A Bayesian mixing model framework for quantifying temporal variation in source of sediment to lakes across broad hydrological gradients of floodplains
Bibliographic record
Abstract
Abstract Paleolimnological reconstructions provide insights into hydrological variability of dynamic floodplain lakes. However, spatial and temporal integration of multiple reconstructions often remains underdeveloped because the efficacy of different paleolimnological measurements varies among lakes due to gradients in energy of floodwaters and sediment composition. Here, we use linear discriminant analysis to identify 10 significant elemental concentrations in sediment obtained from multiple sampling campaigns that distinguish among three end‐member allochthonous sources for lakes in the Peace‐Athabasca Delta (PAD; Canada): Athabasca River, Peace River, and local catchment runoff. Over 90% of sediment samples were correctly classified into original groups after cross‐validation due to the distinctiveness of the three end‐members, which permitted development of a robust Bayesian mixing model to discern the relative contributions of sediment from the three sources. We evaluate performance of the mixing model via application to sediment cores from two adjacent lakes in the Athabasca sector of the PAD and demonstrate its effectiveness to discriminate three known hydrological phases during the past 300 years. Notably, model results indicated that ~ 60% of the sediment originated from the Peace River during the largest ice‐jam flood event on record (1974), which was unrecognized by other methods. The approach provides a new, universal method that can be applied across the full range of sediment composition to quantify changes in source, frequency, and magnitude of sediment delivery by river floodwaters to lakes and is transferable to other dynamic floodplain landscapes where broad range of sediment composition challenges application of other approaches.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".