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Record W3125967539

An Assessment Of The Proposed New Risk Management Programs

2003· article· en· W3125967539 on OpenAlexaboutno aff
Larry J. Martin, Al Mussell, Nancy Brown Andison, Harry Stoddart, Lloyd Davenport

Bibliographic record

VenueMiscellaneous Publications · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsCrop insuranceMandateRisk managementBusinessContext (archaeology)PaymentProduction (economics)Scope (computer science)Risk poolAgribusinessAgricultureActuarial scienceRisk analysis (engineering)FinanceInsurance policyEconomicsComputer scienceKey person insurance
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this assessment as outlined in the terms of reference is: "To obtain an assessment by an independent third party of the expected performance of the proposed new business risk management program's proposed New NISA and production insurance relative to the current set of risk management programming, including NISA, CFIP, crop insurance and companion programs." Within this context, the specific mandate and scope is to assess "the extent to which the current and proposed programs meet the objectives set out by Agriculture Ministers for business risk management programming, as follows: · to ensure programs are responsive to demand and that government dollars are directed to areas of need with respect to income stabilization, disaster mitigation, insurance coverage and investment; · to provide equal treatment for farmers across Canada facing similar risk situations; · to minimize the distortion of farmers' production and marketing decisions; · to focus on management of risks related to the stability of the entire farm and to avoid duplication of payments; · to be relatively simple and easy to understand; and · to facilitate long term planning by farmers."

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0040.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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
Published2003
Admission routes1
Has abstractyes

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