{"id":"W4206728070","doi":"10.1007/978-3-030-92300-6_23","title":"Blabbeur - An Accessible Text Generation Authoring System for Unity","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Computer science; Scripting language; Generative grammar; Syntax; Programming language; Context (archaeology); Simple (philosophy); Rule-based machine translation; Grammar; Text generation; Natural language processing; Artificial intelligence; Linguistics","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009550162,0.0004933081,0.0004891777,0.0006918803,0.0006696234,0.00181367,0.003687631,0.0003546104,0.00002009666],"category_scores_gemma":[0.00009314517,0.0004938592,0.0001436818,0.0007544233,0.0001876965,0.002210767,0.001265022,0.0007398729,0.000020784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007123438,"about_ca_system_score_gemma":0.000876885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004981528,"about_ca_topic_score_gemma":0.0004501297,"domain_scores_codex":[0.9961102,0.00007267469,0.0006195381,0.001788788,0.0007927548,0.0006160313],"domain_scores_gemma":[0.9962392,0.0004549089,0.000377761,0.001913455,0.0007933824,0.000221266],"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":[0.000008549964,0.00009905921,0.00005284244,0.0003577481,0.00004060559,0.0001226313,0.001909509,0.1529547,0.004907003,0.171079,0.0001145802,0.6683538],"study_design_scores_gemma":[0.0001717337,0.0001366345,0.00003215596,0.0004747415,0.00001412443,0.00011341,0.000001798791,0.975337,0.009992313,0.01047476,0.002656468,0.0005948422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003856888,0.0001715899,0.9898812,0.0007121821,0.006041956,0.0005014931,0.00001189137,0.0003195539,0.00197448],"genre_scores_gemma":[0.1871783,0.00001290138,0.8082191,0.001186488,0.002369848,0.00006198989,0.00004583018,0.00006618191,0.0008593329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8223823,"threshold_uncertainty_score":0.9997513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05518078337297638,"score_gpt":0.3078544213443861,"score_spread":0.2526736379714097,"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."}}