Zoning of Trace Metals Sediments, in the Lagoa do Carro-PE Dam, From TEL/ PEL and ERL/ ERM Parameters
Bibliographic record
Abstract
Objective: The objective of this study is to analyze the presence of trace metals (Cd, Hg, Pb, Zn, Cr and Ni), using the TEL and PEL and ERL and ERM parameters of the Environmental Agencies of Canada and the United States, as well as CONAMA Resolution 357/05, in the bottom sediments of the Lagoa do Carro Dam, in the state of Pernambuco. Theoretical Framework: Considered one of the five largest water reservoirs in the state of Pernambuco, located in the Médio Capibaribe microbasin and responsible for supplying a population of approximately 300 thousand inhabitants in the municipalities of Carpina, Feira Nova, Glória do Goitá, Lagoa de Itaenga, Lagoa do Carro and Limoeiro. Method: The methodology adopted for this research includes obtaining the results using FUS-ICP (Fusion Inductively Coupled Plasma), where an oxidized sample is dissolved in a borate stream and then diluted in aqueous nitric acid; TD-ICP (Total Digestion Inductively Coupled Plasma and INAA (Instrumental Neutron Activation Analysis). Results and Discussion: The results obtained demonstrate that two of the metals analyzed are above those recommended by the WHO and pose risks to the dam's biota, as well as to users of the supply system. The numbers are well above acceptable limits, requiring urgent measures to maintain the status quo. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of Geography and water reservoirs. Originality/Value: This study contributes to the literature by standing out for the originality of the research, whether through an innovative approach, new discoveries or practical contributions. The relevance and value of this research are evidenced by analyzing one of the five largest water reservoirs in the state of Pernambuco.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".