533006677822,"score_spread":0.0663055669514428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093729006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012634309,0.0005936214,0.98291117,0.0007006621,0.00029942274,0.00087088894,0.00009003284,0.0003466409,0.0015533575],"genre_scores_gemma":[0.29122263,0.00070216553,0.70114356,0.0005694662,0.0003620274,0.004391518,0.00022798066,0.0003722113,0.0010083421],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7399312,0.21517207,0.010024963,0.018521743,0.014763741,0.0015861788],"domain_scores_gemma":[0.32592213,0.60093683,0.020518098,0.039600823,0.011484271,0.0015378222],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20457685,0.0040998748,0.004374988,0.005999249,0.0034681207,0.005554785,0.006264794,0.004423241,0.009098556],"category_scores_gemma":[0.6226934,0.0025018693,0.0054125213,0.007436273,0.0067970906,0.012947776,0.008490038,0.010006606,0.0008954719],"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":[0.0019126941,0.0011660533,0.07520883,0.002644122,0.006434141,0.0012862417,0.014335686,0.07183813,0.002977443,0.38284832,0.0071452498,0.43220317],"study_design_scores_gemma":[0.0007041203,0.0024695592,0.016752055,0.0014533623,0.002165994,0.00070307864,0.0014444249,0.3572637,0.004491608,0.5987761,0.013317426,0.0004584493],"about_ca_topic_score_codex":0.0033129517,"about_ca_topic_score_gemma":0.003462494,"teacher_disagreement_score":0.20457685,"about_ca_system_score_codex":0.0025162797,"about_ca_system_score_gemma":0.006495285,"threshold_uncertainty_score":0.9808984},"labels":[],"label_agreement":null},{"id":"W2103615853","doi":"10.1080/10705510802154323","title":"Avoiding and Correcting Bias in Score-Based Latent Variable Regression With Discrete Manifest Items","year":2008,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Latent variable; Structural equation modeling; Latent variable model; Ordinary least squares; Econometrics; Statistics; Local independence; Regression; Regression analysis; Item response theory; Latent class model; Mathematics; Computer science; Psychometrics","score_opus":0.5332678160292689,"score_gpt":0.43389796024411,"score_spread":0.09936985578515889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103615853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026030904,0.00067202444,0.97074395,0.0004796645,0.00007019397,0.00015837084,0.000081313214,0.00059401896,0.0011695187],"genre_scores_gemma":[0.27436286,0.0008505858,0.7213241,0.00032941843,0.000120588506,0.0007269827,0.00035775822,0.00034834424,0.0015793644],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89429456,0.09306292,0.002248007,0.0036252425,0.005754001,0.0010152395],"domain_scores_gemma":[0.720468,0.23768745,0.011454769,0.020998664,0.008627141,0.0007640398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10329424,0.0022399516,0.003332552,0.0044518146,0.0011353294,0.0029574006,0.0035877067,0.0029271913,0.0022078278],"category_scores_gemma":[0.34863338,0.0013725079,0.0027822554,0.007905537,0.0040673497,0.004796694,0.0060164286,0.0045720716,0.0014055758],"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":[0.0006282062,0.00023021498,0.03639488,0.0009327878,0.0012790485,0.0005704307,0.0032107776,0.25890818,0.0016719388,0.22617002,0.002971705,0.46703184],"study_design_scores_gemma":[0.00020215595,0.00036717317,0.005767115,0.00040745808,0.00022737458,0.00025415252,0.00038153594,0.76961416,0.0024236816,0.215316,0.004862828,0.00017630657],"about_ca_topic_score_codex":0.007971122,"about_ca_topic_score_gemma":0.006602189,"teacher_disagreement_score":0.10329424,"about_ca_system_score_codex":0.0018122197,"about_ca_system_score_gemma":0.0033849317,"threshold_uncertainty_score":0.5462787},"labels":[],"label_agreement":null},{"id":"W2118846274","doi":"10.1080/10705511.2014.919819","title":"Effect Size, Statistical Power, and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis","year":2014,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":456,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"National Cancer Institute; National Institute on Drug Abuse","keywords":"Sample size determination; Statistics; Latent class model; Statistical power; Likelihood-ratio test; Mathematics; Sample (material); Population; Monte Carlo method; Econometrics; Class (philosophy); Type I and type II errors; Power (physics); Computer science; Artificial intelligence; Demography","score_opus":0.03295265194356398,"score_gpt":0.34019338394351684,"score_spread":0.30724073199995283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118846274","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012921785,0.001686426,0.9711194,0.003539799,0.00029017212,0.0014152499,0.00076653424,0.0004707208,0.0077899275],"genre_scores_gemma":[0.22587766,0.0013658708,0.7556546,0.0014948089,0.00046641112,0.012325896,0.0011218945,0.0005933607,0.0010995457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8485617,0.12362293,0.005947094,0.005655742,0.015261672,0.0009508497],"domain_scores_gemma":[0.31365117,0.6524284,0.006682066,0.015912721,0.010381207,0.0009444294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15243092,0.001258758,0.0033504632,0.0037582442,0.0013081995,0.0033683265,0.0037440096,0.0051312232,0.010477699],"category_scores_gemma":[0.6035471,0.0013972849,0.0024035778,0.0037391859,0.005436583,0.0070810774,0.0036481076,0.0066219578,0.002385603],"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":[0.0025456909,0.0005188216,0.013785854,0.0031171124,0.0006824125,0.0006047638,0.0022690056,0.034722432,0.0053041494,0.4859482,0.021838041,0.42866346],"study_design_scores_gemma":[0.0014485344,0.0018344006,0.02030744,0.0019642566,0.0004680792,0.0018038574,0.00083276787,0.16503258,0.0069967834,0.77869195,0.020293804,0.0003255866],"about_ca_topic_score_codex":0.0012824368,"about_ca_topic_score_gemma":0.0012577041,"teacher_disagreement_score":0.15243092,"about_ca_system_score_codex":0.001702981,"about_ca_system_score_gemma":0.003057818,"threshold_uncertainty_score":0.8061414},"labels":[],"label_agreement":null},{"id":"W2129251172","doi":"10.1080/10705511.2014.935265","title":"Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis","year":2014,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":257,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Drug Abuse","keywords":"Latent class model; Class (philosophy); Simplicity; Computer science; Latent variable; Sample (material); Quality (philosophy); Sample size determination; Latent variable model; Machine learning; Empirical research; Artificial intelligence; Econometrics; Statistics; Mathematics","score_opus":0.41345061215472545,"score_gpt":0.4494748969907112,"score_spread":0.03602428483598574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129251172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037335271,0.00011462341,0.99487466,0.00026417233,0.00003769943,0.00017672071,0.000054826458,0.00019640646,0.00054742553],"genre_scores_gemma":[0.104824975,0.0002670246,0.89119273,0.00046702876,0.00012588437,0.0019512438,0.00026728777,0.00020064037,0.00070319953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94394237,0.044572264,0.0014371995,0.0033916908,0.006206236,0.00045033952],"domain_scores_gemma":[0.8209569,0.14695157,0.006588405,0.018339906,0.0065064006,0.00065683055],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05574513,0.0019661945,0.0024598378,0.0040131323,0.0024516433,0.0033783766,0.0029624428,0.0025201233,0.004614747],"category_scores_gemma":[0.1969837,0.0010958507,0.0023263986,0.0034710122,0.0032429674,0.004508701,0.0048394557,0.0056855,0.0013163289],"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":[0.0005265756,0.0005486801,0.015533455,0.0008769835,0.001128579,0.00016211158,0.0027396695,0.044604443,0.0022368592,0.43308136,0.0053026956,0.49325866],"study_design_scores_gemma":[0.00015457685,0.0002556733,0.003660523,0.00023831827,0.00021991412,0.00014708191,0.000395009,0.37472537,0.0029898968,0.61031455,0.006806995,0.00009209507],"about_ca_topic_score_codex":0.001922696,"about_ca_topic_score_gemma":0.0030327633,"teacher_disagreement_score":0.9442549,"about_ca_system_score_codex":0.0017883852,"about_ca_system_score_gemma":0.0033017853,"threshold_uncertainty_score":0.2948119},"labels":[],"label_agreement":null},{"id":"W2144487201","doi":"10.1080/10705510903008220","title":"Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching","year":2009,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":1080,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Economic and Social Research Council","keywords":"Structural equation modeling; Confirmatory factor analysis; Goodness of fit; Latent variable; Psychology; Statistics; Measurement invariance; Environmental