Bibliographic record
Abstract
The ability of wetlands to sequester carbon has given them a considerable amount of attention, especially in light of global climate change. To date, many wetland studies have focused on peatlands, however very few studies have been conducted on marshes. This study used the eddy covariance (EC) technique to measure net ecosystem carbon exchange (NEE) and energy exchange at a temperate freshwater cattail marsh near Ottawa, Canada. The objectives of the study were to use a four year dataset to determine the environmental controls on the variability of carbon and water vapour exchange. The annual cumulative NEE was on average -246 ± 31 gCm-2yr-1 ranging from -216 to -260 gCm-2yr-1. The variability in accumulation between years was a result of the timing of spring and fall transitions in the carbon uptake and the length of the growing seasons, each of which were determined by prevailing weather conditions. Evaluation of the interannual variability indicated that the marsh may be sensitive to carbon (C) losses through enhanced respiration under warmer autumn periods. Maximum daily average values of evapotranspiration (ET) reached 10.75, 9.07, 11.70 and 8.36 mm day-1 in 2005, 2006, 2007 and 2008, respectively. Bowen ratio values varied seasonally with values well below unity during the growing season (May to October) illustrating the dominance of latent heat. Evaluation of the evaporative fraction and Priestley-Taylor α indicated the seasonal importance of ET and mid-season high values of the decoupling coefficient (Ω) indicated that the marsh ET is radiatively driven owing its smooth aerodynamic surface and abundance of water. Overall, the marsh ecosystem was a large annual sink for CO2 as compared to other wetland ecosystems and ET rates were highly dependent on radiative input.
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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 | 0.000 | 0.001 |
| 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".