{"id":"W1977086895","doi":"10.1016/j.jhydrol.2009.02.030","title":"Distribution and assessment of surface water contamination by application of chemometric and deterministic models","year":2009,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Sixth Framework Programme; Ministerio de Educación, Cultura y Deporte; Generalitat de Catalunya; European Commission","keywords":"Contamination; Environmental science; Water contamination; Surface water; Distribution (mathematics); Surface (topology); Hydrology (agriculture); Statistics; Soil science; Mathematics; Environmental engineering; Geology; Ecology; Geotechnical engineering; Biology","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":[0.0002053671,0.00007218451,0.0003003381,0.0001042259,0.00002068061,0.000005063086,0.00006676794,0.00009344341,0.00002014636],"category_scores_gemma":[0.0000346734,0.00005548072,0.00003591948,0.0001531654,0.0000645902,0.00008583905,0.00001349012,0.0001163805,8.026368e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003961202,"about_ca_system_score_gemma":0.00001504854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000705011,"about_ca_topic_score_gemma":3.054843e-7,"domain_scores_codex":[0.9992821,0.00001460495,0.0003863654,0.00008879209,0.0001326387,0.00009546203],"domain_scores_gemma":[0.9992762,0.00008287072,0.0004065714,0.00007350034,0.0001212041,0.0000396199],"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":[0.00005749736,0.0001821058,0.01039629,0.00006504528,0.00006722386,0.000001946392,0.00004912369,0.0002768626,0.9847219,0.0002872071,0.00005299791,0.003841805],"study_design_scores_gemma":[0.001049249,0.0005654477,0.009747329,0.000009877746,0.0003237698,0.0001268701,0.00005041276,0.03820935,0.9465328,0.003154268,0.0001349865,0.00009556575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714589,0.0007091453,0.02724946,0.000207409,0.000007782523,0.00002276588,0.00001295053,0.000002395228,0.0003292594],"genre_scores_gemma":[0.9993091,0.0002775889,0.0003289169,0.00001661511,0.00001454194,4.438733e-7,0.00002171659,0.000002984213,0.00002805373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03818902,"threshold_uncertainty_score":0.2262438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009090486922841137,"score_gpt":0.2868571700362118,"score_spread":0.2777666831133707,"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."}}