{"id":"W2928870404","doi":"10.3390/rs11070814","title":"Scan Line Intensity-Elevation Ratio (SLIER): An Airborne LiDAR Ratio Index for Automatic Water Surface Mapping","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada); Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Environmental science; Multispectral image; Elevation (ballistics); Intensity (physics); Scan line; Monochromatic color; Optics; Geology; Pixel; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006363433,0.0002579219,0.0003154617,0.00007681305,0.0003787761,0.0001513001,0.0001486529,0.000148693,0.0001024157],"category_scores_gemma":[0.00006087026,0.0002266819,0.00009420201,0.0002966572,0.0001025555,0.0003483726,0.00009480368,0.0002211256,0.0006705415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002820429,"about_ca_system_score_gemma":0.0000279178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000580742,"about_ca_topic_score_gemma":0.0001288095,"domain_scores_codex":[0.9980417,0.00009641836,0.000449306,0.0005914605,0.0003364745,0.0004845657],"domain_scores_gemma":[0.998872,0.00008518034,0.0001519318,0.0006656338,0.00008205568,0.0001432207],"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":[0.00003007402,0.00003454765,0.000356383,0.00004297525,0.00002592129,0.00000289772,0.002515153,0.03534856,0.7392347,0.00001959648,0.0002961794,0.2220931],"study_design_scores_gemma":[0.000408951,0.00006946883,0.003782998,0.00006694724,0.00002103871,0.00003547777,0.0003548493,0.9329244,0.05768827,0.0006191718,0.003708856,0.0003195562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.75968,0.000004684898,0.2369106,0.0009512094,0.0002117079,0.0007300911,0.000002000491,0.0001887486,0.001320892],"genre_scores_gemma":[0.937896,0.000002835878,0.05993829,0.0004780778,0.0001488307,4.919407e-8,0.0001037774,0.00005263245,0.001379544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8975759,"threshold_uncertainty_score":0.9243819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01487698315218319,"score_gpt":0.2396484999298023,"score_spread":0.2247715167776191,"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."}}