scanning electron microscope; Exploratory factor analysis; Econometrics; Mathematics","score_opus":0.3055562692897275,"score_gpt":0.48058043964339947,"score_spread":0.17502417035367196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144487201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13058932,0.0003813372,0.861043,0.00073249213,0.00007245592,0.0015289999,0.0006180906,0.0014596778,0.0035746407],"genre_scores_gemma":[0.347307,0.0002941478,0.6488952,0.00010270846,0.00003085823,0.0023850126,0.00039638046,0.0001340377,0.00045474767],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97309124,0.023224356,0.00081996265,0.00089743675,0.0017188073,0.00024816825],"domain_scores_gemma":[0.91664773,0.0709399,0.0023835455,0.004806654,0.004849082,0.00037309196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039140373,0.0016659264,0.0011949878,0.0042554718,0.0012904794,0.0020984556,0.0010535779,0.0007197165,0.0023498007],"category_scores_gemma":[0.09327305,0.0008967317,0.002463948,0.0060890657,0.000987209,0.0020475248,0.002668435,0.002363415,0.00042462628],"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":[0.00042376292,0.001065445,0.11163333,0.0011012628,0.0016191897,0.00038170777,0.014232084,0.042968303,0.0035196573,0.036244057,0.0055347197,0.7812766],"study_design_scores_gemma":[0.00033657477,0.001831736,0.0867308,0.0011048783,0.00064867054,0.00060407753,0.007931569,0.75027555,0.004237674,0.13403119,0.011954374,0.00031292674],"about_ca_topic_score_codex":0.004991621,"about_ca_topic_score_gemma":0.008192872,"teacher_disagreement_score":0.039140373,"about_ca_system_score_codex":0.0012697163,"about_ca_system_score_gemma":0.0038108951,"threshold_uncertainty_score":0.20699656},"labels":[],"label_agreement":null},{"id":"W2345476363","doi":"10.1080/10705511.2016.1169188","title":"Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models","year":2016,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Latent class model; Covariance; Mathematics; Statistics; Covariance matrix; Mixture model; Residual; Population; Variance (accounting); Econometrics; Algorithm","score_opus":0.12164574989365973,"score_gpt":0.3807370481958413,"score_spread":0.2590912983021816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345476363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55665493,0.0008647219,0.43628323,0.0013166221,0.00008679453,0.00039034584,0.00038243787,0.00041601164,0.0036048363],"genre_scores_gemma":[0.91203004,0.00028386692,0.08591184,0.0002491493,0.000024611552,0.000306725,0.0005141041,0.00010996124,0.00056968885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9671943,0.024986168,0.0017924596,0.002736145,0.002430398,0.00086051103],"domain_scores_gemma":[0.41832045,0.5341681,0.016199593,0.022850193,0.007529321,0.0009323179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07840035,0.0013193745,0.0015456091,0.0015971382,0.0012790858,0.0026158043,0.001967106,0.0022519962,0.0018445783],"category_scores_gemma":[0.37273434,0.0009438346,0.0016507817,0.0013668109,0.0022696347,0.004740867,0.0031255332,0.0037138243,0.0003103074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015872188,0.0003361187,0.05889632,0.0002773035,0.00053189613,0.00024643054,0.0014360052,0.84479946,0.0019017332,0.042996187,0.0009851075,0.046006143],"study_design_scores_gemma":[0.00012164448,0.00048628333,0.009659769,0.00019984797,0.00015848553,0.00017593852,0.00040763538,0.955644,0.0029918647,0.029214729,0.00083142705,0.00010833573],"about_ca_topic_score_codex":0.011644145,"about_ca_topic_score_gemma":0.011017592,"teacher_disagreement_score":0.07840035,"about_ca_system_score_codex":0.0025806685,"about_ca_system_score_gemma":0.0026649728,"threshold_uncertainty_score":0.41462564},"labels":[],"label_agreement":null},{"id":"W2498254812","doi":"10.1080/10705511.2016.1207180","title":"Analysis of Correlation Matrices Using Scale-Invariant Common Principal Component Models and a Hierarchy of Relationships Between Correlation Matrices","year":2016,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Applications","field":"Mathematics","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":"McGill University","funders":"","keywords":"Principal component analysis; Invariant (physics); Correlation; Mathematics; Scale (ratio); Scale invariance; Applied mathematics; Hierarchy; Statistics; Geometry; Physics","score_opus":0.2123378440265293,"score_gpt":0.4079992422006015,"score_spread":0.19566139817407222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498254812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007744849,0.00027809173,0.989666,0.00018611352,0.000025231,0.000055557375,0.00008183799,0.00015453299,0.0018077637],"genre_scores_gemma":[0.43112263,0.0015345092,0.56242496,0.00019842871,0.00015686137,0.00043141507,0.0005703699,0.00022966231,0.0033311385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995456,0.001990402,0.00020434252,0.0010101802,0.0010926324,0.000246455],"domain_scores_gemma":[0.99075174,0.004714571,0.0014073766,0.0014275981,0.0014744321,0.00022430716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056434125,0.0015591877,0.0009148948,0.0038448148,0.0009780863,0.0026746502,0.0014493825,0.0009438188,0.0035158643],"category_scores_gemma":[0.02599279,0.0006134123,0.001710694,0.005330696,0.002465615,0.0045590345,0.0017799693,0.0023627563,0.00092881796],"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":[0.00005106798,0.00009230097,0.00877318,0.00032986098,0.00030638973,0.0004301456,0.0010093626,0.11622435,0.0039372956,0.7234969,0.0042374446,0.14111178],"study_design_scores_gemma":[0.000010768831,0.000059613856,0.004227564,0.000056860077,0.00008046731,0.0001832807,0.00016970524,0.6491434,0.00081929716,0.34079581,0.0043792315,0.00007410112],"about_ca_topic_score_codex":0.006060233,"about_ca_topic_score_gemma":0.0040300176,"teacher_disagreement_score":0.006060233,"about_ca_system_score_codex":0.0011562719,"about_ca_system_score_gemma":0.0018409955,"threshold_uncertainty_score":0.029845595},"labels":[],"label_agreement":null},{"id":"W2755984399","doi":"10.1080/10705511.2017.1364969","title":"Using Latent Variable Modeling for Discrete Time Survival Analysis: Examining the Links of Depression to Mortality","year":2017,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Latent variable; Latent variable model; Structural equation modeling; Latent class model; Depression (economics); Econometrics; Latent growth modeling; Statistics; Psychology; Variable (mathematics); Survival analysis; Mathematics; Economics","score_opus":0.24540594369333424,"score_gpt":0.4508536882828601,"score_spread":0.20544774458952586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755984399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14314435,0.0007615861,0.85054886,0.0024869682,0.00020333206,0.00022382445,0.0007876195,0.00029926555,0.0015441907],"genre_scores_gemma":[0.79888165,0.0011271778,0.19599058,0.00023854335,0.00019111241,0.00086664106,0.0011107932,0.00010696987,0.0014864729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98892784,0.00976604,0.00018413893,0.00055252534,0.00028841489,0.00028110735],"domain_scores_gemma":[0.9613292,0.03417155,0.0018381245,0.0017801719,0.00047891156,0.00040193825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01844948,0.0011187216,0.0012407043,0.002482246,0.0008801319,0.002776397,0.0017284836,0.0012451095,0.0020791227],"category_scores_gemma":[0.046969667,0.0004765345,0.0025194858,0.0038952685,0.001042978,0.0020618986,0.0026765359,0.0027647035,0.00030596086],"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":[0.0005288121,0.00097333634,0.26186785,0.00043304774,0.0035976511,0.0005809604,0.005220986,0.28839275,0.0012356763,0.31105906,0.0049310974,0.12117874],"study_design_scores_gemma":[0.00006750986,0.00022635768,0.017140338,0.0001543016,0.00031151742,0.000116881994,0.0010615968,0.8384451,0.00022467157,0.14007376,0.0020865502,0.00009147474],"about_ca_topic_score_codex":0.015946358,"about_ca_topic_score_gemma":0.017467132,"teacher_disagreement_score":0.01844948,"about_ca_system_score_codex":0.0015883816,"about_ca_system_score_gemma":0.0038318024,"threshold_uncertainty_score":0.09757137},"labels":[],"label_agreement":null},{"id":"W2765291517","doi":"10.1080/10705511.2017.1374187","title":"An Investigation of the Alignment Method With Polytomous Indicators Under Conditions of Partial Measurement Invariance","year":2017,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"University of Connecticut; U.S. Department of Education","keywords":"Polytomous Rasch model; Measurement invariance; Skew; Econometrics; Computer science; Factor analysis; Statistical power; Statistics; Mathematics; Structural equation