{"id":"W2125343508","doi":"10.1007/978-3-642-23716-4_22","title":"A Fast Algorithm to Locate Concepts in Execution Traces","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software Engineering Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Dynamic programming; TRACE (psycholinguistics); Genetic programming; Algorithm; Identification (biology); Genetic algorithm; Segmentation; Theoretical computer science; Artificial intelligence; Machine 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":[0.001028393,0.002529381,0.001485186,0.006101592,0.001709963,0.002911726,0.002977023,0.002113111,0.01332683],"category_scores_gemma":[0.006644052,0.001228257,0.001881769,0.005607374,0.001180669,0.005558166,0.003781162,0.002440731,0.006983833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292738,"about_ca_system_score_gemma":0.003095503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00458263,"about_ca_topic_score_gemma":0.006291002,"domain_scores_codex":[0.9985405,0.0001302561,0.0001550143,0.0003850351,0.0006376172,0.0001515832],"domain_scores_gemma":[0.9960403,0.001715717,0.0002425694,0.0007638212,0.001086213,0.0001513634],"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":[0.0004312409,0.0001736358,0.001001974,0.0005037691,0.00008087197,0.0001703625,0.0002757008,0.01238517,0.0257499,0.03208212,0.01523278,0.9119126],"study_design_scores_gemma":[0.0003258117,0.0003972029,0.001292479,0.0002573469,0.0002161591,0.001042383,0.0004988348,0.6881991,0.08760536,0.1594207,0.06058658,0.0001580507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004113981,0.0002062934,0.9849508,0.00007455317,0.00006108492,0.0001832285,0.0004442216,0.008849259,0.001116636],"genre_scores_gemma":[0.0179576,0.0001295971,0.9776031,0.00003686538,0.00002079782,0.0001720078,0.0009847186,0.0005049843,0.002590185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01332683,"threshold_uncertainty_score":0.04458266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557422175582425,"score_gpt":0.2716429627880335,"score_spread":0.2460687410322093,"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."}}