{"id":"W3023367671","doi":"10.5539/cis.v13n2p54","title":"Increasing Student Engagement with Personalized Emails","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Computer science; Usability; Adaptability; Adaptation (eye); Set (abstract data type); Cover (algebra); World Wide Web; Multimedia; Human–computer interaction; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002893209,0.0001112484,0.0001458987,0.000318083,0.0004786651,0.002023884,0.0006990142,0.00001579236,0.0001440214],"category_scores_gemma":[0.0001765099,0.00007284611,0.00002910094,0.00129853,0.0003074772,0.01306835,0.0004034552,0.00008946806,0.0002930415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259902,"about_ca_system_score_gemma":0.00007817577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003096072,"about_ca_topic_score_gemma":2.487674e-7,"domain_scores_codex":[0.9971267,0.00005433108,0.0004707224,0.0002062719,0.001939305,0.0002026551],"domain_scores_gemma":[0.9988286,0.0001229304,0.0002047057,0.0001842117,0.0004226489,0.0002368821],"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":[0.0003170513,0.00008042514,0.1441659,0.00005921647,0.00003605842,0.000008147565,0.1829394,0.002338863,0.000450531,0.0909755,0.01592817,0.5627007],"study_design_scores_gemma":[0.001598615,0.0003560581,0.5571347,0.00002681283,0.00001730933,0.00002602139,0.01202583,0.1530858,0.0001264756,0.0001099746,0.2750921,0.0004002836],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8381351,0.00001069427,0.1511776,0.002126976,0.0001190424,0.000240996,0.000003866328,0.0000639835,0.008121662],"genre_scores_gemma":[0.9807061,0.000009103916,0.01149891,0.007694949,0.00004912903,0.000005210645,0.000003748421,0.000001798243,0.00003106177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5623004,"threshold_uncertainty_score":0.9990121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2227347223496704,"score_gpt":0.414293765005026,"score_spread":0.1915590426553555,"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."}}