Bibliographic record
Abstract
The importance of native shrubs in the Northern Mixed Prairie of Canada has generally been overlooked; however, restoration specialists have recognized the importance of including shrubs in prairie restorations. Emergence and establishment of winterfat (Krascheninnikovia lanata (Pursh) A. D. J. Meeuse & Smit), a palatable and long-lived shrub, was evaluated in relation to planting time and seedbed preparation in swards of native grasses that had been seeded on previously cultivated cropland in the prairie ecozone of southern Saskatchewan. Diaspores of winterfat were broadcast at 20 m-2 in autumn or spring on upland and lowland sites in seedbed treatments including (1) a control or undisturbed sward, (2) mowing the sward to a 15-cm height, (3) haying, (4) glyphosate application after haying and, (5) tillage. Emergence of winterfat on upland and lowland sites was about three-fold greater with autumn than with spring planting (P < 0.01). Winterfat establishment on upland sites was affected by the interacting influences of planting times and seedbed treatments (P = 0.01), with most winterfat establishing from autumn sowing in the glyphosate and tillage treatments (2.1 and 2.2 plants m-2, respectively). Establishment of 0.9 plants m-2 from autumn planting was greater (P < 0.01) than the 0.1 plants m-2 establishing from spring sowing on lowland sites. The interaction of planting date and seedbed treatment on lowland sites did not influence winterfat establishment (P = 0.06) nor did seedbed treatments (P = 0.07). Winterfat should be planted in late autumn as opposed to spring. Key words: Ceratoides lanata, Eurotia lanata, Krascheninnikovia lanata, Northern Mixed Prairie, restoration
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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 | 0.001 | 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".