Spatial distributions of soil chemical and physical properties prior to planting soybean in soil under ridge‐, no‐ and conventional‐tillage in a maize–soybean rotation
Bibliographic record
Abstract
Abstract Detailed information on the profile distributions of agronomically important soil properties in the planting season can be used as criteria to select the best soil tillage practices. Soil cores (0–60 cm) were collected in May, 2012 (before soybean planting), from soil transects on a 30‐yr tillage experiment, including no‐tillage ( NT ), ridge tillage ( RT ) and mouldboard plough ( MP ) on a Brookston clay loam soil (mesic Typic Argiaquoll). Soil cores were taken every 19 cm across three corn rows and these were used to investigate the lateral and vertical profile characteristics of soil organic carbon ( SOC ), pH, electrical conductivity ( EC ), soil volumetric water content ( SWC ), bulk density ( BD ), and penetration resistance ( PR ). Compared to NT and MP , the RT system resulted in greater spatial heterogeneity of soil properties across the transect. Average SOC concentrations in the top 10 cm layer were significantly greater in RT than in NT and MP ( P = 0.05). NT soil contained between 0.8 and 2.5% (vol/vol) more water in the top 0–30 cm than RT and MP , respectively. MP soil had lower PR and BD in the plough layer compared to NT and RT soils, with both soil properties increasing sharply with depth in MP . The RT had lower PR relative to NT in the upper 35 cm of soil on the crop rows. Overall, RT was a superior conservation tillage option than NT in this clay loam soil; however, MP had the most favourable soil conditions in upper soil layers for early crop development across all treatments.
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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 | 0.000 |
| 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) | 0.001 | 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".