{"id":"W4283781296","doi":"10.1109/cwit55308.2022.9817662","title":"On the Rényi Cross-Entropy","year":2022,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Rényi entropy; Entropy (arrow of time); Cross entropy; Generative grammar; Gaussian; Statistical physics; Markov chain; Min entropy; Alphabet; Exponential family; Shannon's source coding theorem; Maximum-entropy Markov model; Exponential function; Entropy rate; Applied mathematics; Discrete mathematics; Combinatorics; Binary entropy function; Markov model; Statistics; Principle of maximum entropy; Markov property; Mathematical analysis; Artificial intelligence; Computer science; Maximum entropy thermodynamics; Physics","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":[0.008738632,0.001190986,0.001396376,0.002693725,0.0008024884,0.003174208,0.001627734,0.001911068,0.002841516],"category_scores_gemma":[0.02847058,0.0006466167,0.0009442569,0.001768499,0.005889851,0.006730958,0.003406552,0.003540945,0.000599982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002627157,"about_ca_system_score_gemma":0.001105177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001901671,"about_ca_topic_score_gemma":0.0009694693,"domain_scores_codex":[0.9957528,0.001894277,0.0002026989,0.0007172961,0.001151387,0.000281514],"domain_scores_gemma":[0.9842657,0.01236372,0.0008811799,0.001060322,0.001161382,0.0002677543],"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":[0.00005484404,0.00001749202,0.0007956124,0.00009850644,0.00005025271,0.0001154412,0.0001212012,0.1415157,0.0009283799,0.8402178,0.00113453,0.01495021],"study_design_scores_gemma":[0.000008393383,0.00003411909,0.0006204379,0.00009629796,0.0000192769,0.0001317606,0.00002538934,0.3249224,0.0008141674,0.6707108,0.002572099,0.00004480185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03560597,0.005339709,0.9362602,0.001884241,0.0002016331,0.00004601536,0.0002847319,0.0001567725,0.02022078],"genre_scores_gemma":[0.899644,0.007024313,0.08099641,0.001018308,0.001008938,0.0002592809,0.0003916123,0.0002938508,0.009363282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008738632,"threshold_uncertainty_score":0.04621488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03787142083585839,"score_gpt":0.2531293395351752,"score_spread":0.2152579186993168,"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."}}