{"id":"W4405882677","doi":"10.1007/978-981-96-2061-6_30","title":"MLP-AMDC: A MLP Architecture for Adaptive-Mask-Based Dual-Camera Snapshot Hyperspectral Imaging","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Computer science; Hyperspectral imaging; Snapshot (computer storage); Computer vision; Artificial intelligence; Architecture; Dual (grammatical number); Computer graphics (images)","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"],"consensus_categories":[],"category_scores_codex":[0.000395241,0.0007269841,0.0005476851,0.0009969047,0.00015843,0.0004775165,0.0006831459,0.0002835155,0.00002540045],"category_scores_gemma":[0.00008409859,0.000702786,0.0002550264,0.0004532443,0.0006514066,0.0001854266,0.0001381536,0.001226934,0.0000369603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006689893,"about_ca_system_score_gemma":0.000282976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008065026,"about_ca_topic_score_gemma":0.00004119976,"domain_scores_codex":[0.9968163,0.00001617746,0.0004798423,0.00129344,0.0006104037,0.0007838164],"domain_scores_gemma":[0.9981601,0.0005101931,0.0001108876,0.0008852296,0.0001724284,0.000161137],"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":[0.00002333595,0.00001397434,0.000004028808,0.0002623385,0.00004038505,0.0001335542,0.0008567266,0.7153742,0.01213102,0.001706574,0.0002512399,0.2692026],"study_design_scores_gemma":[0.0002578144,0.00007488218,0.00001885219,0.0006913302,0.00004437011,0.00009896093,7.503632e-7,0.9541429,0.006134754,0.03218785,0.005579673,0.0007678426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001868917,0.00128599,0.9890327,0.001389363,0.002381749,0.0007148773,0.00004078612,0.0005310269,0.004436561],"genre_scores_gemma":[0.3677697,0.00002532836,0.6288484,0.0008596378,0.001668559,0.00002477651,0.00009129511,0.0003054353,0.0004069048],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3675828,"threshold_uncertainty_score":0.9995424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801277711339419,"score_gpt":0.2371734883207302,"score_spread":0.219160711207336,"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."}}