{"id":"W2734705500","doi":"","title":"Having Fun With 31.521 Shell Scripts","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure","funders":"","keywords":"Scripting language; Computer science; Shell (structure); Programming language; Parsing; Natural language processing; Syntax; POSIX; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001970075,0.0009450174,0.0004759897,0.002218984,0.001205843,0.001833078,0.0006450293,0.000678195,0.01342217],"category_scores_gemma":[0.01889949,0.000751428,0.0005212511,0.002522681,0.001175665,0.002559155,0.001570313,0.001251611,0.01002955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005339913,"about_ca_system_score_gemma":0.001023626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001745724,"about_ca_topic_score_gemma":0.002556012,"domain_scores_codex":[0.99804,0.0004458637,0.0002378972,0.0006534393,0.0004889605,0.0001338161],"domain_scores_gemma":[0.986845,0.0080631,0.0008464878,0.001897075,0.002039747,0.0003085192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001476963,0.0003066337,0.04808436,0.002703597,0.0001172643,0.00530711,0.02170948,0.002913672,0.04655886,0.03384809,0.2870132,0.5499608],"study_design_scores_gemma":[0.0000601563,0.0001663681,0.08870383,0.0006824483,0.00008986038,0.006847839,0.002677873,0.01500046,0.0628956,0.02243037,0.800244,0.0002010858],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"software","genre_scores_codex":[0.4582117,0.002197115,0.2254057,0.002263085,0.0007883321,0.0006338884,0.1001688,0.127248,0.08308341],"genre_scores_gemma":[0.5539072,0.0009717168,0.1929089,0.0009126954,0.0001971205,0.0005348749,0.1602327,0.06146007,0.02887492],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01342217,"threshold_uncertainty_score":0.04490161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141668654510708,"score_gpt":0.2454500690973523,"score_spread":0.2240333825522452,"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."}}