{"id":"W2135904824","doi":"10.1016/j.jglr.2009.03.002","title":"Assessing the application of SeaWiFS ocean color algorithms to Lake Erie","year":2009,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Ohio Sea Grant College, Ohio State University; U.S. Environmental Protection Agency; Water Resources Research Institute, North Carolina State University; Kent State University; Ohio Lake Erie Commission; National Science Foundation","keywords":"SeaWiFS; Ocean color; Satellite; Algorithm; Structural basin; Remote sensing; Environmental science; Oceanography; Phytoplankton; Satellite imagery; Drainage basin; Geology; Computer science; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004376297,0.0004525914,0.0003701817,0.001042417,0.0007464172,0.001370384,0.00066546,0.001019715,0.0005798806],"category_scores_gemma":[0.01379205,0.0003059054,0.0005318368,0.001215754,0.0004058132,0.001100941,0.0007897884,0.0004203624,0.0002460679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774912,"about_ca_system_score_gemma":0.001484144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.150501,"about_ca_topic_score_gemma":0.163902,"domain_scores_codex":[0.9989671,0.0004050321,0.00006326887,0.0001589245,0.0002852312,0.0001206018],"domain_scores_gemma":[0.9959446,0.001893126,0.0002306414,0.0002500065,0.001563143,0.000118425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002027926,0.0004798415,0.4940433,0.0002180776,0.0007871966,0.0002081025,0.0007099819,0.3482424,0.0136867,0.001647081,0.003466402,0.134483],"study_design_scores_gemma":[0.0002125131,0.000291795,0.2963448,0.00003164897,0.0002603405,0.00005182386,0.0006065948,0.6894502,0.01043457,0.0003918884,0.001874069,0.00004975726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946802,0.00008072128,0.002621485,0.0001875951,0.00001319356,0.00002547436,0.0003751013,0.0001874056,0.001828755],"genre_scores_gemma":[0.990301,0.00006750972,0.008358302,0.00005484625,0.00000774438,0.00001373541,0.0006498705,0.00005439732,0.0004925015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.150501,"threshold_uncertainty_score":0.29925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057098381687621,"score_gpt":0.346989206849829,"score_spread":0.2964182230329528,"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."}}