Temperate grass response to extent and timing of grazing
Bibliographic record
Abstract
Alber, N. B., Brink, G. E. and Jackson, R. D. 2014. Temperate grass response to extent and timing of grazing. Can. J. Plant Sci. 94: 827–833. Considerable differences exist among cool-season grass species in their production potential and response to management variables. We examined the effects of grazing management on forage and root production of two temperate perennial grasses, meadow fescue [Schedonorus pratensis (Huds.) P. Beauv.] and orchardgrass (Dactylis glomerata L.). Grazing factors studied were extent of defoliation (50 or 100% biomass removal) and stage of maturity (vegetative or mature) at grazing. In 2009 and 2010, orchardgrass produced more above-ground biomass than meadow fescue despite yearly precipitation differences. In the drier year (2009), both grasses produced greater above-ground biomass under 100% extent of defoliation at either maturity stage. In 2010, orchardgrass produced greater above-ground biomass when grazed at a mature stage for either extent of defoliation, while few differences existed among grazing treatments imposed on meadow fescue. Grazing treatments had no effect on below-ground growth of orchardgrass either year. Meadow fescue root production was effected in 2010 only; grazing at a mature stage increased below-ground growth for either extent of defoliation. Results suggest that grazing at maturity to remove 100% of biomass maximizes above-ground production of both meadow fescue and orchardgrass, but lengthens the grazing interval and may have a deleterious effect on grass persistence and nutritive value.
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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 | 0.000 | 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".