{"id":"W4390231356","doi":"10.18280/ria.370622","title":"Combined Approach for Answer Identification with Small Sized Reading Comprehension Datasets","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Reading comprehension; Reading (process); Computer science; Comprehension; Natural language processing; Artificial intelligence; Information retrieval; Psychology; Linguistics; Programming language; Philosophy; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003124753,0.001442696,0.001272206,0.004898005,0.0006659593,0.001710949,0.0017852,0.001826458,0.004383373],"category_scores_gemma":[0.01127173,0.0002924577,0.001386219,0.002327112,0.0003130134,0.003451553,0.002482429,0.001459809,0.004080366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006060004,"about_ca_system_score_gemma":0.001068924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002563742,"about_ca_topic_score_gemma":0.004918096,"domain_scores_codex":[0.9972478,0.001047296,0.0002616875,0.0008575052,0.0004486256,0.0001370699],"domain_scores_gemma":[0.9945179,0.002826412,0.0002156087,0.001031268,0.001269183,0.0001396435],"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":[0.0007148701,0.001004135,0.01272824,0.0006267395,0.0004221362,0.0004675864,0.0007488445,0.01404769,0.03963213,0.002740942,0.01298914,0.9138774],"study_design_scores_gemma":[0.0001322189,0.0006007047,0.01461056,0.00005622033,0.0002757515,0.0005612319,0.001474996,0.9162787,0.03334324,0.01414215,0.01842242,0.0001017584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.131983,0.001026468,0.8314856,0.0006794779,0.0001500351,0.0009685179,0.007052857,0.02250748,0.004146608],"genre_scores_gemma":[0.4045374,0.0002040642,0.5699618,0.0002012104,0.0001380886,0.001184108,0.02073414,0.0003907053,0.002648491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004898005,"threshold_uncertainty_score":0.01652551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09111594763010084,"score_gpt":0.2880590670980732,"score_spread":0.1969431194679724,"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."}}