Predicting surface area of coarse-textured soils: Implications for weathering rates
Bibliographic record
Abstract
Whitfield, C. J. and Reid, C. 2013. Predicting surface area of coarse-textured soils: Implications for weathering rates. Can. J. Soil Sci. 93: 621-630. The surface area of soil is an important determinant of mineral weathering rates, but is infrequently measured. Simple texture-based pedotransfer functions (PTFs) have been used to predict the specific surface area (SSA) of coarse-textured soils. Detailed physicochemical properties of 40 upland forest mineral soils from northeastern Alberta were used to evaluate three texture-based PTFs and to calculate weathering rates using a process-oriented soil-chemical model. Evaluation of the PTFs demonstrated that these equations predict only across a limited range of (low) surface areas. Moreover, the fit between predicted and measured SSA was generally poor for soils in this region of Alberta. Improved prediction of SSA was possible using a texture-based PTF calibrated for the region, although differences between measured and predicted values were often large. Mineralogy terms were used in a more comprehensive PTF to account for mineral-specific differences in surface area. This approach proved superior to texture-only approaches; however, it could not be used reliably for site-specific predictions (NRMSE=0.41). Soil-chemical model-generated weathering rates were strongly influenced by the SSA method used in parameterization; weathering estimates and corresponding critical load assessments based on measured SSA (and to a lesser extent SSA derived from the regional PTF) were the most robust. Methods for SSA prediction should be used with caution, particularly in cases where they are applied to soils with different character than those for which they were developed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".