{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008388448,0.0006543904,0.0006466184,0.0005578587,0.0003789902,0.0009198375,0.003587087,0.0002467105,0.00003696923],"category_scores_gemma":[0.0002893374,0.0005828671,0.0001875017,0.0007601607,0.0003366719,0.0007200033,0.002431723,0.001064901,0.0000323545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003465224,"about_ca_system_score_gemma":0.001054401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003429166,"about_ca_topic_score_gemma":0.00008825195,"domain_scores_codex":[0.9946877,0.00005064637,0.0006054502,0.002635129,0.00123679,0.0007842305],"domain_scores_gemma":[0.9964516,0.0002932556,0.0002861412,0.002324946,0.0004509627,0.0001931314],"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":[0.00000203385,0.00003124094,0.0002423296,0.00006792061,0.00002043029,0.0003493129,0.002308537,0.1389252,0.002264244,0.01427585,0.00002050209,0.8414924],"study_design_scores_gemma":[0.000195436,0.00006025218,0.00006402249,0.0002921168,0.000006451956,0.0001835188,3.352885e-7,0.968201,0.001292086,0.02832063,0.000585143,0.0007990205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001021144,0.00173603,0.9897329,0.001217845,0.003274573,0.0003583084,0.000002436257,0.0002009369,0.002455842],"genre_scores_gemma":[0.1479505,0.00005529431,0.8466662,0.001951266,0.002161039,0.00001247012,0.000007103115,0.00004981138,0.001146346],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8406934,"threshold_uncertainty_score":0.9996623,"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."}}