{"id":"W2072812381","doi":"10.1016/j.scitotenv.2015.02.018","title":"Characterization of organic composition in snow and surface waters in the Athabasca Oil Sands Region, using ultrahigh resolution Fourier transform mass spectrometry","year":2015,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Environment and Protected Areas; University of Victoria; Alberta Innovates","funders":"University of Alberta; Alberta Innovates - Technology Futures","keywords":"Oil sands; Snow; Snowmelt; Tributary; Hydrology (agriculture); Fourier transform ion cyclotron resonance; Environmental science; Surface water; Deposition (geology); Geology; Environmental chemistry; Mass spectrometry; Chemistry; Sediment; Geomorphology","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":[0.0007570096,0.00007653932,0.0001089785,0.00004567184,0.0001069772,0.0000208954,0.0002838419,0.00002984193,0.000008514893],"category_scores_gemma":[0.00001391115,0.0000415601,0.00003146895,0.0003595729,0.0004841084,0.0001566876,0.00003729319,0.0001010378,4.410724e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001650566,"about_ca_system_score_gemma":0.00003098085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000664321,"about_ca_topic_score_gemma":0.00000129635,"domain_scores_codex":[0.9990562,0.00004479735,0.000190421,0.0001472096,0.0004136332,0.0001477443],"domain_scores_gemma":[0.9995853,0.00001810126,0.0001293924,0.0002349954,0.000008388778,0.00002382597],"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":[0.00002172218,0.00003883002,0.0006893941,0.0000137925,0.000003700899,3.486905e-7,0.001556441,0.006329636,0.9911142,0.000005493397,2.486052e-7,0.00022622],"study_design_scores_gemma":[0.0004276616,0.00002692151,0.01339918,0.0001395526,0.00004648096,0.00002376778,0.001303978,0.03207984,0.9523246,0.000126129,0.000003659332,0.00009827872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983358,0.00003572807,0.0002901933,0.0009719123,0.00001527043,0.00001387436,0.000003184509,0.000002755491,0.000331227],"genre_scores_gemma":[0.9995885,0.00005596758,0.0001323523,0.000005902843,0.00001103324,0.000001070927,0.000003273591,0.000004133444,0.0001977603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03878963,"threshold_uncertainty_score":0.1783717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207410080200602,"score_gpt":0.2027348588331527,"score_spread":0.1906607580311467,"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."}}