<i>Sphagnum</i> under pressure: towards an ecohydrological approach to examining <i>Sphagnum</i> productivity
Bibliographic record
Abstract
Abstract The genus Sphagnum is the key peat‐forming bryophyte in boreal ecosystems. Relying entirely on passive capillary action for water transport, soil moisture is often the limiting factor in Sphagnum production, and hence peat accumulation. While several hydrological models of peat physics and peatland water movement exist, these models do not readily interface with observations and models of peatland carbon accumulation. A conflict of approaches exists, where hydrological studies primarily utilize variables such as hydraulic head, while ecological models of Sphagnum growth adopt the coarse hydrological variables of water table (WT), volumetric water content (VWC) or gravimetric water content (WC). This review examines the potential of soil pressure head as a measurement to link the hydrological and ecological functioning of Sphagnum in peatlands. The non‐vascular structure of Sphagnum mosses and the reliance on external capillary transport of water in the mosses make them an ideal candidate for this approach. The main advantage of pressure head is the ability to mechanistically link plot‐scale hydrology to cellular‐scale water requirements and carbon exchange. Measurement of pressure head may improve photosynthetic process representation in the next generation of peatland models. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".