{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00102846,0.0004586893,0.0003029664,0.0004099578,0.009635019,0.006632435,0.002445539,0.0001989179,0.005046254],"category_scores_gemma":[0.005212146,0.0003964513,0.0001645941,0.001936925,0.0008989954,0.002109007,0.002547217,0.001442343,0.03543581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006162314,"about_ca_system_score_gemma":0.00006756024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002405275,"about_ca_topic_score_gemma":0.000006552683,"domain_scores_codex":[0.994704,0.001266089,0.0006187538,0.001369143,0.0009998787,0.001042157],"domain_scores_gemma":[0.9968804,0.0005524144,0.0002413135,0.0008992644,0.001024838,0.0004017581],"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":[0.000285287,0.0001691033,2.748771e-7,0.0002785153,0.0001040005,0.0001284335,0.005236092,0.0001148696,0.005384418,0.340997,0.01211931,0.6351827],"study_design_scores_gemma":[0.0004030876,0.0005088394,0.00006193511,0.0005661576,0.00007107887,0.0002096564,0.001672081,0.005779648,0.009531916,0.0112975,0.969395,0.0005031152],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0123016,0.0005740118,0.005549661,0.003483829,0.003282665,0.002014585,0.00258303,0.00255261,0.967658],"genre_scores_gemma":[0.9932962,0.002622582,0.00001907702,0.000187572,0.000470164,3.111674e-7,0.0004296032,0.001298643,0.001675862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809946,"threshold_uncertainty_score":0.9998487,"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."}}