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Record W1927191712 · doi:10.22230/jem.2006v7n2a544

Wildlife/danger tree assessment in unharvested stands attacked by mountain pine beetle in the central interior of British Columbia

2006· article· en· W1927191712 on OpenAlexaffabout
Patience Rakochy, Chris Hawkins

Bibliographic record

VenueJournal of Ecosystems and Management · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsWildlifeMountain pine beetleDiameter at breast heightRecreationGeographySnagHectareForestryTaigaBorealEcologyBaseline (sea)HabitatAgroforestryEnvironmental scienceBiologyArchaeologyAgricultureFishery

Abstract

fetched live from OpenAlex

This extension note outlines work that was part of a broader study designed to collect baseline forest structure data in the Sub-Boreal Spruce dry cool biogeoclimatic subzone (SBSdk) of the Lakes Timber Supply Area (TSA). This data will help to assess the future ecological impacts of the mountain pine beetle. A primary component of our research was to determine the safest possible work or recreation window for individuals planning entry into stands killed by the mountain pine beetle. The provincial Wildlife/Danger Tree Assessment criteria were used to determine the types and frequency of danger trees in these stands. Data collected included species, height, diameter at breast height, and the presence or absence of danger tree characteristics for each mature tree. The majority of the trees in this study were classified as either class 1 (alive and healthy) or class 3 (recently dead). One mountain-pine-beetle-killed tree had fallen. Approximately 85 stems per hectare, or 5.5% of all trees, had a defect considered potentially dangerous. Most defects were found in the smaller diameter classes. The study area was significantly affected by mountain pine beetle; areas of high use (e.g., recreation-, cultural-, or work-related) may require specific mitigation activities to ensure user safety.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

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