{"id":"W3034352268","doi":"10.48550/arxiv.1911.09704","title":"A Conceptual Framework for Lifelong Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Forgetting; Lifelong learning; Computer science; Variety (cybernetics); Transfer of learning; Perspective (graphical); Mechanism (biology); Task (project management); Inductive transfer; Cognitive science; Conceptual framework; Artificial intelligence; Transfer of training; Knowledge management; Human–computer interaction; Robot learning; Cognitive psychology; Epistemology; Psychology; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0003575329,0.000299849,0.0003617413,0.0002108364,0.0002534163,0.0002283621,0.001551288,0.0004250202,0.00005538496],"category_scores_gemma":[0.0003063603,0.0003685537,0.0002866534,0.0003866212,0.0001185526,0.0003334245,0.001220421,0.001185615,0.000265556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001406323,"about_ca_system_score_gemma":0.0002659116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001956124,"about_ca_topic_score_gemma":0.000002936087,"domain_scores_codex":[0.9979486,0.0001858885,0.0002053609,0.001112561,0.0001125491,0.0004349667],"domain_scores_gemma":[0.9978063,0.0006552883,0.0003520431,0.0008232209,0.0001885649,0.0001746265],"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":[0.00001860532,0.0000186252,0.00129123,0.00002751232,0.00004485935,0.00002533245,0.000955933,0.3858682,0.00000462494,0.6108316,0.0001511025,0.0007623924],"study_design_scores_gemma":[0.0006495494,0.0001101287,0.0005798184,0.0001619507,0.00003886045,0.000002369148,0.0008320369,0.8990068,0.00002232536,0.07417896,0.02383254,0.0005846587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02956881,0.00008008065,0.9652019,0.0001598307,0.001132022,0.0004367464,0.000004680051,0.0004098153,0.003006086],"genre_scores_gemma":[0.9527084,0.00005773131,0.04065626,0.0003296679,0.0001602193,0.000002505255,0.00002058193,0.00002814378,0.00603645],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9245456,"threshold_uncertainty_score":0.9998766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003688359967813,"score_gpt":0.2146848974623661,"score_spread":0.1143160614655847,"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."}}