Avaliação dos metais ambientalmente disponíveis em amostras de sedimento de pontos de captação de água para abastecimento público de Palmas, TO
Bibliographic record
Abstract
The sediments are an important compartment used as a tool for assessment of aquatic ecosystems quality, for indicating the presence of contaminants released continuously into the environment as a result of human activities.Among chemical substances discharged to surface water, there are metals that in undesirable amounts, can be toxic to biota.Due to the importance of sediment and of shortage of data of water quality of the Araguaia-Tocantins river system, the present study conducted an assessment of environmentally available metals in sediment samples from water for public supply of the city of Palmas, in Tocantins, Brazil.The concentrations of As, Cd, Pb and Se were analyzed by Graphite Furnace Atomic Absorption Spectrometry (GFAAS), Ag, Al, B, Ba, Be, Ca, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Sb, Sc, Si, Ti, V and Zn were analyzed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Hg by Cold Vapor Atomic Absorption Spectrometry (CVAAS).Two partial solubilization processes were performed for a comparative study, one with HCl 0,1 M and agitation at room temperature, considered a milder method for metal extraction from anthropogenic origin, and another with HNO 3 8 M and microwave heating, considered as an alternative to more complex methods of total digestion, since it provides a good evaluation of the total concentration of the elements.The sediment quality evaluation was realized by comparing the concentration values of the elements As, Cd, Cr, Cu, Hg, Ni, Pb and Zn with the quality guidelines (TEL and PEL) adopted by Canadian Council of Minister of the Environment (CCME), to thereby contribute to the environmental quality of the water of the Araguaia-Tocantins river system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".