{"id":"W7084131731","doi":"10.1109/iri66576.2025.00071","title":"Database Entity Recognition with Data Augmentation and Deep Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Bioeconomy and Sustainability Development","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Named-entity recognition; Annotation; Deep learning; Benchmark (surveying); Security token; Key (lock); Recall; Precision and recall; Relational database","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":[0.002321473,0.001439581,0.001203569,0.002160326,0.0005864073,0.001788645,0.004078655,0.001611614,0.003317934],"category_scores_gemma":[0.005853384,0.0005531249,0.001286115,0.003155839,0.0008056678,0.006309896,0.003062029,0.002951924,0.003595708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337618,"about_ca_system_score_gemma":0.001185593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006181593,"about_ca_topic_score_gemma":0.009021341,"domain_scores_codex":[0.998235,0.0003606997,0.0001467639,0.0006676894,0.0004323058,0.0001575407],"domain_scores_gemma":[0.9966398,0.001153161,0.0001649854,0.001416195,0.0005447957,0.00008108668],"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":[0.0004551766,0.0005110453,0.00330627,0.0003377078,0.0001459268,0.0002819492,0.0001886181,0.1158403,0.01258846,0.007836454,0.03981661,0.8186916],"study_design_scores_gemma":[0.00002545227,0.0001172904,0.0007820349,0.00003243259,0.00004294564,0.0001695043,0.0001097646,0.9445021,0.02329649,0.01344089,0.01744146,0.00003946649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05892326,0.002166036,0.8723168,0.00178405,0.000461857,0.0002924129,0.007618625,0.05080105,0.005635832],"genre_scores_gemma":[0.3505602,0.0007269518,0.6083258,0.001470173,0.0001893286,0.0003727849,0.02962847,0.0005813396,0.008144864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006181593,"threshold_uncertainty_score":0.01229125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507004480349941,"score_gpt":0.2347386580022499,"score_spread":0.2096686131987505,"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."}}