{"id":"W2077439648","doi":"10.1016/j.isprsjprs.2013.10.009","title":"Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis","year":2013,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Aeronautics and Space Administration","keywords":"Wavelet; Robustness (evolution); Wavelet transform; Mean squared error; Reflectivity; Remote sensing; Leaf area index; Mathematics; Transferability; Environmental science; Statistics; Computer science; Database; Artificial intelligence; Chemistry; Botany; Geology; Optics; Physics; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000542965,0.0003356335,0.0008839503,0.0001398866,0.0002864092,0.0002079724,0.000221729,0.0002278048,0.0001253897],"category_scores_gemma":[0.0001684041,0.0002463938,0.0004029267,0.0009351692,0.0004265905,0.0004012419,0.0001402187,0.0005460877,0.000005207467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001750877,"about_ca_system_score_gemma":0.0000179275,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00666204,"about_ca_topic_score_gemma":0.0009051836,"domain_scores_codex":[0.9973771,0.000182487,0.0008372779,0.0003923645,0.0006574005,0.0005533448],"domain_scores_gemma":[0.9980696,0.0002381773,0.0009953763,0.0003153774,0.0001412239,0.0002402452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005672457,0.00003106576,0.00461059,0.00001512523,0.0005198298,0.0001260607,0.002047931,0.001843002,0.9420671,7.085764e-7,0.0001227418,0.04855907],"study_design_scores_gemma":[0.001832052,0.0003314014,0.1093337,0.0009751827,0.001964133,0.00252439,0.01008049,0.5940104,0.2731371,0.002008626,0.00240903,0.001393588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8424042,0.0002210174,0.1564967,0.00008434222,0.0002394236,0.0001400426,0.00001682777,0.0000148281,0.000382672],"genre_scores_gemma":[0.8421094,0.0001333338,0.1573995,0.0001099886,0.0001350985,8.322305e-9,0.000004192559,0.00002118236,0.00008735419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6689301,"threshold_uncertainty_score":0.9999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137247276891826,"score_gpt":0.2332849100558051,"score_spread":0.2195601823666225,"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."}}