Lake sediment-based sediment yields and erosion rates in the Coast Mountains, British Columbia
Bibliographic record
Abstract
Lake sediments have been identified as an alternative to contemporary stream monitoring to establish catchment sediment yields and infer erosion rates. This is due primarily to the longer time period over which the former is based, which makes established yields and rates more representative of means or trends in sedimentation. Studies using lake sediments to establish sediment yields have generally assumed that all the sediment contained within a lake is derived from erosion of the catchment under investigation. This study undermines this assumption by constructing a comprehensive lake sediment budget to asses the relative contributions from various sources. Late Holocene (the last 2350 years) rates of sediment yield and erosion are established for 3 small (<1 km²) catchments that straddle timberline (1620 - 1850 m above sea level) in the Coast Mountains of British Columbia. Due to the temporal and spatial variability of sedimentation in lakes, sediment cores for each lake were taken using a multiple-core approach. Chronology was established by the presence of a dated tephra layer. Once the cores were extracted, corrections were made for sediment derived from aquatic productivity (organic matter and biogenic silica), regional aeolian dust input, the erosion of lake banks and for outflow losses. These sources of sediment could account for between 55 and 99% of the sediment contained within the 3 lakes. Lake trap efficiency ranges from low to >70%. Once corrected, estimates of sediment yield range from 4 and 244 kg km⁻²yr⁻¹. The rate of regional aeolian deposition indicates that, in certain areas, these catchments are undergoing net deposition and not net erosion. The implications for lake sediment-based sediment yields and erosion rates are examined. When placed in a regional context sediment yields are more than 1 order of magnitude lower than larger scale basins due to changes in sediment storage. The spatial and temporal representativeness of the data are also evaluated.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".