{"id":"W2983883725","doi":"10.32877/bt.v2i1.102","title":"Drop Out Students Identification Using Knowledge Base","year":2019,"lang":"en","type":"article","venue":"bit-Tech","topic":"Edcuational Technology Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Drop out; Dropout (neural networks); Identification (biology); Knowledge base; Drop (telecommunication); Computer science; Data collection; Mathematics education; Knowledge management; Psychology; Artificial intelligence; Machine learning; Mathematics; Statistics","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":[0.001458592,0.0004536402,0.0007155273,0.004884121,0.0007677802,0.001908183,0.0009138332,0.0006279811,0.003007518],"category_scores_gemma":[0.01032276,0.0002057126,0.000569699,0.001904478,0.0001708062,0.001614884,0.0008236321,0.0006454136,0.0008732412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204728,"about_ca_system_score_gemma":0.001102136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01569848,"about_ca_topic_score_gemma":0.01631912,"domain_scores_codex":[0.9987103,0.0002411398,0.0001463682,0.0002628788,0.0004603882,0.0001790156],"domain_scores_gemma":[0.9932536,0.003432723,0.0008977171,0.0004512985,0.001738245,0.0002263956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003710344,0.0009254566,0.7147384,0.0001926818,0.0001103232,0.0006942888,0.001711426,0.01115692,0.002064997,0.0008609196,0.00266074,0.2645128],"study_design_scores_gemma":[0.00004542059,0.0007343198,0.5609519,0.0002642632,0.0003407612,0.0005727414,0.006969384,0.4106611,0.009059152,0.003474303,0.006820806,0.0001057676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605497,0.0001806945,0.0285011,0.0003725734,0.00002521271,0.0004205725,0.002633197,0.0004927645,0.006824219],"genre_scores_gemma":[0.9842511,0.0001173789,0.01090207,0.00003541793,0.000008335845,0.000115482,0.002599952,0.00001230324,0.00195807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01569848,"threshold_uncertainty_score":0.03121424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0430946977273137,"score_gpt":0.3348771236426257,"score_spread":0.291782425915312,"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."}}