{"id":"W7117736903","doi":"10.2139/ssrn.5995648","title":"Classification of Filamentous Cyanobacteria Using a Deep Learning Model with Multi-wavelength Laser Microscopic Images","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; McMaster University","funders":"","keywords":"Cyanobacteria; Convolutional neural network; Aphanizomenon; Laser; Pattern recognition (psychology); Microscopy; Multispectral image","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001566057,0.000800992,0.0009403896,0.0004328615,0.0003738445,0.0002019679,0.0008751284,0.0007600663,0.00001762468],"category_scores_gemma":[0.0001304917,0.0007853959,0.0005729631,0.0003033014,0.0002814977,0.00002930137,0.0005962064,0.00416488,0.000001609247],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258777,"about_ca_system_score_gemma":0.006672984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001318222,"about_ca_topic_score_gemma":0.0006818578,"domain_scores_codex":[0.9943956,0.0004628368,0.001241248,0.001140358,0.0003943756,0.002365589],"domain_scores_gemma":[0.9960704,0.00002444251,0.001912583,0.0009326988,0.0009234769,0.0001363843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003609145,0.0003819004,0.001673472,0.0001840421,0.001845676,0.000004170084,0.0001340103,0.01494428,0.9679995,0.00006222916,0.00001086909,0.01239899],"study_design_scores_gemma":[0.002120807,0.001062754,0.0006250562,0.0006917104,0.00192237,0.0003110369,0.001416503,0.2158039,0.7734979,0.001124056,0.0002728016,0.001151045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5102836,0.002994489,0.4860806,0.00002902855,0.00003479939,0.0003655413,0.00001357298,0.00001822593,0.000180114],"genre_scores_gemma":[0.9330797,0.03982558,0.02256932,0.0000325146,0.0001592509,0.0000269446,0.0002417929,0.00008614278,0.00397874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4635113,"threshold_uncertainty_score":0.9994597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134670942145486,"score_gpt":0.2805415012344932,"score_spread":0.2691947918130383,"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."}}