{"id":"W4404801468","doi":"10.1016/j.procs.2024.09.461","title":"Multi-Label Classification with Deep Learning and Manual Data Collection for Identifying Similar Bird Species","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Artificial intelligence; Data collection; Machine learning; Statistics","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":[0.0005072449,0.00009215409,0.00006176193,0.0001241155,0.0003932931,0.000500578,0.0003300617,0.00004203572,0.000001980344],"category_scores_gemma":[0.00009359836,0.00008299835,0.00001173571,0.0003971575,0.0002424214,0.00005655325,0.0001661844,0.00006619015,0.000008753176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001600055,"about_ca_system_score_gemma":0.0001208925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001434474,"about_ca_topic_score_gemma":0.00001843414,"domain_scores_codex":[0.9988151,0.00001597426,0.000148064,0.0006921348,0.0001694827,0.0001592877],"domain_scores_gemma":[0.9993728,0.00002256785,0.0000589079,0.0002677751,0.0002235556,0.00005432847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003385487,0.00007064199,0.002275743,0.0002260938,0.00003065213,6.230185e-7,0.00059692,0.00004085265,0.946466,0.001629349,0.001470974,0.04715827],"study_design_scores_gemma":[0.0002866071,0.0001329263,0.008446703,0.00003463157,0.00001838428,0.00002692277,0.0001529427,0.9312697,0.03909465,0.00002135446,0.02033581,0.0001793437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05794879,0.000480514,0.9403148,0.0003578093,0.0004808076,0.0002854495,0.000006421917,0.00009087782,0.00003448359],"genre_scores_gemma":[0.8347034,0.0001965547,0.1636888,0.0000576206,0.0002263099,0.0000550386,0.0001207025,0.00002096267,0.0009305918],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9312289,"threshold_uncertainty_score":0.4827085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007911516650473,"score_gpt":0.355540649061696,"score_spread":0.2547494973966487,"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."}}