{"id":"W2755579216","doi":"10.1007/s10664-017-9547-8","title":"Inference of development activities from interaction with uninstrumented applications","year":2017,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Documentation; Software engineering; Software development; Generalizability theory; Software; Event (particle physics); Set (abstract data type); Data science; Human–computer interaction; Programming language","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":[0.00009704188,0.0001605831,0.0001854503,0.000123904,0.0001681464,0.000180181,0.0009873809,0.00005936376,0.00001232175],"category_scores_gemma":[0.0006357832,0.0001468193,0.00003131963,0.0001669219,0.0000438869,0.0006407005,0.000372675,0.000238516,0.00001313506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191331,"about_ca_system_score_gemma":0.0001213667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004965039,"about_ca_topic_score_gemma":0.000006499766,"domain_scores_codex":[0.998889,0.000009758797,0.0001982077,0.0003067811,0.0003443359,0.0002519384],"domain_scores_gemma":[0.9980744,0.0008207618,0.0001033,0.0008147003,0.00007428827,0.0001125325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001803411,0.0001245728,0.9194199,0.0001053894,0.0001583889,0.00001423756,0.00107378,0.01426541,0.001113538,0.0003954573,0.00007226006,0.06323904],"study_design_scores_gemma":[0.0004223765,0.00007219061,0.9419943,0.0002270152,0.000007431962,0.000006232012,0.00002074201,0.01234861,0.03129899,0.00005808107,0.0131337,0.0004102931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3586159,0.00001812978,0.6408318,0.00004694673,0.00008478012,0.00009878651,0.000002518207,0.0002850908,0.00001605777],"genre_scores_gemma":[0.8200213,0.000002672423,0.1797991,0.00000754813,0.00003678139,0.00008705775,0.000005806397,0.00001493016,0.00002474381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4614054,"threshold_uncertainty_score":0.5987118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03065809452857621,"score_gpt":0.3057166064988471,"score_spread":0.2750585119702709,"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."}}