{"id":"W4385446408","doi":"10.3390/s23156845","title":"Analysis of Hyperspectral Data to Develop an Approach for Document Images","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Hyperspectral imaging; Preprocessor; Computer science; Data pre-processing; Data science; Field (mathematics); Feature extraction; Image processing; Data mining; Artificial intelligence; Information retrieval; Pattern recognition (psychology); Image (mathematics); Mathematics","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":[0.0004515139,0.0003202763,0.00134077,0.000795629,0.00002961935,0.00005828847,0.0005931362,0.0001589909,0.000003751478],"category_scores_gemma":[0.0003148577,0.0002991073,0.0002162198,0.002627832,0.00002765408,0.00008839757,0.00008975543,0.0001444891,0.00003069866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001799969,"about_ca_system_score_gemma":0.00007481009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001758839,"about_ca_topic_score_gemma":0.000009807632,"domain_scores_codex":[0.9982528,0.00007011647,0.0006064682,0.0005796151,0.0002062067,0.0002847852],"domain_scores_gemma":[0.9979312,0.0001524731,0.0001298761,0.001550914,0.0001356905,0.00009985908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003376923,0.00002777342,9.475858e-7,0.01310521,0.004319716,0.000004977155,0.0002546638,0.04271812,0.0000952831,0.00003622484,0.002313712,0.93712],"study_design_scores_gemma":[0.0001304524,0.00003284341,0.00008533776,0.001374396,0.01450133,0.000009926701,0.0003003157,0.3290606,0.00009875813,0.000007970966,0.6532792,0.00111888],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002404203,0.9503202,0.04209421,0.00003375117,0.0004649134,0.002791289,0.002060096,0.0009948607,0.001000283],"genre_scores_gemma":[0.00002213541,0.8476769,0.1454107,0.000003185479,0.0001280953,0.00002592609,0.006076745,0.0001698549,0.0004863744],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9360011,"threshold_uncertainty_score":0.9999461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.190354951584761,"score_gpt":0.3798717034644761,"score_spread":0.1895167518797152,"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."}}