{"id":"W4313331018","doi":"10.14569/ijacsa.2022.0131234","title":"Transfer Learning for Closed Domain Question Answering in COVID-19","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direktorat Riset and Pengembangan, Universitas Indonesia; Universitas Indonesia","keywords":"Computer science; Benchmark (surveying); Labrador Retriever; Cosine similarity; Transfer of learning; Baseline (sea); Question answering; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Domain (mathematical analysis); Similarity (geometry); Open domain; Machine learning; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001216948,0.00006432529,0.00009516094,0.0003823438,0.0003133886,0.0001576236,0.001195195,0.00001134026,0.0000021394],"category_scores_gemma":[0.00003846334,0.00006713959,0.00003620425,0.000433288,0.00006496316,0.001100751,0.0002146639,0.0001798828,2.25702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002996077,"about_ca_system_score_gemma":0.0003338874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005181243,"about_ca_topic_score_gemma":0.000001878368,"domain_scores_codex":[0.9987091,0.00003609105,0.0003108191,0.0002507727,0.0005512415,0.0001419443],"domain_scores_gemma":[0.9992662,0.0001186504,0.000114396,0.0001213058,0.0002739117,0.0001055215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000155067,0.00004228754,0.0001325717,0.000003645734,0.000004756431,0.000005989638,0.0007987569,0.5394502,0.006704368,0.1182981,0.000006141164,0.3345377],"study_design_scores_gemma":[0.002397319,0.0003231078,0.0006991861,0.00004042926,0.000004093217,0.0004577901,0.0005238093,0.7952869,0.001157356,0.09533571,0.1035077,0.0002666262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07457796,0.00007750777,0.9208938,0.003929408,0.0003077028,0.0001730766,0.000001376166,0.00001977511,0.00001936983],"genre_scores_gemma":[0.7624637,0.00002732082,0.2364157,0.0008799831,0.0001281203,0.0000757895,0.000001010357,0.000003134073,0.000005186664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6878858,"threshold_uncertainty_score":0.2737873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620917970969914,"score_gpt":0.3162011834279427,"score_spread":0.2999920037182436,"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."}}