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Record W2048286529 · doi:10.5558/tfc77667-4

Economic impact of the 1998 ice storm on the eastern Ontario maple syrup industry

2001· article· en· W2048286529 on OpenAlexafffundvenueabout
Jennifer Kidon, Glenn Fox, Daniel W. McKenney, Kimberly Rollins

Bibliographic record

VenueThe Forestry Chronicle · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversity of GuelphCanadian Chiropractic AssociationOntario Forest Research Institute
FundersGovernment of CanadaGovernment of Ontario
KeywordsDamagesBaseline (sea)StormNatural disasterGovernment (linguistics)Economic impact analysisAgricultural economicsEconomicsBusinessGeographyMeteorologyPolitical science

Abstract

fetched live from OpenAlex

Partial equilibrium analysis was used to characterize the economic impact of the 1998 ice storm on the maple syrup industry in Eastern Ontario. Stochastic simulation is used to generate interval estimates of damages. We compare government expenditures on assistance programs to the estimated industry losses. Total losses to the industry in terms of changes in producers' surplus and capital losses for the baseline scenario were estimated to be $5.5 (± $0.5) million. Extensive sensitivity analysis was conducted. Losses were estimated to be $4.7 (± $0.4) million if recovery takes place faster than in the baseline scenario. Losses were estimated to be $7.1 (± $0.4) million when recovery takes longer than in the baseline case. Direct government expenditures on ice storm assistance programs to this industry were approximately $7.3 million. The relevance of this type of analysis to the development of natural disaster relief policies is discussed. Key words: disaster assistance, eastern Ontario, economic, ice storm, maple syrup, producers' surplus

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)0.000
metaresearch head score (Gemma)0.001
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 score0.203
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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

Citations9
Published2001
Admission routes4
Has abstractyes

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