The effect of climate change on peatland plant decomposition rates 
Bibliographic record
Abstract
As peatlands warm as a result of climate change, the balance between production and decomposition is shifting and will ultimately determine if peatlands remain long-term sinks for carbon (C) or become sources to the atmosphere exacerbating climate change. To better understand half of this equation we are measuring plant decomposition rates to warming and elevated CO2 (eCO2) within the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. SPRUCE uses ten 12-m diameter open-top enclosures built in ombrotrophic bog in northern Minnesota to increase air and soil temperatures (+0, +2.25, +4.5, +6.75, +9°C) at ambient and elevated CO2 (+500 ppm). To determine plant decomposition rates, we used litterbags that include black spruce (Picea mariana) needles and fine roots, Labrador tea (Rhododendron groenlandicum) leaves and fine roots, and two species of Sphagnum. The litterbags were deployed in triplicate in both hummock and hollow microtopographies and were retrieved at 0, 0.5, 1, 2 and 6 years after deployment with a final collection planned in 2025 at year 10. Across years, variability in decomposition rates is high and changes in dry mass indicate only a general warming effect on decomposition rates across litter types but no species-specific warming, eCO2, or microtopography treatment effects. We anticipate seeing additional treatment effects following the analysis of year 10 litterbags.
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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 | 0.000 |
| 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) | 0.001 | 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".