{"id":"W4296481997","doi":"10.1007/s10664-022-10187-6","title":"Sources of software development task friction","year":2022,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Task (project management); Computer science; Work (physics); Software; Forcing (mathematics); Software development; Human–computer interaction; Software engineering; Field (mathematics); Development environment; Systems engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01766631,0.0006225606,0.00115144,0.01021997,0.002187275,0.005203305,0.001606568,0.001776454,0.01140629],"category_scores_gemma":[0.2150694,0.001169598,0.0006414941,0.007168464,0.001631938,0.00322571,0.00478467,0.002996968,0.001111816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004052679,"about_ca_system_score_gemma":0.003906024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005062085,"about_ca_topic_score_gemma":0.004030944,"domain_scores_codex":[0.9846663,0.005639459,0.001534333,0.001309873,0.00536883,0.001481212],"domain_scores_gemma":[0.713138,0.2096446,0.03200745,0.029064,0.01305233,0.003093642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002074893,0.002086082,0.6938168,0.00097278,0.0004950733,0.001134695,0.01966778,0.008171675,0.003233287,0.08120406,0.00833756,0.1788053],"study_design_scores_gemma":[0.0003503571,0.0004297428,0.8259795,0.0015201,0.000400428,0.001679565,0.01380979,0.05011866,0.004170429,0.08078702,0.02051603,0.0002383006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418442,0.00183674,0.01737405,0.002719137,0.00007629999,0.0003160106,0.001539691,0.000245316,0.03404846],"genre_scores_gemma":[0.9965153,0.0001405695,0.001536247,0.00008095316,0.00001846089,0.00005584396,0.0003177704,0.00005441254,0.001280433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01766631,"threshold_uncertainty_score":0.09342951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488232714857706,"score_gpt":0.3677832485603151,"score_spread":0.2189599770745445,"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."}}