modeling; Confirmatory factor analysis; Item response theory; Psychometrics","score_opus":0.42639576723060524,"score_gpt":0.4729737817399195,"score_spread":0.04657801450931426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765291517","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07610281,0.00028765027,0.9192025,0.0009351141,0.000112246664,0.0004584461,0.00009190568,0.00039864558,0.0024106116],"genre_scores_gemma":[0.3693265,0.00019578879,0.62795955,0.0002803895,0.00010319351,0.0010847885,0.00027770342,0.0002309823,0.0005410977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9025088,0.08346507,0.0023228526,0.004680825,0.006049993,0.00097250834],"domain_scores_gemma":[0.6294324,0.32067868,0.013865098,0.022506524,0.011688893,0.0018283508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10271674,0.0016754814,0.002553871,0.0031236305,0.0022029402,0.0029861603,0.0033018857,0.0025875203,0.006298724],"category_scores_gemma":[0.3676462,0.00095346407,0.0026585953,0.0068901307,0.0039099976,0.009574613,0.005245998,0.005960978,0.0010607247],"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":[0.0020985485,0.001204636,0.07818362,0.0010366706,0.0014010771,0.0007079114,0.0061077275,0.08653919,0.005966197,0.3257545,0.0049299463,0.48606995],"study_design_scores_gemma":[0.00056607986,0.0014077001,0.017854717,0.00023764119,0.00026542993,0.00047277566,0.0011132783,0.8231849,0.004057782,0.1463646,0.004260245,0.00021489104],"about_ca_topic_score_codex":0.0037119794,"about_ca_topic_score_gemma":0.0026819764,"teacher_disagreement_score":0.10271674,"about_ca_system_score_codex":0.0018222999,"about_ca_system_score_gemma":0.004093822,"threshold_uncertainty_score":0.5432245},"labels":[],"label_agreement":null},{"id":"W2791145377","doi":"10.1080/10705511.2017.1409074","title":"Model Specification Searches in Structural Equation Modeling with <i>R</i>","year":2018,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Tabu search; Computer science; Simulated annealing; Code (set theory); Structural equation modeling; Programming language; Genetic programming; Ant colony optimization algorithms; Source code; Algorithm; Theoretical computer science; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning","score_opus":0.1252571229299742,"score_gpt":0.32702419696353546,"score_spread":0.20176707403356126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791145377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011456459,0.00015562524,0.9906494,0.00035765124,0.00003849446,0.00014332357,0.0006805143,0.00513471,0.0016946321],"genre_scores_gemma":[0.012497074,0.0001553647,0.9834606,0.00012735423,0.000019490417,0.001017095,0.0006403577,0.0014319633,0.00065072265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9845517,0.012344156,0.0007654796,0.0008325459,0.0013339422,0.00017222133],"domain_scores_gemma":[0.9399099,0.053100422,0.0022647094,0.0028309394,0.0016767076,0.00021751027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017441219,0.0028477178,0.0011234826,0.0027337836,0.0008291103,0.0018024456,0.0021640644,0.0013507367,0.02389378],"category_scores_gemma":[0.09047329,0.0015806004,0.0025637483,0.0032585722,0.0014838568,0.0018302796,0.0023788898,0.002562662,0.010418776],"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":[0.00039807317,0.00027628316,0.0072871307,0.0027651393,0.0010767735,0.0007899684,0.0018091197,0.09399361,0.0031550133,0.3123129,0.114721976,0.46141404],"study_design_scores_gemma":[0.00035764452,0.00027587498,0.0030103636,0.00094199454,0.0003019554,0.0009208005,0.000397893,0.5151125,0.0096545415,0.36912817,0.0996834,0.00021484829],"about_ca_topic_score_codex":0.003943033,"about_ca_topic_score_gemma":0.0063273716,"teacher_disagreement_score":0.02389378,"about_ca_system_score_codex":0.0008691049,"about_ca_system_score_gemma":0.003090137,"threshold_uncertainty_score":0.09223908},"labels":[],"label_agreement":null},{"id":"W2793194576","doi":"10.1080/10705511.2018.1442224","title":"Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales","year":2018,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Mental Health Research Topics","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute on Drug Abuse","keywords":"Statistics; Standard deviation; Econometrics; Mathematics; Robustness (evolution); Observational error; Convergence (economics); Statistical physics; Applied mathematics; Physics; Economics","score_opus":0.1699244356728999,"score_gpt":0.41291584362960987,"score_spread":0.24299140795670998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793194576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24215513,0.00026404887,0.7525121,0.00059427746,0.00006555056,0.00010500975,0.00026020728,0.00029384025,0.0037498842],"genre_scores_gemma":[0.9398791,0.00017896426,0.056534026,0.000079421414,0.000038977167,0.00019714888,0.000114917646,0.000034681034,0.0029428345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999548,0.0001588135,0.000022186123,0.00016914899,0.00004929469,0.000052503055],"domain_scores_gemma":[0.99908054,0.0005407097,0.00015871621,0.00009721086,0.00007196929,0.00005086129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015073258,0.0004642294,0.0006725499,0.00047821,0.00037518956,0.0011793689,0.0012247207,0.0010588362,0.0022438949],"category_scores_gemma":[0.00526644,0.00050155586,0.0009790447,0.00045026685,0.00074057054,0.0014136948,0.0011411231,0.0015356146,0.00027652833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016185874,0.00019254061,0.023807427,0.000083323546,0.00027991258,0.0001491023,0.0008861206,0.86600006,0.004812901,0.06362654,0.00084062235,0.039159644],"study_design_scores_gemma":[0.000009553518,0.00002094316,0.0015704883,0.000004805928,0.000015415693,0.000016009206,0.000018916886,0.9898347,0.00008537668,0.008133256,0.00028202898,0.000008409637],"about_ca_topic_score_codex":0.010911722,"about_ca_topic_score_gemma":0.006715698,"teacher_disagreement_score":0.010911722,"about_ca_system_score_codex":0.0007766082,"about_ca_system_score_gemma":0.00069634774,"threshold_uncertainty_score":0.021696448},"labels":[],"label_agreement":null},{"id":"W3030632765","doi":"10.1080/10705511.2020.1745644","title":"Teacher’s Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models","year":2020,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Bayes factor; Structural equation modeling; Bayesian probability; Latent variable; Bayes' theorem; Statistical hypothesis testing; Econometrics; Context (archaeology); Computer science; Machine learning; Artificial intelligence; Confirmatory factor analysis; Bayesian statistics; Bayesian inference; Mathematics; Statistics","score_opus":0.49278769041920467,"score_gpt":0.48494375078867286,"score_spread":0.007843939630531815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030632765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025548062,0.0026090094,0.98405737,0.0054818112,0.00050555915,0.000186185,0.00022458112,0.00038561027,0.0039950674],"genre_scores_gemma":[0.0818195,0.0032778603,0.9052639,0.0024760645,0.0011119001,0.0013145301,0.00041225238,0.0007720512,0.0035518995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95392394,0.037619207,0.0012901986,0.0021252322,0.004685226,0.00035608475],"domain_scores_gemma":[0.7890807,0.19548914,0.0031493062,0.005005418,0.006252048,0.0010233278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05955045,0.0017810932,0.0023576377,0.0044697993,0.0014652447,0.005239321,0.0028105273,0.004097679,0.021586917],"category_scores_gemma":[0.31800047,0.0014248834,0.002685969,0.004273369,0.005120497,0.01012264,0.004340014,0.007711277,0.003198551],"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":[0.00024456772,0.00010689867,0.0028910616,0.0009438269,0.00044578422,0.00027739516,0.0013962219,0.01698102,0.0006021242,0.6034922,0.033969104,0.33864984],"study_design_scores_gemma":[0.000052486248,0.00006407957,0.00060954306,0.00064540503,0.00006929566,0.000081699545,0.00016549358,0.047659744,0.0005789127,0.9358311,0.01418159,0.00006073178],"about_ca_topic_score_codex":0.0025182865,"about_ca_topic_score_gemma":0.0027234969,"teacher_disagreement_score":0.05955045,"about_ca_system_score_codex":0.0021002006,"about_ca_system_score_gemma":0.0035530308,"threshold_uncertainty_score":0.31493664},"labels":[],"label_agreement":null},{"id":"W3162812701","doi":"10.1080/10705511.2021.1877548","title":"Computational Options for Standard Errors and Test Statistics with Incomplete Normal and Nonnormal Data in SEM","year":2021,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Test (biology); Computer science; Standard error; Test data; Statistical hypothesis