The influence of system heterogeneity on peat-surface temperature dynamics
Bibliographic record
Abstract
Abstract Temperatures at the soil–atmosphere interface influence ecosystem function by driving nonlinear terrestrial biogeochemical, ecohydrological, and micrometeorological processes. Whilst climate, soil and vegetation controls on spatially average ecosystem temperatures are recognised, how interacting and heterogeneous ecosystem layers create spatio-temporal complex thermal ecosystems has not been determined. Such thermal hot spots and hot moments may underpin the capability of ecosystems to support biological and biogeochemical diversity and control the likelihood of tipping points in system-regulating feedbacks being locally exceeded. This is of notable importance in peatlands, where soil temperatures control the storage of their associated globally important carbon stocks. Here, through the application of high spatio-temporal resolution surface temperature data and peat thermal modelling, we assess the impact of system heterogeneity (spatio-temporal impact of the following system layers: tree, shrubs, microtopography, groundcover species and sub-surface ice) on surface temperature regimes. We show (a) that peat-surface thermal hotspot intensity and longevity is linked to system heterogeneity and (b) that not all system layers have an equal influence over the peat-surface thermal regime and extreme temperatures; thermal heterogeneity increases up to a maximum of five layers of heterogeneity and decreases thereafter. The results crucially demonstrate that such changes in the spatio-temporal thermal dynamics and extremes may occur without significant changes in median temperatures. This is important to the conceptual understanding of peatland responses and ecosystem resilience to disturbance. It emphasises the need to determine the potential for transitions in magnitude, longevity and locality of small-scale thermal extremes to induce functional transitions that propagate through given ecosystems, and to characterise the impact of such small-scale spatio-temporal complexity on ecosystem scale biogeochemical and ecohydrological function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".