{"id":"W4409972601","doi":"10.1002/ail2.122","title":"A Few‐Shot Learning Approach for a Multilingual Agro‐Information Question Answering System","year":2025,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Bird Study Association; Foreign, Commonwealth and Development Office; International Development Research Centre","keywords":"Question answering; Computer science; One shot; Information retrieval; Shot (pellet); Natural language processing; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004161509,0.0001366152,0.0001529845,0.0001688325,0.0002013192,0.0002251477,0.0003898832,0.00006820007,2.89463e-7],"category_scores_gemma":[0.00002403778,0.0001422231,0.00005413713,0.0002124856,0.00001510028,0.0004374441,0.0001105233,0.0001936964,0.000009949696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370695,"about_ca_system_score_gemma":0.00003442045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002813798,"about_ca_topic_score_gemma":5.782794e-7,"domain_scores_codex":[0.9989505,0.0000252556,0.0002901048,0.0002970914,0.0001682457,0.0002687873],"domain_scores_gemma":[0.9994779,0.00005594851,0.00009593557,0.0002921953,0.00004218459,0.00003578948],"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":[0.00003837467,0.00002741202,0.0001422686,0.000843472,0.00005437885,0.000001723409,0.003490101,0.4358293,0.03173416,0.3706382,0.0006867184,0.1565139],"study_design_scores_gemma":[0.0005039871,0.000008232349,0.00006711543,0.00005195461,0.000009465032,0.000002954914,0.0002636421,0.9934249,0.002947766,0.00006704465,0.002497379,0.0001555506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02312013,0.00001007478,0.9716716,0.000947639,0.0002546418,0.0005124169,6.017336e-7,0.0004551943,0.003027634],"genre_scores_gemma":[0.8434155,3.393162e-7,0.1541815,0.002112166,0.00007100561,0.0001688757,0.00001536343,0.000005888606,0.00002936739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8202953,"threshold_uncertainty_score":0.5799688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099179054557923,"score_gpt":0.2422306152666991,"score_spread":0.2312388247211199,"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."}}