{"id":"W4409310957","doi":"10.1109/tgrs.2025.3559145","title":"Global Adaptability Assessment of Ten Common Topographic Correction Models for Landsat 8 OLI Images","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Adaptability; Remote sensing; Geology; Computer science; Environmental science","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":[],"consensus_categories":[],"category_scores_codex":[0.0003185989,0.0001601453,0.0002124327,0.00007086708,0.0004443387,0.00004760141,0.0001051244,0.0001046817,0.000004084496],"category_scores_gemma":[0.000008143867,0.0001255783,0.0001087603,0.0007485469,0.0004454071,0.0002127366,0.000005951566,0.0001432914,6.801774e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001698076,"about_ca_system_score_gemma":0.00002970548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691482,"about_ca_topic_score_gemma":0.001612095,"domain_scores_codex":[0.998733,0.00006566502,0.0002481747,0.0004399544,0.0002616702,0.0002515162],"domain_scores_gemma":[0.9994628,0.000112146,0.00008346167,0.0002366231,0.00003830714,0.0000666725],"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":[0.00004162837,0.0000902939,0.0003974162,0.00002997372,0.00001701519,0.000001472384,0.0001446106,0.06773048,0.009865762,0.00001429318,0.000219751,0.9214473],"study_design_scores_gemma":[0.000302253,0.000119135,0.04059067,0.0001201063,0.00005700262,0.00003028839,0.0002994986,0.9473997,0.008429044,0.002363862,0.0001129971,0.000175422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2608635,0.000009727056,0.7351534,0.0002907844,0.0007578879,0.0002741087,0.0000131052,0.00004154195,0.002595944],"genre_scores_gemma":[0.9319484,0.0000539277,0.06748747,0.0000917146,0.00001026318,1.46064e-7,0.000001266269,0.000004336931,0.0004024348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9212719,"threshold_uncertainty_score":0.5120932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009926595315550268,"score_gpt":0.2658343676645742,"score_spread":0.255907772349024,"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."}}