{"id":"W4414183863","doi":"10.3390/app151810052","title":"AutoProPos: An Extension of Prototype Scattering and Positive Sampling Clustering for an Unknown Number of Clusters","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Silhouette; Cluster analysis; Pattern recognition (psychology); Subspace topology; Extension (predicate logic); Autoencoder; Image (mathematics); Parametric statistics; Binary number","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008391766,0.0001375025,0.000241549,0.0001909166,0.0003079568,0.0001176436,0.0008463756,0.00005240355,0.000001082],"category_scores_gemma":[0.0000394177,0.0001229856,0.00002836473,0.0006444662,0.0005131726,0.0006644287,0.0005863459,0.00008839345,4.946447e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003142671,"about_ca_system_score_gemma":0.0001145134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005926263,"about_ca_topic_score_gemma":0.00001969333,"domain_scores_codex":[0.9983499,0.00003860158,0.0003063492,0.0006416371,0.0003238292,0.0003396808],"domain_scores_gemma":[0.9990441,0.0001866047,0.000129323,0.0003979582,0.0001609483,0.00008109353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002293118,0.0001935084,0.0004953432,0.000517133,0.00003177351,0.000001469736,0.004357269,0.01822302,0.3709584,0.06342648,0.000004269697,0.5415621],"study_design_scores_gemma":[0.0006628205,0.0007363016,0.003130092,0.0002779379,0.000009516003,0.00001526641,0.0007747817,0.8896386,0.08949234,0.01492162,0.00004862341,0.000292084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433349,0.00002446093,0.75468,0.0001173778,0.00009529134,0.0007994091,0.000003146993,0.00005286463,0.000892512],"genre_scores_gemma":[0.6454868,0.000003339601,0.3543714,0.00004461874,0.00001241669,0.00005550811,0.000001049796,0.000005327434,0.00001947821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8714156,"threshold_uncertainty_score":0.5015209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04704613310433881,"score_gpt":0.3798647348832161,"score_spread":0.3328186017788773,"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."}}