{"id":"W2377453426","doi":"10.18307/2009.0201","title":"Progress in lake water color remote sensing","year":2009,"lang":"en","type":"article","venue":"Journal of Lake Sciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASTER","funders":"","keywords":"Remote sensing; Ocean color; Environmental science; Satellite; Scale (ratio); Water quality; Key (lock); Status quo; Remote sensing application; Computer science; Meteorology; Geology; Geography; Hyperspectral imaging; Engineering","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":[0.001370109,0.0000781946,0.0001819705,0.0001816563,0.0001152302,0.0001382515,0.0002434991,0.00003281029,0.0004743267],"category_scores_gemma":[0.00002328032,0.00004281136,0.00005319883,0.0003023891,0.0001091837,0.000407981,0.000007914554,0.000132302,0.00003046909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002110814,"about_ca_system_score_gemma":0.00006984451,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007828163,"about_ca_topic_score_gemma":0.05802232,"domain_scores_codex":[0.9987721,0.00006479341,0.000356826,0.0001162585,0.0004067043,0.0002833107],"domain_scores_gemma":[0.9996122,0.00003620022,0.0001461761,0.00005706139,0.00005743783,0.00009092516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005343186,0.00001535253,0.2270397,0.000009374075,0.000004709473,0.0003332379,0.0003517009,0.0009774859,0.00005522175,0.00001440774,0.000290885,0.7708545],"study_design_scores_gemma":[0.0008452996,0.002573034,0.8217114,0.0002744357,0.00001703454,0.001644452,0.0008076677,0.06076004,0.0007238898,0.00516392,0.105083,0.0003958952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821748,0.0002165631,0.00003608314,0.002251942,0.0004273983,0.0000568326,0.00000430731,0.000006539144,0.01482561],"genre_scores_gemma":[0.9976053,0.00002000997,0.001799635,0.0002375581,0.0001710058,4.910031e-9,0.000001611851,7.100923e-7,0.0001642263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7704586,"threshold_uncertainty_score":0.9591663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499363992987347,"score_gpt":0.2313325458916339,"score_spread":0.2163389059617604,"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."}}