{"id":"W4416375036","doi":"10.1016/j.marchem.2025.104578","title":"Advancing the characterization of organic Cu(II)-binding ligands in Arctic Ocean waters: Integration of IMAC, SPE, HRMS, and fluorescence techniques","year":2025,"lang":"en","type":"article","venue":"Marine Chemistry","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Canada Foundation for Innovation; National Science Foundation","keywords":"Dissolved organic carbon; Fluorescence; Organic matter; Characterization (materials science); Arctic; Ligand (biochemistry); Metal; Mass spectrometry; Metal-organic framework","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001959424,0.00008188002,0.000140451,0.00004242778,0.00004238083,0.00001639158,0.0001177014,0.00004196911,0.000165683],"category_scores_gemma":[0.00005702971,0.00005909724,0.00001762032,0.0002650631,0.00003823865,0.0001035345,0.00007280409,0.00009793592,4.068991e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007037044,"about_ca_system_score_gemma":0.00002634039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083889,"about_ca_topic_score_gemma":0.0006200636,"domain_scores_codex":[0.9994215,0.00001020016,0.0002499602,0.0001309166,0.00008053029,0.0001068364],"domain_scores_gemma":[0.9996853,0.00003106494,0.0001052724,0.0001240275,0.00003526715,0.00001909893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001548481,0.000008279324,0.4828109,0.0002722968,0.000003794811,8.273003e-7,0.00007684247,0.000001139505,0.4875742,0.000003577599,0.000005546872,0.02922704],"study_design_scores_gemma":[0.00009827771,0.00002568431,0.1037308,0.0002499204,0.000008645062,0.000005613075,0.0002074656,0.001597591,0.8937507,0.0001341451,0.0001229075,0.00006814998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965515,0.00001850185,0.00004507437,0.0001309783,0.00003138062,0.00009424012,0.00001047051,0.00001274877,0.00310508],"genre_scores_gemma":[0.9989158,0.0001455244,0.00009151301,0.00001393803,0.00002240214,7.867352e-7,0.0001684578,0.000001970642,0.0006396417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4061765,"threshold_uncertainty_score":0.2409915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003362147279488165,"score_gpt":0.1850964354359549,"score_spread":0.1817342881564667,"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."}}