Carbon burial and infill rates in small Western Boreal lakes: physical factors affecting carbon storage
Bibliographic record
Abstract
Effects of depression depth (ZT), lake surface elevation (ES), catchment area:lake surface area (AT:AO), trophic status, and surficial geology on sediment burial rates in small Western Boreal Plain lakes were assessed using content and chronology of cores from a relatively large and small lake on each of moraine (M), glaciofluvial (GF), and glaciolacustrine (GL) deposits. Aquatic and terrestrial plant and sediment carbon:nitrogen (C:N) suggested most buried C was aquatic. The rate of long-term total C burial averaged 31 g·m2·year1 (range: 084 g·m2·year1); this was recently 79 g·m2·year1 (range: 40180 g·m2·year1) (higher and more variable rates than previously reported for Boreal lakes). Long-term C accrual rate and sediment depth increased with increasing ZT. In each landform, a relatively low base elevation (EB = ES ZT) lake began accumulating sediment thousands of years before a high EB lake. GF depressions were deeper and had accrued more C·m2 (and infilled) faster than M and GL lakes; a large GF lake had no organic sediment, perhaps because of large groundwater inputs. Increasing AT:AO corresponded with increasing C accrual rates where precipitation and evaporation dominated (surface runoff infrequent) (M and GL) but not where groundwater dominated (GF lakes) lake water budgets, illustrating the importance of landform and depression characteristics in regionalizing lake-C burial estimates.
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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 | 0.000 |
| 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 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".