Disturbance Impacts on Thermal Hot Spots and Hot Moments at the Peatland‐Atmosphere Interface
Bibliographic record
Abstract
Abstract Soil‐surface temperature acts as a master variable driving nonlinear terrestrial ecohydrological, biogeochemical, and micrometeorological processes, inducing short‐lived or spatially isolated extremes across heterogeneous landscape surfaces. However, subcanopy soil‐surface temperatures have been, to date, characterized through isolated, spatially discrete measurements. Using spatially complex forested northern peatlands as an exemplar ecosystem, we explore the high‐resolution spatiotemporal thermal behavior of this critical interface and its response to disturbances by using Fiber‐Optic Distributed Temperature Sensing. Soil‐surface thermal patterning was identified from 1.9 million temperature measurements under undisturbed, trees removed and vascular subcanopy removed conditions. Removing layers of the structurally diverse vegetation canopy not only increased mean temperatures but it shifted the spatial and temporal distribution, range, and longevity of thermal hot spots and hot moments. We argue that linking hot spots and/or hot moments with spatially variable ecosystem processes and feedbacks is key for predicting ecosystem function and resilience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".