Characterizing mass–volume–density–porosity relationships in a sandy loam soil amended with compost
Bibliographic record
Abstract
Although compost is widely used as an organic soil amendment or conditioner, little is known of how it affects the characteristics or interactions among soil constituents. To address this, mixture theory was used to describe the mass–volume–density–porosity attributes and interactions among bulk soil, the mineral constituent, and the organic matter constituent of a sandy loam soil in a no-till corn field that had received one-time additions of yard waste compost at rates of 0 (control), 64, 154, and 380 dry t ha −1 . Bulk density (BD, 0–10 cm depth) decreased consistently and near-linearly with increasing soil organic matter (SOM) mass fraction (F OM ) for all six growing seasons (2012–2017) after compost addition. Fitting mixture theory expressions to BD vs. F OM data and to soil particle density vs. F OM data for 2013–2017 yielded constant mineral and SOM self-packing densities of D M = 1.673 Mg m −3 and D O = 0.335 Mg m −3 , respectively, and constant mineral and SOM particle densities of ρ M = 2.760 Mg m −3 and ρ O = 1.409 Mg m −3 , respectively. On a self-packing basis, soil mineral and SOM domain porosities were constants at n M = 0.39 and n O = 0.76, respectively. On a bulk soil volume basis, soil mineral and SOM porosities and volume ratios were linear functions of F OM . The porosity and volume characteristics of the SOM domain differed substantially from those of bulk soil and the mineral domain, and may therefore control the agri-environmental performance of soil, given that organic matter influences soil functioning more than mineral matter.
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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 | 0.000 | 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 | 0.001 | 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".