{"id":"W2003009239","doi":"10.1016/s0305-9006(03)00062-x","title":"Remote sensing for mapping and monitoring land-cover and land-use change","year":2003,"lang":"en","type":"article","venue":"Progress in Planning","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Land cover; Remote sensing; Cover (algebra); Change detection; Land use; Environmental resource management; Land use, land-use change and forestry; Geography; Environmental science; 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":[0.0003347285,0.0001258516,0.0001651057,0.0001063034,0.0001552143,0.0001671066,0.00003103375,0.00007153318,0.00000347692],"category_scores_gemma":[0.00007670826,0.0001050326,0.00001646804,0.00009977088,0.00004067807,0.0002113803,0.000007330416,0.000122971,0.00000215998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003003937,"about_ca_system_score_gemma":0.000007646354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004779416,"about_ca_topic_score_gemma":0.00005716038,"domain_scores_codex":[0.9991505,0.00005119964,0.0001417428,0.0002466446,0.00009561868,0.0003142904],"domain_scores_gemma":[0.9995447,0.0002257558,0.00005196818,0.00008194808,0.00001629616,0.00007928388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001987777,0.000001140062,0.7439858,0.0000348393,0.000005267865,0.00003278886,0.0007491875,0.000061007,0.00000164021,9.242531e-7,0.000004825715,0.2551027],"study_design_scores_gemma":[0.0006347249,0.00004969335,0.9003965,0.0004988066,0.00001024553,0.0001110768,0.0001532826,0.09069636,0.00005749582,0.0001792963,0.006984027,0.0002285423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923802,0.006353091,0.0003124613,0.00005365011,0.0002762028,0.0001975294,0.000007409695,0.00003973826,0.0003796879],"genre_scores_gemma":[0.9746855,0.0001734736,0.02487371,0.00004881932,0.0001599897,1.353746e-7,0.00001324379,0.000006447432,0.0000386355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2548742,"threshold_uncertainty_score":0.4283104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07275574569961979,"score_gpt":0.2805068656247641,"score_spread":0.2077511199251443,"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."}}