{"id":"W4401352128","doi":"10.1016/j.scitotenv.2024.175279","title":"Investigating the kinetics of marine and terrestrial organic carbon incorporation and degradation in coastal bulk sediment and water settings through isotopic lenses","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental chemistry; Sediment; Phytoplankton; Total organic carbon; Organic matter; Heterotroph; Microcosm; Weathering; Environmental science; Isotopes of carbon; Carbon cycle; Carbon fibers; Deposition (geology); Biomass (ecology); Chemistry; Geology; Oceanography; Nutrient; Ecology; Ecosystem; Geochemistry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000123369,0.0001842949,0.0001728898,0.0002678873,0.0001640532,0.000394279,0.0001529444,0.0002294365,0.0005851634],"category_scores_gemma":[0.0001986994,0.0001539205,0.0001651086,0.0003138854,0.000143386,0.0002770387,0.0002257192,0.0003657634,0.0001973161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002566886,"about_ca_system_score_gemma":0.0002023273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003785346,"about_ca_topic_score_gemma":0.006550511,"domain_scores_codex":[0.9999033,0.000006177706,0.000006859272,0.00003615888,0.0000268353,0.00002058491],"domain_scores_gemma":[0.9998227,0.00002614882,0.00005269769,0.00001201657,0.00006177281,0.00002472069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001176354,0.00001834794,0.008933739,0.00003593541,0.000009806641,0.00003743096,0.00007001473,0.00007634882,0.9875997,0.00003443353,0.000024102,0.003042533],"study_design_scores_gemma":[0.000006900952,0.0006099122,0.2195839,0.00001783145,0.0000443421,0.0001397875,0.0004306168,0.00248649,0.7737364,0.00009027829,0.002826387,0.00002712668],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971324,0.0004277969,0.0009289124,0.00001748704,0.000007671289,0.00001168648,0.0005813803,0.00001855387,0.0008739912],"genre_scores_gemma":[0.9935966,0.0005572074,0.002384281,0.00004239247,0.000005754299,0.00003619425,0.000751273,0.00002069051,0.002605679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003785346,"threshold_uncertainty_score":0.007526636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009599329838583892,"score_gpt":0.1781413856292928,"score_spread":0.1685420557907089,"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."}}