{"id":"W2185266635","doi":"10.1016/b978-0-12-800283-4.00005-8","title":"Memory Recruitment","year":2014,"lang":"en","type":"book-chapter","venue":"The Psychology of learning and motivation/The psychology of learning and motivation","topic":"Memory Processes and Influences","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of Calgary","funders":"","keywords":"Priming (agriculture); Context (archaeology); Task (project management); Second line; Contrast (vision); Psychology; Cognitive psychology; Response priming; First line; Computer science; Neuroscience; Artificial intelligence; Cognition; Lexical decision task; History; Biology; Medicine","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":[0.002068397,0.0006181053,0.0008904285,0.000459887,0.0009851706,0.00005211267,0.0005591683,0.0007849286,0.0002471293],"category_scores_gemma":[0.00133572,0.0004502331,0.0001842676,0.0002199822,0.002391733,0.0001412599,0.0001668198,0.00210328,0.00003158743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001814828,"about_ca_system_score_gemma":0.00005905311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001904178,"about_ca_topic_score_gemma":0.000006274996,"domain_scores_codex":[0.9956138,0.0009132253,0.001201552,0.001268687,0.0005119804,0.0004907966],"domain_scores_gemma":[0.9943796,0.002389987,0.002220884,0.0006015625,0.0002900076,0.0001179655],"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":[0.002487713,0.0006758339,0.09191653,0.001557981,0.001082998,0.00003676388,0.01288231,0.004275561,0.1713072,0.04620587,0.009961488,0.6576097],"study_design_scores_gemma":[0.003480398,0.002868899,0.06813938,0.0009388444,0.0003577506,0.0003945615,0.000904208,0.0003000942,0.002177203,0.01275411,0.9064595,0.001225044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6808246,0.003501243,0.007258605,0.009316107,0.001668875,0.002597331,0.00002116435,0.0003826145,0.2944294],"genre_scores_gemma":[0.8701777,0.002854879,0.0001613536,0.001713496,0.0002345016,0.00006750353,0.00003828742,0.00009793462,0.1246544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.896498,"threshold_uncertainty_score":0.999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08710036490593182,"score_gpt":0.3353741105442797,"score_spread":0.2482737456383479,"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."}}