Assessment of uncertainty in soil test phosphorus using kriging techniques and sequential Gaussian simulation: implications for water quality management in southern Quebec
Bibliographic record
Abstract
Missisquoi Bay, located in southern Quebec at the north-eastern extremity of Lake Champlain, is subject to eutrophication arising from excess nutrients, predominantly phosphorus (P), contributed by agricultural watersheds. Factors such as land use pattern, management practices, soil properties and geomorphology have an impact on soil P levels. Land patches under different management practices introduce a cyclic pattern, especially when fitting the variogram. Geostatistics procedures were used to model soil test phosphorus (STP) within the 11 km2 Castor Watershed in southern Quebec, Canada. An ordinary kriging (OK) method was used to estimate STP at unsampled points, but this had a smoothing effect, resulting in an underestimation of high values and overestimation of low values. Therefore, a more efficient technique was needed to draw predictions from the conditional probability distribution at the simulation grid nodes. A sequential Gaussian simulation (SGS) was adopted for this purpose, and used to create 50 equal probable realizations. Uncertainty was modelled using the E-type (mean) of the realizations, which ranged from 12.5 to 223 mg P kg–1 soil. The adequate spatial probability pattern for STP is a valuable criterion when seeking to delineate areas of high STP for site-specific best management practices (BMPs).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".