{"id":"W2912612993","doi":"10.5539/cis.v12n1p59","title":"Feature Models Preconfiguration Based on User Profiling","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Profiling (computer programming); Computer science; Software; Feature model; Software product line; Feature (linguistics); User requirements document; Software engineering; Data mining; Human–computer interaction; Software development","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001246172,0.00118388,0.0006080426,0.001322201,0.0004466769,0.001838027,0.001144989,0.0007191714,0.002067963],"category_scores_gemma":[0.005432415,0.0006197043,0.00115813,0.0006544542,0.0003921377,0.002381638,0.001500337,0.001124081,0.001267348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006568807,"about_ca_system_score_gemma":0.0007658614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003081214,"about_ca_topic_score_gemma":0.00383027,"domain_scores_codex":[0.997676,0.0007020808,0.0001689113,0.0004769926,0.0008089648,0.0001671089],"domain_scores_gemma":[0.995638,0.001113329,0.0003908528,0.002089778,0.0006427949,0.0001252865],"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":[0.001310826,0.0007474739,0.0298205,0.0004243118,0.0002105634,0.001438115,0.003499498,0.1990684,0.1062384,0.03763316,0.01085324,0.6087556],"study_design_scores_gemma":[0.00002959504,0.0003111825,0.006697216,0.00008273013,0.00009691033,0.0005809476,0.0003018372,0.8912555,0.06091218,0.01252312,0.02707756,0.0001312052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07607188,0.0001017662,0.9094299,0.00009812708,0.00002283172,0.0002582546,0.0005848466,0.007981366,0.005450984],"genre_scores_gemma":[0.7367535,0.0001617156,0.2557548,0.00007690009,0.00001755937,0.0003391891,0.00217144,0.0007855813,0.003939362],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003081214,"threshold_uncertainty_score":0.006918013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110567316289229,"score_gpt":0.256623008577113,"score_spread":0.2355173354142207,"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."}}