{"id":"W7105671492","doi":"10.24400/527896/a03-2025.4115","title":"A Benchmark Dataset of Water Levels and Waves for SWOT Calibration and Validation: Insights from the St. Lawrence Estuary and Saguenay Fjord","year":2025,"lang":"","type":"article","venue":"Open MIND","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Environment and Climate Change Canada","funders":"","keywords":"SWOT analysis; Estuary; Benchmark (surveying); Satellite; Calibration; Satellite imagery; Tide gauge; Altimeter","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007357138,0.0008051283,0.0006254998,0.001604036,0.0008063514,0.0009280811,0.001771699,0.0009122312,0.001417475],"category_scores_gemma":[0.001953806,0.0002247978,0.0005595525,0.003284516,0.0005381377,0.0005146919,0.001208341,0.0006055777,0.001387715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002826486,"about_ca_system_score_gemma":0.005033144,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6341063,"about_ca_topic_score_gemma":0.801037,"domain_scores_codex":[0.9991979,0.00006558212,0.00006868596,0.0002011452,0.0002866007,0.0001800669],"domain_scores_gemma":[0.9976594,0.0001363571,0.0001372348,0.0003570233,0.001535149,0.00017473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006608612,0.0009969133,0.3439203,0.001013765,0.0006060631,0.001310772,0.000883579,0.07509694,0.01127679,0.002752468,0.4190485,0.142433],"study_design_scores_gemma":[0.0002813754,0.0001713368,0.6123862,0.0004896396,0.0001225463,0.0002749963,0.001965185,0.1347657,0.006333433,0.001164971,0.2418695,0.0001750764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3483637,0.0007238043,0.00561521,0.0003961569,0.0001542003,0.0002496849,0.6363484,0.002346513,0.005802281],"genre_scores_gemma":[0.1356937,0.0001573869,0.006507938,0.0000898869,0.00002887219,0.0001475824,0.8555568,0.0001234761,0.00169443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3658937,"threshold_uncertainty_score":0.7360971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906742541347007,"score_gpt":0.2600078404310994,"score_spread":0.2209404150176293,"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."}}