Landscape segmentation modeling in agricultural fields: Correlating soil pH to herbicide persistence
Bibliographic record
Abstract
There is increasing recognition that spatial variability in soils occurs across the landscape within a field and that differential management practices are warranted. Differences in herbicide persistence in the soil can result in variation in crop injury across the landscape. Soil pH, which is also reported to vary across the landscape, influences herbicide persistence. Thus, this study investigates the ability to map and delineate within-field soil pH variability using landscape segmentation analysis and provide a tool for predicting herbicide persistence. The area, comprising a conventional and a no-till field in southern Alberta, was surveyed using a dual frequency global positioning system; a digital elevation model was created and landform segments defined. Four landform segments were effective in delineating soil pH zones that could be related to herbicide persistence. The lower slopes in both fields, with pH < 6.0, showed imidazilinone persistence. Carryover of sulfonylurea and triazine herbicides was evident in the upper slopes of the conventional till field only (pH 7.8(7.9). The results suggest that landscape segmentation modeling could provide a valuable tool in managing field spatial variation in soil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".