Predicting the spatial mud energy and mud deposition boundary depth in a small boreal reservoir before and after draw down
Bibliographic record
Abstract
We predicted the distribution of coarse- and fine-grained substrata in a small boreal lake at natural lake level and then assessed if the extents of sediment focusing due to water level manipulation could be predicted. The littoral substratum and upper limit to the distribution of mud was mapped completely prior to experimental draw down and the upper depth limit of mud was mapped in each of the first 2 years of a new water level regime. Six published equations for estimating the position of the mud boundary and the lower limit of surface wave energy were applied using maps of fetch, slope, and depth. At natural lake level, the agreement between observed and estimated mud boundaries in deep water was remarkable (<5 m horizontal). Agreement between the depth of mixing by surface waves and mud boundaries in shallow water was closest, at times exact, for equations with estimates similar to maximum wave height. However, the size of waves responsible for the shallow sediment boundaries remains unclear due to natural variation of lake level. All models overestimated energy in shallow depositional settings where exposure and slope was low. Refocusing of sediment due to maximum winter draw down of 3 m resulted in contraction and expansion of the profundal zone; a net decrease in area of 3% was evident after 2 years. Our results demonstrate that mud deposition models can be used in deep lake basins to map the littoral and profundal zones. Sediment refocusing in the first few years of winter draw down is forced mainly by falling lake levels. The interpretation of littoral habitat complexity can be simplified by understanding the lake-wide spatial pattern of erosion, transport, and deposition of sediments.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".