{"id":"W2015996042","doi":"10.1177/0309133308098363","title":"Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends","year":2008,"lang":"en","type":"article","venue":"Progress in Physical Geography Earth and Environment","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; U.S. Geological Survey; National Oceanic and Atmospheric Administration; Government of Canada","keywords":"Land cover; Remote sensing; Change detection; Variety (cybernetics); Environmental resource management; Geography; Temporal scales; Computer science; Cover (algebra); Data science; Land use; Cartography; Environmental science; Ecology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006789548,0.0001652817,0.0002277245,0.00006582981,0.0001132079,0.00001556599,0.0000500548,0.00004448926,0.00002056723],"category_scores_gemma":[6.846028e-7,0.0001310855,0.0000450038,0.0001204805,0.0002607487,0.0001673632,0.0001752793,0.0001045958,0.00000220353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007512242,"about_ca_system_score_gemma":7.424882e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004545149,"about_ca_topic_score_gemma":0.0003052334,"domain_scores_codex":[0.9990391,0.00003471798,0.0001580585,0.0003280355,0.0001814245,0.0002587379],"domain_scores_gemma":[0.9996806,0.00001891004,0.00007070266,0.0001255701,0.000001395816,0.0001027708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000118166,0.0000343984,0.619799,0.00002054346,0.000008425586,0.00001119539,0.0003804419,0.000008186211,0.00003016226,4.775851e-7,3.995504e-7,0.379695],"study_design_scores_gemma":[0.0004562516,0.00009721812,0.8581507,0.00004268789,0.00001478132,0.00001768967,0.00006840564,0.1408134,0.0001192711,0.00002689939,0.00003736559,0.0001553096],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974688,0.002210406,0.00002477465,0.00008669757,0.00002826627,0.0001174999,0.0000097929,0.00001196572,0.00004180304],"genre_scores_gemma":[0.9980555,0.001245374,0.0006107101,0.00002474821,0.00003449478,0.000002132941,0.0000139084,0.000009909058,0.000003179766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3795397,"threshold_uncertainty_score":0.534551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416703821536338,"score_gpt":0.2145475299649302,"score_spread":0.2003804917495668,"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."}}