{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008130423,0.0003891128,0.0003214577,0.003199313,0.0001328124,0.001418785,0.0004844497,0.0003792025,0.001271037],"category_scores_gemma":[0.001240225,0.0001460065,0.0002820249,0.004888763,0.0002373952,0.001137068,0.0002688363,0.0002754188,0.0003172298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006443313,"about_ca_system_score_gemma":0.00103613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04888802,"about_ca_topic_score_gemma":0.1575617,"domain_scores_codex":[0.9997051,0.00006306894,0.00002729608,0.00004698996,0.0001329227,0.00002459293],"domain_scores_gemma":[0.9995732,0.0001204141,0.0001272581,0.00004602438,0.0001135994,0.00001953734],"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":[0.00003758211,0.00007366504,0.08950426,0.001809564,0.0001772748,0.0001451146,0.0004750944,0.0126505,0.01395356,0.004111291,0.006733177,0.870329],"study_design_scores_gemma":[0.00002073869,0.0001460443,0.7749716,0.0009934559,0.0002970276,0.001232667,0.002939716,0.05240905,0.005999165,0.00911496,0.1517264,0.0001491923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6239653,0.1254737,0.163334,0.006921478,0.0004456304,0.0004088535,0.02380892,0.001333889,0.05430818],"genre_scores_gemma":[0.6923055,0.08281385,0.2010861,0.0007070275,0.00032863,0.0002430922,0.01161186,0.0001099725,0.0107939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04888802,"threshold_uncertainty_score":0.09720695,"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."}}