{"id":"W2129414299","doi":"10.1145/1370175.1370194","title":"Jigsaw","year":2008,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reuse; Computer science; Code reuse; Jigsaw; Source code; KPI-driven code analysis; Context (archaeology); Code review; Code (set theory); Software engineering; Overhead (engineering); Software quality; Open source; Database; Software; Programming language; Software development; Engineering; Set (abstract data type)","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":[0.00004851788,0.00002654927,0.00002725886,0.00003813548,0.00003329034,0.00001427579,0.0003997769,0.00001131818,0.00004762585],"category_scores_gemma":[0.0001043094,0.00002249781,0.00001241796,0.0001871771,0.00001153844,0.0001252951,0.0001055609,0.00004283289,0.0004919301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001016243,"about_ca_system_score_gemma":0.00002317581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009494374,"about_ca_topic_score_gemma":2.448533e-7,"domain_scores_codex":[0.9996051,0.000004409439,0.00003295989,0.00009619127,0.0001435218,0.0001177848],"domain_scores_gemma":[0.9995444,0.0001255985,0.000002577323,0.0002637286,0.00001931616,0.00004434641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00000276533,0.0001643314,0.2261675,0.00002316062,0.00003421303,0.0009196043,0.001914208,0.00102307,0.002812348,0.359597,0.3237616,0.08358023],"study_design_scores_gemma":[0.000558429,0.0001362558,0.7672216,0.000008406124,6.291355e-7,0.0006379323,0.000005294443,0.07401386,0.02440082,0.001997537,0.1305076,0.0005116178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05067861,0.00002939905,0.9411738,0.0003386364,0.0001131546,0.00002490597,4.708125e-8,0.000579825,0.007061624],"genre_scores_gemma":[0.9138247,0.000004454082,0.08256164,0.00008486881,0.00002234307,0.000002777753,8.409604e-8,0.000002457744,0.003496694],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8631461,"threshold_uncertainty_score":0.632293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02905687703687048,"score_gpt":0.2535442770321326,"score_spread":0.2244873999952621,"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."}}