{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842163,0.001344445,0.001052815,0.002491042,0.00048694,0.0007409966,0.001613032,0.0006119183,0.01790954],"category_scores_gemma":[0.001028789,0.0002935774,0.0008000938,0.005018442,0.0002216675,0.000681616,0.000887407,0.0008586842,0.03166418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007780864,"about_ca_system_score_gemma":0.001664012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02766655,"about_ca_topic_score_gemma":0.03953389,"domain_scores_codex":[0.9994715,0.00006314122,0.00008557495,0.0001317064,0.0001580586,0.00009005787],"domain_scores_gemma":[0.9986252,0.00008951974,0.0001140729,0.0003416979,0.0006858192,0.0001437688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004319026,0.0003048678,0.01125681,0.0007456894,0.0001539093,0.0003124364,0.0001443507,0.002947738,0.002410784,0.001062984,0.9509564,0.02927218],"study_design_scores_gemma":[0.0004474744,0.0001206243,0.1037409,0.0001641023,0.0001177621,0.0004138327,0.000466783,0.01005352,0.005623422,0.001081108,0.8776666,0.0001039714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00513162,0.00006636461,0.0003951052,0.00005085067,0.00002343714,0.0001023305,0.9909966,0.0004982958,0.002735493],"genre_scores_gemma":[0.002525321,0.00002754995,0.0006858271,0.00001853208,0.000004305873,0.0001378402,0.9957111,0.00003174935,0.0008578119],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02766655,"threshold_uncertainty_score":0.05991346,"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."}}