{"id":"W4287773619","doi":"10.5281/zenodo.3748768","title":"Order in Chaos: Prioritizing Mobile App Reviews using Consensus Algorithms","year":2020,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université de Montréal; Polytechnique Montréal","funders":"","keywords":"CHAOS (operating system); Computer science; Order (exchange); Algorithm; Consensus algorithm; Data mining; Artificial intelligence; Computer security","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001228588,0.0003815143,0.0006527331,0.0004819547,0.001363275,0.001630936,0.002594871,0.0002415995,0.002632195],"category_scores_gemma":[0.0004333198,0.0004004239,0.0001141762,0.001782328,0.0001212096,0.0002350364,0.002408007,0.0008337286,0.04182112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869702,"about_ca_system_score_gemma":0.00002925129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008804897,"about_ca_topic_score_gemma":4.297079e-7,"domain_scores_codex":[0.9958963,0.001047425,0.000765291,0.001092338,0.000544386,0.0006542332],"domain_scores_gemma":[0.9978719,0.00004242028,0.0003909231,0.0009462829,0.0004848,0.0002636253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001144065,0.00008605015,3.256737e-7,0.0004038565,0.00003654571,0.00009163498,0.003854593,0.0002846104,0.001768431,0.0005295136,0.8827138,0.1102192],"study_design_scores_gemma":[0.0003684571,0.0001435358,0.000001759348,0.000448871,0.00000687377,0.0002429774,0.0001707795,0.02493741,0.00007384348,0.00001186311,0.9731777,0.0004159023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008729633,0.02651551,0.3673649,0.002314113,0.006396152,0.007675227,0.0007962158,0.003563002,0.5845019],"genre_scores_gemma":[0.09918442,0.04680956,0.3094704,0.009274649,0.0264558,0.00001288696,0.03516479,0.05440405,0.4192234],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1652786,"threshold_uncertainty_score":0.9999368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08106516786073853,"score_gpt":0.3028904543558362,"score_spread":0.2218252864950977,"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."}}