{"id":"W4292260751","doi":"10.3389/fmars.2022.934536","title":"CHLNET: A novel hybrid 1D CNN-SVR algorithm for estimating ocean surface chlorophyll-a","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"People's Government of Guangxi Zhuang Autonomous Region; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"SeaWiFS; Algorithm; Computer science; Convolutional neural network; Remote sensing; Support vector machine; Chlorophyll a; Environmental science; Artificial intelligence; Pattern recognition (psychology); Geography; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005929179,0.001063846,0.0005455912,0.0006186208,0.0002137328,0.0004485723,0.001331806,0.0006693903,0.001717008],"category_scores_gemma":[0.0008693103,0.0004664631,0.0005146067,0.0005297543,0.0002227094,0.0009301335,0.000823778,0.0007562743,0.0006899931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006763429,"about_ca_system_score_gemma":0.001099205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01447853,"about_ca_topic_score_gemma":0.01945405,"domain_scores_codex":[0.9997998,0.00002683026,0.00001096327,0.00006413012,0.00006962533,0.00002865028],"domain_scores_gemma":[0.9998364,0.00003018554,0.0000228025,0.00001672705,0.00008168298,0.00001220819],"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":[0.0001987441,0.0001172576,0.004959697,0.0001055607,0.000181908,0.0001094664,0.00005522312,0.4497752,0.02124855,0.003543968,0.01128374,0.5084206],"study_design_scores_gemma":[0.0000067737,0.00002060731,0.0002455369,0.000002645222,0.000005206356,0.00001282032,0.000003681487,0.9970536,0.001372471,0.0003694337,0.0009021726,0.000005138863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03273234,0.0003662108,0.9595731,0.0002057876,0.0001011502,0.00008014696,0.0003812906,0.004736194,0.001823794],"genre_scores_gemma":[0.3768991,0.0003616418,0.6089894,0.0004520131,0.00008733624,0.0002467472,0.002949711,0.0004276479,0.009586493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01447853,"threshold_uncertainty_score":0.02878851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00848877022433544,"score_gpt":0.2001798886665051,"score_spread":0.1916911184421697,"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."}}