{"id":"W4409787746","doi":"10.61091/jcmcc127a-485","title":"A quantitative study of stylistic differences between Giacometti and his contemporaries based on big data modeling","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Psychology; Artificial intelligence; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004678153,0.0002059515,0.0009919979,0.0004424046,0.0005488294,0.0002330577,0.0005726253,0.00009905622,0.000002004493],"category_scores_gemma":[0.002575868,0.0001743319,0.00008817093,0.0005628784,0.0002058221,0.0001624363,0.0002822669,0.0003207316,1.295188e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000496104,"about_ca_system_score_gemma":0.0003756881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002992178,"about_ca_topic_score_gemma":0.00001527754,"domain_scores_codex":[0.9968274,0.0007120167,0.001140933,0.000259579,0.0008623336,0.0001977846],"domain_scores_gemma":[0.9921672,0.005787874,0.0009653872,0.0002509194,0.0007080323,0.0001205315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002165365,0.001425446,0.009873854,0.0001853554,0.0004691835,0.000007121417,0.007698856,0.0005297578,0.00001225572,0.9683692,0.00003062634,0.01118184],"study_design_scores_gemma":[0.004818158,0.001830842,0.001870262,0.0006800016,0.0006000139,7.229964e-7,0.01221681,0.2936446,0.000007863732,0.6839262,0.0001286038,0.0002758477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9195367,0.0002948296,0.07563356,0.0002588217,0.003214965,0.0002871916,0.000006177227,0.00001500486,0.000752789],"genre_scores_gemma":[0.9951757,0.00001734899,0.004354803,0.00001381989,0.0004207276,9.903871e-7,0.000001960922,0.000009516572,0.000005166542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2931149,"threshold_uncertainty_score":0.710905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530500144728349,"score_gpt":0.3918670727446805,"score_spread":0.2388170582718455,"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."}}