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Is Geographical Targeting Cost-Effective? The Case of the Conservation Reserve Enhancement Program in Illinois

2005· article· en· W2041144461 on OpenAlex
Wanhong Yang, Madhu Khanna, Richard L. Farnsworth, Hayri Önal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReview of Agricultural Economics · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Guelph
FundersUniversity of GuelphFarm Service AgencyUniversity of New South WalesIllinois Department of Natural ResourcesU.S. Department of Agriculture
KeywordsConservation Reserve ProgramNatural resource economicsEconomicsNature reserveBusinessGeographyArchaeology

Abstract

fetched live from OpenAlex

This paper uses economic, hydrologic, and GIS modeling to assess the effectiveness of the Illinois Conservation Reserve Enhancement Program in the Lower Sangamon watershed. Our results show that for a representative five-year storm event, the acres currently enrolled in the program result in a 12% reduction in sediment loading, which is below the program goal of 20% and four times the least-cost solution. We also analyze the design of alternative rental payment instruments for improving the cost-effectiveness of geographical targeting for land retirement. Policy implications for the characteristics of the land parcels that should be targeted for enrollment in the program are discussed.

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.

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)0.001
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 score0.215
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.049
GPT teacher head0.253
Teacher spread0.203 · 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