{"id":"W4379537355","doi":"10.1080/07038992.2023.2215333","title":"Comparative Analysis of Empirical and Machine Learning Models for Chl <i>a</i> Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Water Security Agency; University of Regina; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey; Canada First Research Excellence Fund; National Aeronautics and Space Administration; Canada Research Chairs; Global Institute for Water Security, University of Saskatchewan; Global Water Futures; University of Regina; Canada Foundation for Innovation","keywords":"Remote sensing; Atmospheric correction; Environmental science; Support vector machine; Empirical modelling; Chlorophyll a; Earth observation; Satellite; Computer science; Geography; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003786799,0.0008673506,0.0005118279,0.0007405374,0.0002309335,0.000619162,0.0009400146,0.0005750771,0.0006632715],"category_scores_gemma":[0.006176401,0.0003149258,0.0007007933,0.0004647379,0.0002653006,0.001076498,0.0003650683,0.0007152849,0.0002246608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001031046,"about_ca_system_score_gemma":0.001004223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01960069,"about_ca_topic_score_gemma":0.01370041,"domain_scores_codex":[0.999537,0.0002210692,0.00003232986,0.00009503235,0.00007971511,0.00003498546],"domain_scores_gemma":[0.9964573,0.002571944,0.0002139881,0.0001137254,0.0005963663,0.00004682743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001509702,0.0001450396,0.01502144,0.00008557944,0.0001587523,0.00002951758,0.00004591417,0.9171385,0.001083664,0.0008289206,0.0007072758,0.06460453],"study_design_scores_gemma":[0.000002605524,0.00002114948,0.0009777989,0.000005720888,0.000007633254,0.000003382145,0.000007062091,0.998481,0.0002648158,0.0001539472,0.00007157971,0.000003459958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7819626,0.002468237,0.2096867,0.001053365,0.00007375079,0.00008050461,0.0004665121,0.001370722,0.002837744],"genre_scores_gemma":[0.9651093,0.0006821146,0.03230752,0.0001192456,0.00003517394,0.00006601664,0.0007179354,0.00006613473,0.000896512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01960069,"threshold_uncertainty_score":0.03897321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3895752542072285,"score_gpt":0.3135454720213864,"score_spread":0.07602978218584217,"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."}}