{"id":"W2777645374","doi":"10.1007/s00128-017-2262-8","title":"Testing the Underlying Chemical Principles of the Biotic Ligand Model (BLM) to Marine Copper Systems: Measuring Copper Speciation Using Fluorescence Quenching","year":2017,"lang":"en","type":"article","venue":"Bulletin of Environmental Contamination and Toxicology","topic":"Chemical and Physical Properties in Aqueous Solutions","field":"Chemical Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copper; Genetic algorithm; Environmental chemistry; Biotic Ligand Model; Quenching (fluorescence); Ecotoxicology; Fluorescence; Ligand (biochemistry); Chemistry; Ecology; Biology; Organic chemistry; Physics; Biochemistry; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001788594,0.0001251901,0.0001949126,0.00001832526,0.0003105927,0.00002537138,0.0002806942,0.00008740078,0.00002425441],"category_scores_gemma":[0.0004960665,0.00008670927,0.00004978757,0.00002563466,0.0003307857,0.00003275358,0.0004841672,0.0001799399,0.000003215351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001506488,"about_ca_system_score_gemma":0.00001234218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001313566,"about_ca_topic_score_gemma":0.000004317508,"domain_scores_codex":[0.9990984,0.0000378844,0.0003121916,0.0001952684,0.0001851193,0.0001711402],"domain_scores_gemma":[0.999235,0.0002013357,0.0002193512,0.000272529,0.00002021053,0.00005153552],"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":[0.00001554587,0.00006293386,0.001972972,0.00005475534,0.00001642874,2.9508e-7,0.0001395224,0.007544896,0.9867419,0.001558759,0.00001423414,0.001877753],"study_design_scores_gemma":[0.0007038643,0.00007650777,0.03184637,0.0003205516,0.00007958055,0.00002147771,0.0002179138,0.4363433,0.5294445,0.0001618614,0.0005067342,0.000277359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963617,0.00006122026,0.0006590217,0.000652378,0.00007927531,0.0002694578,0.00001180936,0.00001140989,0.001893718],"genre_scores_gemma":[0.9987155,0.000006910147,0.0009560366,0.00006178991,0.00004726132,0.00001351188,0.000001814277,0.00001298251,0.0001841667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4572975,"threshold_uncertainty_score":0.3535902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06882364086221783,"score_gpt":0.2453992677760812,"score_spread":0.1765756269138634,"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."}}