A framework for <sup>210</sup> Pb model selection and its application to 37 cores from Eastern Canada to identify the dynamics and drivers of lake sedimentation rates
Bibliographic record
Abstract
Abstract Lake sedimentation rate represents a synthetic metric of ecosystem functioning. Many localized studies have reported a significant association between land use/land cover changes and lake sediment mass accumulation rates, with a few global syntheses echoing these findings at larger scales. In the literature, studies evaluating lead‐210 ( 210 Pb) for establishing sediment chronologies will report at least one of three dating models, but the constant rate of supply (CRS) model is the most widely used. However, it is often unclear how or why this model is selected, despite its influence on the interpretation of many subsequent analyses about ecosystem dynamics and functioning. It would thus be advantageous to design an objective and semi‐automated way of choosing among dating models. We measured radioisotopic activities in 37 sediment cores across four ecozones of Eastern Canada and developed an approach to assess model fit for the three commonly applied dating models. The derived chronologies were then used to evaluate the spatial and temporal variation in sedimentation rates across four ecozones in Canada (covering a surface area of 2.2 × 10 6 km 2 ). We observed a recent increase in lake sedimentation rates across most lakes, as has been observed globally, albeit with significant differences in the magnitude of sedimentation rates across ecozones. Across all lakes, we found that regional human population counts and mean annual air temperatures were significant temporal predictors of variation in mass accumulation rates. Overall, this analytical framework offers an objective approach for assessing fit and selecting among sediment age models, which contributes to a more robust quantification of sedimentation rates. With this first application, we provide a quantitative assessment of how lake sedimentation rates have varied across a northern lake‐rich region and have responded to environmental change.
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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 | 0.000 |
| 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) | 0.000 | 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".