Modelling the interannual variability of net ecosystem CO<sub>2</sub> exchange at a subarctic sedge fen
Bibliographic record
Abstract
Abstract This paper presents an empirical model of net ecosystem CO2 exchange (NEE) developed for a subarctic fen near Churchill, Manitoba. The model with observed data helps explain the interannual variability in growing season NEE. Five years of tower‐flux data are used to test and examine the seasonal behaviour of the model simulations. Processes controlling the observed interannual variability of CO2 exchange at the fen are examined by exploring the sensitivity of the model to changes in air temperature, precipitation and leaf area index. Results indicate that the sensitivity of NEE to changing environmental controls is complex and varies interannually depending on the initial conditions of the wetland. Changes in air temperature and the timing of precipitation events have a strong influence on NEE, which is largely manifest in gross ecosystem photosynthesis (GEP). Climate change scenarios indicate that warmer air temperatures will increase carbon acquisition during wet years but may act to reduce wetland carbon storage in years that experience a large water deficit early in the growing season. Model simulations for this subarctic sedge fen indicate that carbon acquisition is greatest during wet and warm conditions. This suggests therefore that carbon accumulation was greatest at this subarctic fen during its early developmental stages when hydroclimatic conditions were relatively wet and warm at approximately 2500 years before present.
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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 | 0.001 | 0.000 |
| 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".