Multidecadal carbon sequestration in a headwater boreal lake
Bibliographic record
Abstract
Abstract Dissolved organic carbon (DOC) was measured continuously since 1970 in a pristine headwater boreal lake and its catchment at the IISD‐Experimental Lakes Area (Ontario, Canada). Mass balanced accounting of DOC concentrations in precipitation, watershed runoff, and inflow and outflow streams, and integrated weekly hydrological data determined annual mass flux of DOC to and from the lake. Inputs minus outputs represented two residual terms: (1) mineralization and evasion of CO2 and (2) DOC flocculation and sediment burial. Accumulation of organic carbon in sediment cores estimated permanent storage and evasion was calculated by subtraction of burial from the annual retention over 40 yr (1971–2010). Terrestrial sources accounted for 92% ± 1% of DOC load; 37% ± 2% of which was lost via the outflow. About 40% ± 3% of DOC load accumulated in sediments and 23 ± 3% was lost as CO2. Over 40 yr, C sequestration in sediments was a more important sink than evasion or outflow. We explore the fate of DOC during decade long periods of differing precipitation patterns. Loading and loss via the outflow was higher in wet (1990–2010) compared to dry (1980–1990) years. Due to longer DOC processing times when water residence times are longer, it is possible that if drought increases in the boreal forest, the efficiency of headwater lakes to sequester C in sediments maybe greater than in wet periods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".