Soil structure dynamics in annual croplands under controlled traffic management
Bibliographic record
Abstract
Soil samples were collected from commercial agriculture sites within western Canada that were subjected to compaction from farm equipment in both conventional (imposed) traffic and controlled traffic regimes. Soil characteristics such as bulk density, pore volume fractions, and unsaturated hydraulic conductivity were compared with soil physical quality parameters, such as S-index and mass fractal aggregation between trafficked and untrafficked field areas. Our results showed that untrafficked soil characteristics displayed substantial improvements over those exposed to equipment compaction. Untrafficked soils in the controlled traffic regime exhibited total porosity improvements up to 15% in more than half of the study sites. In addition, spatial reductions of equipment compaction increased the volume of soil pore diameters associated with preferential water transmission from 40% to 180%. Changes in these soil characteristics within untrafficked soils correlated well with enhancements in the soil structure metrics, as improvements to the S-index were coupled with evidence of hierarchical aggregation. Irrespective of the positive changes to soil structure, significant increases in crop yield were rarely observed in favor of a controlled traffic regime. Our results suggest that the integration of controlled traffic farming into management systems may take several years for the benefits to soil physical quality to translate into observable improvements in crop yield.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".