{"id":"W3209322995","doi":"10.1145/3479242.3487321","title":"Automated and Reproducible Application Traces Generation for IoT Applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"CHIST-ERA; Agence Nationale de la Recherche","keywords":"Computer science; Testbed; TRACE (psycholinguistics); Focus (optics); Internet of Things; Software; Replication (statistics); Usability; Scale (ratio); Software engineering; Distributed computing; Embedded system; World Wide Web; Human–computer interaction; Operating system","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":[0.0004881127,0.0001796442,0.0002104439,0.00009849865,0.0002587242,0.0007730304,0.0004828732,0.000181004,7.868061e-7],"category_scores_gemma":[0.00003217275,0.0001843346,0.00006444129,0.0002310824,0.00002102635,0.0001472524,0.0006717083,0.0001524753,0.000005303464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004178194,"about_ca_system_score_gemma":0.0001884897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004874391,"about_ca_topic_score_gemma":0.000008982116,"domain_scores_codex":[0.9980422,0.00003442125,0.000322639,0.001270564,0.000134121,0.0001960888],"domain_scores_gemma":[0.9982178,0.00006134159,0.0001807911,0.00122202,0.0002583039,0.0000597615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003395799,0.0002403255,0.0002108302,0.0007020385,0.0001050386,0.000001178713,0.001766731,0.006432111,0.03987138,0.02337201,0.03278627,0.8945087],"study_design_scores_gemma":[0.00009115261,0.00001119098,0.0002413589,0.0000158608,0.00001380336,0.000006121246,0.00001218538,0.9742495,0.00890119,0.002288361,0.01394445,0.0002247852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01171759,0.0008115544,0.9812841,0.001528579,0.001822808,0.00124798,6.813508e-7,0.001158662,0.0004280843],"genre_scores_gemma":[0.1054583,0.0000811723,0.8868049,0.0002651074,0.004644963,0.0019171,0.0003539492,0.00002754025,0.0004470336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9678174,"threshold_uncertainty_score":0.7516948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03937044197141514,"score_gpt":0.3011663749263245,"score_spread":0.2617959329549094,"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."}}