{"id":"W7113160520","doi":"","title":"Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases","year":2025,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Set (abstract data type); Feature (linguistics); Action (physics); Key (lock)","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":[0.0008104522,0.001231819,0.0007704081,0.0009064755,0.0006850048,0.001894984,0.001754881,0.001272985,0.0311253],"category_scores_gemma":[0.004548231,0.0008877217,0.0006405752,0.0008093952,0.0007417353,0.002307036,0.003806611,0.001351681,0.01072964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003612455,"about_ca_system_score_gemma":0.000697349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001331388,"about_ca_topic_score_gemma":0.002479981,"domain_scores_codex":[0.9993205,0.0001828839,0.00004695948,0.0002100906,0.0001526648,0.00008686403],"domain_scores_gemma":[0.9981297,0.001075644,0.00006635831,0.0004153502,0.0002319796,0.00008094947],"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":[0.00132579,0.0002105295,0.002026683,0.0005466701,0.00007349801,0.001445802,0.001716228,0.01423573,0.1072461,0.02922779,0.04511474,0.7968304],"study_design_scores_gemma":[0.0003463044,0.0003517341,0.002804017,0.0001119884,0.0001400373,0.001024354,0.001501708,0.6959547,0.1645539,0.07947498,0.05356027,0.0001760665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1355116,0.0002792189,0.7557698,0.0005521406,0.0005489654,0.0004270101,0.004473918,0.07568018,0.02675723],"genre_scores_gemma":[0.635981,0.0001590115,0.3328266,0.0001507834,0.0001309849,0.0002101311,0.007414527,0.008410593,0.01471637],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0311253,"threshold_uncertainty_score":0.1041245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066384881160835,"score_gpt":0.3305961335946331,"score_spread":0.2239576454785496,"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."}}