{"id":"W2077570405","doi":"10.1080/01431160412331291297","title":"GLC2000: a new approach to global land cover mapping from Earth observation data","year":2005,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1867,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land cover; Vegetation (pathology); General partnership; Geography; Remote sensing; Geomatics; Earth observation; Database; Product (mathematics); Environmental resource management; Cover (algebra); Cartography; Land use; Computer science; Satellite; Political science; Environmental science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006004677,0.002024754,0.001526286,0.01770366,0.0008412219,0.00504965,0.003273556,0.001808889,0.01241549],"category_scores_gemma":[0.0161034,0.001368519,0.001833634,0.02094534,0.0004647271,0.002820489,0.003448514,0.00215634,0.008124659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001791151,"about_ca_system_score_gemma":0.002791155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0426783,"about_ca_topic_score_gemma":0.01740923,"domain_scores_codex":[0.9968459,0.0006938783,0.0003900461,0.0006637221,0.001240895,0.0001656242],"domain_scores_gemma":[0.99266,0.002068906,0.0006839763,0.00220579,0.001863301,0.0005180945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002933898,0.0002590273,0.01132091,0.001046751,0.0005589797,0.0004719636,0.000777847,0.03109059,0.004307471,0.01533275,0.4550312,0.4795091],"study_design_scores_gemma":[0.0003480531,0.00007619397,0.03135045,0.0006687001,0.0002661631,0.0004281948,0.0005845698,0.119936,0.004431802,0.02525998,0.8163251,0.0003248448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01565844,0.001272529,0.4500939,0.001230783,0.0005783797,0.001260092,0.4667536,0.04996958,0.01318273],"genre_scores_gemma":[0.02737821,0.0007132845,0.5501179,0.00036736,0.0001875263,0.001716998,0.4082296,0.007869027,0.003420179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0426783,"threshold_uncertainty_score":0.08485979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04154139217003906,"score_gpt":0.2669772099775927,"score_spread":0.2254358178075537,"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."}}