{"id":"W6949763089","doi":"10.5281/zenodo.3588525","title":"Microservices: A Performance Tester's Dream or Nightmare? - Replication package","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Queen's University","funders":"","keywords":"Microservices; Scripting language; Automation; Replication (statistics); Software; Point (geometry); Software performance testing","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01428526,0.0009200594,0.0006916114,0.001014428,0.001089362,0.004844718,0.002953363,0.001401685,0.06500757],"category_scores_gemma":[0.04245527,0.0006630667,0.0006477201,0.001135122,0.001524046,0.007852526,0.004238869,0.003317641,0.04055269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083797,"about_ca_system_score_gemma":0.002637775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294608,"about_ca_topic_score_gemma":0.001641685,"domain_scores_codex":[0.9925297,0.002477873,0.0003392265,0.0008535764,0.003349893,0.0004497845],"domain_scores_gemma":[0.9559704,0.01010797,0.001427281,0.02079016,0.008938987,0.002765262],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005495981,0.0003804106,0.004015655,0.0003869276,0.00005421969,0.0003466744,0.00137046,0.00140405,0.00864925,0.04125216,0.5303518,0.4112388],"study_design_scores_gemma":[0.0001038656,0.000438524,0.005584735,0.0003952564,0.00003368382,0.000480509,0.0008864297,0.008160007,0.01743673,0.02947196,0.936879,0.0001293015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.02936333,0.001674105,0.466758,0.05126426,0.004658636,0.001094679,0.009638839,0.2679162,0.1676321],"genre_scores_gemma":[0.3119316,0.002323924,0.2836857,0.01170359,0.001756792,0.00242901,0.0150159,0.1244128,0.2467407],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.9857147,"threshold_uncertainty_score":0.217472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05010193129510802,"score_gpt":0.309471852889387,"score_spread":0.259369921594279,"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."}}