{"id":"W4387444958","doi":"10.1109/caisais59399.2023.10270552","title":"Exploring Correlated Features in Deep Learning Models for Academic Advising","year":2023,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Academic advising; Software deployment; Scalability; Machine learning; Naive Bayes classifier; Process (computing); Data science; Knowledge management; Higher education; Software engineering; Database; Support vector machine","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":[0.002494741,0.0006801504,0.0005870851,0.0007733992,0.0003170971,0.001426132,0.0009958997,0.001147801,0.001777718],"category_scores_gemma":[0.009108622,0.0003646224,0.0004571843,0.0009121156,0.0005521518,0.001510056,0.0008583888,0.002283837,0.0004145585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302257,"about_ca_system_score_gemma":0.001327933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006545579,"about_ca_topic_score_gemma":0.01255083,"domain_scores_codex":[0.9991807,0.0004268007,0.00003507319,0.0001382436,0.000115919,0.0001032894],"domain_scores_gemma":[0.9958271,0.002980833,0.000395582,0.0002350885,0.0004120359,0.0001492733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002371148,0.0003771978,0.01423934,0.0001085385,0.00009568535,0.0001281051,0.0002266321,0.8063573,0.00130801,0.01155955,0.004637113,0.1607253],"study_design_scores_gemma":[0.000004281015,0.00001489002,0.0004523154,0.000009629786,0.000004835275,0.000006088032,0.00001364014,0.9934869,0.0002936681,0.005415573,0.0002939222,0.000004231398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3115624,0.00168006,0.6757066,0.003734759,0.0001308487,0.000093627,0.0006667133,0.001580641,0.0048443],"genre_scores_gemma":[0.9585196,0.000240144,0.0383247,0.0002087739,0.000033122,0.00004478272,0.0003464458,0.00003429313,0.002248209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006545579,"threshold_uncertainty_score":0.01319361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.120613588228019,"score_gpt":0.3157207123548512,"score_spread":0.1951071241268322,"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."}}