{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004760538,0.0008263785,0.0007157305,0.00898458,0.0009814093,0.003192122,0.001325339,0.001347036,0.005594215],"category_scores_gemma":[0.05235687,0.0002122732,0.001046732,0.004668622,0.002690855,0.006498048,0.003007344,0.001516544,0.0006887082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002669304,"about_ca_system_score_gemma":0.001243107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002513258,"about_ca_topic_score_gemma":0.002618775,"domain_scores_codex":[0.9933918,0.001434578,0.0005816306,0.00080562,0.003433554,0.00035285],"domain_scores_gemma":[0.9575095,0.02869837,0.00386256,0.00313564,0.00526,0.001534088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007527464,0.0008277865,0.1229497,0.0008606255,0.000502648,0.0002620935,0.002045072,0.04153214,0.008340728,0.2603393,0.01300285,0.5485842],"study_design_scores_gemma":[0.000102606,0.001967296,0.1375678,0.0002355504,0.0001892951,0.001338114,0.001410559,0.2029682,0.008224703,0.6166667,0.02897309,0.0003560465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2410208,0.002066438,0.6832986,0.00114609,0.0003101229,0.0009804564,0.003115073,0.00122606,0.06683633],"genre_scores_gemma":[0.793878,0.0005572367,0.1981106,0.0001707299,0.000151887,0.001051882,0.002059515,0.0001636251,0.003856545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00898458,"threshold_uncertainty_score":0.02517641,"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."}}