{"id":"W6949444982","doi":"10.5281/zenodo.14167443","title":"Pronoun Generation for Text Summarization and Question Answering","year":2006,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Question answering; Automatic summarization; Pronoun; Natural language; Corpus 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003175916,0.001535354,0.001480807,0.004048654,0.00145029,0.002197882,0.001753035,0.001733976,0.0178897],"category_scores_gemma":[0.01003841,0.0006764444,0.001133979,0.003329783,0.0005802584,0.003635311,0.001955663,0.001326744,0.0142011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007838659,"about_ca_system_score_gemma":0.001129865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989239,"about_ca_topic_score_gemma":0.003003448,"domain_scores_codex":[0.9970892,0.001519,0.0002155662,0.0005970095,0.0003815161,0.0001976182],"domain_scores_gemma":[0.9950998,0.002789525,0.0002534428,0.0007068035,0.0010166,0.0001337442],"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":[0.0004132515,0.0001631068,0.0007474145,0.001179473,0.000136305,0.0002540797,0.0007363095,0.008999197,0.02610222,0.01767701,0.08312779,0.8604638],"study_design_scores_gemma":[0.0002067701,0.0003492448,0.001652794,0.0002203629,0.000257537,0.0005468013,0.001108982,0.5960758,0.07208115,0.1434696,0.1839172,0.0001136636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01115571,0.003133674,0.9447892,0.001412904,0.000689154,0.0006396675,0.005179113,0.02638551,0.006614995],"genre_scores_gemma":[0.1359282,0.001544179,0.8291373,0.0003368597,0.0006795783,0.0006999773,0.01914825,0.001658546,0.01086705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0178897,"threshold_uncertainty_score":0.05984706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167901767740509,"score_gpt":0.2348188671750112,"score_spread":0.2031398494976062,"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."}}