testing; Mathematics; Econometrics; Geology","score_opus":0.043143828677021046,"score_gpt":0.33808818414545927,"score_spread":0.2949443554684382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162812701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00138569,0.000553974,0.9948561,0.00094919855,0.00005239903,0.00006007252,0.00012467298,0.00027591095,0.0017419292],"genre_scores_gemma":[0.034758743,0.0007964627,0.96170527,0.0003044949,0.00011568163,0.00062197336,0.00019564056,0.0003682537,0.0011334157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9668754,0.028118093,0.0013416829,0.0011874945,0.0022369814,0.00024037709],"domain_scores_gemma":[0.7784835,0.20785809,0.0026510952,0.007638383,0.0029082338,0.00046075496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045144606,0.001936785,0.0018705443,0.0033010507,0.0012144106,0.004235797,0.0049069165,0.0029442164,0.015788836],"category_scores_gemma":[0.23659177,0.0013606747,0.002535302,0.0038693047,0.004213161,0.0075407047,0.0051119756,0.0053893323,0.0031941466],"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":[0.00014567822,0.00006444894,0.0013111616,0.0005057244,0.00013571195,0.00021808618,0.00059307186,0.060698424,0.0003650905,0.8111681,0.004487892,0.12030665],"study_design_scores_gemma":[0.000046898713,0.000039059418,0.00025084874,0.00022263252,0.000026540944,0.00011322866,0.00012965771,0.14518109,0.0006079697,0.8486424,0.004692549,0.000047208836],"about_ca_topic_score_codex":0.0029924917,"about_ca_topic_score_gemma":0.005148892,"teacher_disagreement_score":0.045144606,"about_ca_system_score_codex":0.0020093254,"about_ca_system_score_gemma":0.0027922462,"threshold_uncertainty_score":0.23875034},"labels":[],"label_agreement":null},{"id":"W3178307030","doi":"10.1080/10705511.2021.1937177","title":"Multilevel Dynamic Twin Modeling","year":2021,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Mental Health Research Topics","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Twin study; Computer science; Dizygotic twins; Multilevel model; Set (abstract data type); Population; Missing data; Interpretation (philosophy); Monozygotic twin; Genetic model; Econometrics; Mathematics; Machine learning; Heritability; Biology; Evolutionary biology","score_opus":0.1529738349086613,"score_gpt":0.45256614895364455,"score_spread":0.29959231404498327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178307030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031444203,0.0005420543,0.9587257,0.0011691162,0.00012048414,0.00023279384,0.0023556424,0.00029957044,0.005110602],"genre_scores_gemma":[0.49078763,0.0013506566,0.4899317,0.00041816037,0.00018174465,0.0015828204,0.0050762584,0.0002941141,0.010376818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947461,0.0033930263,0.0002507497,0.0008098924,0.00052131945,0.00027893044],"domain_scores_gemma":[0.9939353,0.0035264634,0.00059576763,0.00089833053,0.00081325445,0.00023093424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006538546,0.0007556601,0.0021260118,0.0019198442,0.0012057992,0.002157116,0.0038467895,0.0014418784,0.012960677],"category_scores_gemma":[0.023743736,0.00084456796,0.002687653,0.0036637625,0.0007206478,0.0019673787,0.0031668774,0.0029254202,0.0013770957],"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":[0.0002267955,0.00013745678,0.027388072,0.00028322902,0.0010531658,0.00053905294,0.0013736243,0.19542426,0.0006362293,0.6686007,0.010765195,0.093572214],"study_design_scores_gemma":[0.00008479234,0.00006466767,0.0044597425,0.00011503495,0.00026226725,0.0002819807,0.00019819224,0.72299606,0.00019253927,0.25986266,0.011433235,0.00004871583],"about_ca_topic_score_codex":0.021478567,"about_ca_topic_score_gemma":0.020311872,"teacher_disagreement_score":0.021478567,"about_ca_system_score_codex":0.0016519318,"about_ca_system_score_gemma":0.001826003,"threshold_uncertainty_score":0.04335779},"labels":[],"label_agreement":null},{"id":"W4226220491","doi":"10.1080/10705511.2021.1977648","title":"Prior Predictive Checks for the Method of Covariances in Bayesian Mediation Analysis","year":2021,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wishart distribution; Prior probability; Inverse-Wishart distribution; Bayesian probability; Computer science; Covariance; Covariance matrix; Inverse; Mediation; Prior information; Conjugate prior; Econometrics; Mathematics; Algorithm; Artificial intelligence; Machine learning; Statistics; Multivariate statistics; Political science","score_opus":0.04888226668248531,"score_gpt":0.34602703161759263,"score_spread":0.29714476493510733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226220491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022849257,0.00018464636,0.9928415,0.0010626267,0.00009224823,0.00011916924,0.000052169587,0.0002661193,0.003096574],"genre_scores_gemma":[0.18810193,0.0007524455,0.80502874,0.0010314423,0.00047412486,0.001463779,0.00024119357,0.0007261724,0.0021802695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90849644,0.073299,0.002407582,0.0038986653,0.010936652,0.00096171023],"domain_scores_gemma":[0.4758633,0.472844,0.006251994,0.029105622,0.014878416,0.0010565993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14020087,0.0018211128,0.0022953346,0.005175547,0.0035363417,0.005538736,0.005184703,0.004428702,0.013787504],"category_scores_gemma":[0.5180104,0.002204024,0.0033243867,0.0047077644,0.011626121,0.013181052,0.0068395655,0.014273129,0.0023393265],"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":[0.000117778996,0.000055742938,0.0010036787,0.00014427997,0.00008177951,0.000114602226,0.0007359784,0.00936104,0.00029215738,0.94458044,0.001967886,0.041544564],"study_design_scores_gemma":[0.000072092866,0.00005673932,0.0004603123,0.00032370572,0.000062008105,0.00010408436,0.000100577505,0.07279433,0.0010510269,0.92116094,0.0037447456,0.000069426715],"about_ca_topic_score_codex":0.0049694073,"about_ca_topic_score_gemma":0.0037513713,"teacher_disagreement_score":0.14020087,"about_ca_system_score_codex":0.0033201831,"about_ca_system_score_gemma":0.0069538443,"threshold_uncertainty_score":0.74146193},"labels":[],"label_agreement":null},{"id":"W4236987526","doi":"10.1080/10705510709336736","title":"The Effect of the Number of Observations per Parameter in Misspecified Confirmatory Factor Analytic Models","year":2007,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Sample size determination; Statistics; Structural equation modeling; Context (archaeology); Mathematics; Confirmatory factor analysis; Covariance; Econometrics; Sample (material); Physics; Geography","score_opus":0.4246153844163098,"score_gpt":0.4715834738566535,"score_spread":0.0469680894403437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236987526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33146593,0.0067177936,0.6134875,0.016644109,0.0027129166,0.0053634928,0.0011517868,0.0015135144,0.020943044],"genre_scores_gemma":[0.7717317,0.00074620877,0.21460362,0.002847317,0.0004349705,0.0071193012,0.00045609626,0.00046730743,0.001593617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.25304663,0.58240724,0.06698235,0.047975972,0.046788048,0.0027998271],"domain_scores_gemma":[0.022849495,0.920634,0.014205029,0.034461584,0.007065311,0.0007845376],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.52418214,0.0031355005,0.0043884763,0.00394302,0.006931295,0.007652592,0.0055457344,0.007935125,0.006515443],"category_scores_gemma":[0.8887018,0.0046254857,0.0062961667,0.0064866776,0.013734536,0.022276338,0.009572446,0.013052769,0.0014491708],"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":[0.026366485,0.0036379574,0.31178445,0.005536087,0.008000396,0.0029188003,0.037041176,0.049656034,0.011859754,0.09639828,0.0154249,0.4313757],"study_design_scores_gemma":[0.009405231,0.016693793,0.3266108,0.006657084,0.00780443,0.005164266,0.0080840895,0.18808939,0.028774008,0.33929446,0.061045498,0.0023768868],"about_ca_topic_score_codex":0.0036879617,"about_ca_topic_score_gemma":0.004685813,"teacher_disagreement_score":0.52418214,"about_ca_system_score_codex":0.005298094,"about_ca_system_score_gemma":0.0065827053,"threshold_uncertainty_score":0.58676815},"labels":[],"label_agreement":null},{"id":"W4255033255","doi":"10.1207/s15328007sem1401_5","title":"Multiplicity Control in Structural Equation Modeling","year":2007,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","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":"York University","funders":"","keywords":"Type I and type II errors; Multiplicity (mathematics); Statistical power; Sample size determination; False discovery rate; Statistics; Multiple comparisons problem; Mathematics; Structural equation modeling; Computer