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Distribution of Public and Private Benefits on Federally Managed Community Pastures in Canada

2008· article· en· W4253924460 on OpenAlexfundaboutno aff
Suren Kulshreshtha, George D. Pearson, Brant Kirychuk

Bibliographic record

VenueRangelands · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsDistribution (mathematics)RangelandBusinessAgricultural economicsGeographyEnvironmental protectionAgroforestryEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.192
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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