{"id":"W2210676602","doi":"10.1016/j.jag.2015.03.003","title":"New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities","year":2015,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":234,"is_retracted":false,"has_abstract":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"International Centre for Integrated Mountain Development","keywords":"Vegetation (pathology); Remote sensing; Geography; Satellite; Vegetation Index; Cartography; Satellite imagery; Vegetation type; Physical geography; Normalized Difference Vegetation Index; Geology; Grassland; Ecology; Oceanography; Climate change; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002754499,0.0001097551,0.0001549013,0.00006370708,0.00003685849,0.00008507136,0.00007464602,0.00006747001,0.000005480694],"category_scores_gemma":[0.00003579919,0.00008899334,0.00001945358,0.0001536738,0.00006146875,0.0007933306,0.0000345106,0.00008710704,0.000008245757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085668,"about_ca_system_score_gemma":0.0000606027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235987,"about_ca_topic_score_gemma":0.0001098232,"domain_scores_codex":[0.9986805,0.00002139357,0.0005086467,0.00009044923,0.0006109175,0.00008805941],"domain_scores_gemma":[0.9987776,0.00002523731,0.0007498455,0.00006144355,0.0002890913,0.0000967612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001620048,0.00003619857,0.04202349,0.00009937597,0.0001654612,0.000004614575,0.01743952,0.1394977,0.004979323,0.001105892,0.0007117936,0.7923166],"study_design_scores_gemma":[0.002420438,0.0003005172,0.6694988,0.0002587505,0.00007426008,0.0002295923,0.001980688,0.3070245,0.003740483,0.00689945,0.007296972,0.0002755341],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9705989,0.00009152204,0.02568803,0.0002254387,0.0002874674,0.0001607415,0.000001745581,0.00001333454,0.002932765],"genre_scores_gemma":[0.8498452,0.00002138374,0.1498903,0.0001095046,0.00006234566,2.602615e-8,0.00003802272,0.000004101985,0.00002916719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.792041,"threshold_uncertainty_score":0.3629043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02404416794757535,"score_gpt":0.2480575096441058,"score_spread":0.2240133416965304,"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."}}