{"id":"W2157007405","doi":"10.3390/rs6076446","title":"An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; Institut National de la Recherche Scientifique","funders":"Goddard Space Flight Center; Québec Ministère du Développement Durable, de l’Environnement et de la Lutte Contre les Changements Climatiques; Fonds Québécois de la Recherche sur la Nature et les Technologies; National Aeronautics and Space Administration","keywords":"Environmental science; Chlorophyll a; Estimator; Mean squared error; Calibration; Remote sensing; Mathematics; Statistics; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002908857,0.0004680132,0.0001615261,0.0002460826,0.0003673617,0.0004527641,0.000758128,0.0002914531,0.0007602043],"category_scores_gemma":[0.0005976837,0.0001405123,0.0002247772,0.0003221716,0.0001744706,0.0002509005,0.0001859045,0.0003311624,0.0001117841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004267411,"about_ca_system_score_gemma":0.002525518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8801677,"about_ca_topic_score_gemma":0.8583441,"domain_scores_codex":[0.9999272,0.00001193823,0.000002645887,0.00003179927,0.0000115184,0.00001494346],"domain_scores_gemma":[0.9998472,0.00004201481,0.00002091915,0.000007736383,0.0000708924,0.00001109642],"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":[0.0001184094,0.0001269214,0.05872336,0.0000351243,0.00007232597,0.00009627844,0.00009947543,0.8928264,0.005126758,0.0004347097,0.001231415,0.04110889],"study_design_scores_gemma":[0.000004973464,0.000008541598,0.006615405,0.00000110715,0.00000451693,0.000002620751,0.00001206409,0.9929945,0.0001851328,0.00003189603,0.0001359866,0.00000329975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971561,0.0001123007,0.02575399,0.0001298833,0.00001328054,0.00005339272,0.0004213013,0.0003219385,0.001632858],"genre_scores_gemma":[0.9886686,0.00004239459,0.00941644,0.00002710959,0.000003975531,0.00003255048,0.0003347462,0.00001666433,0.001457503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1198323,"threshold_uncertainty_score":0.2410761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239468344405735,"score_gpt":0.2569329636999083,"score_spread":0.2329861292593348,"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."}}