{"id":"W4248762168","doi":"10.32920/ryerson.14647026","title":"Accuracy assessment of GlobeLand30: a case study of Ontario,Canada","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Land cover; Geospatial analysis; Data set; Scale (ratio); Computer science; Cover (algebra); Data mining; Set (abstract data type); Remote sensing; Cohen's kappa; Geography; Environmental resource management; Land use; Cartography; Environmental science; Artificial intelligence; Machine learning; Civil engineering; Engineering; Business","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":[0.001634376,0.0006091778,0.0004196361,0.001545182,0.00310587,0.00163301,0.001388949,0.000647236,0.001785929],"category_scores_gemma":[0.00456723,0.00018325,0.0005089168,0.004290817,0.001161427,0.0006579511,0.0007980171,0.0004784117,0.0003608271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03499611,"about_ca_system_score_gemma":0.02269158,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932877,"about_ca_topic_score_gemma":0.9960563,"domain_scores_codex":[0.9982004,0.0001487305,0.0000774293,0.0002384238,0.001032809,0.0003022032],"domain_scores_gemma":[0.9935614,0.0008491023,0.0002371718,0.0003163652,0.004815376,0.0002206125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001166372,0.0005570941,0.5256953,0.001251953,0.0004390512,0.005575648,0.007672598,0.13953,0.01600833,0.01021827,0.04744031,0.2444451],"study_design_scores_gemma":[0.0001230164,0.0002443336,0.6812789,0.0004060984,0.000285996,0.0008176144,0.01641274,0.2118443,0.01718392,0.001647937,0.06953914,0.0002160273],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577652,0.000923189,0.005893657,0.0009915865,0.00004401017,0.0002589672,0.008591296,0.0003026461,0.0252295],"genre_scores_gemma":[0.9696971,0.0006069941,0.0101957,0.0001418642,0.00001052824,0.00005271849,0.009630994,0.0001184326,0.009545598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03499611,"threshold_uncertainty_score":0.2539157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376608181049021,"score_gpt":0.2631192033240647,"score_spread":0.2493531215135745,"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."}}