Timothy Response to Increasing Rates of Selenium Fertilizer in Eastern Canada
Bibliographic record
Abstract
Selenium is an essential micronutrient given to ruminants by injection or orally or applied as an additive to fertilizers to raise crop Se concentrations in areas with low‐Se soils. We determined the response of timothy (Phleum pratense L.) Se concentration to increasing rates (0, 5, 10, 15, 20, and 25 g Se ha−1) of a slow‐release Se fertilizer (Selcote Ultra) applied in spring of 2010 at three sites in Québec, Canada, and investigated the possibility of predicting forage Se concentration by near‐infrared reflectance spectroscopy (NIRS). The response of timothy Se concentration (y, mg kg−1 dry matter, DM) to increasing Se rates (x) was similar at the three sites; averaged across sites, it increased linearly with increasing Se rates at the first (y = 0.012 + 0.0292x; R2 = 0.98) and second harvests (y = 0.052 + 0.0091x; R2 = 0.96) in the year of spring Se application and in the first harvest of the subsequent year (y = 0.012 + 0.0117x; R2 = 0.97). Selenium fertilization did not affect timothy DM yield, fiber concentration, or digestibility. Timothy Se concentration could not be successfully predicted by NIRS. A spring application of 10 g Se ha−1 as a slow‐release fertilizer and its residual effect are sufficient to produce timothy with an adequate Se concentration (>0.1 mg kg−1 DM) to prevent deficiency diseases in livestock and allow diet formulation to meet optimal Se levels.
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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 | 0.001 | 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".