{"id":"W4239926161","doi":"10.32920/ryerson.14654136.v1","title":"Generating Artificial Data for Scalability Test of a Followee Twitter Recommender System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Scalability; Recommender system; Computer science; Software deployment; Cloud computing; Test (biology); Machine learning; Task (project management); Artificial intelligence; Software; Data mining; Database; Software engineering; Engineering","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001801623,0.0002790098,0.0005425268,0.00009004551,0.0001514784,0.0004768706,0.003038081,0.0001521255,0.000007761643],"category_scores_gemma":[0.0002737869,0.0002431285,0.0002214848,0.0001954628,0.00003241814,0.00002563862,0.01300384,0.0002890471,0.000002785835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007568,"about_ca_system_score_gemma":0.0001606072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051745,"about_ca_topic_score_gemma":0.00004777802,"domain_scores_codex":[0.9968888,0.0001580463,0.000824681,0.001435137,0.0003594518,0.0003339464],"domain_scores_gemma":[0.9945469,0.0005259067,0.0003732634,0.004258501,0.000220736,0.00007471791],"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":[0.00003728023,0.002911223,0.004133204,0.0170211,0.001545793,0.00008745391,0.005287204,0.2223108,0.002675238,0.01027435,0.07957029,0.6541461],"study_design_scores_gemma":[0.0001364762,0.00002924143,0.0001533602,0.0003040891,0.00004220335,0.000003718992,0.0003282116,0.9974875,0.0004086224,0.0001794538,0.0006525591,0.0002745365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.115353,0.00007538734,0.8789705,0.001731266,0.001715688,0.0006849569,0.00003624889,0.0002619987,0.001170973],"genre_scores_gemma":[0.7042085,5.687075e-7,0.2946627,0.0002343003,0.0004515861,0.00004723464,0.00008799419,0.00001769565,0.0002894706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7751768,"threshold_uncertainty_score":0.9949788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09490493922037319,"score_gpt":0.3020290099950328,"score_spread":0.2071240707746596,"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."}}