Elevation‐Based Soil Sampling to Assess Temporal Changes in Soil Constituents
Bibliographic record
Abstract
Agricultural land currently is used not only for producing food, fiber, and other bioproducts, but also for disposing of and recycling industrial, municipal, and agricultural wastes. Because such uses may potentially have negative effects on the environment, the fate of applied elements or of those originally present in the soil are under intensive scrutiny. Temporal change in the mass of a given soil element or constituent per unit area typically is calculated as the difference between constituent masses in a fixed soil depth at two sampling times. This method, however, is adequate for only rare cases when soil volume remains unchanged (i.e., when there are no changes in soil bulk density or thickness). Other methods based on “fixed soil mass,” “equivalent soil depth,” or “cumulative mass coordinate” have been developed to account for bulk density changes but they still do not account for changes in soil mass, such as those associated with waste inputs or soil redistribution through erosion and deposition, or imports and exports. We propose an alternative method based on elevation‐based soil sampling to account for the effects of changes in both soil bulk density and soil mass. Unlike methods that assume soil mass remains unchanged, the proposed method would also be applicable to sites with appreciable additions or removals of soil mass or volume. We discuss the merits of elevation‐based soil sampling to assess temporal changes in soil constituents, and present an example of its application to a site receiving heavy applications of livestock manure for 30 yr.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".