{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002397387,0.0002831133,0.0001882513,0.00096502,0.0002179248,0.0003033078,0.0002357898,0.0002136535,0.0008611169],"category_scores_gemma":[0.0006078045,0.0001590857,0.0001299469,0.001060195,0.0001575525,0.0004134463,0.0002396592,0.00009136871,0.0001711521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008402706,"about_ca_system_score_gemma":0.0002774876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04462923,"about_ca_topic_score_gemma":0.07362439,"domain_scores_codex":[0.9998558,0.00002712662,0.000009308926,0.00003064769,0.00005194201,0.00002516965],"domain_scores_gemma":[0.9997759,0.00003927647,0.00005244396,0.00002223342,0.00006744272,0.00004267248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001153052,0.0003312839,0.7479261,0.0002177084,0.0002022815,0.002096262,0.0009319227,0.02790642,0.06480446,0.0004003685,0.001885735,0.1521444],"study_design_scores_gemma":[0.00002011996,0.00007495294,0.9599035,0.000005457742,0.00003961656,0.0001256941,0.0003014957,0.03412179,0.00459999,0.0000797228,0.0007132824,0.00001451846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984788,0.00004351311,0.0003359014,0.00002026535,0.000001481091,0.00001042174,0.000191778,0.00001630421,0.0009016233],"genre_scores_gemma":[0.9982766,0.00003026547,0.0007798819,0.00000429496,0.00000199141,0.000007378916,0.0004437497,0.000002778305,0.0004530058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04462923,"threshold_uncertainty_score":0.08873892,"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."}}