Potassium Accumulation in Perennial Cool-Season Grass Forage
Bibliographic record
Abstract
Perennial grasses are well adapted to the northern U.S.A. and Canada, but the potassium (K) content of grass forage is a major concern in regard to dairy cattle nutrition. Our goal was to identify factors influencing potassium content of grasses, so that a grass management strategy for controlling potassium content could be formulated. Separate experiments were conducted with several perennial coolseason grass species and varieties. Nitrogen fertilization and harvest management also were evaluated. Aside from the obvious positive effect on K concentration caused by commercial K fertilizer or by animal manure application, K concentration was influenced by grass species, grass maturity, time of season and N fertilization. Orchardgrass (Dactylis glomerata L.) was consistently high in K content, while timothy (Phleum pratense L.) and smooth bromegrass (Bromus inermis Leyss.) were up to 10 g kg-1 lower in K concentration. Reed canarygrass (Phalaris arundinacea L.) remained high in K content until inflorescence emergence and then declined. Grass regrowth was consistently lower in K content than primary spring growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".