{"id":"W4283161067","doi":"10.1007/s00163-022-00391-2","title":"“Why couldn’t we do this more often?”: exploring the feasibility of virtual and distributed work in product design engineering","year":2022,"lang":"en","type":"article","venue":"Research in Engineering Design","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Product design; Design review (U.S. government); Engineering design process; Product (mathematics); Product engineering; New product development; Process (computing); Work (physics); Virtual prototyping; Design technology; Engineering; Computer science; Process management; Knowledge management; Engineering management; Systems engineering; Product testing; Business; Operations management; Simulation; Marketing; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003886406,0.0002679491,0.0003216431,0.0006890376,0.00009299165,0.0000912387,0.0005346551,0.00007441618,0.00003223259],"category_scores_gemma":[0.000437414,0.0002655961,0.0000474026,0.002596921,0.00009304977,0.000483453,0.0001708559,0.001666763,0.000002775194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006577467,"about_ca_system_score_gemma":0.00007175128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002097482,"about_ca_topic_score_gemma":6.182418e-7,"domain_scores_codex":[0.9972464,0.0002318294,0.0005694331,0.0003478394,0.0008421486,0.0007623236],"domain_scores_gemma":[0.9979473,0.001330426,0.00002624854,0.000505088,0.00006651017,0.0001244554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006796963,0.00005068818,0.000697846,0.0002702301,0.00002523382,0.00002469345,0.001300071,0.9930219,0.001757559,0.0002559165,0.0009022404,0.001625619],"study_design_scores_gemma":[0.001457661,0.0003523704,0.009483928,0.0009435897,0.00001281894,0.00004159418,0.002010349,0.9688528,0.01148236,0.0001992425,0.004257725,0.0009056077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5009446,0.00374534,0.4874871,0.0012982,0.001029952,0.004304657,0.0001580765,0.0008254007,0.0002067251],"genre_scores_gemma":[0.9967113,0.0001806902,0.002064529,0.000004627225,0.00004612043,0.0008973196,0.000009379424,0.00007427407,0.00001173689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4957668,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1828289422781856,"score_gpt":0.3129804667637322,"score_spread":0.1301515244855466,"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."}}