Inferring northern peatland methane emissions from testate amoebae: A proof of concept study
Bibliographic record
Abstract
Peatlands are efficient carbon sinks due to waterlogged soils causing oxygen depletion and slowing organic matter decomposition, leading to peat accumulation. However, peatlands are also a natural source of methane (CH4), a powerful greenhouse gas, to the atmosphere. Methane production (by methanogens) and oxidation (by methanotrophs) are controlled by water table depth, soil temperature and hydrochemistry. Measuring CH4 emissions is resource demanding. Several measurements method are used, which introduces potential bias for comparisons among studies. Thus, a simple and reliable indicator tool would be desirable for both researchers and managers. Currently, such a tool does not exist. Testate amoebae (TA), an abundant and diverse group of shelled protists occurring in peatlands, are well-established proxies of present water table depth (WTD). As their shells are well preserved in peat, they are commonly used to infer past hydrological changes using predictive mathematical models called transfer functions. As CH4 emissions are also tightly linked to WTD, and although TA are not directly involved in CH4 production or consumption, we hypothesised that CH4 emissions would be significantly correlated to TA community composition and could therefore be inferred from TA communities living in peatland mosses. We tested this hypothesis using compilations of CH4 plot emissions measurements from European and North American bogs and fens, and TA data from moss samples collected from the same plots. Testate amoeba communities were significantly correlated to CH4 fluxes. As our models were based on several independent studies for both flux measurements and TA communities, methodological differences among studies (e.g., CH4 emission measurements, TA taxonomy) may potentially cause bias in the model. Nevertheless, the results are promising, and this proof-of-concept study suggests that past and present peatland CH4 emissions could be inferred from TA shells preserved in peat over centuries and in mosses growing at the surfaces of peatlands.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".