Yields, Quality and Suitability of Four Annual Forages for Deer Pasture in North Central Alberta
Bibliographic record
Abstract
Field pea (Pisum sativum), canola (Brassica napus), turnips (Brassica rapa) and Berseem clover (Trifolium alexandrinum) were grown in north central Alberta to assess their potential yield and quality and to evaluate their utilization and preference by white-tailed deer. Herbage DM yield, crude protein (CP), neutral detergent fiber (NDF), absolute and relative herbage utilization, and dietary preference were the criteria used. The four annual forages produced over 3, 000 to 11, 000 kg ha-1 DM with canola and turnips producing higher DM yields than field pea and berseem clover. Forage CP for the four seeded forages ranged from 140 to 305 g kg-1 DM with canola and turnips having higher CP concentrations than field pea and berseem clover. NDF concentrations ranged from 246 to 480 g kg-1 DM and were lowest in turnips, intermediate in field pea, and highest in canola and berseem clover. Absolute herbage utilization remained similar (P>0.05) among the four forage species. In contrast, relative herbage utilization was greater from berseem clover (66% DM) than field pea (42% DM) or canola (22% DM) or turnips (20% DM). Differences in dietary preference occurred among the four forages (berseem clover ≥ field pea ≥ turnips ≥ canola). These results suggest that annual forages, especially berseem clover and field pea, show promise for increasing forage yield and quality and suitability for late season grazing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".