{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001939877,0.00008872656,0.00007575336,0.00001596532,0.0000462846,0.0001811019,0.0005477119,0.00006231361,0.001201847],"category_scores_gemma":[0.00006605929,0.00006760597,0.000009568854,0.0002246896,0.00008346522,0.0004458929,0.0007416656,0.0001995293,0.001474912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005238366,"about_ca_system_score_gemma":0.00001002478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847775,"about_ca_topic_score_gemma":0.002126906,"domain_scores_codex":[0.9990567,0.00002456361,0.0001219089,0.0004264294,0.0001921343,0.0001782941],"domain_scores_gemma":[0.9990234,0.00007655504,0.00001219211,0.0008409588,8.767061e-7,0.00004601418],"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":[0.000003661269,0.00001282153,0.001800441,0.00001018405,0.000003368436,0.0002170201,0.00004644765,0.0000263742,0.0006063102,0.00001394689,0.9769008,0.02035866],"study_design_scores_gemma":[0.00006139877,0.00001241917,0.01444595,0.00003572377,0.000005093037,0.00007516211,0.00001027216,0.002942037,0.0001725234,0.0001181541,0.9820143,0.0001069549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6839671,0.004955167,0.003607933,0.02819779,0.007464039,0.001600803,0.07341121,0.0009255744,0.1958704],"genre_scores_gemma":[0.8091284,0.0004818879,0.01598268,0.01380583,0.001179826,0.000004318458,0.1527951,0.0000919315,0.006529993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1893404,"threshold_uncertainty_score":0.9997112,"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."}}