{"id":"W2290975004","doi":"10.1016/j.jhazmat.2016.03.001","title":"A new analytical approach to understanding nanoscale lead-iron interactions in drinking water distribution systems","year":2016,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network","keywords":"Chemistry; Iron oxide; Adsorption; Natural organic matter; Lead (geology); Environmental chemistry; Absorbance; Size-exclusion chromatography; Colloid; Elution; Analytical Chemistry (journal); Chromatography; Organic chemistry","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":[0.0003292785,0.0001160292,0.0002322033,0.00008636627,0.00006274407,0.0001244166,0.00009588065,0.00004029777,0.0004016057],"category_scores_gemma":[0.00001892674,0.00006181045,0.00005686349,0.00008699378,0.0000244569,0.0004296644,0.00006122753,0.00004531918,0.0002047256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187626,"about_ca_system_score_gemma":0.000009484452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003564362,"about_ca_topic_score_gemma":0.00005913302,"domain_scores_codex":[0.9988543,0.00008832349,0.0004357257,0.0001470866,0.0002321311,0.0002423873],"domain_scores_gemma":[0.9996355,0.00002434708,0.0001226809,0.00009564136,0.000007198042,0.0001146209],"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":[0.0003119806,0.0002488299,0.03926772,0.00001582719,0.00003506488,0.00003388351,0.0005270079,0.00117207,0.952037,0.0006491028,0.005047948,0.000653568],"study_design_scores_gemma":[0.006687164,0.001186484,0.05505189,0.001153943,0.0003260516,0.001467071,0.001604852,0.0003822386,0.9127974,0.006257018,0.01197369,0.001112222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9354406,0.000004086678,0.06203177,0.0005808958,0.0007555435,0.0001432371,0.000005421853,0.00001210917,0.001026344],"genre_scores_gemma":[0.9988562,0.0000044469,0.0002293613,0.0000122958,0.0001798239,0.000003820342,0.000005759692,0.000009792622,0.0006984447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06341566,"threshold_uncertainty_score":0.4397301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0332396634313203,"score_gpt":0.2544141611000491,"score_spread":0.2211744976687288,"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."}}