{"id":"W2182679614","doi":"","title":"LAND COVER AND LAND USE CHANGE DETECTION OF BEIJING WITH TEXTURAL INFORMATION FROM SATELLITE REMOTE SENSING DATA","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Beijing; Remote sensing; Multispectral image; Land cover; Geography; China; Land use; Satellite; Change detection; Environmental resource management; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Satellite imagery; Environmental planning; Environmental science; Digital elevation model; Engineering; Civil engineering","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":[0.0002261974,0.00007796013,0.0001010878,0.00006346968,0.0000698326,0.00008974921,0.00004258332,0.00004827489,0.00002207986],"category_scores_gemma":[0.00002500389,0.00004946611,0.000007884817,0.000104922,0.00003028683,0.001047398,0.00001060801,0.00006783243,0.0000167582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001586706,"about_ca_system_score_gemma":0.00000601207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06358016,"about_ca_topic_score_gemma":0.05455169,"domain_scores_codex":[0.9994467,0.00001980992,0.0001451564,0.0001172469,0.000135735,0.0001353629],"domain_scores_gemma":[0.999512,0.0001401509,0.0000756485,0.0001841073,0.00003026778,0.00005780216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001110446,5.712101e-7,0.2693381,0.00000660715,0.000008695296,0.000003819469,0.0002384928,0.00002400573,0.00004663614,6.988356e-8,0.000002825486,0.7302191],"study_design_scores_gemma":[0.0002987258,0.00005263909,0.8612955,0.00003992093,0.00001764227,0.00003337235,0.00007046758,0.1323185,0.00029892,0.000009803442,0.005470984,0.00009355795],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895097,0.0001408887,0.008978438,0.00002816084,0.00008544605,0.00007978798,0.00005654099,0.00002611287,0.001094919],"genre_scores_gemma":[0.9925821,0.0001454897,0.006568081,0.0001414358,0.0000835776,8.278448e-10,0.0004508142,0.000001694792,0.00002683806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7301256,"threshold_uncertainty_score":0.9627003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03784890967893584,"score_gpt":0.2175928975215593,"score_spread":0.1797439878426235,"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."}}