{"id":"W4389004940","doi":"10.23977/jeis.2023.080602","title":"A Deep Neural Network for Image Segmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Computer science; Annotation; Artificial intelligence; Metric (unit); Convolutional neural network; Segmentation; Quality (philosophy); Convolution (computer science); Artificial neural network; Automatic image annotation; Data mining; Image (mathematics); Architecture; Pattern recognition (psychology); Machine learning; Image retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000731391,0.00004223525,0.00005887478,0.0001883025,0.0001076524,0.0001755119,0.00008111363,0.0000155451,9.161715e-7],"category_scores_gemma":[0.0001055966,0.00003726956,0.00002072855,0.0005796802,0.0000417821,0.003826472,0.000007471644,0.00007265055,0.000004481949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007997607,"about_ca_system_score_gemma":0.00005022756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.341115e-7,"about_ca_topic_score_gemma":3.997312e-7,"domain_scores_codex":[0.9993488,0.000003797212,0.0002563827,0.00002890126,0.0001854312,0.0001767373],"domain_scores_gemma":[0.9994998,0.000034258,0.0001256103,0.00004766839,0.000251262,0.00004140866],"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":[0.00002598841,0.000004120956,0.00007267216,0.00006826571,0.0000118584,7.162289e-7,0.001251758,0.5813376,0.1538399,0.003575304,0.004820324,0.2549915],"study_design_scores_gemma":[0.0001633708,0.00006030232,0.002228867,0.00000565827,0.000004105974,0.00002869333,0.00008442505,0.9890394,0.00350362,0.0004156059,0.004423656,0.00004234264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4523045,0.0002153728,0.5453389,0.0005279093,0.0005834406,0.0001874867,0.000001274877,0.00007138819,0.0007697512],"genre_scores_gemma":[0.9832327,0.0005447366,0.01602371,0.00009091102,0.00008901012,0.00000147617,0.000005700386,0.000005197676,0.00000651298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5309283,"threshold_uncertainty_score":0.27741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049056525711602,"score_gpt":0.2539490747978226,"score_spread":0.2434585095407066,"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."}}