{"id":"W4225308848","doi":"10.36227/techrxiv.19400612.v1","title":"Anomaly Detection Based on Sigmoid Metric and Object Area Filtering in Hyperspectral Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Hyperspectral imaging; Pixel; Metric (unit); Sigmoid function; Mean squared error; Artificial intelligence; Window (computing); Object (grammar); Anomaly (physics); Pattern recognition (psychology); Mathematics; Image (mathematics); Anomaly detection; Computer science; Computer vision; Statistics; Physics; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001158421,0.0008625285,0.0009842517,0.001856457,0.0003263623,0.001101177,0.0008452648,0.0006645825,0.000422558],"category_scores_gemma":[0.002257978,0.0002217141,0.000730817,0.001800464,0.0008242771,0.001632956,0.0008217002,0.0006844181,0.0002735771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000612919,"about_ca_system_score_gemma":0.0005827722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002068753,"about_ca_topic_score_gemma":0.001610903,"domain_scores_codex":[0.9990888,0.0001369194,0.00006637388,0.0002537721,0.0003692976,0.00008493593],"domain_scores_gemma":[0.9989164,0.0003063527,0.0001984853,0.0001219294,0.0003886276,0.00006817267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004343883,0.0001871803,0.0134858,0.0002676922,0.0002066855,0.0002839098,0.0003210508,0.1437888,0.1159509,0.0118716,0.001697778,0.7115042],"study_design_scores_gemma":[0.000005930595,0.000097891,0.005797801,0.000009318309,0.00002287365,0.0002465589,0.00004042481,0.9608666,0.02777688,0.003849696,0.00125334,0.00003261659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07118303,0.0004029583,0.9269537,0.00006188737,0.00003835626,0.00003421016,0.00006267185,0.0005825477,0.0006806508],"genre_scores_gemma":[0.6297348,0.0005665655,0.367437,0.0000676921,0.00009090102,0.00006816709,0.0003370325,0.0001117252,0.001586168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002068753,"threshold_uncertainty_score":0.006126344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801771498601864,"score_gpt":0.2253410697097361,"score_spread":0.2073233547237175,"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."}}