{"id":"W4379107014","doi":"10.1093/jrsssb/qkad056","title":"Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe &amp; Zeng","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Varimax rotation; Zhàng; Inference; Vintage; Statistics; Econometrics; Mathematics; History; Computer science; Artificial intelligence; Archaeology; China","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01836924,0.002346708,0.001471763,0.003144583,0.001687195,0.004149456,0.00326739,0.005561345,0.006971849],"category_scores_gemma":[0.04882903,0.001442079,0.002882099,0.006362904,0.008721804,0.007202839,0.002947153,0.009055521,0.00265451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00316439,"about_ca_system_score_gemma":0.001842655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005783407,"about_ca_topic_score_gemma":0.004618036,"domain_scores_codex":[0.9844056,0.01075079,0.0006761065,0.001937428,0.001859117,0.0003711676],"domain_scores_gemma":[0.9766028,0.01956553,0.0007503331,0.001425889,0.001447156,0.0002083329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003650186,0.0000259797,0.00102015,0.0003785134,0.0001476583,0.0001644618,0.000989052,0.003674105,0.0002211984,0.8786508,0.05086168,0.06382992],"study_design_scores_gemma":[0.00001575855,0.00003477124,0.001260605,0.0002090114,0.00004693206,0.0001669621,0.0001817624,0.0157133,0.0006160497,0.720356,0.2613074,0.00009145769],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.00223741,0.0413505,0.8459371,0.07975713,0.006791719,0.0001100052,0.0003137823,0.0003889448,0.02311339],"genre_scores_gemma":[0.1796141,0.052485,0.6443216,0.0544509,0.02142391,0.001173854,0.0006286414,0.001559727,0.04434241],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01836924,"threshold_uncertainty_score":0.09714699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340494173261374,"score_gpt":0.3226808834483902,"score_spread":0.2892759417157764,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}