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Record W63398390

Valuing old-growth forests and related qualities in southwest mainland British Columbia using contingent choice

2009· dissertation· en· W63398390 on OpenAlexaboutno aff
Philip G. Bradshaw

Bibliographic record

VenueSummit (Simon Fraser University) · 2009
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsMainlandGeographyForestryMainland ChinaArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The Northern Spotted Owl is an endangered species that requires old-growth forests in southwest mainland British Columbia for its survival. Policies to save the owl focus on habitat preservation and include trade-offs between removing old-growth forests from timber harvesting allocations, and preservation of an animal with no direct market value. The majority of forests are public land and determining the proper trade-offs requires knowledge of the general public’s preferences. Existence values for the Spotted Owl may be confounded with their habitat (i.e. old-growth forests), or other old-growth dependent species at risk, where the old-growth forests can exist without these species, but not vice versa. This study uses a stated preference, multi-attribute, trade-off approach to measure these values separately, but in the context of each other. Risk (success of conservation) is also included into the valuation. The study surveyed the general public of the Lower Mainland using a web-based format.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.198
Teacher spread0.168 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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