The Annual Carbon Budget for Fen and Forest in a Wetland at Arctic Treeline
Bibliographic record
Abstract
ABSTRACT. Three separate research efforts conducted in the same wetland-peatland system in the northern Hudson Bay Lowland near the town of Churchill, Manitoba, allow a comparison of two carbon budget estimates, one derived from long-term growth rates of organic soil and the other based on shorter-term flux measurements. For a tundra fen and an open subarctic forest, calculations of organic soil accumulation or loss over the last half-century indicate that while the fen on average has lost small amounts of carbon from the ecosystem, the adjacent forest has gained larger amounts of atmospheric carbon dioxide. These longer-term data are supported by shorter-term flux measurements and estimates, which also show carbon loss by the fen and carbon uptake by the forest. The shorter-term data indicate that the fen’s carbon loss is largely attributable to exceptionally dry years, especially if they are warm. The forest may gain carbon at an increased rate as it matures and during warm growing seasons. Also, the changes in relief of the dynamic hummock-hollow landscape in the fen may inhibit photosynthesis. Key words: Subarctic fen and forest, net carbon loss, temperature and water balance variability RÉSUMÉ. Trois travaux de recherche distincts portant sur le même système de marécages/tourbières situés dans la partie septentrionale des basses-terres de la baie d’Hudson, près de la ville de Churchill au Manitoba, permettent de comparer deux estimations du budget de carbone, l’une tirée des taux de croissance à long terme du sol organique et l’autre fondée sur des mesures du flux à plus court terme. Pour une tourbière basse de toundra et une forêt claire subarctique, les calculs de l’accumulation ou de la perte de sol organique au cours des cinquante dernières années révèlent que, si la tourbière basse a perdu en moyenne de petites
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".