{"id":"W4297825063","doi":"10.1080/09658211.2022.2104317","title":"Understanding autobiographical memory content using computational text analysis","year":2022,"lang":"en","type":"article","venue":"Memory","topic":"Identity, Memory, and Therapy","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autobiographical memory; Argument (complex analysis); Psychology; Content (measure theory); Scope (computer science); Cognitive psychology; Valence (chemistry); Scale (ratio); Computer science; Cognitive science; Recall","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007530354,0.0001890253,0.0003593455,0.0009927981,0.0007829648,0.00005692637,0.0003010069,0.00007550375,0.01133651],"category_scores_gemma":[0.00001054948,0.0002100913,0.0004431737,0.001493001,0.0001686082,0.00007812453,0.0001116591,0.0003752054,0.00007008869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003697482,"about_ca_system_score_gemma":0.00006220159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005331731,"about_ca_topic_score_gemma":0.00008290896,"domain_scores_codex":[0.997742,0.0004786988,0.0003800367,0.0004712253,0.000531344,0.0003966854],"domain_scores_gemma":[0.9991511,0.0001529469,0.0001647439,0.0003706785,0.00005129874,0.0001092064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002035756,0.005851129,0.2928533,0.0001351974,0.02988163,0.002219671,0.04242755,0.3817463,0.006794655,0.1815176,0.04723154,0.007305657],"study_design_scores_gemma":[0.01841888,0.001274354,0.6145989,0.00004389697,0.006791031,0.0008041641,0.1730208,0.1062571,0.0002991688,0.06524593,0.009218951,0.004026806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408569,0.0007330625,0.03679141,0.0005792033,0.002867571,0.000322162,0.00006045672,0.0001786254,0.01761059],"genre_scores_gemma":[0.995561,0.000003679959,0.0003201573,0.0009826367,0.0001856545,0.00003318566,0.00007249264,0.000032223,0.002808989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3217456,"threshold_uncertainty_score":0.9895673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2229522282896605,"score_gpt":0.3447666119380852,"score_spread":0.1218143836484247,"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."}}