{"id":"W3165731670","doi":"10.3390/app11114852","title":"A New Computational Method for Arabic Calligraphy Style Representation and Classification","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Calligraphy; Arabic; Style (visual arts); Computer science; Artificial intelligence; Natural language processing; Representation (politics); Set (abstract data type); Pattern recognition (psychology); Linguistics; Art; Painting; Literature; Visual arts; Programming language","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":[0.000525522,0.0007600011,0.0009524598,0.002342305,0.000494341,0.001213944,0.001284729,0.0007386745,0.004473431],"category_scores_gemma":[0.001685244,0.0002534945,0.00114656,0.002086079,0.0004910748,0.001008531,0.0009040078,0.001056134,0.002315112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005312721,"about_ca_system_score_gemma":0.001150321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004447552,"about_ca_topic_score_gemma":0.004570629,"domain_scores_codex":[0.9993559,0.00006713862,0.00006235216,0.0001949469,0.0002484908,0.00007120636],"domain_scores_gemma":[0.9995551,0.00009656416,0.00004055023,0.00009007553,0.0001862733,0.00003149885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001366129,0.00009348874,0.0007761428,0.0001353924,0.00005924579,0.00006776996,0.00008022181,0.01574965,0.0325739,0.006052726,0.005948388,0.9383265],"study_design_scores_gemma":[0.00002539173,0.0001267448,0.001978595,0.00002348396,0.00003999098,0.0003004875,0.00009559509,0.9634883,0.01732745,0.006411488,0.0101352,0.00004726496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007988016,0.0001792939,0.9888058,0.00009212657,0.0000951482,0.0001357703,0.0002028321,0.001164567,0.001336504],"genre_scores_gemma":[0.1241035,0.0003347301,0.8676335,0.0001599405,0.0001528505,0.0004161759,0.001248774,0.000148875,0.005801591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004473431,"threshold_uncertainty_score":0.01496506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05514340961661588,"score_gpt":0.3481415503591823,"score_spread":0.2929981407425664,"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."}}