{"id":"W4380992126","doi":"10.1109/access.2023.3286853","title":"Learning to Generate Popular Headlines","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007567266,0.001494277,0.0005348914,0.001862845,0.0003903018,0.0008101283,0.0008363182,0.0009001691,0.003792812],"category_scores_gemma":[0.005577119,0.0003966871,0.0007227672,0.001065916,0.0002679913,0.001841938,0.0005273015,0.0007661296,0.003257222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005465989,"about_ca_system_score_gemma":0.0005451039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00245712,"about_ca_topic_score_gemma":0.006484696,"domain_scores_codex":[0.9995071,0.0001114704,0.0000324911,0.0001988164,0.00009780886,0.00005228131],"domain_scores_gemma":[0.9971038,0.001313033,0.0003514108,0.0003284637,0.0007600898,0.0001432792],"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":[0.0007131502,0.0004130541,0.01047109,0.0006941227,0.0001896541,0.0005474208,0.0006086529,0.06956744,0.03856302,0.004326791,0.0632431,0.8106624],"study_design_scores_gemma":[0.0001161445,0.0004152321,0.002792492,0.00003995313,0.0001169097,0.0002827591,0.0001910434,0.9487725,0.02743556,0.004755982,0.01504619,0.00003511603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2008809,0.002437976,0.7445605,0.0008290075,0.0004462725,0.0005387182,0.004849885,0.03703216,0.008424501],"genre_scores_gemma":[0.6467115,0.0009814227,0.3191571,0.0004436359,0.0006325746,0.0005220852,0.01514263,0.001605617,0.01480357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003792812,"threshold_uncertainty_score":0.01268828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08285575657819608,"score_gpt":0.347523334990131,"score_spread":0.2646675784119349,"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."}}