The Effect of Grazing on Etiolated Regrowth in Eight Grass Species
Bibliographic record
Abstract
Grazing by herbivores affects grass species both morphologically and physiologically. A grazing study was conducted on an irrigated pasture near Outlook, Saskatchewan, Canada during the summers of 1991 and 1992 to determine etiolated regrowth after grazing of reed canarygrass (Phalaris arundinacea L.), slender wheatgrass (Elymus trachycaulus (Link) Gould ex Shinners subsp. trachycaulus), intermediate wheatgrass (Thinopyrum intermedium (Host) Barkw. & D.R. Dewey subsp. intermedium), orchardgrass (Dactylis glomerata L.), meadow bromegrass (Bromus beibersteinii Roem. & Schult.), smooth bromegrass (Bromus inermis Leyss.), tall fescue (Festuca arundinacea (Schreb.) Wimm.) and timothy (Phleum pratense L.) in order to evaluate the suitability of these species for grazing. All eight species were mob-grazed by sheep at a stocking rate of 30 animals ha-1. Carbohydrate reserves were estimated by determination of etiolated regrowth. Meadow bromegrass produced the greatest etiolated regrowth and timothy the least. Based on production of etiolated regrowth the grasses were ranked into three groups - meadow bromegrass and intermediate wheatgrass ranked highest, smooth bromegrass, reed canarygrass, slender wheatgrass and orchardgrass ranked intermediate and timothy and tall fescue ranked lowest.
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 | 0.000 | 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) | 0.000 | 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".