{"id":"W4353100309","doi":"10.18280/ts.400111","title":"Prediction of Vegetation Change by Discrete Wavelet Decomposition Based on Remote Sensing Time Series Images","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Education Department of Hunan Province","keywords":"Remote sensing; Series (stratigraphy); Vegetation (pathology); Wavelet; Decomposition; Change detection; Time series; Discrete wavelet transform; Computer science; Wavelet transform; Environmental science; Artificial intelligence; Pattern recognition (psychology); Geology; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003059643,0.0003902389,0.0002126127,0.0009816446,0.0001150322,0.0003411995,0.0002112791,0.0002361408,0.0004035133],"category_scores_gemma":[0.0008344011,0.0001236763,0.0004153863,0.0008008934,0.0001302774,0.0005374749,0.0001592634,0.0003763278,0.0001169628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002234762,"about_ca_system_score_gemma":0.0002468537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006998939,"about_ca_topic_score_gemma":0.003833521,"domain_scores_codex":[0.999886,0.00001670438,0.000006923771,0.00003474414,0.00003726713,0.00001827319],"domain_scores_gemma":[0.9998626,0.00004958782,0.00002532698,0.00001310739,0.00003784691,0.00001150806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003410965,0.00029345,0.05148885,0.0001031853,0.0001246659,0.0003115984,0.0001721842,0.6072125,0.03977923,0.002496014,0.001706068,0.2959712],"study_design_scores_gemma":[0.000002711522,0.00001460173,0.007232364,0.000002215899,0.000006239669,0.00000905321,0.00001853981,0.9909106,0.001446282,0.0002495331,0.0001039206,0.000003869288],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7526309,0.0001983327,0.2449789,0.0001373661,0.00007098735,0.00003417115,0.0003286176,0.0003411747,0.001279475],"genre_scores_gemma":[0.9785711,0.0001430926,0.02034337,0.000008898368,0.00001189496,0.000014104,0.0003208066,0.0000147896,0.0005718508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006998939,"threshold_uncertainty_score":0.01391637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026192505511664,"score_gpt":0.2213720567027032,"score_spread":0.2011101316475866,"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."}}