Distribution of Public and Private Benefits on Federally Managed Community Pastures in Canada
Bibliographic record
Abstract
Public lands traditionally managed for agricultural purposes are seeing increased usage and value for other uses.The Prairie Farm Rehabilitation Administration (PFRA) managed Community Pasture Program (CPP) in Canada is no exception.Although the program was developed for both conservation and livestock production purposes, there is a realization of CPP lands' value and contribution to other sectors of society.The CPP is a unique grazing land management program in that it provides full care for livestock during the grazing season, and recovers the costs associated with providing grazing and breeding services from those clients.Recognizing that there are multiple users and benefi ts to Canadian society, a study was undertaken to examine the costs and benefi ts associated with these uses, and the relationship to setting grazing and breeding service fees. History of the Community Pasture ProgramPrairie agriculture was severely challenged in the 1930s by rangeland degradation resulting from drought, economic depression, and inappropriate policies for marginal land use.i These events left the lands in the region severely eroded, resulting in a loss of means to provide decent economic returns to the farmers and a good quality of life to their families.Many of the farmers decided to leave the Prairies, particularly in southeastern Alberta and southwestern Distribution of Public and Private Benefi ts on Federally Managed Community Pastures in Canada
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".