{"id":"W2947034755","doi":"10.5220/0007708201580166","title":"Generalized Dirichlet Regression and other Compositional Models with Application to Market-share Data Mining of Information Technology Companies","year":2019,"lang":"en","type":"article","venue":"","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Data modeling; Regression analysis; Regression; Topic model; Data mining; Econometrics; Data science; Artificial intelligence; Statistics; Machine learning; Mathematics; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044742,0.0001128412,0.0002540464,0.0004291675,0.00008595435,0.00008483632,0.0008809729,0.00007261876,0.0003184179],"category_scores_gemma":[0.00008576104,0.00007233271,0.00001366541,0.0008033267,0.00007746751,0.0008325924,0.000416447,0.00004692008,0.0001654333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001589363,"about_ca_system_score_gemma":0.0000305293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002222842,"about_ca_topic_score_gemma":0.00002432189,"domain_scores_codex":[0.9983178,0.00008393838,0.0005308964,0.0003490065,0.0006010422,0.0001173417],"domain_scores_gemma":[0.9976346,0.0002966447,0.0003580716,0.001311424,0.0003425568,0.00005672192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006978358,0.0001296281,0.0712861,0.00008506986,0.0000759851,2.689797e-7,0.002088984,0.009314367,0.01581945,0.8328675,0.03566185,0.03197296],"study_design_scores_gemma":[0.001777965,0.0001931905,0.01432605,0.0002456773,0.00003100618,0.00006419494,0.006846402,0.8577075,0.002452613,0.05798329,0.05786465,0.0005074717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5303851,0.00003403333,0.453494,0.001234074,0.00002509814,0.001006634,0.0003938799,0.00007425057,0.0133529],"genre_scores_gemma":[0.9215238,7.277952e-7,0.07762355,0.0002658661,0.000008201057,0.00007288714,0.000109509,0.000006955666,0.000388467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8483931,"threshold_uncertainty_score":0.3486453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07290700291791297,"score_gpt":0.3502497025460311,"score_spread":0.2773426996281181,"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."}}