Bibliographic record
Abstract
Herbage mass estimation is a crucial tool for producers to increase pasture efficiency. Increasing pasture use efficiency through improved grazing management practices is critical for effective livestock production and results in both economic and environmental benefits. Herbage mass (HM) can be determined through the use of the rising plate meter (RPM), a tool that estimates biomass within pastures. The RPM has been used in New Zealand and Ireland, places with simple pastures (composed of 3 or less species), and the RPM is well calibrated for these areas. In Ontario, complex mixed-species perennial pastures (composed of 3 or more species) are common and to date, the RPM has not been calibrated under these conditions. This study will calibrate the RPM for mixed-species perennial pastures in Ontario. Specifically, this project addresses the question: is the RPM able to accurately estimate HM in mixed-species perennial pastures? For this study, RPM compressed sward measurements were collected every 7 to 14 days in four locations across Ontario. Corresponding dry matter estimates using quadrats were also obtained at each site. Preliminary linear regression analysis using data from the 2021 growing season shows a positive relationship between HM and compressed sward height measurements using the RPM. It is expected that the linear regressions will have strong R squared values and equations will be significantly different amongst sites and seasons. The results of this study will provide livestock producers with a simple method that will lead to increased pasture use efficiencies. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance (Special Initiatives Program)
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".