Evaluation of the content of total mercury in waters, soils and sediments of the river suratá, using a system for direct analysis of mercury
Bibliographic record
Abstract
En este trabajo de aplicación se llevó a cabo la evaluación del contenido de mercurio total en aguas, suelos y sedimentos del rio Suratá, empleando un sistema de análisis directo de mercurio total el cual fue validado al interior del laboratorio de aguas y suelos de la CDMB, encontrándose que el mercurio en las aguas superficiales del río Suratá y sus confluencias Vetas, Charta y Tona, se encuentran dentro del criterio establecido en los artículos 38 y 39 del Decreto 1594/84 del Ministerio de Salud, donde se establece 2.0 µg Hg/L como criterio de calidad admisible para la destinación del recurso para consumo humano y doméstico, previo tratamiento convencional, articulo 41 del Decreto 1594/84 donde se establece 10 µg Hg/L como criterio de calidad admisible para la destinación del recurso para uso pecuario. Los suelos aledaños al rio Suratá se encuentran dentro de los niveles de suelos no contaminados de acuerdo a los resultados reportados en la literatura. Los sedimentos en los puntos SA-03, RT-01, RCH-01, SA-RT-01 y SA-06 se encuentran libres de contaminación, el punto SA-05 tiene un nivel de contaminación tolerable por la mayoría de los organismos que viven en los sedimentos y los puntos SA-PtePaneca y RV-01 son sedimentos contaminados, según Canadian Sediment Quality Guidelines for the Protection of Aquatic Life, Ontario, 2002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".