Changes in lake sediment carbon accumulation rates in southwestern Canada since the mid-1800s
Bibliographic record
Abstract
Carbon (C) storage in lakes is an increasingly recognized component of the global C cycle. Yet, rates of C accumulation in lake sediments remain poorly quantified in some regions such as in Canada. This study assessed C stocks and C accumulation rates (CARs) in sediments from 18 lakes across four provinces and seven national parks in southwestern Canada. We analyzed temporal and spatial variability in CARs and examined their relationship with landscape characteristics (e.g., land use and lake morphology) and climate variables (e.g., temperature and precipitation). Fourteen lakes showed increasing trends in CAR between 1830 and 2009. The average CAR during the modern period (1980–2009) was 42.8 ± 2.6 g/m 2 /year, representing a 14% increase compared to the historical period (1920–1949). Variability in CARs was primarily explained by temperature-related factors, including mean annual temperature, degree-days under 0 °C, and seasonal temperatures, particularly in spring and summer. Land use also played a significant role as the percentage of catchment area dedicated to agriculture and development was a strong predictor of CAR increases. These findings indicate that rising temperatures and intensified land use are key drivers of enhanced C accumulation in southwestern Canadian lakes, trends likely to continue under ongoing climate change.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".