{"id":"W1975329034","doi":"10.3390/rs5020891","title":"Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations","year":2013,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University; Algoma University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mangrove; Rhizophora mangle; Hyperspectral imaging; Environmental science; Nitrogen; Range (aeronautics); Remote sensing; Biology; Ecology; Chemistry; Geology; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002093335,0.000203977,0.0001206735,0.0002783057,0.0001085832,0.0002376706,0.00008633154,0.0001959929,0.0003845994],"category_scores_gemma":[0.0006686345,0.0001097409,0.00009782449,0.0002000543,0.0001420606,0.0002139534,0.0001198702,0.000149138,0.00008583572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592766,"about_ca_system_score_gemma":0.0000665102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005209178,"about_ca_topic_score_gemma":0.008639697,"domain_scores_codex":[0.9998796,0.00002122756,0.000007506837,0.00004579275,0.00003437118,0.0000115862],"domain_scores_gemma":[0.9996136,0.0001551359,0.0001112608,0.00003135701,0.00006663015,0.00002201827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002861094,0.0001020102,0.7865894,0.00005581685,0.0001540719,0.0001379201,0.0001847442,0.008130476,0.1878241,0.00005310385,0.00009538157,0.01638681],"study_design_scores_gemma":[0.000001933695,0.00004293939,0.9782386,0.000002550837,0.00001139765,0.00006995156,0.00008492633,0.01467982,0.006718345,0.00003223569,0.0001114728,0.000005824009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99905,0.00002943148,0.0006474988,0.000004332764,7.312655e-7,0.000001952674,0.00006973294,0.00001234815,0.0001839675],"genre_scores_gemma":[0.9992083,0.00001975275,0.0005369124,0.00000372033,8.415049e-7,0.000002669181,0.0001333833,0.000002064827,0.00009247837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005209178,"threshold_uncertainty_score":0.01035774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03644754604145694,"score_gpt":0.2385227933624707,"score_spread":0.2020752473210138,"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."}}