{"id":"W2903639127","doi":"10.3390/app8122680","title":"Modeling Sea Bottom Hyperspectral Reflectance","year":2018,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Hyperspectral imaging; Irradiance; Remote sensing; Vegetation (pathology); Environmental science; Radiative transfer; Reflectivity; Absorption (acoustics); Spectral line; Mineral; Mineralogy; Materials science; Chemistry; Optics; Geology; 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":[0.0001608684,0.0005220382,0.0002552037,0.0002645981,0.0002124188,0.0005548235,0.0006379824,0.0006143422,0.0008271714],"category_scores_gemma":[0.0004389876,0.0002859246,0.0005359654,0.0004266811,0.000225984,0.0005834528,0.0003364297,0.0003394836,0.0003113814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006813224,"about_ca_system_score_gemma":0.0005027849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02283514,"about_ca_topic_score_gemma":0.01644857,"domain_scores_codex":[0.9999011,0.00001229358,0.000004303497,0.00003273985,0.00003348016,0.00001611742],"domain_scores_gemma":[0.999899,0.00003066773,0.00001594914,0.00001104289,0.00003562741,0.000007579308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001882863,0.00003210142,0.003615779,0.00004434725,0.00002082687,0.00007317827,0.00004606765,0.9666529,0.0197304,0.0009849393,0.0002602456,0.00852044],"study_design_scores_gemma":[0.000002688927,0.000005738692,0.00137928,0.00000173593,0.000004215464,0.00001257653,0.000008329546,0.9967858,0.001439902,0.0001612927,0.0001930078,0.000005449933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7156345,0.0002501202,0.2717553,0.0001345606,0.00004210883,0.00006600187,0.001251183,0.0009925044,0.009873677],"genre_scores_gemma":[0.9686216,0.0002040846,0.02708757,0.00003090799,0.00001048176,0.00005204691,0.0005424579,0.0001198033,0.003331021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02283514,"threshold_uncertainty_score":0.04540443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02497403931745827,"score_gpt":0.2343429607836824,"score_spread":0.2093689214662241,"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."}}