{"id":"W7116285713","doi":"10.5281/zenodo.17982447","title":"The Active Inference Institute & Active Inference Ecosystem","year":2024,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Inference; Context (archaeology); Variety (cybernetics); Active learning (machine learning); State (computer science); Active database","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":[0.01460411,0.001160123,0.001422514,0.003561328,0.002927688,0.01897447,0.004633325,0.004770409,0.03024769],"category_scores_gemma":[0.02331673,0.001239396,0.001440568,0.003612387,0.01023563,0.02853104,0.01109426,0.007288484,0.01120821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003042084,"about_ca_system_score_gemma":0.006180342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003729651,"about_ca_topic_score_gemma":0.002678757,"domain_scores_codex":[0.9913093,0.003086514,0.0005482138,0.00211773,0.002367582,0.0005706454],"domain_scores_gemma":[0.9822834,0.008389249,0.0007143895,0.00452716,0.002413197,0.00167264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006476413,0.00004979999,0.0008502642,0.0001939184,0.00004114657,0.00006629933,0.0004234194,0.002655664,0.0002250016,0.8688247,0.01805093,0.1085541],"study_design_scores_gemma":[0.0000201805,0.00003241138,0.0004300218,0.0003129278,0.00003298294,0.0001793234,0.0003012951,0.02068293,0.0007497196,0.7187545,0.2584486,0.00005509967],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.004252235,0.01063004,0.7645467,0.02079178,0.001567903,0.000171203,0.0009951451,0.004129575,0.1929155],"genre_scores_gemma":[0.2648826,0.01940422,0.631716,0.005136861,0.003713673,0.0007756762,0.003996068,0.003120912,0.06725411],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03024769,"threshold_uncertainty_score":0.1011886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06301421984906667,"score_gpt":0.2980920396210765,"score_spread":0.2350778197720098,"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."}}