Bioavailability of heavy metals in mangrove soil in Alagoas, Brazil
Bibliographic record
Abstract
The mangrove forest is an important transitional ecosystem consisted of terrestrial and marine environment located in tropical and subtropical regions with average temperatures above 20 °C. In Alagoas, the mangrove forests are found on the entire coastline from Maragogi to Piaçabuçu. In the last 20 years, due to the pollution of water resources, studies of coastal aquatic ecosystems have been developed. The objective of this study was to analyze the physicochemical properties and determine the levels of heavy metals in mangrove sediments of the Mundaú-Manguaba estuary lagoon complex (MMELC) and Meirim River in Alagoas. Zinc, copper, lead, cadmium and chromium were chosen due to their relationship with sewer, agricultural, and industrial wastes. 22 soil samples were collected in the MMELC and in the Meirim River. The samples were submitted to soil routine analyses of Embrapa. The heavy metals were extracted by the Mehlich-1 method and analyzed by atomic absorption spectrometry. The mean concentrations of these metals in the sediment samples followed the order Mn > Zn >Pb > Cr> Cu >Cd in MMELC and Zn > Mn >Pb > Cr> Cu >Cd in Meirim River. All proposed heavy metals were found in sediments, however, the cadmium levels were above the normal levels proposed by Environment National Council (CONAMA) and Canadian Council of Ministers of the Environment. The study shows that the analysis of sediment can contribute to environmental monitoring actions and development of public policies aimed to controlling the sustainable use of natural resources of the studied areas.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".