Organic and Inorganic Geochemical Characterization of Mangrove Sediments of the São Francisco Estuary, Sergipe
Bibliographic record
Abstract
Mangrove ecosystems develop in coastal areas and possess intrinsic characteristics being considered in Brazil environmental preservation areas, however, anthropogenic impacts associated with property speculation and petroleum activities are altering the landscape of these ecosystems. This study aimed to perform organic and inorganic geochemical characterization (pH, organic matter content (MO), elemental analysis, determination of metals -Al, Na, K, Fe, Cd, Cr, Cu, Ni and Pb) in mangrove sediments of the So Francisco Estuary, Sergipe. Mineralogical characteristics defined the sediments as high acidity (pH <4.9), consisting of little decomposed organic matter and low humification. Measurements of elemental analysis with C/N ratio > 10% and MO greater than 15% are related to the proximity of the sampling stations to urban and industrial centers, intrinsic characteristics of native vegetation and different contributions from biomass. The Cd, Cu and Ni metals in sediments showed some upper bounds when compared to the legislation Threshold Effect Level (TEL) e Probable Effect Level (PEL) of Canadian Sediment Quality Guidelines for the Protection of Aquatic Life and Conselho Nacional do Meio Ambiente (CONAMA), being linked to anthropogenic activity in the region and alerting for toxicological reasons.
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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 | 0.000 |
| 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".