Spatial Variability of Soil Test Phosphorus, Potassium, and pH of Ontario Soils
Bibliographic record
Abstract
Grid soil sampling is typically used for establishing management zones for site‐specific application of nutrients. The geostatistical procedures used to estimate values between sample locations require samples to be taken close enough to together that they are correlated to one another. An evaluation of the scale of variability of soil test P (STP), soil test K (STK), and soil pH for Ontario soils was conducted using autocorrelation analysis of 23 Ontario farm fields, which were grid‐sampled using a 30‐m spacing. The results of the autocorrelation analysis indicated that 13 of the 23 farm fields would require a grid spacing of less than 30 m to adequately assess their spatial variability. For only one site was the commonly used 100‐m grid spacing adequate for the assessment of the spatial patterns of STP and STK. Further analysis using F tests compared the residuals from three gridding procedures (kriging, inverse distance, and nearest neighbor) using 60‐ and 90‐m grid data to that of the residuals using the field mean soil test value. In most cases, soil test variation maps based on 60‐ or 90‐m grid soil samples did not result in an increased ability to predict the soil test level at a given location in the field. It was concluded that a grid spacing of 30 m or less would be required to adequately assess the spatial variation of STP, STK, and soil pH. Sampling at this intensity would require approximately 11 times as many soil samples as the commonly used 100‐m grid, which is likely to make the cost prohibitive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".