{"id":"W2950355150","doi":"10.1007/s13762-019-02440-1","title":"Heavy metals on sediments of a Mexican tropical lake: chemical speciation, metal uptake capacity, and chemical states","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Science and Technology","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Ministère de l’Éducation, Gouvernement de l’Ontario; Consejo Nacional de Ciencia y Tecnología; U.S. Environmental Protection Agency","keywords":"Aluminosilicate; Adsorption; Metal; Hydroxide; Oxide; Inorganic chemistry; X-ray photoelectron spectroscopy; Extraction (chemistry); Iron oxide; Chemistry; Environmental chemistry; Materials science; Chemical engineering; Metallurgy; Catalysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005490568,0.0001693862,0.0003023845,0.0002208168,0.00004497779,0.0000236702,0.0006325238,0.0001090787,0.001104444],"category_scores_gemma":[0.0001316292,0.00013901,0.00005707709,0.0002104502,0.002351334,0.0003636739,0.0005683763,0.0002660258,0.00005886744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003454522,"about_ca_system_score_gemma":0.00001891714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006391267,"about_ca_topic_score_gemma":0.000002299445,"domain_scores_codex":[0.9973974,0.00002705204,0.0005279877,0.0003627421,0.001419884,0.0002649835],"domain_scores_gemma":[0.9992493,0.00006146231,0.000331645,0.0001821123,0.00001794693,0.0001574932],"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":[0.00007183879,0.0003821015,0.09181297,0.000002581466,0.00006944221,0.000013943,0.0001145842,0.00004185981,0.8932796,0.0008746666,0.00004378333,0.01329267],"study_design_scores_gemma":[0.00141683,0.0007087716,0.2110291,0.0000379954,0.0000482281,0.0004390493,0.0003087145,0.000316055,0.7731718,0.004627846,0.007619326,0.0002763512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975964,0.00005127838,0.00004127645,0.001428224,0.0002129998,0.0001342141,0.00002624654,0.000007371035,0.0005020123],"genre_scores_gemma":[0.9976112,0.0001652028,0.001934007,0.0001917224,0.00003955827,0.000003157511,0.000002044288,0.000009055342,0.00004409206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201078,"threshold_uncertainty_score":0.9998087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006987459079147825,"score_gpt":0.2301765740765114,"score_spread":0.2231891149973636,"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."}}