{"id":"W1991867644","doi":"10.1007/s10664-015-9375-7","title":"Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews","year":2015,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Queen's University","funders":"","keywords":"App store; Computer science; World Wide Web; Download; Internet privacy; Mobile apps; Mobile device; Analytics; Notice; Data science","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":[0.006229083,0.0008031299,0.0007328273,0.01231966,0.001039726,0.002766008,0.0007881423,0.001449913,0.001066602],"category_scores_gemma":[0.08608788,0.0005188828,0.0006743261,0.004139348,0.0003684386,0.002666769,0.001302344,0.001066866,0.001180554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993829,"about_ca_system_score_gemma":0.001927762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003718195,"about_ca_topic_score_gemma":0.01187222,"domain_scores_codex":[0.9889125,0.003520149,0.001347609,0.001193675,0.004675177,0.0003509439],"domain_scores_gemma":[0.8451639,0.1085085,0.0188138,0.003606429,0.02274385,0.00116364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001159987,0.000435178,0.360364,0.007534947,0.0004687168,0.002870316,0.01288431,0.001387844,0.05643315,0.002963752,0.04310456,0.5103933],"study_design_scores_gemma":[0.0001166104,0.001112487,0.6913583,0.003118112,0.001489217,0.008753801,0.01374798,0.07616106,0.06316012,0.005673286,0.1348716,0.0004373917],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218228,0.008429823,0.0438677,0.002083048,0.0007294635,0.001015965,0.006626593,0.003083073,0.01234146],"genre_scores_gemma":[0.9067219,0.002408757,0.07367112,0.0005298289,0.0004055584,0.0006024953,0.008159434,0.0003939672,0.007106913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01231966,"threshold_uncertainty_score":0.03294295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05224016304009076,"score_gpt":0.3190022140689671,"score_spread":0.2667620510288763,"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."}}