Seasonal Dynamics of Dissolved Methane in Lakes of the Mackenzie Delta and the Role of Carbon Substrate Quality
Bibliographic record
Abstract
Abstract Dissolved CH 4 among lake waters of the Mackenzie River Delta was tracked in 2014 to assess how river‐to‐lake connection times, plus carbon substrate quantity and quality, affects the patterns and dynamics of CH 4 . An under‐ice survey of 29 lakes, three open‐water surveys of 43 lakes, and weekly surveys of 6 lakes revealed that CH 4 among lake‐waters ranged from very high concentrations at the end of winter, with highest concentrations linked to shortest annual river connection times, to considerably lower concentrations as open water progressed, with a limited concentration range among lakes by late summer. Lakes most strongly affected by thermokarst varied irregularly from this pattern and did not have the highest CH 4 concentrations. CH 4 among lake waters was generally related to measures of carbon substrate quantity, where relations with macrophyte biomass and dissolved organic carbon in lake water were statistically stronger than % organic matter within the lake sediments. CH 4 was also directly related to the molecular weight (a[250]:a[365]) of dissolved organic matter at the end of winter, but was inversely related to this measure during open water. Carbon quality per se, after accounting for differences in carbon quantity (macrophyte biomass, dissolved organic carbon concentrations, or organic content of lake sediments), appears to play a significant role in controlling CH 4 concentrations among the lake waters, particularly during winter ice cover. Carbon quality in lake sediments and of dissolved organic matter in winter lake waters appears to be as important as thermokarst augmentation of carbon quantity for enhancing methanogenesis in this lake‐rich Arctic system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".