Scaling net ecosystem CO<sub>2</sub> exchange from the community to landscape‐level at a subarctic fen
Bibliographic record
Abstract
Summary Landscape‐ and community‐level CO2 measurements were made at a subarctic sedge fen near Churchill Manitoba during the 1997 growing season. Climatic conditions were warmer and drier than the 30‐y normal. Landscape‐scale micrometeorological measurements indicated that the wetland gained 49 g CO2 m−2 during the growing season. Chamber‐scale measurements from the main vegetation community types showed that small hummocks (Carex spp. sites) dominated the CO2 exchange, yielding an effective scaling factor of 70%. Scaled parameters of two algorithms describing photosynthesis and respiration for each community type show strong similarity to those derived at the landscape level. Scaling photosynthesis, respiration, and net ecosystem CO2 exchange from the community to landscape‐level over the season is within the maximum probable error of each methodological approach and helps substantiate the 1997 CO2 budget. We explore the equilibrium response of net ecosystem CO2 exchange of this fen to climatic change by examining the feedback of water table position on vegetation distribution and nitrogen availability. Based on the effective scaling factors computed for each community type, we hypothesize that a small decrease in mean water table position could nearly triple the net uptake of CO2 at this wetland.
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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 | 0.000 | 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".