{"id":"W4220871005","doi":"10.1007/s11270-022-05592-y","title":"Modelling Metribuzin Removal Efficiency Through Adsorption Using Activated Carbon of Olive-waste Cake","year":2022,"lang":"en","type":"article","venue":"Water Air & Soil Pollution","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Activated carbon; Metribuzin; Adsorption; Correlation coefficient; Chemistry; Pulp and paper industry; Mathematics; Coefficient of determination; Statistics; Carbon fibers; Environmental engineering; Biological system; Environmental science; Algorithm; Engineering; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002409988,0.0005043301,0.0006112028,0.0003194703,0.0002864652,0.0006533462,0.0007331466,0.0009483349,0.0005830956],"category_scores_gemma":[0.0003540406,0.000244095,0.0008312085,0.000389426,0.0002190154,0.0004114643,0.000216781,0.0003368643,0.0002098635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168701,"about_ca_system_score_gemma":0.0009792229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02588232,"about_ca_topic_score_gemma":0.01521173,"domain_scores_codex":[0.9998584,0.00002171777,0.000010574,0.00003762517,0.00004519721,0.00002642464],"domain_scores_gemma":[0.9998597,0.0000753263,0.00001849279,0.000007051141,0.00003480544,0.000004585781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000660533,0.00004651811,0.00169218,0.0001246229,0.0000317248,0.00006716167,0.00001949319,0.9657899,0.02864002,0.0003813912,0.00004344649,0.003097386],"study_design_scores_gemma":[0.000005331078,0.00004765206,0.0006852598,0.000003469846,0.00001079413,0.00001376905,0.000008526206,0.9865559,0.0123787,0.0001247461,0.0001588325,0.00000693935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822439,0.0004141319,0.1125561,0.0001130463,0.00001945514,0.00009786739,0.0004495827,0.0002296893,0.003876208],"genre_scores_gemma":[0.9917111,0.000164677,0.005916803,0.000009174657,0.000001338491,0.00006684945,0.0001242291,0.0000116442,0.001994126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02588232,"threshold_uncertainty_score":0.05146337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266558600722532,"score_gpt":0.2272800912292762,"score_spread":0.2046145052220509,"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."}}