{"id":"W3093432495","doi":"10.21432/cjlt27847","title":"High Potential of Computer-Based Reading Assessment","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Learning and Technology","topic":"Reading and Literacy Development","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reading (process); Reading comprehension; Computer science; Comprehension; Reliability (semiconductor); Computer-Assisted Instruction; Variance (accounting); Structural equation modeling; Test (biology); Natural language processing; Interpretation (philosophy); Software; Artificial intelligence; Multimedia; Machine learning; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008846628,0.0003805344,0.0003896954,0.002772034,0.0002133488,0.001471463,0.0007463839,0.0007554851,0.004000673],"category_scores_gemma":[0.04302604,0.0001645801,0.0003552164,0.001837213,0.0006021016,0.001604147,0.0009338475,0.0005714832,0.001054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003715643,"about_ca_system_score_gemma":0.0008495277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009113915,"about_ca_topic_score_gemma":0.001485051,"domain_scores_codex":[0.9892558,0.006308279,0.0003646659,0.0006953777,0.003246776,0.0001290809],"domain_scores_gemma":[0.8959919,0.08790322,0.002669218,0.004590738,0.008076063,0.0007688901],"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":[0.0007306713,0.0002892021,0.1644532,0.0005177491,0.000102379,0.0003447381,0.0008944635,0.001085871,0.00522196,0.003321863,0.001239443,0.8217984],"study_design_scores_gemma":[0.0002326884,0.00472426,0.8500742,0.001764077,0.0005373177,0.01287499,0.002863355,0.02529639,0.0204621,0.03316029,0.04777552,0.0002347985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7936783,0.01760024,0.1145142,0.003800681,0.0004339698,0.0004638231,0.000963521,0.001690964,0.06685429],"genre_scores_gemma":[0.9695508,0.00141645,0.02601604,0.0002062267,0.0001715379,0.00009850434,0.0001898949,0.00005001573,0.002300629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008846628,"threshold_uncertainty_score":0.04678601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001967562552831,"score_gpt":0.2627583290424904,"score_spread":0.2527386534169621,"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."}}