{"id":"W3161838415","doi":"10.1007/978-3-030-75765-6_52","title":"SILVER: Generating Persuasive Chinese Product Pitch","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Product (mathematics); Rank (graph theory); Natural language generation; Artificial intelligence; Deep learning; Hierarchy; Statistic; Generator (circuit theory); Artificial neural network; Natural language processing; Information retrieval; Natural language; Power (physics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0007162976,0.001165944,0.0005093606,0.0007540646,0.0004920576,0.001098764,0.0008318611,0.0006961915,0.03408262],"category_scores_gemma":[0.003714473,0.0003282117,0.0004207882,0.0005867399,0.0002884257,0.001718658,0.001442562,0.0006235859,0.00787151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002958053,"about_ca_system_score_gemma":0.0003515246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009430228,"about_ca_topic_score_gemma":0.001180418,"domain_scores_codex":[0.9996276,0.0001058912,0.00001441452,0.0000910079,0.0001310344,0.00002993856],"domain_scores_gemma":[0.9990477,0.0006181106,0.0000288176,0.0000948316,0.0001651416,0.00004528477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008878146,0.0002179396,0.001492482,0.0007614966,0.00006416715,0.0005053037,0.001904531,0.008496239,0.03229503,0.01340081,0.08148815,0.8584861],"study_design_scores_gemma":[0.0005867204,0.001813291,0.006630562,0.0003147892,0.0003199832,0.0008989864,0.003103151,0.6621862,0.09721779,0.04798469,0.1787753,0.0001685265],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1725466,0.001355832,0.6698558,0.0009146151,0.001810134,0.001058638,0.002824269,0.05098129,0.09865279],"genre_scores_gemma":[0.5305633,0.0005033262,0.3831812,0.0003109691,0.0002823513,0.0006965371,0.005162391,0.003261575,0.07603855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03408262,"threshold_uncertainty_score":0.1140177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742694147783169,"score_gpt":0.2492963406584512,"score_spread":0.2318693991806195,"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."}}