Effects of Long-term Cultivation on a Morainal Landscape
Bibliographic record
Abstract
Agriculture and Agri-Food Canada established a benchmark site network in the early 1990s to monitor soil quality change over time on reference agricultural landscapes. This approach assumes that monitoring selected soil variables for 10 or more years will show changes in soil conditions. An alternative approach is to compare cultivated and uncultivated (native) soils in a landscape. This approach requires less research time and provides an estimate of changes in soil characteristics over the entire cultivation period. Opportunities for such research are rare in the agricultural parts of western Canada. An opportunity presented itself in conjunction with work at the Provost (05-AB) benchmark site. A parcel of native land only 1.6 km away was studied and sampled using the same methodology. The similarity of the two sites provided an opportunity to examine soil attributes that approximated conditions prior to cultivation, and, by comparison, to assess changes brought about by 80 years of cultivation. Literature Review The impact of soil erosion on soil quality has been investigated using long-term cropping system studies1,2,7 and retrospective views of management-induced soil changes by comparing cultivated with uncultivated soils in the same landscape6,7. Differences in organic
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".