{"id":"W6976831743","doi":"10.60692/2m81x-g0y08","title":"Feeding What You Need by Understanding What You Learned","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Pipeline (software); Interpretation (philosophy); Comprehension; Training set; Reading (process); Curriculum; Deep learning","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":[0.001553457,0.001514397,0.0008109732,0.001229219,0.0005218633,0.00243112,0.001387591,0.001838481,0.01353625],"category_scores_gemma":[0.0183821,0.0005962086,0.001135781,0.001086286,0.0004768337,0.006851956,0.002497848,0.002620915,0.005926637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006579085,"about_ca_system_score_gemma":0.001446869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002966991,"about_ca_topic_score_gemma":0.004523426,"domain_scores_codex":[0.9990031,0.0003713899,0.00005648637,0.0003608313,0.0001498296,0.00005843822],"domain_scores_gemma":[0.9944624,0.003774387,0.0002574386,0.000761646,0.0005459964,0.0001979745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004591481,0.001001076,0.02456116,0.001488284,0.0001770226,0.0004316668,0.003810702,0.03701868,0.0306895,0.01476102,0.03248483,0.8531169],"study_design_scores_gemma":[0.0001161627,0.0008588476,0.01687452,0.0005830711,0.0002630297,0.0006170107,0.003546778,0.7312581,0.04213548,0.1216505,0.08188514,0.0002113103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2313326,0.001144098,0.7222999,0.005410756,0.0003191118,0.0006022021,0.004202355,0.01239589,0.02229299],"genre_scores_gemma":[0.5394021,0.001128001,0.4384375,0.0008774003,0.0001217707,0.0006084441,0.007920262,0.0007022737,0.01080223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01353625,"threshold_uncertainty_score":0.04528326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08032085247982688,"score_gpt":0.2248874978158338,"score_spread":0.1445666453360069,"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."}}