{"id":"W4379792752","doi":"10.5194/essd-15-2347-2023","title":"An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach","year":2023,"lang":"en","type":"article","venue":"Earth system science data","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Sun Yat-sen University; Joint Research Centre; Tsinghua University; U.S. Geological Survey; Wuhan University; National Natural Science Foundation of China; Chinese Academy of Sciences; Natural Resources Canada; National Science Fund for Distinguished Young Scholars; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China-Guangdong Joint Fund; Aberystwyth University","keywords":"Land cover; Product (mathematics); Pixel; Cover (algebra); Computer science; Grid; Class (philosophy); Resolution (logic); Environmental science; Process (computing); Point (geometry); Remote sensing; Reliability (semiconductor); Land use; Data mining; Mathematics; Geography; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001461811,0.0007916941,0.0005174177,0.002527532,0.0002889828,0.0006630433,0.0005586506,0.0005604284,0.00124245],"category_scores_gemma":[0.001404372,0.0002323793,0.0009651938,0.002686667,0.0002574172,0.0008854016,0.0008728024,0.0004107258,0.0004395501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005389692,"about_ca_system_score_gemma":0.000914138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01829872,"about_ca_topic_score_gemma":0.02157604,"domain_scores_codex":[0.9994704,0.00008291401,0.00002642605,0.0001582611,0.0001948893,0.00006710381],"domain_scores_gemma":[0.9993601,0.00005646422,0.00006089044,0.0001103741,0.0003735598,0.00003853993],"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":[0.0005642559,0.0004150833,0.1479868,0.0005454288,0.0007514653,0.0009668802,0.0006359068,0.2237904,0.07209036,0.002634004,0.0167173,0.5329021],"study_design_scores_gemma":[0.00008954947,0.0001499526,0.2392807,0.00007283509,0.0002937595,0.0002608743,0.0002580665,0.7204554,0.02366198,0.002192611,0.01314177,0.0001425351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8055563,0.0008934344,0.1678814,0.0005307985,0.0002291267,0.0002240306,0.01284014,0.005319844,0.006524897],"genre_scores_gemma":[0.8138126,0.0001512298,0.1742494,0.0001034177,0.00003518148,0.00007206259,0.01051908,0.0001560098,0.000901051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01829872,"threshold_uncertainty_score":0.0363844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038896172341181,"score_gpt":0.2477707216813091,"score_spread":0.2273817599578973,"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."}}