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Record W2071835348 · doi:10.1139/x99-213

Regional forest resource accounting: a northern Alberta case study

2000· article· en· W2071835348 on OpenAlexfundvenueaboutno aff
Michel K. Haener, Wiktor Adamowicz

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersU.S. Forest ServiceAlberta-Pacific Forest Industries
KeywordsNonmarket forcesRecreationResource (disambiguation)SustainabilityContext (archaeology)Forest managementEnvironmental resource managementBusinessFishingGeographyNatural resource economicsForestryEnvironmental scienceEcologyEconomics

Abstract

fetched live from OpenAlex

This study outlines the development of a resource accounting system for a region of public forestland in northern Alberta. The purpose of this exercise is to provide a clearer picture of the market and nonmarket benefits provided by the forest. The services valued include commercial activities such as forestry, trapping, and fishing plus noncommercial or nonmarket activities. Nonmarket services include recreational activities (fishing, hunting, and camping), subsistence resource use, and environmental control services (carbon sequestration and biodiversity maintenance). The case study provides the basis for future estimates that when tracked over time can provide information regarding the sustainability of income flows from the region. Many of the complexities of resource accounting at this finer resolution parallel those of resource accounting at the national level. The case study also illustrates constraints and challenges unique to the regional and forestry context.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.163
GPT teacher head0.278
Teacher spread0.115 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2000
Admission routes3
Has abstractyes

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