{"id":"W4398778252","doi":"10.3390/rs16111870","title":"Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Context (archaeology); Environmental science; Random forest; Computer science; Gradient boosting; Extreme learning machine; Remote sensing; Chlorophyll a; Machine learning; Artificial neural network; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001384691,0.0004542989,0.0004946458,0.0002022149,0.0003719968,0.0003893161,0.0002763936,0.0003092514,0.00001924016],"category_scores_gemma":[0.001038576,0.0004030571,0.0000806448,0.001112317,0.0002732478,0.000628921,0.001036961,0.001253514,0.00004859501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006263638,"about_ca_system_score_gemma":0.00003741632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004468973,"about_ca_topic_score_gemma":0.0008211989,"domain_scores_codex":[0.995942,0.0007938738,0.0006383635,0.00133198,0.0005925914,0.0007011907],"domain_scores_gemma":[0.9982442,0.0005191474,0.0001559999,0.0008538828,0.00002364972,0.0002031492],"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":[0.00002610803,0.000004973126,0.00009319522,0.00007270987,0.00002334731,0.000435669,0.0007312141,0.07467375,0.1871255,0.000001712417,0.0001165785,0.7366952],"study_design_scores_gemma":[0.0002276689,0.00003170695,0.001086925,0.0007297886,0.00007077156,0.00136348,0.0001334024,0.9876071,0.003547404,0.0007826063,0.003947231,0.0004718719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5544038,0.0005226446,0.4422761,0.0007026476,0.000461344,0.0002593111,0.000003970597,0.0002477123,0.001122519],"genre_scores_gemma":[0.3696054,0.0001066139,0.6297064,0.00009156542,0.0001608879,9.397586e-10,0.00004608608,0.00006872333,0.0002143054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9129334,"threshold_uncertainty_score":0.9998421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03205942482826322,"score_gpt":0.318785294554801,"score_spread":0.2867258697265377,"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."}}