{"id":"W4387376837","doi":"10.1364/oe.500340","title":"A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content","year":2023,"lang":"en","type":"article","venue":"Optics Express","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; ArcticNet; Japan Aerospace Exploration Agency; National Aeronautics and Space Administration","keywords":"Ocean color; Arctic; Environmental science; Phytoplankton; Oceanography; Climate change; Colored dissolved organic matter; Remote sensing; Chlorophyll a; Irradiance; Marine ecosystem; The arctic; Climatology; Algorithm; Ecosystem; Satellite; Computer science; Ecology; Geology; Physics; Optics; Biology","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":[0.0009598343,0.001012634,0.0005950715,0.0008709818,0.0005817528,0.0008579007,0.001182868,0.001298891,0.001360437],"category_scores_gemma":[0.002269498,0.0003427076,0.0007598277,0.0007767492,0.0003937882,0.0006884476,0.0008307296,0.0009503835,0.000728522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000608645,"about_ca_system_score_gemma":0.002082044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02227174,"about_ca_topic_score_gemma":0.02693612,"domain_scores_codex":[0.9996501,0.0000601818,0.00002124636,0.0001261646,0.00007692012,0.00006550902],"domain_scores_gemma":[0.9995605,0.0001051862,0.00004163201,0.00004847018,0.0002124489,0.00003177506],"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":[0.0005778543,0.0003009255,0.01311516,0.0001745574,0.0002731818,0.0002500162,0.0002127793,0.3674583,0.05337021,0.00296043,0.008287613,0.553019],"study_design_scores_gemma":[0.00005100551,0.0000352517,0.00199885,0.000009008728,0.00002430796,0.00004793855,0.00005192621,0.9890159,0.005999396,0.0006696934,0.002080077,0.00001672964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1687518,0.0006093447,0.8216962,0.0004205038,0.0002222527,0.0001403652,0.0004107994,0.005640894,0.002107775],"genre_scores_gemma":[0.2364699,0.0001714227,0.7591454,0.0002770406,0.00005235847,0.00009065215,0.001060149,0.0004763939,0.002256595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02227174,"threshold_uncertainty_score":0.04428422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02589627677130609,"score_gpt":0.2037497369289328,"score_spread":0.1778534601576267,"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."}}