{"id":"W6966906508","doi":"10.48448/qj1x-sh92","title":"Event Transition Planning for Open-ended Text Generation","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Text generation; Natural language generation; Transition (genetics); Continuation; Event (particle physics); Generator (circuit theory); Coherence (philosophical gambling strategy); Planner","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001897036,0.0003486829,0.0003513145,0.001083686,0.0008443929,0.0004418096,0.001594175,0.0001461836,0.005883811],"category_scores_gemma":[0.000110511,0.0003554392,0.00007963279,0.001177475,0.0003838385,0.0004877695,0.0002987861,0.0002805657,0.0004138137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00081652,"about_ca_system_score_gemma":0.001052805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002027365,"about_ca_topic_score_gemma":0.0004587643,"domain_scores_codex":[0.9967368,0.00009607561,0.0003897013,0.001099698,0.001083506,0.0005942165],"domain_scores_gemma":[0.998681,0.00004124949,0.0004062766,0.0005954629,0.0001200258,0.0001559896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004206087,0.0002234289,0.000006770583,0.00004213441,0.00003626881,0.00000805271,0.0006489857,0.01624899,0.03084525,0.005106965,0.9409921,0.005798984],"study_design_scores_gemma":[0.001390188,0.0003001636,0.00001323845,0.00009688559,0.00007599808,0.00002036705,0.0004910616,0.2316245,0.0009715018,0.0007845485,0.7635278,0.0007037842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005646933,0.001380564,0.2299526,0.00160902,0.004192491,0.01021627,0.003765387,0.001592158,0.7467269],"genre_scores_gemma":[0.1313005,0.00003669934,0.1273489,0.002958783,0.004792201,0.002685836,0.0142642,0.003777018,0.7128359],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2153755,"threshold_uncertainty_score":0.9998897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07716378672298892,"score_gpt":0.3637185555266449,"score_spread":0.286554768803656,"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."}}