Predicting surface area of coarse-textured soils: Implications for weathering rates
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it