{"id":"W2148423108","doi":"10.1109/igarss.2008.4779436","title":"A Practical Analytical Approach for Predicting Sand Spectral Signatures","year":2008,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Principal component analysis; Monte Carlo method; Representation (politics); Computer science; Reflectivity; Component (thermodynamics); Algorithm; Spectral signature; Data mining; Artificial intelligence; Mathematics; Statistics; Remote sensing; Geology; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004170949,0.0007436409,0.000546447,0.0006747951,0.0004758328,0.0007869923,0.0007647095,0.0009994436,0.002515624],"category_scores_gemma":[0.001938616,0.0004523556,0.0004287954,0.000492081,0.0005120287,0.0008420949,0.0004190285,0.0007365973,0.0008519827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889264,"about_ca_system_score_gemma":0.001174883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005152273,"about_ca_topic_score_gemma":0.004109797,"domain_scores_codex":[0.9998153,0.00004295724,0.000006732536,0.0000306637,0.00008663115,0.0000178291],"domain_scores_gemma":[0.9996597,0.0001669734,0.0000275464,0.00003749538,0.00009727584,0.00001109927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001182262,0.00003709005,0.0003093325,0.00004398681,0.00001350373,0.00006859626,0.00002785414,0.9413423,0.007025158,0.02207796,0.0007017009,0.02834065],"study_design_scores_gemma":[0.000001131345,0.000006468418,0.0000281098,0.000002229817,0.000001881753,0.00001503402,0.000003601927,0.9952343,0.000848289,0.003382389,0.0004728796,0.000003539853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005248354,0.00005972838,0.9921764,0.0001034312,0.00001860874,0.00001634988,0.00003819682,0.0004311724,0.001907831],"genre_scores_gemma":[0.4886197,0.0006424674,0.5024325,0.0001419568,0.0000591126,0.000220028,0.0001828624,0.0002196344,0.007481796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005152273,"threshold_uncertainty_score":0.01024461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122512462071256,"score_gpt":0.284421235714805,"score_spread":0.2531961110940924,"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."}}