Effect of mine dewatering on the peatlands of the James Bay Lowland: the role of marine sediments on mitigating peatland drainage
Bibliographic record
Abstract
Abstract The wetlands of the James Bay Lowland comprise one of largest wetland complexes in the world, in part due to the properties (thickness and hydraulic conductivity) of the marine sediment (MS) that underlay them. Dewatering of an open‐pit diamond mine is depressurizing the surrounding Silurian bedrock below the MS. Prior to mining, it was assumed that these MS would largely isolate the overlying peatlands from the depressurized regional bedrock aquifer. To assess this isolation, we instrumented a 1.5 km long transect of wells and piezometers located within the zone of the mine's influence that crossed a sequence of bogs, fens, and bedrock outcrops (bioherms). Results were differentiated between those areas with no MS (near bioherms) and those underlain by MS (non‐bioherm) along the transect. Between 2007 and 2010 at near‐bioherm and non‐bioherm locations, average peat water tables declined 71 and 31 cm, and hydraulic head declined 66 and 32 cm, in bioherm and non‐bioherm locations, respectively. Gradients varied from near zero (−0.001) at the start of dewatering to −0.03 (after 5 years) in non‐bioherm areas and from −0.20 to −0.45 in near‐bioherm areas. These gradients corresponded to fluxes (groundwater recharge) of approximately −0.26 mm/day and −2.1 mm/day, in non‐ and near‐bioherm areas, respectively. Specific discharge (recharge) determined using the known mine dewatering rate and drawdown cone heads and areas corresponded well with measured recharge determined in the non‐bioherm transect locations. A simple rearrangement of Darcy's Law used to calculate the specific discharge highlighted how the ratio of hydraulic conductivity to the thickness of the MS can be used to assess vulnerable areas. Therefore, given the increasing development in Ontario's Far North, considerable attention must be given to both the thickness and hydraulic conductivity of MS. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 | 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 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".