{"id":"W2035251626","doi":"10.1109/mcse.2006.122","title":"Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive","year":2006,"lang":"en","type":"article","venue":"Computing in Science & Engineering","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Lawrence Livermore National Laboratory","keywords":"Carpentry; Computer science; Software; Code (set theory); Software engineering; Programming language; Engineering","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01171868,0.0003089564,0.0003599552,0.001157247,0.0007143852,0.002181093,0.003129808,0.0000460226,0.00001907816],"category_scores_gemma":[0.005275499,0.0002827222,0.00007895076,0.007428819,0.0003376759,0.0007882078,0.002125166,0.0002926631,0.0001445706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003402242,"about_ca_system_score_gemma":0.0001000759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001013428,"about_ca_topic_score_gemma":0.0000167845,"domain_scores_codex":[0.9927222,0.00005938508,0.000938542,0.002205833,0.002823067,0.001250998],"domain_scores_gemma":[0.9971688,0.0006115739,0.0002321142,0.001420552,0.0003808683,0.0001861411],"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":[0.000005752857,0.00009005288,0.1450028,0.00001767752,0.000004938676,0.00004278583,0.002283983,0.7603309,0.01250015,0.001365806,0.02167368,0.0566815],"study_design_scores_gemma":[0.0003676119,0.00002730824,0.172063,0.0004146401,0.000007850187,0.00003629983,0.0007541124,0.7794389,0.004191716,0.0007782378,0.04099668,0.0009236786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.73984,0.00006123365,0.2565485,0.0005433798,0.001997947,0.0003066693,0.00001579595,0.0002303135,0.0004561703],"genre_scores_gemma":[0.9266514,1.428557e-7,0.07252131,0.0002345206,0.0002474785,0.000006315717,0.000005982263,0.00002150351,0.0003113427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1868114,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04395400164416501,"score_gpt":0.3354911798575602,"score_spread":0.2915371782133952,"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."}}