science; Algorithm","score_opus":0.3975708787621388,"score_gpt":0.4703581866455891,"score_spread":0.07278730788345028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255033255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01884042,0.002107814,0.9684579,0.002324844,0.00087770616,0.0016243621,0.00019670844,0.00046230122,0.0051079732],"genre_scores_gemma":[0.43902245,0.0014956957,0.54850763,0.0014069184,0.00095837784,0.006152521,0.0002999224,0.0004117074,0.0017448206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.4798814,0.44095856,0.019727675,0.022819374,0.034461666,0.0021513158],"domain_scores_gemma":[0.13277325,0.78810525,0.023411216,0.040788468,0.013776214,0.0011456101],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3270911,0.0028897116,0.0046096533,0.00731129,0.004997692,0.0071713864,0.005534989,0.0046554236,0.0074323253],"category_scores_gemma":[0.7744489,0.0022993498,0.0043894183,0.009394391,0.012207132,0.011765917,0.00846406,0.010048564,0.0008359171],"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":[0.0015069521,0.00057880755,0.07983252,0.0034183743,0.0036712857,0.001153247,0.0142764775,0.025645262,0.0015634042,0.5441996,0.00943767,0.31471646],"study_design_scores_gemma":[0.0008070436,0.0018845806,0.024349228,0.0022655313,0.001886753,0.0013202289,0.0020788019,0.10793807,0.0051139696,0.8292236,0.022640837,0.00049145584],"about_ca_topic_score_codex":0.0025240316,"about_ca_topic_score_gemma":0.0020131625,"teacher_disagreement_score":0.6729089,"about_ca_system_score_codex":0.003247428,"about_ca_system_score_gemma":0.00674853,"threshold_uncertainty_score":0.82981646},"labels":[],"label_agreement":null},{"id":"W4293225463","doi":"10.1080/10705511.2021.1962325","title":"R-squared Measures for Multilevel Mixture Models with Random Effects","year":2022,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multilevel model; Random effects model; Statistics; Econometrics; Mean squared error; Variance (accounting); Mathematics; Regression; Interpretation (philosophy); Regression analysis; Computer science; Meta-analysis","score_opus":0.10316251204698246,"score_gpt":0.36780228202806053,"score_spread":0.2646397699810781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293225463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015039128,0.00067571964,0.99141484,0.00042433405,0.00013159298,0.0001883597,0.0015577342,0.0013870362,0.0027163972],"genre_scores_gemma":[0.04493664,0.00070088974,0.9424981,0.00060317304,0.00028720355,0.0033732567,0.002713946,0.0025583077,0.002328476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9568897,0.029005447,0.0028680894,0.004494639,0.0061642453,0.00057796633],"domain_scores_gemma":[0.8471603,0.11604713,0.011695974,0.017520413,0.0067679095,0.00080836006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038697664,0.002667609,0.0017768254,0.0046398635,0.0011934061,0.003455431,0.0040342514,0.00250396,0.031006446],"category_scores_gemma":[0.21333563,0.0012587928,0.0035972446,0.0061577614,0.0027494205,0.005491891,0.0037506195,0.0058229347,0.011047924],"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":[0.00017350937,0.00018024388,0.010755926,0.0017882339,0.00090613344,0.00031482117,0.0012407412,0.028619548,0.001660414,0.68773705,0.056411825,0.21021152],"study_design_scores_gemma":[0.00007727775,0.00023862999,0.005948959,0.00065673713,0.0002643046,0.0005506525,0.0003042151,0.09485561,0.0018810846,0.7857905,0.109254524,0.00017762215],"about_ca_topic_score_codex":0.0020474663,"about_ca_topic_score_gemma":0.0022618899,"teacher_disagreement_score":0.038697664,"about_ca_system_score_codex":0.0015802265,"about_ca_system_score_gemma":0.0027976395,"threshold_uncertainty_score":0.20465523},"labels":[],"label_agreement":null},{"id":"W4301373880","doi":"10.1080/10705511.2022.2112199","title":"Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data","year":2022,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normality; Covariance; Path (computing); Parametric statistics; Nonlinear system; Mathematics; Linearity; Parametric model; Computer science; Path analysis (statistics); Algorithm; Applied mathematics; Statistics","score_opus":0.10362328176336716,"score_gpt":0.32550032161704967,"score_spread":0.2218770398536825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301373880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13237584,0.00010664556,0.864421,0.00039971556,0.000038070826,0.00020910338,0.000529964,0.0004418018,0.0014778404],"genre_scores_gemma":[0.71926403,0.0001315176,0.27693018,0.00020430057,0.000045228022,0.0010833556,0.001612661,0.00024206059,0.00048665158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94325846,0.044524852,0.0016883634,0.005765269,0.0037973798,0.00096561323],"domain_scores_gemma":[0.6487621,0.31571496,0.008644169,0.020319572,0.005380768,0.0011784506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05586995,0.0022644405,0.0020598245,0.003086452,0.0013134273,0.0022676478,0.0023984164,0.002049567,0.005525519],"category_scores_gemma":[0.29366285,0.00090435275,0.0033471405,0.0039683552,0.0028556553,0.005570538,0.0037500958,0.003947645,0.0007474027],"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":[0.0015737895,0.0012377083,0.21682203,0.0012186472,0.006523546,0.0012947806,0.0039261077,0.32527465,0.004051922,0.20665208,0.0049768863,0.22644785],"study_design_scores_gemma":[0.00018736615,0.0008323037,0.020875836,0.00013708472,0.0004298013,0.00027539887,0.00083077524,0.7774898,0.0021269782,0.1948779,0.0018504707,0.000086279506],"about_ca_topic_score_codex":0.0033494683,"about_ca_topic_score_gemma":0.003800849,"teacher_disagreement_score":0.05586995,"about_ca_system_score_codex":0.0014349179,"about_ca_system_score_gemma":0.0042931847,"threshold_uncertainty_score":0.2954721},"labels":[],"label_agreement":null},{"id":"W4309499037","doi":"10.1080/10705511.2022.2134140","title":"Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling: A Comment on Yuan and Deng (2021)","year":2022,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","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":"Concordia University","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Structural equation modeling; Partial least squares regression; Covariance; Mathematics; Null hypothesis; Zero (linguistics); Null (SQL); Econometrics; Applied mathematics; Statistics; Computer science; Data mining; Philosophy","score_opus":0.4530253858465273,"score_gpt":0.4324932390906678,"score_spread":0.020532146755859515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309499037","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024633508,0.0095568,0.018544631,0.9522944,0.009634465,0.000047381043,0.0006042955,0.00016152914,0.006693154],"genre_scores_gemma":[0.11267001,0.007695915,0.03833188,0.79689926,0.034728747,0.0009800348,0.0004085066,0.00034743614,0.007938203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9552006,0.02010889,0.004526952,0.0070965993,0.011834446,0.0012324404],"domain_scores_gemma":[0.84125865,0.12760283,0.005094507,0.0059515056,0.018831432,0.0012610366],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06680903,0.0017161178,0.002384961,0.0017674824,0.0037053337,0.005253302,0.0061744847,0.020292703,0.003557215],"category_scores_gemma":[0.1545463,0.0010928956,0.0021633655,0.0028335708,0.024631267,0.011048023,0.005153554,0.050412886,0.0033106206],"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":[0.00009668327,0.000038510978,0.0014389742,0.00028467053,0.000052736148,0.00015437821,0.0033104878,0.00047671798,0.00018449307,0.69904715,0.2746655,0.020249577],"study_design_scores_gemma":[0.0001369227,0.00009694709,0.0031964285,0.0010724717,0.00006721883,0.00041557383,0.0011696594,0.0031993582,0.0010248774,0.65891254,0.3305093,0.0001985961],"about_ca_topic_score_codex":0.010323636,"about_ca_topic_score_gemma":0.007302453,"teacher_disagreement_score":0.93319094,"about_ca_system_score_codex":0.0067312475,"about_ca_system_score_gemma":0.0062865647,"threshold_uncertainty_score":0.35332412},"labels":[],"label_agreement":null},{"id":"W4386156211","doi":"10.1080/10705511.2023.2234086","title":"Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes","year":2023,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Component (thermodynamics); Component analysis; Computer science; Artificial intelligence; Function (biology); Flexibility (engineering); Artificial neural network; Independent component analysis; Predictive power; Deep learning; Multivariate statistics; Machine learning; Power (physics); Mathematics; Statistics; Biology","score_opus":0.04006853568200957,"score_gpt":0.2990695866781405,"score_spread":0.2590010509961309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386156211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022564078,0.00033815883,0.9743853,0.0005853787,0.00006564386,0.00006444225,0.00040895314,0.00041852923,0.0011694841],"genre_scores_gemma":[0.65624243,0.0008513656,0.3331594,0.00035325516,0.00016907329,0.00048621342,0.0014706983,0.00021016643,0.0070573627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998971,0.00053253805,0.000034942586,0.00025044475,0.00013556273,0.0000756128],"domain_scores_gemma":[0.9980667,0.0010895575,0.00020884858,0.0001882067,0.00038022368,0.00006644842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023786589,0.0012786369,0.0010103269,0.0010344505,0.0004020059,0.0015091462,0.0017891319,0.0010726413,0.0019963174],"category_scores_gemma":[0.0071802805,0.0005349824,0.0011157703,0.001440897,0.0011509379,0.0014893429,0.0014210532,0.0027239968,0.0004474101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109756045,0.00006936898,0.003274702,0.00008262531,0.00018197471,0.000098501296,0.000118989985,0.8666793,0.00086844026,0.05313249,0.004153022,0.071230926],"study_design_scores_gemma":[0.000003012197,0.0000064145456,0.00020316399,0.0000049564787,0.0000073119604,0.0000053685517,0.0000033553786,0.9856118,0.00007943908,0.013718213,0.00035175774,0.000005204386],"about_ca_topic_score_codex":0.014467889,"about_ca_topic_score_gemma":0.014685697,"teacher_disagreement_score":0.014467889,"about_ca_system_score_codex":0.0012820514,"about_ca_system_score_gemma":0.0019274877,"threshold_uncertainty_score":0.028767347},"labels":[],"label_agreement":null},{"id":"W4387040294","doi":"10.1080/10705511.2023.2243387","title":"Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples","year":2023,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","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":"McGill University","funders":"","keywords":"Structural equation modeling; Statistics; Econometrics; Factor analysis; Sample (material); Reflection (computer programming); Mathematics; Computer science; Psychology; Physics","score_opus":0.9078051462744978,"score_gpt":0.4546789784729287,"score_spread":0.4531261678015691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387040294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039832897,0.0004882963,0.9561242,0.00038243813,0.0001112944,0.0005779668,0.00008394246,0.00041105034,0.001987938],"genre_scores_gemma":[0.27042955,0.00036449954,0.7262852,0.00018903348,0.000066434695,0.0016686923,0.00023072048,0.00024127495,0.00052459585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.81855965,0.16601303,0.0027369861,0.0057698605,0.006385852,0.0005346629],"domain_scores_gemma":[0.41110164,0.53838354,0.008718099,0.030920373,0.009995897,0.0008805795],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14965613,0.001902436,0.0018202412,0.00444315,0.0012743808,0.002206031,0.0022903471,0.0013269212,0.004272624],"category_scores_gemma":[0.40846723,0.00080178404,0.002310925,0.004272208,0.0033634782,0.0044618705,0.0036636442,0.0030739973,0.0006433045],"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":[0.0012195416,0.0007267103,0.05507239,0.0015870398,0.0044906964,0.00023430891,0.0049161813,0.075219646,0.0017987512,0.16771756,0.0046320935,0.6823851],"study_design_scores_gemma":[0.0006184846,0.0019383836,0.028001254,0.0007132115,0.0008220672,0.00038569275,0.0021937874,0.64847344,0.0032637634,0.30124447,0.012120827,0.00022461917],"about_ca_topic_score_codex":0.0030507385,"about_ca_topic_score_gemma":0.0056170872,"teacher_disagreement_score":0.8503439,"about_ca_system_score_codex":0.0016562594,"about_ca_system_score_gemma":0.002931878,"threshold_uncertainty_score":0.7914667},"labels":[],"label_agreement":null},{"id":"W4389942765","doi":"10.1080/10705511.2023.2272294","title":"GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling","year":2023,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Multi-Criteria Decision Making","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":"McGill University","funders":"","keywords":"Structural equation modeling; Component (thermodynamics); Software; Computer science; Software engineering; Programming language; Machine learning; Physics","score_opus":0.34141222945545857,"score_gpt":0.45342396747973723,"score_spread":0.11201173802427866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389942765","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026259187,0.0009489999,0.6409646,0.0010727504,0.0005743638,0.0014410147,0.0790445,0.25080302,0.022524808],"genre_scores_gemma":[0.014148091,0.0015064793,0.82969457,0.001130358,0.00020669938,0.008051899,0.05946213,0.06462894,0.021170795],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980566,0.0008302021,0.00027605382,0.0002731827,0.00046263108,0.00010139495],"domain_scores_gemma":[0.9792564,0.01573096,0.0008749125,0.001510713,0.0023517439,0.00027521732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005217796,0.00282246,0.0015742802,0.0030429373,0.0005617726,0.002495067,0.0030552084,0.0014576092,0.25450963],"category_scores_gemma":[0.028042994,0.0022087733,0.0024026646,0.0035384644,0.0005553132,0.0032212653,0.0022865366,0.0035773488,0.095058575],"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":[0.00018012225,0.00016920792,0.0011537586,0.0024864306,0.00028224176,0.0001524296,0.00044029762,0.006210714,0.0016459532,0.025296578,0.7486592,0.21332312],"study_design_scores_gemma":[0.00053699256,0.000171801,0.0053101517,0.0013744296,0.00024781114,0.00063853,0.00029324324,0.06182178,0.005544948,0.10391541,0.81986743,0.00027746838],"about_ca_topic_score_codex":0.0037917027,"about_ca_topic_score_gemma":0.0056896824,"teacher_disagreement_score":0.25450963,"about_ca_system_score_codex":0.0008645716,"about_ca_system_score_gemma":0.0031282164,"threshold_uncertainty_score":0.8514195},"labels":[],"label_agreement":null},{"id":"W4391875809","doi":"10.1080/10705511.2023.2300079","title":"Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators","year":2024,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Latent variable; Pooling; Missing data; Latent variable model; Continuous variable; Computer science; Econometrics; Variable (mathematics); Latent class model; Statistics; Data mining; Data science; Artificial intelligence; Mathematics; Machine learning","score_opus":0.2921339497355178,"score_gpt":0.4098606816698565,"score_spread":0.11772673193433869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391875809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074820807,0.00086196314,0.9877879,0.002325681,0.00018670163,0.00031593954,0.00021941817,0.0002475266,0.0005728226],"genre_scores_gemma":[0.21882243,0.0011253286,0.77123886,0.002297614,0.00067164405,0.0037775764,0.0009671375,0.0002863598,0.00081307656],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8032315,0.16573341,0.008981796,0.011873521,0.008686141,0.0014936825],"domain_scores_gemma":[0.59042746,0.3389827,0.018726587,0.039912976,0.010120499,0.0018297652],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21755055,0.0026852626,0.004848236,0.003835574,0.0041930513,0.0064451736,0.008141919,0.0051706145,0.0044810227],"category_scores_gemma":[0.45309675,0.0025659916,0.0047442676,0.00915567,0.006177083,0.010762949,0.009577412,0.009286262,0.0008581529],"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":[0.001468101,0.0006576984,0.039754435,0.0051042307,0.0057732672,0.0022361383,0.017138088,0.061688304,0.0031682868,0.38256285,0.01639019,0.4640584],"study_design_scores_gemma":[0.00042935583,0.00051101664,0.0075074662,0.0014056974,0.0009759586,0.0009620638,0.0021383138,0.14690438,0.0036011995,0.8197262,0.015498552,0.00033988786],"about_ca_topic_score_codex":0.004816424,"about_ca_topic_score_gemma":0.0053749713,"teacher_disagreement_score":0.21755055,"about_ca_system_score_codex":0.0024690903,"about_ca_system_score_gemma":0.008326005,"threshold_uncertainty_score":0.9648995},"labels":[],"label_agreement":null},{"id":"W4399068220","doi":"10.1080/10705511.2024.2350023","title":"Investigating Structural Model Fit Evaluation","year":2024,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Evaluation and Performance Assessment","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":"York University","funders":"","keywords":"Structural equation modeling; Econometrics; Goodness of fit; Psychology; Statistics; Computer