Compaction of Coarse-Textured Soils: Balance Models across Mineral and Organic Compositions
Bibliographic record
Abstract
Soil bulk density (BD), degree of compactness (DC), maximum bulk density (MBD), and critical water content (CWC) at which MBD is reached are commonly used to characterize soil compaction, and can be predicted from soil texture and organic matter content, omitting other components such as sand sub-classes and soil cementing agents and potential biases such as data redundancy and sub-compositional incoherence. Compositional data analysis is needed to account for interactions among soil components and to avoid biases. The aim of this study was to relate soil compaction indexes to the basic components of coarse-textured soils using unbiased numerical techniques. Soil samples collected in horizons A and B at 49 sites in Quebec, Canada, were analyzed for gravimetric water content, BD, particle-size distribution, MBD, CWC, organic C, total N, Si, Fe, Al, Mn, Mg and Ca. DC was calculated as the ratio of BD to MBD. The 14 physical-chemical soil properties were expressed as isometric log-ratios balances. We conducted principal component analysis to identify the components most correlated with compaction indexes. We used regression analysis to predict MBD and CWC, and used linear mixed-effects models to predict BD and DC. The regression models accounted for up to 83% of total variation in MBD and CWC, and the linear mixed-effects models explained 58–64% of total variation in BD and DC. BD and DC were found to decrease with clay content, and increase with larger proportion of coarser particles. Organic matter content tended to reduce BD and DC, and showed little effects on MBD. Increasing evenness of sand fractions resulted in a higher MBD value. Relationships between CWC and soil texture, and between CWC and organic C were not significant. Mineral cementing agents were the major contributors to soil compaction indexes. Si, Al, Fe and Ca oxides increased BD, DC and CWC, but reduced MBD. The sensitivity of coarse-textured soils to compaction could be predicted to support decisions on soil resilience after ripping and on the need to implement corrective chemical, biological and physical methods such as soil amendments, structure-building crops or textural mixtures to rebalance soil compositions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".