{"id":"W4402575256","doi":"10.1093/comjnl/bxae082","title":"Efficient object detector via dynamic prior and dynamic feature fusion","year":2024,"lang":"en","type":"article","venue":"The Computer Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Computation; Overhead (engineering); Detector; Artificial intelligence; Generator (circuit theory); Inference; Object detection; Pattern recognition (psychology); Object (grammar); Fusion; Process (computing); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002941813,0.0001772857,0.0001363077,0.0001138148,0.0005482323,0.0005409448,0.0008706545,0.0000503876,0.000004626892],"category_scores_gemma":[0.000004168078,0.0001107828,0.00008576463,0.0004905403,0.00006754671,0.0001462436,0.0005133264,0.0006673794,0.00004305533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054868,"about_ca_system_score_gemma":0.00004691666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.268728e-7,"about_ca_topic_score_gemma":0.000003486409,"domain_scores_codex":[0.9987864,0.0000986806,0.0001946409,0.000350553,0.000264035,0.0003056852],"domain_scores_gemma":[0.9990883,0.0002243792,0.00007832765,0.0004359527,0.00004977173,0.0001232629],"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":[0.000006354638,0.00002308445,0.00000448878,0.00001569076,0.00002812919,0.00008019543,0.0006095171,0.0217708,0.004638363,0.001734934,0.0007255989,0.9703628],"study_design_scores_gemma":[0.000128306,0.00006442,0.001580109,0.00006514916,0.00001175286,0.002829511,0.000003867751,0.9868018,0.0000551916,0.00587528,0.002445579,0.0001390336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06978108,0.00330563,0.9193738,0.005939261,0.001161696,0.0001971815,0.000001331541,0.0002133446,0.00002665783],"genre_scores_gemma":[0.8942538,0.000200506,0.1046128,0.0004293911,0.0003187826,0.000008431113,8.261526e-7,0.00002106223,0.0001543684],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9702238,"threshold_uncertainty_score":0.5216343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005758767124131667,"score_gpt":0.2469275378236761,"score_spread":0.2411687706995445,"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."}}