PSLBI-2 Alberta lamb producers sheep/goat vegetation management accreditation course
Bibliographic record
Abstract
Abstract Sheep and goats present land stewards with a unique opportunity to graze areas that cattle cannot access and by consuming plants that cattle find unpalatable. While Alberta has a thriving sheep industry and ranks third in Canadian sheep and lamb inventories, producers continue to seek new opportunities to improve land stewardship and better understand grazing animal management. Resources are limited for flock managers that provide the details necessary to successfully graze sheep or goats and maintain forage health under typical and novel circumstances. These typical and uncommon grazing situations include invasive weeds, solar farms, low-quality marginal lands, forests, inner city vegetation control, and powerline cut blocks. In partnership with Lakeland College, the Alberta Lamb Producers and the Alberta Goat Association obtained funding from Alberta Environment and Parks through their Rangeland Sustainability Program to develop a Sheep/Goat Vegetation Management Accreditation Course. In consultation with some knowledgeable Alberta sheep and goat producers, summer students developed a series of vegetation management modules for Alberta sheep and goat producers to learn best management practices for grazing forages under typical and unique situations. These modules form a vegetation management accreditation program. The modules include 1) Grazing Principles; 2) Grazing Forages – native and tame; 3) Stockmanship and Animal Welfare; 4) Building a Business Case; and 5) Unique Grazing – under solar panels, powerline cut blocks, meeting landscape goals and controlling invasive species, forest grazing, and urban grazing. The course will be offered through Lakeland College in the D2L continuing education platform after a group of sheep/goat producers have gone through the course and provided their feedback. The D2L platform will monitor the number of producers and landowners who complete the course to determine the effectiveness of the project. The modules will educate flock owners and serve as a go-to resource for comprehensive grazing and sheep/goat management under specific situations. Stakeholders requiring or offering vegetation management services using livestock would benefit from these resources for contract and partnership development purposes. The project engages land and animal stakeholders to balance and show both perspectives and needs related to vegetation management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.340 | 0.120 |
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".