{"id":"W4367693472","doi":"10.1016/j.eti.2023.103179","title":"Tracking the sources of dissolved organic matter under bio- and photo-transformation conditions using fluorescence spectrum-based machine learning techniques","year":2023,"lang":"en","type":"article","venue":"Environmental Technology & Innovation","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Korea Institute of Marine Science and Technology promotion; Korea Environmental Industry and Technology Institute; Ministry of Oceans and Fisheries; Ministry of Environment","keywords":"Dissolved organic carbon; Compost; Colored dissolved organic matter; Mixing (physics); Transformation (genetics); Support vector machine; Biogeochemical cycle; Environmental science; Chemistry; Biological system; Environmental chemistry; Machine learning; Computer science; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000304128,0.0001232486,0.0001205632,0.0002802159,0.0003330674,0.0000209644,0.0001393967,0.0001056071,0.0003814488],"category_scores_gemma":[0.00001123481,0.0001017151,0.00002744129,0.001241707,0.0005151472,0.0001802483,0.000087635,0.0002138435,0.0000476287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001066774,"about_ca_system_score_gemma":0.000002673293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009507261,"about_ca_topic_score_gemma":0.000004012814,"domain_scores_codex":[0.9990742,0.00004693725,0.0003036966,0.0002085682,0.0001892564,0.0001772937],"domain_scores_gemma":[0.9996238,0.00002877372,0.0001664791,0.0001658748,0.000002850154,0.00001220212],"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":[0.000002402655,0.00002092991,0.2444701,0.000005179408,0.000009908991,5.253168e-7,0.0001434947,0.0003892159,0.7544239,0.00005006747,0.000004109794,0.0004801895],"study_design_scores_gemma":[0.0001104281,0.00003312338,0.05324621,0.00002328937,0.00002636095,0.000005745134,0.0007105549,0.00230282,0.9422724,0.001094745,0.00006104275,0.0001132602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955096,0.00001265531,0.00265879,0.001451929,0.00001894863,0.0001507848,0.00001110119,0.0001654239,0.00002076901],"genre_scores_gemma":[0.9993262,0.00001642171,0.0004379689,0.00004920225,0.00001077561,0.00001459669,0.00008319522,0.00001496996,0.00004666202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1912239,"threshold_uncertainty_score":0.4176596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552628938658035,"score_gpt":0.2464717783748141,"score_spread":0.2309454889882337,"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."}}