Surface and Ground Waters Concentrations of Metal Elements in Central Cross River State, Nigeria, and their Suitability for Fish Culture
Bibliographic record
Abstract
One of the requirements for fish farming is good water quality, void of pollutants. Some heavy metals such as Magnesium, Calcium, Zinc and Iron, which are important in daily life processes, could become pollutants above the required concentrations. Others such as Mercury (Hg), Arsenic (As), Silver (Ag), and Cadmium (Cd) are not required by organisms even at low concentrations. Surface and ground water were investigated for heavy metals concentration to establish their suitability for fish culture. Three surface water bodies (a river, stream and fish pond) and ground water stations (dugout well and two bore holes) were sampled. Heavy metals were analyzed spectrophotomically at different wave lengths. Data were collated and subjected to analysis of variance which showed that heavy metal concentration in order of abundance was pond, river, stream, dug out well and the bore holes. Heavy metals were therefore, more concentrated in surface water than in ground water (p < 0.05). Surface water contain run off from within their basins while ground water contain what has been sieved into it from the surrounding. Hardness which is a measure of Calcium (Ca) and magnesium (Mg) salts were higher in ground water compared to surface water. Heavy metal concentration were Cu = 0.06 - 0.97 mg/l, Cd = 0.0 - 0.0013 mg/l, Zn = 0.04 – 2.97 mg/l, Ni = 0.0 – 0.43 mg/l, Mn = 0.1 – 3.67 mg/l, Fe = 0.95 – 5.11 mg/l and Al = 0.02 - .02 mg/l. Though heavy metals have no safe concentration for living organisms, the metals were observed to be lower than concentrations recommended by several bodies including Food and Nutrition Board (USA) and Food and Drug Administration Control (NAFDAC in Nigeria).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".