science; Mathematics","score_opus":0.42721309084629544,"score_gpt":0.5352635135144597,"score_spread":0.10805042266816423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399068220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30953997,0.0014377072,0.6627698,0.002211763,0.0003609361,0.0021719995,0.0015822337,0.001083187,0.018842526],"genre_scores_gemma":[0.8313416,0.00034221992,0.16352375,0.00020592213,0.00003976707,0.0021627522,0.0014895711,0.0003623856,0.0005320222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9066186,0.068067834,0.0062041534,0.0055323993,0.012452417,0.0011246046],"domain_scores_gemma":[0.5549362,0.38353544,0.011638123,0.016398445,0.03203376,0.0014581253],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1520108,0.0027876997,0.0016965977,0.008506636,0.0019686643,0.0054805484,0.00200701,0.0019192848,0.008392957],"category_scores_gemma":[0.46613872,0.00079735805,0.0049202335,0.010343649,0.00300607,0.0074669523,0.0042162905,0.0038970932,0.00065951684],"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":[0.001170783,0.0010117055,0.26297674,0.0028988253,0.006030909,0.0010035008,0.020301657,0.09720103,0.0028410917,0.19465372,0.015108601,0.39480147],"study_design_scores_gemma":[0.00046996624,0.0020937675,0.06866207,0.002700701,0.0020675005,0.0006503725,0.015779903,0.7413699,0.00443056,0.1473105,0.014080323,0.00038440654],"about_ca_topic_score_codex":0.0035257281,"about_ca_topic_score_gemma":0.004223865,"teacher_disagreement_score":0.8479892,"about_ca_system_score_codex":0.003262237,"about_ca_system_score_gemma":0.006380671,"threshold_uncertainty_score":0.80391955},"labels":[],"label_agreement":null},{"id":"W4406855839","doi":"10.1080/10705511.2024.2443943","title":"Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Latent variable; Latent variable model; Structural equation modeling; Latent class model; Econometrics; Variable (mathematics); Local independence; Continuous variable; Psychology; Statistics; Mathematics; Mathematical analysis","score_opus":0.1174367802356366,"score_gpt":0.38886316277846367,"score_spread":0.2714263825428271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406855839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041334733,0.00055744284,0.95359874,0.001365076,0.000065450105,0.00025465016,0.0008828328,0.00023483558,0.0017062093],"genre_scores_gemma":[0.5132096,0.00058237393,0.48141566,0.0002880191,0.00019187198,0.0015872198,0.0014540395,0.00008623747,0.0011850457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95479226,0.037828505,0.0010197096,0.002836302,0.0029941008,0.0005290908],"domain_scores_gemma":[0.8904169,0.094658434,0.00569349,0.0056207497,0.002860799,0.0007496191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032247763,0.0013426463,0.0023116055,0.002844458,0.0010175229,0.003784902,0.00410071,0.0017999755,0.0049709454],"category_scores_gemma":[0.10551578,0.00094225415,0.0027183003,0.005104003,0.002214012,0.0059655234,0.004117364,0.005145161,0.00057207444],"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":[0.0005109028,0.0005362003,0.06510165,0.0007759366,0.0018833579,0.00019822184,0.0037152634,0.14554842,0.0005422581,0.63040304,0.0042287144,0.14655611],"study_design_scores_gemma":[0.00009720783,0.00018516796,0.0090753045,0.00019943155,0.00023640784,0.00006049469,0.00043492194,0.4948894,0.00018054679,0.4910224,0.0035247817,0.00009403663],"about_ca_topic_score_codex":0.007244551,"about_ca_topic_score_gemma":0.009367069,"teacher_disagreement_score":0.032247763,"about_ca_system_score_codex":0.0029820136,"about_ca_system_score_gemma":0.0024983422,"threshold_uncertainty_score":0.1705445},"labels":[],"label_agreement":null},{"id":"W4407514557","doi":"10.1080/10705511.2025.2459768","title":"Effect Size Interpretation in Structural Equation Models","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Mental Health Research Topics","field":"Psychology","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":"York University","funders":"","keywords":"Structural equation modeling; Interpretation (philosophy); Mathematics; Econometrics; Statistics; Computer science","score_opus":0.06840088426035913,"score_gpt":0.44606267269832933,"score_spread":0.3776617884379702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407514557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011015756,0.019821225,0.9322945,0.0093388995,0.0029859392,0.0024717504,0.0027632862,0.0009887359,0.01831988],"genre_scores_gemma":[0.23235607,0.0079738265,0.736946,0.0050078565,0.001556921,0.012906965,0.0013675959,0.00077560305,0.0011092442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5424492,0.3740038,0.029698228,0.021502454,0.031448957,0.0008973307],"domain_scores_gemma":[0.14842604,0.7977933,0.017008578,0.022423636,0.013809354,0.00053901627],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.30702746,0.0029406394,0.004031997,0.011804419,0.0022310417,0.007954146,0.0055783857,0.003745618,0.010642445],"category_scores_gemma":[0.7067618,0.0016451684,0.007229964,0.0129401125,0.012489146,0.009932853,0.0064209066,0.008956313,0.001073988],"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":[0.0004863797,0.00021882121,0.024229942,0.028929675,0.006170535,0.00041224848,0.012647933,0.0076395525,0.001078954,0.48675776,0.027315168,0.40411305],"study_design_scores_gemma":[0.0003861279,0.00049370975,0.015778325,0.013924022,0.0030316967,0.0004427979,0.002779294,0.015369073,0.0023459308,0.89057773,0.054586522,0.00028480464],"about_ca_topic_score_codex":0.002420026,"about_ca_topic_score_gemma":0.0023810973,"teacher_disagreement_score":0.69297254,"about_ca_system_score_codex":0.0038420402,"about_ca_system_score_gemma":0.0056844866,"threshold_uncertainty_score":0.8545585},"labels":[],"label_agreement":null},{"id":"W4410764745","doi":"10.1080/10705511.2025.2497088","title":"Comparison of Component-Based Structural Equation Modeling Methods in Testing Component Interaction Effects","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Structural equation modeling; Component (thermodynamics); Computer science; Physics; Thermodynamics; Machine learning","score_opus":0.10165504055142977,"score_gpt":0.4290779753891268,"score_spread":0.327422934837697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410764745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15387006,0.0044489824,0.8329796,0.00101696,0.00026910377,0.0023365663,0.0006428365,0.0009683676,0.0034674492],"genre_scores_gemma":[0.30187565,0.0023434744,0.6892955,0.00020686266,0.00006282633,0.004280618,0.001164056,0.00024605385,0.0005250405],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9326464,0.058493137,0.00208793,0.0023206559,0.00398164,0.00047019345],"domain_scores_gemma":[0.79230016,0.18446024,0.0034370432,0.005517403,0.013344669,0.00094045926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.077207185,0.0024773944,0.0014344512,0.0055334875,0.0010661534,0.0020179083,0.0023207169,0.0016805154,0.0023749492],"category_scores_gemma":[0.15023986,0.0007746432,0.002663146,0.0069611054,0.0011558579,0.0043454817,0.0024706821,0.002842349,0.0005693275],"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":[0.0035510699,0.001997051,0.085699745,0.0029759337,0.0064761164,0.00020894535,0.0049449345,0.1405699,0.0030779021,0.075618036,0.005994611,0.6688857],"study_design_scores_gemma":[0.0008996474,0.0023838612,0.036710445,0.00069734605,0.0015285935,0.00023960961,0.0015972673,0.8990232,0.0028879696,0.047716178,0.005922737,0.00039324933],"about_ca_topic_score_codex":0.008370441,"about_ca_topic_score_gemma":0.0138521595,"teacher_disagreement_score":0.077207185,"about_ca_system_score_codex":0.0016124516,"about_ca_system_score_gemma":0.0046328106,"threshold_uncertainty_score":0.40831548},"labels":[],"label_agreement":null},{"id":"W4412837935","doi":"10.1080/10705511.2025.2531528","title":"Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","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":"McGill University","funders":"","keywords":"Imputation (statistics); Missing data; Structural equation modeling; Generative grammar; Adversarial system; Computer science; Data mining; Econometrics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.12601208025692712,"score_gpt":0.39790019171302876,"score_spread":0.27188811145610164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412837935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39180577,0.004631347,0.5884042,0.0020929414,0.00050874805,0.00040324614,0.0010273518,0.0028886271,0.008237812],"genre_scores_gemma":[0.865015,0.000840596,0.1295272,0.00052108144,0.00008013465,0.00021137668,0.001481326,0.00020838995,0.0021148992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963905,0.0024165572,0.00013838602,0.00039292773,0.00044628847,0.00021532235],"domain_scores_gemma":[0.9662999,0.029573657,0.0007551744,0.0013338707,0.0015569304,0.00048040252],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014751396,0.0015117279,0.0011652197,0.0007835011,0.0006563053,0.0011487363,0.0014582854,0.0016801745,0.0024151593],"category_scores_gemma":[0.036888003,0.00035890663,0.00092559523,0.0006544907,0.001090163,0.0014279765,0.0018314064,0.0023053533,0.00056521344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011023306,0.00032448166,0.0105338665,0.00043979223,0.00034028874,0.0001781382,0.0002930837,0.85414946,0.0011614115,0.010467926,0.003423347,0.117585875],"study_design_scores_gemma":[0.000041337207,0.00016944439,0.0010309692,0.00005649286,0.000038244303,0.00004143501,0.000044809196,0.9930478,0.0009659241,0.004080361,0.0004638125,0.000019434317],"about_ca_topic_score_codex":0.0074353195,"about_ca_topic_score_gemma":0.0060728462,"teacher_disagreement_score":0.9852486,"about_ca_system_score_codex":0.0009384208,"about_ca_system_score_gemma":0.0015010458,"threshold_uncertainty_score":0.07801372},"labels":[],"label_agreement":null},{"id":"W4414110945","doi":"10.1080/10705511.2025.2537946","title":"Regularized