Forest soil CO2 efflux models improved by incorporating topographic controls on carbon content and sorption capacity of soils
Bibliographic record
Abstract
Improved models are needed to predict the fate of carbon in forest soils under changing environmental conditions. Within a temperate sugar maple forest, soil CO 2 efflux averaged 3.58 µmol m −2 s −1 but ranged from 0.02 to 25.35 µmol m −2 s −1 . Soil CO 2 efflux models based on temperature and moisture explained approximately the same amount of variance on gentle and steep hillslopes (r 2 = 0.506, p < 0.05 and r 2 = 0.470, p < 0.05 respectively). When soil carbon content and sorption capacity were added to the models, the amount of explanation increased slightly on a gentle hillslope (r 2 = 0.567, p < 0.05) and substantially on a steep hillslope (r 2 = 0.803, p < 0.05). Within the organic-rich surface of the mineral soil, carbon content was positively related and sorption capacity was negatively related to soil CO 2 efflux rates. There were general patterns of smaller carbon pools and lower sorption capacity in the upland positions than in the lowland and wetland positions, likely a result of hydrological transport of particulate and dissolved substances downslope, leading to higher soil CO 2 efflux in the upland positions. However, the magnitude of the soil CO 2 efflux was mitigated by the higher sorption capacity of the organic-rich surface layer of the mineral soils, which was negatively correlated to soil CO 2 efflux. More accurate estimates of forest soil CO 2 efflux must take into account topographic influences on the carbon pool, the environmental factors that affect rates of carbon transformation, as well as the physicochemical factors that determine the fraction of the carbon pool that can be transformed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".