{"id":"W1524510684","doi":"","title":"Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine.","year":2009,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Boltzmann machine; Computer science; Restricted Boltzmann machine; Artificial intelligence; Machine learning; Binary number; Deep learning; 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":[0.0004821506,0.0003942765,0.0004217532,0.0002023236,0.0001924406,0.0003605747,0.0008269602,0.0008292785,0.001724327],"category_scores_gemma":[0.001635157,0.0003223354,0.0005232582,0.0002158844,0.0007466094,0.0006311623,0.0005817142,0.001150615,0.0002276864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005259607,"about_ca_system_score_gemma":0.0005400231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007858885,"about_ca_topic_score_gemma":0.008893155,"domain_scores_codex":[0.9998593,0.00004942087,0.000004506802,0.00003942927,0.00001824106,0.00002906633],"domain_scores_gemma":[0.9995927,0.0002645078,0.00004841242,0.00003385119,0.00003095447,0.00002962335],"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":[0.00004218374,0.00001821698,0.0008725016,0.00001523064,0.00002240481,0.00002645706,0.00002111704,0.9863912,0.00169022,0.007021132,0.0002726026,0.003606775],"study_design_scores_gemma":[0.000002402525,0.000005141503,0.0001101691,8.423528e-7,0.000001717651,0.000004802368,0.000001214712,0.9977124,0.0001220134,0.00197672,0.00006079548,0.000001755552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1421155,0.000351939,0.8532674,0.0005307572,0.00007292719,0.00004893747,0.0002914205,0.0005367565,0.002784305],"genre_scores_gemma":[0.9441237,0.0001247101,0.05131685,0.0001263632,0.00002050998,0.0001138411,0.0002068091,0.00006113686,0.003906065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007858885,"threshold_uncertainty_score":0.01562625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03380569312864074,"score_gpt":0.2523828971768214,"score_spread":0.2185772040481807,"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."}}