Assessment of forage species and varieties for the central interior of British Columbia
Bibliographic record
Abstract
1982 experiments were conducted to examine several aspects of forage quality in relation to animal nutrition, including the differences in quality between forage types (legumes or grasses), species and varieties; between years; between two hay mixes; and between three harvest dates. In addition, the importance of quality relative to yield is examined. In the first experiment, acid detergent fibre, neutral detergent fibre, crude protein, and nylon bag dry matter disappearance determinations were used to assess the variation in quality between forage types, species and varieties, and between years. In the second, voluntary dry matter intake and digestibility results were used to assess the variation in quality between hay mixes and harvest dates. The results of the first experiment indicate that the legumes were of higher quality than the grasses; red and alsike clover were of higher quality than alfalfa, and orchardgrass was of higher quality than timothy. With the exception of red clovers, where Lakeland and Pacific varieties were of higher quality than Altaswede, there was little difference in quality between varieties within a species. Neutral detergent fibre analysis results suggest a difference in intake between forages grown in different years while acid detergent fibre analysis results indicate no difference in digestibility would be expected between years. The results of the second experiment indicate there was a difference in quality between forage mixtures (the early maturing mixture was best), and harvests (early and mid bloom harvests were better than the late bloom harvest). The parameter with the largest variability was yield. Differences were greater between years than between types and species (the clovers highest, alfalfa and timothy intermediate, and orchardgrass lowest) with the least variation occurring between varieties within species. The red clover-timothy (late maturing) forage mixture was the highest yielding. Within forage mixtures the full bloom harvest (100% bloom of the legume component) had the highest yields. Since yield was more variable than the quality parameters studied, it was concluded that the most important consideration when selecting a forage mixture was yield. Since there tended to be little difference in quality parameters between varieties within a species, selecting the highest yielding combination would provide the largest amount of useable nutrients per hectare of land base.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".