{"id":"W4319431216","doi":"10.1109/icacrs55517.2022.10029277","title":"Prediction of YouTube View Count using Supervised and Ensemble Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Upload; Random forest; Laptop; Decision tree; Social media; Key (lock); Regression analysis; Linear regression; Variable (mathematics); Variables; Machine learning; Artificial intelligence; World Wide Web; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001000993,0.0001586616,0.0002886795,0.0003430014,0.0005549485,0.0002730032,0.0003564989,0.00005111575,0.00003882106],"category_scores_gemma":[0.00006198055,0.0001527051,0.00004975033,0.0003726671,0.0000351518,0.0002945503,0.0003510619,0.0001778051,7.91063e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001198818,"about_ca_system_score_gemma":0.00009834392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001093749,"about_ca_topic_score_gemma":0.00001217524,"domain_scores_codex":[0.9978492,0.000361963,0.0005786113,0.0004184878,0.0006381006,0.0001536607],"domain_scores_gemma":[0.9988779,0.00008508308,0.0004102947,0.0002111461,0.0003586391,0.00005694125],"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":[0.00007020246,0.0003991587,0.04514215,0.0006392903,0.0006173205,0.00001936681,0.007187537,0.471906,0.1164671,0.2853175,0.0006021912,0.07163212],"study_design_scores_gemma":[0.000241073,0.0001160246,0.0008873132,0.0001540863,0.00001664637,0.00004359466,0.000415302,0.9956754,0.0004854988,0.0003938542,0.00143883,0.000132315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.204373,0.0008120253,0.7894132,0.0006773918,0.001020243,0.0004390449,0.00006165247,0.0004455894,0.002757918],"genre_scores_gemma":[0.9959702,0.0001770588,0.00324185,0.00004589589,0.00007932357,0.00001467639,0.0001009728,0.00001086557,0.0003591122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7915972,"threshold_uncertainty_score":0.6227133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03606848058555161,"score_gpt":0.260111665657059,"score_spread":0.2240431850715073,"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."}}