Decomposition of <i>Betula papyrifera</i> leaf litter under the independent and interactive effects of elevated CO<sub>2</sub> and O<sub>3</sub>
Bibliographic record
Abstract
Abstract Litter decay dynamics of paper birch (Betula papyrifera) were assessed at the Aspen free‐air CO2 enrichment (FACE) facility in northern Wisconsin, USA. Leaf litter was decomposed for 12 months under factorial combinations of 360 vs. 560 μL CO2 L−1, crossed with 36 vs. 55 nL O3 L−1. To differentiate between substrate quality and environment effects, litterbags were placed in their Native Plots of origin or transplanted into the other treatments. CO2 enrichment, regardless of O3 concentration, produced poorer quality litter (high C/N, lignin/N and condensed tannins) than did ambient CO2 (low C/N, lignin/N and condensed tannins). Substrate quality differences were reflected in the mass loss rates (k‐values), which were high for litter generated under ambient CO2 (0.887 year−1) and low for litter generated under elevated CO2 (0.674 year−1). The rate‐retarding effects of CO2 enrichment were neither alleviated nor exacerbated by O3 exposure. Decay rates varied, however, depending on whether litter was placed back into its plot of origin or transplanted to Common Gardens. The results of this study are species specific, but they have important implications for understanding the processes regulating storage of fixed C and the release of CO2 from northern forest ecosystems.
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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".