Reconstructing past hydrology of eastern Canadian boreal catchments using clastic varved sediments and hydro-climatic modeling: 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 laminated sediments record. The great depth of Grand Lake, the availability of fine sediments along its tributaries, and its important seasonal river inflow have favored the formation of a 160 years-long clastic varved sequence. Each varve represents one hydrological year. Varve formation is mainly related to spring discharge conditions with minor 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. Mean detrital layer thickness and the grain-size (99th percentile) series extracted from each varve yields the strongest correlations with instrumental data (r = 0.69 and 0.76) and have been used to reconstruct Naskaupi River mean and maximum annual discharges, respectively, over the 1856–2016 period. The reconstructed Q-mean series suggest that high Q-mean years occurred during the 1925–1960 period and a slight decrease in Q-mean take place during the second half of the 20th century. Independent reconstructions based on rainfall-runoff modeling 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 hydroclimatic signal as suggested by the statistically significant relation between mean detrital layer thickness series and the observed Labrador region Q-mean series extracted from five watersheds of different size and location.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".