{"id":"W4317932986","doi":"10.3390/rs15030687","title":"Merged Multi-Sensor Ocean Colour Chlorophyll Product Evaluation for the British Columbia Coast","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of Victoria","funders":"Canadian Space Agency; Hakai Institute; Natural Sciences and Engineering Research Council of Canada; Marine Environmental Observation Prediction and Response Network","keywords":"Environmental science; Product (mathematics); Scale (ratio); Seasonality; Chlorophyll a; Phytoplankton; Meteorology; Remote sensing; Climatology; Computer science; Statistics; Geography; Mathematics; Cartography; Nutrient; Ecology; Geology","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":[0.001086631,0.0006360493,0.0002937403,0.001397593,0.0005423074,0.001353628,0.0005306688,0.0003967285,0.001590095],"category_scores_gemma":[0.003240014,0.0002233749,0.0003400545,0.002541718,0.0002418606,0.0007480402,0.0005947216,0.0003338649,0.0005407182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003613219,"about_ca_system_score_gemma":0.003853288,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7706159,"about_ca_topic_score_gemma":0.8364803,"domain_scores_codex":[0.9994476,0.00006501544,0.00003890663,0.0001024475,0.000291864,0.0000539784],"domain_scores_gemma":[0.9984409,0.0001697495,0.0001117258,0.0001463728,0.001042363,0.00008879215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001789246,0.0004865596,0.5262407,0.0004647227,0.0007963076,0.001203508,0.000650803,0.1426876,0.04107425,0.00129916,0.02340446,0.2599027],"study_design_scores_gemma":[0.0001163951,0.0001158554,0.7006143,0.00007480309,0.0001167318,0.0001135487,0.000692935,0.2795982,0.008711514,0.0001828044,0.009581248,0.00008164213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757587,0.0002703695,0.004499862,0.0001399855,0.00002659421,0.0001147124,0.01435333,0.0005818649,0.004254573],"genre_scores_gemma":[0.9514068,0.0001580126,0.01800103,0.00004590902,0.000006393185,0.0001037619,0.02670353,0.0001584622,0.003416056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2293841,"threshold_uncertainty_score":0.46147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612722769124001,"score_gpt":0.2445006898430813,"score_spread":0.2083734621518413,"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."}}