{"id":"W4400614786","doi":"10.1016/j.dib.2024.110739","title":"GloCAB cropland field boundary dataset","year":2024,"lang":"en","type":"article","venue":"Data in Brief","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Air Force; Nuclear Safety and Security Commission; National Aeronautics and Space Administration","keywords":"Field (mathematics); Boundary (topology); Range (aeronautics); Product (mathematics); Crop; Geography; Remote sensing; Cartography; Physical geography; Computer science; Forestry; Mathematics; Geometry","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":[0.0004847726,0.00104318,0.0007092836,0.001832219,0.0005961251,0.0007260847,0.001561176,0.001054326,0.01207936],"category_scores_gemma":[0.001384352,0.0002438317,0.0005797493,0.002351641,0.0002809375,0.0007212819,0.0008394236,0.000960984,0.01794913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000881099,"about_ca_system_score_gemma":0.0009739775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04064474,"about_ca_topic_score_gemma":0.08611837,"domain_scores_codex":[0.9995395,0.0000537571,0.00003238531,0.0001604664,0.0001396224,0.00007424432],"domain_scores_gemma":[0.9994017,0.00009501949,0.00004933398,0.0001447032,0.000246014,0.00006326398],"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":[0.0001622565,0.0001676933,0.006014266,0.0004448115,0.00005076148,0.0001317485,0.00006540974,0.002676522,0.001563291,0.0007302804,0.9659157,0.02207721],"study_design_scores_gemma":[0.0003854561,0.00009998023,0.04464266,0.0002763058,0.00004606019,0.0002909678,0.0003850006,0.01429045,0.002545809,0.001798405,0.9351618,0.00007702361],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009355046,0.0003663644,0.0008399054,0.0001576873,0.00005064435,0.0001015154,0.9841157,0.001892717,0.003120407],"genre_scores_gemma":[0.00459716,0.00005860576,0.001778682,0.00005296928,0.000005990714,0.0001032729,0.9925938,0.00006587025,0.0007436849],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04064474,"threshold_uncertainty_score":0.08081633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509014584873995,"score_gpt":0.265903804795031,"score_spread":0.250813658946291,"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."}}