Influence of Hydraulic Connectivity on Carbon Burial Efficiency in Mackenzie Delta Lake Sediments
Bibliographic record
Abstract
Abstract The Arctic is undergoing accelerated changes in response to ongoing modifications to the climate system, and there is a need for local to regional scale records of past climate variability in order to put these changes into context. The Mackenzie Delta region in northern Canada is populated by numerous small shallow lakes. They are classified as no‐, low‐, and high‐closure (NC, LC, and HC, respectively) lakes, reflecting varying degrees of connection to the river main stem, and have different sedimentation characteristics. This study examines sedimentological (mineral surface area, grain size), carbon isotopic (bulk and molecular‐level) and inorganic isotopic (neodymium) characteristics of sediment cores from three lakes representing each class. We find that HC lake sediments exhibit strikingly different properties from the other lake sediments. Specifically, they are characterized by higher organic carbon loadings per unit mineral surface area and with relatively minor influence from allochthonous, petrogenic (rock‐derived) organic carbon. In contrast, LC and NC lakes have the potential to record basin‐scale climatic changes at a high resolution by virtue of enhanced detrital sedimentation. Overall the delta lakes have the capacity to bury about 2 MtC year−1, with little changes in the last 200 years. However, in the (near) future, an increased number of high closure lakes might change the carbon burial efficiency of the Mackenzie Delta as they seem to retain less carbon than NC and LC lakes.
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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 | 0.000 | 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".