Spatial and Temporal Variability of Lake Accumulation Rates in Subarctic Northwest Territories, Canada
Bibliographic record
Abstract
We examined the spatial and temporal variability of Holocene lake sediment accumulation at 22 sites from 18 lakes transecting boreal forest, tree line, and tundra zones in the central Northwest Territories, Canada.Over 140 radiocarbon dates were obtained, and accumulation rates (AR) were calculated at 100-year intervals from agedepth models constructed using the age-depth modeling software Clam.Sites with the shortest mean AR of 25±10yr/cm (1σ) occur primarily in the boreal zone.Sites with moderate (70±22yr/cm) and long (160±56yr/cm) AR are north of the treeline and display higher variability, strongly influenced by bathymetry.Many age-depth models are characterized by fluctuations in ARs that coincide with paleogeographical changes associated with proglacial lake evolution during the early Holocene, and subsequent climate changes inferred from proxy data.The insights gained on the spatial and temporal trends in ARs across the region are valuable for developing higher resolution age-depth models using the Bayesian software Bacon.me the ropes in the lab and who has been a mentor to me over the past few years.In the spring of 2012 I was fortunate to spend three months at Queen's University in Belfast taking courses and absorbing knowledge from Dr. Paula Reimer and Dr.Maarten Blaauw -world experts in radiocarbon dating and age-depth modeling.I was also lucky to work with Dr. Helen Roe, who is one of the most caring and dedicated advisors I have ever met.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".