{"id":"W2997824220","doi":"10.3390/su12010274","title":"Uncertainty Problems in Image Change Detection","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Wuhan University","keywords":"Stratified sampling; Thresholding; Sampling (signal processing); Change detection; Variance (accounting); Binary number; Computer science; Systematic sampling; Random forest; Statistics; Set (abstract data type); Land cover; Image (mathematics); Data mining; Artificial intelligence; Mathematics; Land use; Computer vision","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":[0.01769769,0.0008248841,0.001180392,0.004194499,0.0009448482,0.003044701,0.001412628,0.001371164,0.0006227971],"category_scores_gemma":[0.08026872,0.0005323787,0.0008252201,0.003512847,0.003096966,0.003396914,0.001911079,0.001240048,0.0001775916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880947,"about_ca_system_score_gemma":0.000926109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005157105,"about_ca_topic_score_gemma":0.002844285,"domain_scores_codex":[0.9829667,0.005953505,0.001343496,0.002662136,0.006554985,0.0005192375],"domain_scores_gemma":[0.9444454,0.04195261,0.004998891,0.003148323,0.005211401,0.0002434104],"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":[0.0003696798,0.00008786461,0.04850705,0.001181334,0.0004912894,0.000635338,0.001661841,0.2387012,0.01001159,0.08286565,0.003142401,0.6123448],"study_design_scores_gemma":[0.00002912872,0.000189459,0.03797115,0.0002960424,0.0001842996,0.001038997,0.0007763322,0.7330688,0.02224833,0.1940246,0.009935402,0.0002373693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04684526,0.002311212,0.9466097,0.0006273688,0.00008280178,0.0001165983,0.0001930358,0.0002856398,0.002928404],"genre_scores_gemma":[0.7206475,0.001027609,0.2766646,0.0002401679,0.0001471498,0.0001745092,0.0003597688,0.00008914222,0.0006496408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01769769,"threshold_uncertainty_score":0.09359545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055794092616517,"score_gpt":0.2275945150014048,"score_spread":0.2170365740752396,"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."}}