{"id":"W4382241254","doi":"10.1007/s11042-023-16007-3","title":"Efficient semi-supervised learning model for limited otolith data using generative adversarial networks","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Adversarial system; Machine learning; Deep learning; Otolith; Data set; Generative grammar; Pattern recognition (psychology); Artificial neural network; Set (abstract data type); Fish <Actinopterygii>; Fishery","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":[0.0002317937,0.00009869636,0.0001080924,0.0000261784,0.0004153377,0.0000760004,0.0002563985,0.00006202269,0.0001775641],"category_scores_gemma":[0.00007229025,0.00009115557,0.00002361842,0.0003287509,0.000119522,0.00009482689,0.000617163,0.0001328374,0.00002632444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003536753,"about_ca_system_score_gemma":0.00001721512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001070471,"about_ca_topic_score_gemma":0.00002607427,"domain_scores_codex":[0.9989922,0.00002404116,0.0001460876,0.0003961083,0.0001555741,0.0002859554],"domain_scores_gemma":[0.9993204,0.0001799482,0.00003575114,0.000335317,0.00001531013,0.0001132657],"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":[0.00001953168,0.00003880051,0.003726537,0.000007160528,0.00001105607,3.404973e-7,0.0002902729,0.8769581,0.001671886,0.00005069332,0.001060421,0.1161652],"study_design_scores_gemma":[0.0003660116,0.00001160618,0.0007338122,0.000001599549,0.00001245673,4.035516e-7,0.0001021236,0.9785772,0.0000194676,0.0000319645,0.02003326,0.0001100926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06354076,0.00001162801,0.9303439,0.000296376,0.00003934898,0.001759887,0.000320995,0.0001234987,0.003563655],"genre_scores_gemma":[0.8333042,0.0003374908,0.1483214,0.0003863835,0.001155325,0.002929288,0.007121,0.0001129998,0.006331895],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7820225,"threshold_uncertainty_score":0.3717216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1037068700470745,"score_gpt":0.3152649413857523,"score_spread":0.2115580713386778,"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."}}