A method for error analysis of sediment yields derived from estimates of lacustrine sediment accumulation
Bibliographic record
Abstract
The logistical demands of coring lake sediments tend to preclude the replicate coring necessary to establish error estimates for measured sedimentary parameters. However, if such parameters are to be used to reconstruct sediment yield, and particularly to identify temporal variability of sediment yield, reasonable error estimates are required. In this paper data from a series of alpine lakes in British Columbia are applied to develop a new method for deriving such estimates. Regression surfaces fitted to point values of sediment mass are used to model the physically controlled spatial variability of sedimentation. Deviations from these surfaces are assumed to represent remaining unstructured variance, which constitutes a conservative error estimate. Application of the technique to the alpine lake dataset gives sediment yield estimates with error ranges of ±7–21 per cent. The potential error is minimized where the spatial variability of sedimentation is strongly predictable. The best fits were achieved for elongate lakes of simple basin morphology. The range of the error estimates is sufficiently low to allow detection of variability in Holocene sediment yield to one of the lakes. By using this technique, absolute sediment yields with associated error estimates may be derived. The associated gains in precision justify multicore approaches to lake sediment-based reconstructions of sediment yield. Copyright © 2000 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".