Sensitivity of catchment‐aggregated estimates of soil carbon dioxide efflux to topography under different climatic conditions
Bibliographic record
Abstract
Soil respiration (Rs) is an important component of regional carbon budgets in forested landscapes. Within a sugar maple forest on the Algoma Highlands of the Canadian Shield, mosaics of topographic features create gradients in environmental conditions and carbon pools that influence the pattern of Rs within the region. The sensitivity of catchment‐aggregated Rs (CAR) to different spatial partitioning schemes of the landscape under different climate scenarios was examined in two contrasting catchments: one dominated by uplands (C35); the other containing uplands, critical transition zones (transiently saturated areas in isolated depressions or adjacent to wetlands, streams and lakes) and wetlands. CAR was estimated using a six topographic feature representation of the catchments including crest, backslope, footslope, toeslope, inner and outer wetland. CAR was underestimated (−7.4%) or overestimated (30.8%) if coarser spatial partitions were used, but the amount of error differed between catchments and with climatic conditions. A single feature (upland) partitioning scheme performed poorly under all climatic conditions (warm‐wet, warm‐dry, cool‐wet and cool‐dry). A two feature (upland and wetland) partitioning scheme showed improvement, but a partitioning scheme with a minimum of three features (upland, transition and wetland) was needed for accurate estimates of CAR in topographically varying catchments. The critical transition zone had the highest rates of Rs under all climate scenarios, and the critical transition zone and wetland became increasingly larger contributors to CAR under warmer and drier conditions. These observations point to the importance of accounting for the differential contribution of topographic features to Rs in carbon budget models. Failure to do so may lead to inaccurate estimates of landscape‐scale Rs.
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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 | 0.004 |
| 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 | 0.000 |
| 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".