A new 1523-year-long varve sequence reveals the influence of the Atlantic Multidecal Variability on Eastern Canada hydroclimate
Bibliographic record
Abstract
Grand Lake, Labrador, is a 245-m deep and 55 km long fjord lake deglaciated c.a. 8000 years ago, located at the eastern margin of North America in the high boreal forest ecozone. The lake is fed by two large rivers that transport a substantial amount of sediments, mainly during the snowmelt season. As a result, up to 13 mm thick varves are preserved in the proximal zone of the two main tributaries, while distal varves are 1.26 mm thick on average. Proximal and distal varves can be correlated thanks to cross-correlation of distinctive varves. Varve counts were made from high-resolution images of thin sections at the scanning electron microscope, and from 100 µm-resolution µXRF profiles. The age model was validated by 210Pb, 137Cs and 14C dating. The proximal varves are composed of 3 distinct laminae, while the distal varves contain 2 layers. This paper outlines how the proximal and distal sequences were combined to produce a 1523-year-long record allowing a very long reconstruction of past river mean discharge (Q-mean). The river discharge was higher during the Medieval Climate Anomaly (1050–1225 CE) and lower during the Little Ice Age (15th–19th centuries). The reconstructed Q-mean shows a significant co-variability with Atlantic Multidecadal Variability reconstructions and with reconstructed summer Northern Hemisphere temperature based on tree rings. This suggests that river discharge in Labrador was influenced by ocean-atmosphere interactions across the North Atlantic, and that a longer varved record from Grand Lake has the potential to reconstruct the supra-regional modes of climatic variability for most of the Holocene.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".