Bibliographic record
Abstract
Peat formation is a slow process and the formation of thick peat layers in large parts of e.g. Russia, Canada and Indonesia has generally taken thousands of years. Due to degradation of peatlands throughout the world, as a result of changed land use and pollution, many ecosystem services provided by peatlands have disappeared. It is therefore necessary to restore degraded systems or create new peat-forming wetlands. Information on the early stages of peat formation is scarce, however, and the biogeochemical conditions that stimulate the transition of mineral sand to growing peatland (which would have happened thousands of years ago in e.g. Russia, Canada and Indonesia) remain largely unknown. In this thesis, several pathways of peat formation are studied using three model species: Stratiotes aloides, which grows in the aquatic phase, Typha spp., which grow in the semi-terrestrial phase, and Sphagnum mosses, which grow in the terrestrial or floating mire stage. Using a combination of lab studies, mesocosm experiments and field measurements, the biogeochemical conditions and biotic interactions (such as facilitation) that stimulate or limit growth of these ecosystem engineers were studied. Furthermore, for each of these species, the contribution to the net C sequestration rate of a system -or the net build-up of an organic layer that can form peat- was determined. We found that there is a huge difference between starting peat formation “from scratch” (on mineral soils) or restoring peat formation in a degraded peatland. In this latter case, secondary peat formation can be started after habitat conditions are suitable for growth of Sphagnum mosses (e.g. by topsoil removal and rewetting). For primary peat formation, on the other hand, the main concern is the low colonisation rates of low-nutrient, mineral soils. Therefore, modifying habitat conditions to suit the requirements of target species and harnessing inter- and intraspecific facilitation is essential to transform such a system into a net C sink without having to wait a thousand years.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.027 |
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".