Bibliographic record
Abstract
Turnip tops or greens, the early leaves of rutabaga (Brassica napus var. rapifera L.), are a traditional Newfoundland vegetable. Commercial farmers currently grow and market forage rape (B. napus L.) as greens. Our objectives were to determine why forage rape is now grown in preference to other Brassica crops and to examine potential greens alternatives. Seed from two cultivars each of three Brassicas [rutabaga, forage rape and forage kale (Brassica oleracea var. medullosa L.)] was used in: 1) a germination study at 5, 10, 15 and 20°C; 2) a growth study at constant temperature regimes of 12 and 18°C; 3) a 2 yr agronomic study; and 4) a sensory evaluation for appearance and taste as a boiled vegetable. Hobson rape, Dwarf Essex rape and the locally bred Brookfield rutabaga germinated, emerged and grew faster than both kale cultivars and Laurentian rutabaga at all controlled-temperature regimes. The two kale cultivars and Laurentian rutabaga did not exhibit adequate agronomic potential. Although the rape cultivars were among the top-yielding entries at most harvests, Brookfield rutabaga yielded greater leaf weight in both years of the agronomic study. Judges preferred the visual appearance of greens with dark green leaves, a characteristic of the forage rape cultivars studied, but favored the taste of boiled kale. Key words: Forage rape, kale, rutabaga, SPAD chlorophyll meter
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".