{"id":"W4362597934","doi":"10.48550/arxiv.2304.00078","title":"A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Carnegie Mellon University; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense; National Science Foundation","keywords":"Systematic review; Resource (disambiguation); Field (mathematics); Knowledge management; Data science; Computer science; Medical education; Engineering ethics; Engineering; Political science; MEDLINE; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005913752,0.0002967622,0.0005576606,0.0006946035,0.00008981615,0.00007741102,0.001630154,0.0001597995,0.000007516583],"category_scores_gemma":[0.0004171746,0.0003039555,0.0001076268,0.001599705,0.0001174202,0.0005070862,0.001708773,0.0006327976,0.000007374645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002394184,"about_ca_system_score_gemma":0.0002867675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223906,"about_ca_topic_score_gemma":0.0001108099,"domain_scores_codex":[0.9975085,0.0002115589,0.0002461523,0.001356777,0.0002897705,0.0003872863],"domain_scores_gemma":[0.9971384,0.001221256,0.0002771638,0.001051643,0.0002102005,0.0001012908],"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":[0.0002305943,0.000558598,0.02259273,0.001038191,0.00383192,0.002576351,0.03447261,0.9223174,0.001642757,0.009970392,0.0002221223,0.0005463187],"study_design_scores_gemma":[0.004605963,0.0008416168,0.1154729,0.004762173,0.001503839,0.0000416313,0.02795529,0.7966036,0.01821425,0.02457445,0.0006526478,0.004771679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486724,0.0006561566,0.04912987,0.000246683,0.0004076288,0.0004244768,0.000009925579,0.0003620547,0.0000907818],"genre_scores_gemma":[0.9873357,0.0005152776,0.01186238,0.000004460908,0.00003839003,0.00001315831,0.000008841008,0.00002767418,0.0001941537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1257138,"threshold_uncertainty_score":0.9999412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3087562264377492,"score_gpt":0.2472846301889575,"score_spread":0.06147159624879178,"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."}}