{"id":"W6968017633","doi":"10.5281/zenodo.14108991","title":"The Active Inference Institute & Active Inference Ecosystem","year":2024,"lang":"en","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003627162,0.0001603884,0.0001109166,0.0001645374,0.003345862,0.001801378,0.0009714458,0.00006157335,0.001458338],"category_scores_gemma":[0.002654753,0.0001278225,0.00005513825,0.000742521,0.0002618734,0.0008815546,0.0008005599,0.0004791971,0.01359881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888722,"about_ca_system_score_gemma":0.00001666393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000876064,"about_ca_topic_score_gemma":0.000002655537,"domain_scores_codex":[0.9980264,0.0003937107,0.0002151531,0.0005464535,0.0004155474,0.0004026771],"domain_scores_gemma":[0.9988798,0.0002417892,0.00007200999,0.0003686683,0.0002989723,0.0001386845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009379646,0.00005592558,2.358639e-7,0.00006497919,0.00002305079,0.00004649858,0.001720887,0.00005062222,0.01244416,0.4647352,0.008388611,0.5123761],"study_design_scores_gemma":[0.000180888,0.0001501086,0.00003981988,0.0001126605,0.00001318893,0.00009067989,0.0004216824,0.00192608,0.01635518,0.01624176,0.9642758,0.0001921814],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01685059,0.00007976842,0.003681997,0.001847634,0.0008284702,0.0007309642,0.000579374,0.002104178,0.973297],"genre_scores_gemma":[0.9980658,0.0003327898,0.00001621605,0.0001390496,0.0001591974,2.165043e-7,0.0001559619,0.000439687,0.0006911349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9812152,"threshold_uncertainty_score":0.9994544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06535789803859422,"score_gpt":0.2962463326808042,"score_spread":0.23088843464221,"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."}}