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Record W2067870029 · doi:10.1139/x03-133

Strategic reserve design in the central coast of British Columbia: integrating ecological and industrial goals

2003· article· en· W2067870029 on OpenAlexfundvenueaboutno aff
Emily K. Gonzales, Peter Arcese, Rueben J. Schulz, Fred L. Bunnell

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStakeholderNature reserveEnvironmental resource managementProtected areaBusinessMarine reserveEcologyEnvironmental planningComputer scienceGeographyEnvironmental scienceEconomicsHabitat

Abstract

fetched live from OpenAlex

Few conservation reserves worldwide have been created in ways that are likely to promote the persistence of species, ecosystems, and ecological processes regarded as being representative of biological diversity. We demonstrate the application of newer approaches to systematic reserve design that could help stakeholders find designs that maximize simultaneously ecological, societal, and industrial goals. We created example reserve designs using the simulated annealing algorithm of SITES 1.0 and then contrasted these designs with a proposed reserve design negotiated as a multi stakeholder process for British Columbia's central coast. Our strategic approach recommended reserve designs that included greater proportions of key conservation elements identified by stakeholders without increasing the land area or timber volume in reserves currently under consideration for protection. Our examples demonstrate that strategic approaches to reserve design can facilitate the repeatable and efficient allocation of land to conservation and development and, therefore, represent an improvement on ad hoc methods. Readily available software facilitates the exploration of alternative conservation and societal values, incorporate the interests of multiple stakeholders, and provides a focus and catalyst for discussion at the planning table.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.282
Teacher spread0.178 · 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 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

Citations24
Published2003
Admission routes3
Has abstractyes

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