{"id":"W4312178470","doi":"10.18357/otessac.2022.2.1.76","title":"Investigating the Effects of Computer-Generated Contextual Landmarks on Short-Term Recall of E-Texts","year":2022,"lang":"en","type":"article","venue":"The Open/Technology in Education Society and Scholarship Association Conference","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Athabasca University","keywords":"Recall; Computer science; Term (time); Recall rate; Natural language processing; Artificial intelligence; Test (biology); Information retrieval; Human–computer interaction; Cognitive psychology; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.005157937,0.0008833051,0.0006770097,0.0007062595,0.0004950502,0.002488707,0.001102906,0.001115074,0.005433348],"category_scores_gemma":[0.1009306,0.000542392,0.0006027294,0.0005674428,0.0006472471,0.002143861,0.001245051,0.001128709,0.0009305044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000524169,"about_ca_system_score_gemma":0.0003993935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002183309,"about_ca_topic_score_gemma":0.002663712,"domain_scores_codex":[0.9959356,0.001857827,0.000494158,0.0007087842,0.0007827451,0.0002209638],"domain_scores_gemma":[0.8088317,0.1660923,0.01047236,0.007328896,0.005099447,0.002175202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.05923219,0.01941195,0.1962057,0.005146175,0.001622157,0.001199069,0.05143582,0.005699655,0.1738294,0.001215463,0.00336656,0.481636],"study_design_scores_gemma":[0.002193072,0.1011581,0.7866231,0.0008240715,0.002589561,0.001010074,0.01277818,0.01044572,0.07083406,0.001996999,0.00904073,0.000506437],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958206,0.0003239129,0.001056471,0.00005341377,0.00002881907,0.0001737674,0.0001079574,0.0001072794,0.00232768],"genre_scores_gemma":[0.9893695,0.0003965558,0.005619795,0.0001207401,0.00004798049,0.0004234381,0.0003029928,0.00009691697,0.003622003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005433348,"threshold_uncertainty_score":0.02727807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03256114804462039,"score_gpt":0.3072386214578118,"score_spread":0.2746774734131914,"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."}}