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Record W2082750213 · doi:10.5558/tfc83806-6

Forest management in a changing climate: building the environmental information base for southwest Yukon

2007· article· en· W2082750213 on OpenAlexaffvenueabout
Aynslie Ogden

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsYukon Department of EnvironmentYukon Department of Tourism and CultureYukon Health and Social ServicesYukon University
Fundersnot available
KeywordsClimate changeEnvironmental resource managementBaseline (sea)Forest managementGeographyEnvironmental planningEnvironmental scienceEcologyForestryPolitical science

Abstract

fetched live from OpenAlex

This paper provides an overview of a project that synthesized available information on climate change for the southwest Yukon. This was done as a first step in a longer-term process of evaluating climate impacts, assessing risks to ecosystem and community values, and developing scenarios for adaptation. The overall intent of the work was to support informed forest management decision-making for the Champagne-Aishihik Traditional Territory (CATT) in the light of climate change. The objectives of this stage of the project were to: compile and improve access to existing baseline information needed to support informed management decisions in the face of climate change; to make this information available using several communication tools for various target audiences; and to create an opportunity for scientists, government; and local residents to share observations and concerns on climate change as related to the management of forest resources within the study region. Key words: climate change, impacts, adaptation, sustainable forest management, southwest Yukon, Dendroctonus rufipennis, spruce bark beetle, Yukon Territory, champagne and Aishihik Traditional Territory

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.631

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 studies0.0010.000
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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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

Citations12
Published2007
Admission routes3
Has abstractyes

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