Reconstructing a sediment pulse: Modeling the effect of placer mining on Fraser River, Canada
Bibliographic record
Abstract
Abstract Gold mining along 525 km of the Fraser River between 1858 and 1909 added an estimated 1.1 × 108 t of tailings, half gravel and the rest finer, to the river's natural sediment load. We simulate the response using a 1‐D multigrain size morphodynamic model. Since premining conditions are unknown and modern data are insufficient for tuning the process representation, we devised a novel modeling strategy which may be useful in other data‐poor applications. We start the model from a smoothed version of the modern longitudinal profile with bed grain size distributions optimized to match alternative assumptions about natural sediment supply and compare runs that include mining with control runs that can be used to quantify the effects of deficiencies in process representation and initialization. Simulations with an appropriate choice of natural supply rate closely match the best available test data, which consist of a detailed 1952–1999 gravel budget for the distal part of the model domain. The simulations suggest that the main response to mining was rapid bed fining, which allowed a major increase in bed load transport rate with only slight (~0.1 m) mean aggradation within the mining region and most of the excess sediment exported well beyond the mountain front within the mining period or soon afterward. We compare this pattern of response by a large, powerful river with previous case studies of river adjustment to sediment supply change.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".