{"id":"W4391117032","doi":"10.1609/aaaiss.v2i1.27709","title":"Bridging Generative Networks with the Common Model of Cognition","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generative grammar; Bridging (networking); Computer science; Cognition; Shadow (psychology); Generative model; Artificial intelligence; Cognitive science; Field (mathematics); Cognitive model; Artificial neural network; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004361969,0.0001904736,0.0002406809,0.00005795878,0.0002075341,0.0002902317,0.001157364,0.00005250396,0.000001419556],"category_scores_gemma":[0.00001561256,0.0001036922,0.000103381,0.0006429782,0.0003566086,0.0008992241,0.0007175492,0.0002298462,9.380868e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000371005,"about_ca_system_score_gemma":0.00006451914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001718865,"about_ca_topic_score_gemma":0.00000817722,"domain_scores_codex":[0.9987458,0.00001923142,0.0002726771,0.0003514393,0.0003771703,0.000233686],"domain_scores_gemma":[0.9990663,0.00009000032,0.0001844064,0.0002523503,0.0003724703,0.00003446937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002238956,0.0002970691,0.002428809,0.001257211,0.0004880121,0.000003545186,0.0331261,0.2693973,0.2264736,0.4456302,0.005369267,0.01530491],"study_design_scores_gemma":[0.00008772005,0.000149142,0.0001736298,0.0001585214,0.00004820155,0.00003562857,0.0001265376,0.9153852,0.07392026,0.009720548,0.00005562685,0.000138962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4853184,0.000931189,0.4687929,0.03438842,0.000771654,0.001213214,0.00001913968,0.0005281185,0.008036965],"genre_scores_gemma":[0.9878828,0.00003313438,0.01155618,0.0001452849,0.0001148596,0.0000347034,7.761069e-7,0.0000161155,0.0002161437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6459879,"threshold_uncertainty_score":0.4228444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135116480114124,"score_gpt":0.2110483439820031,"score_spread":0.1996971791808619,"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."}}