Reconstructing past hydrology of eastern Canadian boreal catchments using clastic varved sediments and hydro-climatic modelling: 160 years of fluvial inflows
Bibliographic record
Abstract
Abstract. Analysis of short sediment cores collected in Grand Lake, Labrador, revealed that this lake is an excellent candidate for the preservation of a laminated sediment record. The great depth of Grand Lake, the availability of fine sediments along its tributaries and its important seasonal river inflow have favoured the formation of a 160-year-long clastic varved sequence. Each varve represents 1 hydrological year. Varve formation is mainly related to spring discharge conditions with contributions from summer and autumn rainfall events. The statistically significant relation between varve parameters and the Naskaupi River discharge observations provided the opportunity to develop local hydrological reconstructions beyond the instrumental period. The combined detrital layer thickness and the particle size (99th percentile) series extracted from each varve yield the strongest correlations with instrumental data (r=0.68 and 0.75 respectively) and have been used to reconstruct the respective Naskaupi River mean and maximum annual discharges over the 1856–2016 period. The reconstructed Q-mean series suggest that high Q-mean years occurred during the 1920–1960 period, and a slight decrease in Q-mean took place during the second half of the 20th century. Independent reconstructions based on rainfall–runoff modelling of the watershed from historical reanalysis of global geopotential height fields display a significant correlation with the reconstructed Naskaupi River discharge based on varve physical parameters. The Grand Lake varved sequence contains a regional hydrological signal, as suggested by the statistically significant relation between the combined detrital layer thickness series and the observed Labrador region Q-mean series extracted from five watersheds of different sizes.
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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".