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Record W2037012782 · doi:10.1504/ijarge.2008.022746

Accounting precautionary measures in agriculture through pathway analysis: the case of the Environmental Farm Plan

2008· article· en· W2037012782 on OpenAlexaffabout
Robert Summers, Ryan Plummer, John FitzGibbon

Bibliographic record

VenueInternational Journal of Agricultural Resources Governance and Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsUniversity of GuelphBrock UniversityUniversity of Alberta
Fundersnot available
KeywordsStewardship (theology)Environmental stewardshipPlan (archaeology)Precautionary principleBusinessAgriculturePrincipal (computer security)Environmental planningRisk analysis (engineering)Environmental resource managementEconomicsComputer scienceEnvironmental sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

Stewardship programs are an important element of agroecosystems management. These programs are confronted with the difficult challenges of demonstrating their positive impacts on the environment. This paper argues that under the precautionary principle, evaluation of stewardship programs should be aimed at identifying risks mitigated, as opposed to actual changes in environmental conditions. The Ontario Environmental Farm Plan Program is presented as a case study. A pathways model is developed and applied to the case. It demonstrates the types of risks mitigated, a way to account for preventative actions, and insights into how future evaluations based on the precautionary principal can be undertaken.

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)0.005
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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

Citations7
Published2008
Admission routes2
Has abstractyes

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