{"id":"W2662539824","doi":"10.5555/arwg.3.2.r1r7840m633848r3","title":"Asian 30-Second Land Cover Dataset","year":2011,"lang":"en","type":"article","venue":"Arab world geographer","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Land cover; Thematic map; Ground truth; Cover (algebra); Remote sensing; Geography; Vegetation (pathology); Vegetation Index; Physical geography; Land use; Environmental science; Cartography; Geology; Computer science; Ecology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001510712,0.0001354259,0.0001358263,0.0001216526,0.0001241849,0.00005273467,0.0001676291,0.00004128481,0.07428402],"category_scores_gemma":[0.000006003628,0.00009922156,0.00006152318,0.0002942785,0.00007797033,0.0001483739,0.000008714886,0.0001355713,0.003471872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":6.389662e-7,"about_ca_system_score_gemma":0.00001147878,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003981715,"about_ca_topic_score_gemma":0.02696786,"domain_scores_codex":[0.9990893,0.00005108293,0.0001466456,0.0002568031,0.0001408802,0.0003152922],"domain_scores_gemma":[0.9994266,0.00003326256,0.00004699908,0.000330129,0.00001376443,0.0001492274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002925983,0.00001617179,0.9094278,0.000005153365,0.00003151805,0.00004630141,0.0001291534,0.00001370563,7.988505e-7,0.00001325066,0.05756465,0.03272229],"study_design_scores_gemma":[0.0001837516,0.00003042407,0.6637148,0.000006912101,0.00001257026,0.000008051576,0.0000135056,0.0001603396,0.00001422901,0.0003878826,0.335337,0.0001305098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4335613,0.0005030068,0.00001224904,0.0001077796,0.000548503,0.0001186635,0.000928491,0.00007809306,0.5641419],"genre_scores_gemma":[0.993323,0.00003242875,0.0005109721,0.0009887476,0.0001076837,1.190608e-7,0.001017696,0.000004944251,0.004014451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5601274,"threshold_uncertainty_score":0.997304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831099030414311,"score_gpt":0.2055674579307321,"score_spread":0.187256467626589,"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."}}