{"id":"W4391930960","doi":"10.30554/archmed.23.2.4825.2023","title":"Modelo predictivo en comprensión de lectura impresa y digital en estudiantes universitarios","year":2024,"lang":"en","type":"article","venue":"Archivos de Medicina (Manizales)","topic":"Literacy and Educational Practices","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Open access publishing; Computer science; Art; Humanities; Library science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01688119,0.002778471,0.001699517,0.002760563,0.0008494731,0.003186231,0.002147396,0.001197039,0.008636989],"category_scores_gemma":[0.04114749,0.0006600394,0.003197427,0.001439642,0.0009333698,0.00163303,0.001735495,0.001977731,0.001101864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001857795,"about_ca_system_score_gemma":0.00370312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02836882,"about_ca_topic_score_gemma":0.01273652,"domain_scores_codex":[0.994082,0.003884457,0.0002146899,0.001051039,0.0004791876,0.0002885587],"domain_scores_gemma":[0.9547236,0.03968609,0.001855038,0.001124046,0.001680764,0.000930551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001733633,0.001076753,0.9419524,0.0004891369,0.001352694,0.0002827007,0.002286735,0.01383675,0.0002157637,0.001846611,0.002470645,0.03245619],"study_design_scores_gemma":[0.0007894668,0.004119801,0.573171,0.002575675,0.004045371,0.0008465163,0.005980709,0.3871179,0.0009636885,0.010363,0.009850146,0.000176728],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706726,0.001436976,0.01637601,0.001662642,0.0002472593,0.0008214114,0.002413549,0.0003996975,0.005969849],"genre_scores_gemma":[0.9877159,0.0006698651,0.007443762,0.00009587102,0.00005987977,0.0008702344,0.001402831,0.00004365954,0.001697883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02836882,"threshold_uncertainty_score":0.08927739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007223947957441001,"score_gpt":0.3097117094958731,"score_spread":0.3024877615384321,"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."}}