Characterizing turbidity and identifying sediment sources in Norway House Cree Nation drinking water using sediment fingerprinting
Bibliographic record
Abstract
The Jack River, located along the Nelson River system in north-central Manitoba, Canada, is the drinking water source for the Norway House Cree Nation (NHCN). The Jack River lies downstream of the 2-Mile Channel which was built by Manitoba Hydro to increase the outflow capacity of Lake Winnipeg. The erosion of the 2-Mile Channel and the subsequent sedimentation downstream has been an on-going environmental concern for NHCN. This study characterizes the source water quality in NHCN with respect to sediments and turbidity, and uses a sediment fingerprinting technique to investigate the sources of sediment at the Jack River drinking water intake. The historic data show that turbidity has been gradually increasing over time (p<0.05) and is weakly positively correlated with the previous-day mean wind (p<0.05) and the previous 10-day accumulated precipitation (p<0.05). The gradual increase of turbidity over time indicates that environmental factors in the Lake Winnipeg Basin including precipitation, land use changes, and multi-decadal climate oscillations are the major influencing factors. To determine the origins of sediments at the Jack River intake, sediment fingerprinting was used and identified four discriminable source areas using three sediment colour properties. Modelling results using MixSIAR showed that 79.3 % (SD 31.2 %) of sediment was reflective of upstream sources while 9.3 % (SD 18.8 %) was reflective of the 2-Mile Channel erosion. These results show that NHCN drinking water was not significantly impacted by the 2-Mile Channel erosion throughout the summer of 2014 and the spring of 2015.
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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 | 0.001 |
| 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 | 0.001 | 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".