Comparison of decomposition of belowground and aboveground plant litters in peatlands of boreal Alberta, Canada
Bibliographic record
Abstract
Studies examining the decomposition rates of belowground plant tissues in peatlands are scarce despite the significant contribution these tissues make to total plant production. Therefore, we measured mass losses of Carex aquatilis Wahlenb. leaves and rhizomes and Salix planifolia Pursh leaves and roots in a rich, sedge-dominated fen and Sphagnum fuscum (Schimp.) Klinggr. plants in a forested bog using the litter bag technique over a 2-year period in southern boreal Alberta. After 2 years, mass losses of C. aquatilis rhizomes (75%) were significantly higher than those of C. aquatilis leaves and Salix planifolia leaves, which were similar to each other (54 and 48%, respectively). Sphagnum fuscum and Salix planifolia root mass losses also were similar to each other (21 and 29%, respectively), but they were significantly lower than those of the other three litter types. Different tissue nutrient concentrations as well as alkalinity- and phosphorus-related surface water chemistry variables correlated significantly with mass losses of different litter types; however, they alone did not explain all of the mass loss trends. The majority of sedge peat and carbon in the fen originates from C. aquatilis leaves (188 and 86 g·m-2, respectively), with the remainder originating from C. aquatilis rhizomes (102 and 47 g·m-2, respectively) after the first 2 years of decomposition. Conversely, the majority of Salix planifolia peat and carbon originates from its roots (33 and 16 g·m-2, respectively) and the remainder from its leaves (24 and 11 g·m-2, respectively) over the same period. After the first 2 years of decomposition, 150 g·m-2of peat and 71 g·m-2of carbon remained from the decomposing Sphagnum fuscum in the bog.Key words: bog, fen, mass losses, Carex aquatilis, Salix planifolia, Sphagnum fuscum.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".