{"id":"W1978677100","doi":"10.1117/12.811707","title":"A model of memory for incidental learning","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Traverse; Memorization; TRACE (psycholinguistics); Task (project management); Artificial intelligence; Perception; Tree traversal; Machine learning; Event (particle physics); Cognitive psychology; Algorithm; Psychology","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":[0.001081503,0.0006585142,0.0009667313,0.0008276646,0.0007830238,0.001586099,0.003736579,0.001959637,0.00775244],"category_scores_gemma":[0.003438208,0.0003466498,0.001266848,0.0006283416,0.001359561,0.004018936,0.001143671,0.00155156,0.002464543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189501,"about_ca_system_score_gemma":0.001457563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007643078,"about_ca_topic_score_gemma":0.003799981,"domain_scores_codex":[0.999454,0.0001339329,0.00002481042,0.0001263141,0.000138534,0.0001223966],"domain_scores_gemma":[0.9988089,0.0004059132,0.0001215192,0.0002349706,0.0003024526,0.0001262956],"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":[0.0002002546,0.0001477271,0.001778521,0.00011266,0.00007115032,0.0003714488,0.0002917455,0.3844665,0.003229803,0.5712605,0.003723635,0.03434617],"study_design_scores_gemma":[0.00002981256,0.00006783292,0.0002765263,0.0000119691,0.00001704374,0.0001555922,0.00002189042,0.8627656,0.0004514553,0.1331939,0.002990182,0.00001826503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07277094,0.0008457222,0.8942644,0.001521888,0.0001747353,0.00009218096,0.0004169169,0.0009510859,0.02896208],"genre_scores_gemma":[0.8533539,0.0009474477,0.1003588,0.0003598043,0.0002209902,0.0003925583,0.0003949828,0.0001735098,0.04379805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00775244,"threshold_uncertainty_score":0.02593452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547390414894246,"score_gpt":0.2375635839068959,"score_spread":0.2220896797579535,"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."}}