{"id":"W4417311715","doi":"10.18280/isi.301003","title":"Shrimp Classification Using Generative Adversarial Network with ResNet","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Residual neural network; Artificial neural network; Identification (biology); Pattern recognition (psychology); Identity (music)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007469364,0.00126816,0.0008484946,0.0006717329,0.0002247142,0.0006045086,0.0008752984,0.0008440217,0.001759692],"category_scores_gemma":[0.001060953,0.0003571109,0.0009726857,0.0003827612,0.0004616457,0.0006626981,0.0008624405,0.001265522,0.0006675902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005774135,"about_ca_system_score_gemma":0.0004136643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590674,"about_ca_topic_score_gemma":0.003638306,"domain_scores_codex":[0.9997271,0.00006748053,0.00001296922,0.00008343237,0.00006518071,0.00004377942],"domain_scores_gemma":[0.9995893,0.0001965234,0.0000546133,0.00005819896,0.00007229838,0.0000290592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002494749,0.0001005351,0.001586492,0.0000609035,0.00007911713,0.0001574598,0.00003367025,0.8427728,0.007477241,0.002091012,0.003364644,0.1420266],"study_design_scores_gemma":[0.000002400036,0.00001518592,0.00007869727,0.00000272321,0.000003454453,0.000009205816,0.0000019876,0.9985348,0.0006853642,0.0005384042,0.0001248453,0.000002912553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1093034,0.001131852,0.8774806,0.000677989,0.0002969409,0.0001529237,0.0003299188,0.003587193,0.007039172],"genre_scores_gemma":[0.894377,0.0003667917,0.09587255,0.0005342644,0.0001079504,0.0001319199,0.0009991063,0.0001392929,0.00747116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003590674,"threshold_uncertainty_score":0.007139564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03444713277326734,"score_gpt":0.264042060166617,"score_spread":0.2295949273933496,"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."}}