{"id":"W2945536980","doi":"10.3389/fmars.2019.00251","title":"Going Beyond Standard Ocean Color Observations: Lidar and Polarimetry","year":2019,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Centre National d’Etudes Spatiales; California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Ocean color; Remote sensing; Radiance; Polarimetry; Lidar; Environmental science; Geology; Meteorology; Geography; Scattering; Physics; Optics; Satellite","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":[],"consensus_categories":[],"category_scores_codex":[0.0009629808,0.0001216505,0.0002068568,0.0002901361,0.0001743441,0.0001818648,0.0003815635,0.00004140339,0.0003690918],"category_scores_gemma":[0.0001151071,0.0001077616,0.00002231528,0.00106058,0.0002143845,0.0007845444,0.0001601188,0.0001565829,0.00004255212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002035298,"about_ca_system_score_gemma":0.0001806824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036016,"about_ca_topic_score_gemma":0.000525881,"domain_scores_codex":[0.9985569,0.00003344284,0.0002266955,0.0004079502,0.0003890283,0.0003860454],"domain_scores_gemma":[0.9994566,0.00004866049,0.00007189274,0.000232842,0.00005347616,0.000136528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002642888,0.000004322682,0.9444452,0.00002165236,0.000002499255,0.000004318255,0.00005031812,0.00006525798,0.00002224756,0.0001931331,0.0004986138,0.05466601],"study_design_scores_gemma":[0.0002903024,0.0001507358,0.9658008,0.00001691962,0.000003386241,0.000007937299,0.0003516327,0.0150075,0.00006042546,0.002627234,0.01550995,0.000173151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629824,0.0001223018,0.0002363393,0.0001525098,0.001137803,0.0002441214,0.00002706014,0.00002807715,0.03506943],"genre_scores_gemma":[0.9473453,0.00003940442,0.05005419,0.0002243476,0.00004000597,2.832406e-7,0.00002102778,0.000003132836,0.002272313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05449286,"threshold_uncertainty_score":0.4394389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006188706973341378,"score_gpt":0.1880917262763487,"score_spread":0.1819030193030073,"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."}}