Untangling sedimentation processes in a deep fjord lake in Labrador: A high‐resolution archive of past environment dynamics at Grand Lake
Bibliographic record
Abstract
Abstract Grand Lake is a large 250 m deep fjord lake located in Labrador, Canada. Previous studies on short and shallow sediment cores identified seasonal hydrological signals and connections with North Atlantic modes of climate variability. This study presents a new 20 m composite sequence from the deepest basin of Grand Lake, providing high‐resolution insights into sedimentary processes over the last ca. 3300 years. As a potential key environmental archive for north‐eastern Canada, a region where high‐resolution palaeoclimate records are scarce, Grand Lake offers a unique opportunity to examine long‐term sedimentary and climatic interactions. Previous research did not examine temporal changes in sedimentary processes or the specific mechanisms driving mass sediment deposition, limiting the distinction and interpretation of climate controls on longer time scales. Here, sedimentological and geochemical characteristics are used to reconstruct sedimentation dynamics and erosional processes. Several rapidly deposited layers are characterised over changing depositional environments during the Late Holocene, from a phase when the lake was connected to the sea to a more stable state conducive to varve formation. A combination of end‐member modelling analysis, lithofacies descriptions and high‐resolution μ‐XRF proxies revealed density currents as the dominant sedimentation process. Their origins ranged from proximal sources (gully systems) to distal sources (tributary rivers), with contributions varying over time, reflecting the transition from a marine‐influenced system to a post‐glacial fjord lake. The results provide a framework for future palaeoclimate studies in the region by contributing to a better understanding of sedimentary dynamics in a deep glacial lake, with implications for regional palaeoclimate reconstructions. Additionally, this study highlights the broader applicability of statistical unmixing for interpreting grain‐size variations in both lacustrine and marine environments.
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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".