{"id":"W4394760812","doi":"10.1111/coin.12641","title":"Novel mixture allocation models for topic learning","year":2024,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Inference; Topic model; Dirichlet distribution; Computer science; Mixture model; Artificial intelligence; Prior probability; Latent variable; Categorization; Machine learning; Pattern recognition (psychology); Mathematics","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":[0.008335629,0.001341454,0.002400427,0.003043614,0.001328952,0.003513985,0.003953056,0.002899031,0.006266231],"category_scores_gemma":[0.01705857,0.001309777,0.003084317,0.002981982,0.001826636,0.004437193,0.0027832,0.003759684,0.002491114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002337498,"about_ca_system_score_gemma":0.001322674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007725293,"about_ca_topic_score_gemma":0.007712372,"domain_scores_codex":[0.9951068,0.002661582,0.000208346,0.001039889,0.0006722087,0.0003112024],"domain_scores_gemma":[0.9909527,0.006940756,0.0004014594,0.0006306589,0.0008472862,0.000227267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003071718,0.000220509,0.002671561,0.0002710258,0.0002782094,0.000176483,0.0007034317,0.4957242,0.001937099,0.3588181,0.009627714,0.1292644],"study_design_scores_gemma":[0.00001202403,0.000009937756,0.0001464211,0.00001463163,0.00001215967,0.00002273647,0.00001310548,0.9482309,0.0001372108,0.04999737,0.001391459,0.00001208429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006014743,0.0007271921,0.9909745,0.0003542855,0.00008055718,0.00007041698,0.0001621197,0.0003347918,0.001281414],"genre_scores_gemma":[0.4431865,0.002019971,0.5310256,0.0006483169,0.000810086,0.001478536,0.002495976,0.0006483887,0.01768668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008335629,"threshold_uncertainty_score":0.0440836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05465376630462006,"score_gpt":0.3294408408894598,"score_spread":0.2747870745848398,"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."}}