{"id":"W4401419335","doi":"10.3390/rs16162903","title":"LAQUA: a LAndsat water QUality retrieval tool for east African lakes","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kenya Marine and Fisheries Research Institute; Kent State University; University of Minnesota; University of Wisconsin-Madison; University of Windsor; George Mason University; Bowling Green State University; National Science Foundation","keywords":"Water quality; Environmental science; Satellite; Remote sensing; Chlorophyll a; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0006597727,0.0006897289,0.0003660627,0.00154137,0.0004201475,0.0006213353,0.0006255342,0.0004318953,0.003681789],"category_scores_gemma":[0.001067398,0.0003168997,0.0005864764,0.001478068,0.0001446276,0.00119154,0.0008103368,0.0004034181,0.001367552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000463556,"about_ca_system_score_gemma":0.000690032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01590871,"about_ca_topic_score_gemma":0.02803844,"domain_scores_codex":[0.999803,0.00003479623,0.00001818629,0.00004313891,0.00007067817,0.00003012495],"domain_scores_gemma":[0.9997781,0.00004115982,0.00004020585,0.00003166188,0.00009211386,0.00001684495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001018137,0.0004491719,0.09779102,0.002119779,0.0006105516,0.0009188342,0.002308985,0.03562533,0.09736451,0.00353908,0.1224732,0.6357813],"study_design_scores_gemma":[0.0006071565,0.0002483712,0.2351447,0.0003923872,0.0003840027,0.0005273249,0.001954293,0.4747984,0.06185471,0.003522357,0.2202655,0.0003007376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5883272,0.00265128,0.2132561,0.001397872,0.0002656059,0.001461147,0.09830331,0.07251405,0.02182343],"genre_scores_gemma":[0.5667449,0.0009430621,0.3740947,0.0002919725,0.00005218399,0.001129124,0.04826233,0.002029958,0.006451808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01590871,"threshold_uncertainty_score":0.03163224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02497217270680318,"score_gpt":0.2403148912156407,"score_spread":0.2153427185088375,"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."}}