Stratifying soils into pedogenically similar categories for modeling forest soil carbon
Bibliographic record
Abstract
Most forest ecosystem carbon (C) models are designed to estimate total ecosystem C including soil C stocks and fluxes. Stratification by tree species is often used in these models to reduce uncertainty, but the potential of stratification by soil taxon has received little attention. This potential can be realized only if meaningful modeling strata are identified. Therefore, the objectives of this study were: (a) to distinguish strata of soil C modeling cateogories (SCMC) on the basis of soil C stocks of taxonomic categories that are characterized by similar pedogenic processes important to C dynamics, and (b) to review the literature to test the robustness of the SCMC scheme. Carbon stocks of 1383 forest soil pedons were analyzed by multiple means comparisons for soil orders and by orthogonal contrasts between pedologically related sets of subgroups within soil orders. Eleven SCMCs were distinguished with mean total C stocks varying from 325 ± 37.2 t ha-1 for the gleyed Cryosol SCMC to 94 ± 3.9 t ha-1 for the Brunisolic Gray Luvisol SCMC. A review of the literature relevant to each SCMC demonstrated that there is a scientific basis for using these strata to model forest soil C dynamics. Key words: Forest soil, carbon, modeling, pedology, genesis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".