The effect of rejuvenation of Aspen Parkland ecoregion grass-legume pastures on botanical composition
Bibliographic record
Abstract
A 3-yr study was conducted at five different sites in the Aspen Parkland of Saskatchewan to determine the effect of spiking, burning, mowing, deep-banding (Trt) and applications of N, P, K and S liquid and granular fertilizers (Fert) on changes in botanical composition of predominantly smooth bromegrass (Bromus inermis Leyss.) and alfalfa (Medicago sativa L.) pastures established on Black Chernozemic and Gray Luvisolic soils in central Saskatchewan. Fertilizer application was in a liquid form blended to provide 100 kg N ha–1, 45 kg P2O5 ha–1, 23 kg K2O ha–1 and 12 kg S ha–1 in 350 kg of fertilizer ha–1. The experimental design at each site was a randomized complete block in a split-plot arrangement. Main plots were spike, burn, mow, deep-band, deep-band liquid fertilizer and control. The split-plot treatment was granular fertilizer broadcast at 0 and 350 kg ha–1 (providing 100 kg N ha–1, 45 kg P2O5 ha–1, 23 kg K2O ha–1 and 12 kg S ha–1). All treatments were applied in the spring of 1994. Interaction effects of Trt × Yr and Fert × Yr were significant (P < 0.05), indicating a wide range of response to the rejuvenation methods among years. Spiking reduced grass and legume composition, and increased (P < 0.05) the presence of annual weeds and bare ground. Burning increased (P < 0.05) alfalfa composition in years 2 and 3 at three sites and tended to decrease (P > 0.05) bluegrass composition in all 3 yr. Broadcast and liquid fertilizer, at 200 kg N ha–1 decreased (P < 0.05) the alfalfa component in years 2 at four sites and increased (P < 0.05) the smooth bromegrass component at two sites in year 1 and all sites in years 2 and 3. Fertilizer (granular or liquid) alone or combined with mechanical treatments (deep-band, mow, spike or burn) increased (P < 0.05) the composition of smooth bromegrass and decreased (P < 0.05) the composition of bluegrass, weeds and bare ground variably over 3 yr. Mowing and deep-banding had minimal effects on botanical composition. Key words: Rejuvenation, fertilizer, spike, burn, deep-band, botanical composition
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".