{"id":"W2398184019","doi":"","title":"Understanding Expert Perception in Software Estimation Effort: a Cognitive Approach Using Software Chunks.","year":2014,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analogy; Computer science; Intuition; Categorization; Software development; Software; Perception; Artificial intelligence; Cognition; Estimation; Machine learning; Cognitive science; Psychology; Engineering; Systems engineering; Programming language","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":[0.003116605,0.0003570167,0.0002352042,0.002145257,0.0006522838,0.003047726,0.0006992129,0.001012685,0.002133981],"category_scores_gemma":[0.03054667,0.0004349505,0.0004355623,0.0007376819,0.002071162,0.005425991,0.001979212,0.0009490082,0.0001659528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805788,"about_ca_system_score_gemma":0.0009177877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01507152,"about_ca_topic_score_gemma":0.01434654,"domain_scores_codex":[0.9985602,0.0005325903,0.00005983763,0.000294132,0.0004407286,0.0001124406],"domain_scores_gemma":[0.9815011,0.01276081,0.002212546,0.000965151,0.001864023,0.0006964269],"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":[0.000701219,0.000710481,0.2802951,0.0007913531,0.0002594464,0.0009649654,0.2225272,0.01393799,0.03609906,0.07766665,0.003923733,0.3621227],"study_design_scores_gemma":[0.00009118935,0.0005674062,0.4917027,0.000485432,0.0002198574,0.001148155,0.1199663,0.192481,0.008159931,0.1728739,0.01195541,0.0003486805],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9065109,0.0005422594,0.07762723,0.001058476,0.00001918015,0.0000704394,0.00005909228,0.00005921283,0.01405322],"genre_scores_gemma":[0.9926086,0.00008804831,0.006861076,0.00005691521,0.000005218722,0.0000175559,0.00002216038,0.000006865176,0.0003335784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01507152,"threshold_uncertainty_score":0.02996755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028197548921514,"score_gpt":0.328239075028085,"score_spread":0.2254193201359336,"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."}}