Structural Equation Modeling with Both Factors and Components","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; York University","funders":"","keywords":"Work (physics); Constraint (computer-aided design); Stability (learning theory); Estimation theory; Component (thermodynamics)","score_opus":0.05276099914351777,"score_gpt":0.31903475484060184,"score_spread":0.26627375569708406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414110945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009079718,0.0001721016,0.98953295,0.00023420552,0.000026458385,0.000057473295,0.000172663,0.00022623729,0.00049814803],"genre_scores_gemma":[0.2375927,0.00046156623,0.75707334,0.00028157423,0.00010277838,0.000707042,0.0014263114,0.00015278661,0.002201939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903984,0.00682864,0.00030380642,0.0013124787,0.0009274868,0.0002291926],"domain_scores_gemma":[0.9916911,0.0048648342,0.0010154332,0.0013465976,0.0009366796,0.00014538053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079811625,0.0016811283,0.0016875057,0.0018728167,0.00065748004,0.0016140237,0.0020760412,0.0014783625,0.0024042572],"category_scores_gemma":[0.021231413,0.00084773335,0.002120733,0.0031366514,0.0014849387,0.0018550706,0.002490521,0.0029166832,0.0007596348],"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":[0.00017523194,0.00023125256,0.008920734,0.00035895128,0.001491837,0.00021542059,0.00079637504,0.4421293,0.0026975113,0.297348,0.0070176017,0.23861767],"study_design_scores_gemma":[0.000028965238,0.00007213199,0.0011083288,0.00005423487,0.000099901816,0.000046829915,0.00004177634,0.89622253,0.00053096405,0.09846333,0.0032947895,0.000036208792],"about_ca_topic_score_codex":0.0042011845,"about_ca_topic_score_gemma":0.005950449,"teacher_disagreement_score":0.0079811625,"about_ca_system_score_codex":0.0009289268,"about_ca_system_score_gemma":0.0031147774,"threshold_uncertainty_score":0.04220897},"labels":[],"label_agreement":null},{"id":"W4415059812","doi":"10.1080/10705511.2025.2559270","title":"Controlling for Large Sets of Measured Confounders in Mediation Analysis: Comparison of Bayesian Model Averaging, the LASSO, and Path Analysis","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Bayesian probability; Path analysis (statistics); Path (computing); Bayesian inference; Confounding; Mediation","score_opus":0.16874481179621337,"score_gpt":0.44927888921299813,"score_spread":0.28053407741678477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415059812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027852079,0.0017713272,0.9673145,0.0011352712,0.000066028675,0.00024757505,0.00009476634,0.00022549577,0.0012929704],"genre_scores_gemma":[0.32785782,0.0024263416,0.66636914,0.00044321662,0.0001910076,0.0014989794,0.00033155244,0.00027719143,0.0006047193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8900596,0.10028581,0.0014845674,0.0024683883,0.004943841,0.00075765664],"domain_scores_gemma":[0.6876426,0.28710827,0.007458411,0.0122772595,0.004609983,0.00090342696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12937315,0.0020403233,0.0031458894,0.0029782222,0.0013720681,0.0023018376,0.0023924864,0.0017767585,0.0018345988],"category_scores_gemma":[0.27498367,0.00096258026,0.002883626,0.0032450934,0.0030881502,0.00583698,0.0055584926,0.0034958806,0.00023498341],"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":[0.002015359,0.0005354086,0.021555377,0.0012450558,0.0047986507,0.00026457044,0.0033718834,0.14739822,0.0016228139,0.31507343,0.0031773914,0.4989418],"study_design_scores_gemma":[0.00046384643,0.0007781883,0.0073010465,0.00044883406,0.0012340047,0.0002355228,0.00057208637,0.52749735,0.0016316359,0.45427084,0.005394822,0.00017186248],"about_ca_topic_score_codex":0.0032111746,"about_ca_topic_score_gemma":0.0027024122,"teacher_disagreement_score":0.12937315,"about_ca_system_score_codex":0.0011450142,"about_ca_system_score_gemma":0.003909981,"threshold_uncertainty_score":0.68419874},"labels":[],"label_agreement":null},{"id":"W7083288263","doi":"10.1080/10705511.2025.2555612","title":"A Comparison of Scaled Difference Tests for Forming Confidence Intervals in SEM","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Education, Psychology, and Social Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confidence interval; Interval (graph theory); Confidence region; Noise (video)","score_opus":0.23375005693690765,"score_gpt":0.5592372911300127,"score_spread":0.3254872341931051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083288263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09626095,0.002251844,0.8902726,0.0008216108,0.00043681366,0.0005404162,0.0007984001,0.0019972904,0.00662003],"genre_scores_gemma":[0.38847727,0.0004779426,0.6073402,0.00024786283,0.000097360076,0.0010305427,0.0009400135,0.000812995,0.00057577284],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.89492214,0.08791205,0.0044555226,0.004194431,0.00794535,0.0005705348],"domain_scores_gemma":[0.4350059,0.5131633,0.008978946,0.024285741,0.017248055,0.0013179977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.094827294,0.0011272641,0.0013948812,0.004336156,0.0007428762,0.0033630978,0.00294383,0.0018814004,0.008668923],"category_scores_gemma":[0.52928007,0.00066512113,0.0018295959,0.0049328897,0.0025663404,0.006747635,0.0028472724,0.0029088212,0.0010374063],"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":[0.0027384015,0.00057790877,0.032317616,0.001660934,0.0018947815,0.00032034,0.003028437,0.05915649,0.0023171972,0.19704425,0.011749823,0.6871938],"study_design_scores_gemma":[0.001706984,0.0043960176,0.043853186,0.0019553814,0.00087807985,0.000962298,0.0028381632,0.64001536,0.010349818,0.2670763,0.025264967,0.00070335675],"about_ca_topic_score_codex":0.0015758316,"about_ca_topic_score_gemma":0.0014800057,"teacher_disagreement_score":0.094827294,"about_ca_system_score_codex":0.0011303718,"about_ca_system_score_gemma":0.0019220994,"threshold_uncertainty_score":0.50150067},"labels":[],"label_agreement":null},{"id":"W7110836110","doi":"10.1080/10705511.2025.2588572","title":"Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reflection (computer programming); Latent variable; Bayesian probability; Variable (mathematics); Sign (mathematics); Pattern recognition (psychology)","score_opus":0.20543495730840183,"score_gpt":0.37482561792634794,"score_spread":0.1693906606179461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110836110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020514477,0.00091809384,0.9748574,0.0009717996,0.00008982398,0.00029437465,0.00012498375,0.0006790401,0.0015499964],"genre_scores_gemma":[0.13889438,0.0005291473,0.8579695,0.00037987853,0.00008526502,0.00087351113,0.0003812708,0.00047621143,0.00041088182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91338617,0.07361551,0.0032696745,0.0038059838,0.0050245165,0.00089811074],"domain_scores_gemma":[0.4218009,0.53896886,0.0098289475,0.017303856,0.009921358,0.002176136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15940392,0.0030422849,0.0021352994,0.005484968,0.0024995285,0.006081527,0.0055020656,0.005543518,0.0063675484],"category_scores_gemma":[0.5267564,0.0023841418,0.0032891768,0.0047364947,0.0038482994,0.010359197,0.0073969583,0.0074332156,0.001146439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011711563,0.0005031063,0.028956443,0.0015955442,0.0022697852,0.00032939197,0.0031504598,0.373786,0.0015019729,0.21415986,0.0062630954,0.36631316],"study_design_scores_gemma":[0.00025748924,0.00027794894,0.0018735824,0.00059484714,0.00026344918,0.00016702975,0.0004742279,0.79962665,0.0014435183,0.1920176,0.0028597408,0.00014386485],"about_ca_topic_score_codex":0.008095411,"about_ca_topic_score_gemma":0.011312181,"teacher_disagreement_score":0.15940392,"about_ca_system_score_codex":0.0038654432,"about_ca_system_score_gemma":0.0058375387,"threshold_uncertainty_score":0.8430186},"labels":[],"label_agreement":null}]}