{"id":"W4401997064","doi":"10.2139/ssrn.4939059","title":"Using Soft-Nms and Mask-Scoring R-Cnn to Improve the Detection of Overlapping Insects","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insect Resistance and Genetics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","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":[0.001480956,0.001729163,0.00117311,0.001217364,0.0005272758,0.001179698,0.001428316,0.001766735,0.005532181],"category_scores_gemma":[0.002855736,0.0005284978,0.001042914,0.0007908156,0.0003872217,0.001282421,0.001729566,0.001336246,0.00415851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005620426,"about_ca_system_score_gemma":0.00086877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004064393,"about_ca_topic_score_gemma":0.009588776,"domain_scores_codex":[0.9991208,0.0001251135,0.00004229861,0.0003172481,0.0002347207,0.0001598738],"domain_scores_gemma":[0.9987994,0.0003899391,0.0000979449,0.0003038963,0.0003213613,0.00008735275],"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":[0.0007217875,0.0002025834,0.004644644,0.0002943976,0.0002800576,0.0003394682,0.0001010968,0.03500245,0.1799482,0.001979586,0.01149511,0.7649907],"study_design_scores_gemma":[0.00002614992,0.0001283067,0.005086354,0.00002717044,0.0001042922,0.0002947056,0.00003758765,0.9380614,0.05059772,0.002354893,0.003255159,0.00002635477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1921722,0.001943502,0.7733812,0.0005932148,0.0005184469,0.0001882201,0.001502212,0.02074073,0.008960267],"genre_scores_gemma":[0.5103395,0.0004628531,0.4693357,0.0007289309,0.0001789239,0.0001409572,0.004085956,0.001369294,0.01335785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005532181,"threshold_uncertainty_score":0.01850694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009248197614928,"score_gpt":0.254049793569569,"score_spread":0.244801595954641,"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."}}