{"id":"W1527607785","doi":"10.1109/coginf.2003.1225966","title":"A cognitive complexity metric based on category learning","year":2004,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Program comprehension; Comprehension; Computer science; Process (computing); Identifier; Set (abstract data type); Software; Software development; Metric (unit); Cognition; Software maintenance; Artificial intelligence; Software engineering; Software system; Human–computer interaction; Programming language; Engineering; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003037977,0.0000934258,0.00009367772,0.0003612234,0.00009379745,0.0000917904,0.0004179785,0.0000332258,0.00005397815],"category_scores_gemma":[0.001324616,0.00008433453,0.00003956799,0.001147382,0.00004110642,0.0001314767,0.0001100955,0.0002908424,0.0003983916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109697,"about_ca_system_score_gemma":0.0001138785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001393637,"about_ca_topic_score_gemma":0.000003984013,"domain_scores_codex":[0.9988995,0.0000454006,0.00008476541,0.0002845577,0.0004086674,0.0002771026],"domain_scores_gemma":[0.9985198,0.001039985,0.00001700509,0.0002246235,0.00009428467,0.0001042825],"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":[0.00007214351,0.001038397,0.04131421,0.0001160858,0.00009303514,0.0004828241,0.001440598,0.5759706,0.0004160857,0.2703167,0.0006704924,0.1080688],"study_design_scores_gemma":[0.003793343,0.001300947,0.305326,0.0001157565,0.000008004165,0.0000212392,0.0000900587,0.6632654,0.01653872,0.007996964,0.0007359657,0.0008075698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02635196,0.0000175047,0.9677049,0.0003349124,0.00007934903,0.0001107338,3.883872e-7,0.0006465266,0.004753747],"genre_scores_gemma":[0.9710952,6.301294e-7,0.02845204,0.0002393873,0.00001902087,0.00001228002,0.000002024061,0.000008382708,0.0001710561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9447432,"threshold_uncertainty_score":0.5120651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057610787086489,"score_gpt":0.2989768848936994,"score_spread":0.2484007770228346,"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."}}