{"id":"W4318336231","doi":"10.1016/j.jenvman.2023.117287","title":"Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Groundwater and Isotope Geochemistry","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Waterloo","funders":"Iran National Science Foundation","keywords":"Environmental science; Groundwater; Contamination; Risk assessment; Aquifer; Arsenic; Fluoride; Hydrogeology; Environmental engineering; Water resource management; Computer science; Engineering; Chemistry; 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":[0.0005887579,0.0001271683,0.0001239085,0.00008642366,0.0003173409,0.0001133409,0.0002220258,0.00003543524,0.0003194873],"category_scores_gemma":[0.000001993435,0.00008496355,0.00007714647,0.00008785151,0.00005659916,0.0005004136,0.00005594872,0.0001744579,0.00006286782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003401083,"about_ca_system_score_gemma":0.000005859541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002298832,"about_ca_topic_score_gemma":0.00005902953,"domain_scores_codex":[0.9988463,0.00008451616,0.0003198879,0.0001499293,0.0003691796,0.0002302015],"domain_scores_gemma":[0.9995295,0.00002880715,0.0002305495,0.0001301399,0.000007620398,0.00007331638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005088184,0.0001207431,0.08111657,0.0000275011,0.0001317351,0.0001512472,0.0009100202,0.8689885,0.001295794,0.000009495484,0.00002353803,0.04717404],"study_design_scores_gemma":[0.0005316804,0.0001468085,0.5770389,0.00002300584,0.00008276498,0.00006779666,0.00625412,0.4149425,0.0004214666,0.0001924497,0.0001548268,0.0001437519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854861,0.0001167444,0.0137815,0.00003183795,0.0002124024,0.0001318043,0.00002708005,0.000008725306,0.0002038079],"genre_scores_gemma":[0.9973171,0.0002393084,0.001931162,0.00003277581,0.0001073323,4.852351e-7,0.00004274625,0.000005500172,0.0003235786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4959223,"threshold_uncertainty_score":0.3498162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07134260222857618,"score_gpt":0.2581816620706431,"score_spread":0.1868390598420669,"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."}}