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Record W1818855069 · doi:10.22230/jem.2006v7n1a507

Arrow IFPA Series: Note 8 of 8: Criterion 9: Quality-of-life indicators

2006· article· en· W1818855069 on OpenAlexafffund
Michael J. Meltner, Howard W. Harshaw, Stephen R.J. Sheppard, Paul Picard

Bibliographic record

VenueJournal of Ecosystems and Management · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsRecreationSustainable forest managementEnvironmental resource managementForest managementUnit (ring theory)Quality (philosophy)Sustainable developmentEnvironmental economicsBusinessArrowSustainable managementEnvironmental planningComputer scienceGeographyEnvironmental scienceEconomicsSustainabilityForestryEcologyMathematics

Abstract

fetched live from OpenAlex

This extension note is the eighth in a series of eight that describes a set of tools and processes developed to support sustainable forest management planning and its pilot application in the Arrow Timber Supply Area (TSA). It summarizes the criterion and indicators used to evaluate quality-of-life opportunities for the sustainable forest management (SFM) pilot basecase analysis of the Lemon Landscape Unit. The management of forests has broadened to include various social values and amenities that were considered during the development of criteria and indicators for the Arrow Innovative Forestry Practices Agreement. The quality-of-life criterion was assessed through indicators that addressed outdoor recreation opportunities and visual quality of the managed landscape. This assessment was informed by public input from area residents and stakeholders. Measurable quality-of-life indicators allowed trade-offs with other resources in the SFM pilot basecase analysis; protection of these quality-of-life values did not overly constrain other values modelled in the project.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.409

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2006
Admission routes2
Has abstractyes

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