{"id":"W2364913443","doi":"10.48550/arxiv.1605.03072","title":"Semi-Supervised Representation Learning based on Probabilistic Labeling","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Artificial intelligence; Representation (politics); Computer science; Machine learning; Supervised learning; Natural language processing; Pattern recognition (psychology); Artificial neural network; Political science","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":[0.005098469,0.001428401,0.002573233,0.002471682,0.001032049,0.002260237,0.004748637,0.002285329,0.002507102],"category_scores_gemma":[0.01592257,0.0009701937,0.001761658,0.002375142,0.002422011,0.00498366,0.003554164,0.004098273,0.001723568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515913,"about_ca_system_score_gemma":0.001903072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818721,"about_ca_topic_score_gemma":0.001785128,"domain_scores_codex":[0.9935323,0.002766141,0.0003154212,0.001503016,0.001596557,0.0002866153],"domain_scores_gemma":[0.9880989,0.006546021,0.000982377,0.002461072,0.00166392,0.0002476867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002411184,0.0002841314,0.001210895,0.0003505443,0.0001947129,0.0001221765,0.0002889139,0.3664792,0.005128807,0.06265347,0.009958618,0.5530873],"study_design_scores_gemma":[0.00001302443,0.00003084624,0.00007876555,0.00001448516,0.000008320701,0.00004372185,0.00001365362,0.9646649,0.001350064,0.03286604,0.0009023835,0.00001388753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001475235,0.00006075826,0.9975018,0.000080302,0.00001449781,0.00004004765,0.00003325746,0.0005029188,0.0002911825],"genre_scores_gemma":[0.1469347,0.0002300623,0.84854,0.000275651,0.0001744371,0.0005749412,0.001061551,0.0002923149,0.001916371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005098469,"threshold_uncertainty_score":0.02696359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08014613218572131,"score_gpt":0.206100075125378,"score_spread":0.1259539429396567,"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."}}