{"id":"W2909724110","doi":"10.1109/icmla.2018.00195","title":"Asymmetric Gaussian-Based Statistical Models Using Markov Chain Monte Carlo Techniques for Image Categorization","year":2018,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Reversible-jump Markov chain Monte Carlo; Probabilistic latent semantic analysis; Markov chain Monte Carlo; Mixture model; Artificial intelligence; Computer science; Pattern recognition (psychology); Scale-invariant feature transform; Gaussian process; Gaussian; Machine learning; Markov chain; Feature extraction; Bayesian probability","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":[0.0007750391,0.0001968743,0.0002324916,0.0002430922,0.0001835561,0.0002087665,0.0004704552,0.0001212792,0.00001064996],"category_scores_gemma":[0.0001036162,0.0001660367,0.0000684047,0.0006655493,0.00008860952,0.0005259229,0.00008684152,0.00008956339,0.000002249373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008171044,"about_ca_system_score_gemma":0.0001545671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000169413,"about_ca_topic_score_gemma":0.00001228147,"domain_scores_codex":[0.998406,0.0001394273,0.0002952338,0.0005232517,0.0002373748,0.0003987354],"domain_scores_gemma":[0.9987573,0.000212767,0.00009517665,0.0004943921,0.0003028942,0.0001374566],"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":[0.00002327364,0.00006883533,0.000007516057,0.00004036584,0.00001209906,0.000006937186,0.0001019503,0.00008622911,0.002581502,0.6780154,0.002203062,0.3168529],"study_design_scores_gemma":[0.000205866,0.0001493044,0.00001025241,0.00001130543,0.00001188302,0.000004907734,0.000001925111,0.8833279,0.01889023,0.09699151,0.0001881626,0.0002067523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003655714,0.00003225141,0.9942369,0.0003592892,0.0001790827,0.0005347288,0.00002102913,0.0003057201,0.004294455],"genre_scores_gemma":[0.1507045,0.00000263685,0.8483933,0.0004935155,0.0001517432,0.00004148733,0.000004571056,0.00002186212,0.0001864422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8832417,"threshold_uncertainty_score":0.6770779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02955404527103057,"score_gpt":0.3110658572995906,"score_spread":0.2815118120285601,"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."}}