{"id":"W4416296533","doi":"10.1080/01431161.2025.2574517","title":"ACIX-III Aqua: evaluation of atmospheric correction for hyperspectral PRISMA imagery over inland and coastal waters","year":2025,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Agenzia Spaziale Italiana; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Space Agency","keywords":"Hyperspectral imaging; Atmospheric correction; Multispectral image; Satellite; Radiometer; In situ; Satellite imagery; Reference data","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":[0.004506495,0.0009633353,0.0004105492,0.0008594175,0.0009228226,0.001051724,0.001399953,0.0006957909,0.001210127],"category_scores_gemma":[0.003222127,0.0003847961,0.0008294603,0.001263836,0.0005290572,0.001294197,0.001122104,0.0007822195,0.0003611422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395569,"about_ca_system_score_gemma":0.002071332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0458768,"about_ca_topic_score_gemma":0.05435069,"domain_scores_codex":[0.9987219,0.0002523704,0.00006549814,0.0002957114,0.0005402979,0.0001242727],"domain_scores_gemma":[0.9984446,0.0003278719,0.0001642586,0.0002405057,0.0007094488,0.0001133067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003510466,0.001542476,0.150531,0.0007544316,0.001391474,0.0004276084,0.002026117,0.2027732,0.22354,0.002849643,0.01750118,0.3931525],"study_design_scores_gemma":[0.001045739,0.0009543481,0.1915093,0.00008348402,0.0003550547,0.0002566604,0.001138477,0.5924638,0.1813892,0.001168643,0.02937729,0.0002580837],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359201,0.0002504222,0.04289768,0.0004285322,0.0001358588,0.0004581484,0.004321528,0.007322081,0.008265571],"genre_scores_gemma":[0.8058789,0.0001158518,0.1806843,0.0003000296,0.00002452979,0.0002260794,0.008497579,0.001263379,0.003009388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0458768,"threshold_uncertainty_score":0.09121954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099254735595401,"score_gpt":0.2514862098896618,"score_spread":0.2404936625337078,"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."}}