Distribution and Pollution Assessment of Critical Nutrients and Heavy Metals in the Sediments of Loktak Lake, a Ramsar Site in the Indo‐Myanmar Hotspot Region of Manipur Valley (India)
Bibliographic record
Abstract
ABSTRACT The assessment of nutrient and heavy metal accumulation in lake sediments is essential for understanding their role in the ecohydrological dynamics of lake ecosystems, as sediments can function both as a sink and source of nutrients, metals and various contaminants aquatic ecosystem. The main objective of this study was to evaluate the concentrations of nutrients and heavy metals and their accumulation in the sediments across different sampling locations of Loktak Lake and to compare them against geochemical background values and sediment quality guidelines. The key findings of the study revealed substantial nutrient accumulation in sediments from the Keibul Lamjao National Park site, which can be attributed to the decomposition of organic matter from dense floating vegetative masses called ‘ Phumdis ’. Iron concentrations in sediments were significantly high, ranging from 909.83 to 1004 mg/kg and its enrichment is likely due to diffused surface runoff from the iron‐rich soils from the surrounding catchment entering the lake through feeder rivers and other anthropogenic influences. The average metal concentrations were in the order of Fe > Mn > Zn > Cu, and all values were below the average shale concentrations and the Interim freshwater Sediment Quality Guidelines (ISQG) set by the Canadian Council of Ministers of the Environment (CCME). The evaluation of the heavy metal pollution status of the lake sediments based on various pollution indices such as the contamination factor, degree of contamination, modified degree of contamination, geoaccumulation index, pollution load index and potential ecological risk index, suggested an overall low level of metal pollution in the sediments of Loktak Lake. The findings of this study provide a crucial baseline for understanding on the sediment‐associated nutrient and metals dynamics in Loktak Lake which can aid in the formulation of long‐term lake management strategies for preserving the ecological integrity and health of this precious lake ecosystem of the region.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".