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Record W1827980117 · doi:10.22230/jem.2009v10n1a410

Aerial overview survey of the mountain pine beetle epidemic in British Columbia: Communication of impacts

2009· article· en· W1827980117 on OpenAlexafffundabout
Michael A. Wulder, Joanne C. White, Danny Grills, Trisalyn Nelson, Nicholas C. Coops, Tim Ebata

Bibliographic record

VenueJournal of Ecosystems and Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsGovernment of British ColumbiaUniversity of VictoriaNatural Resources CanadaUniversity of British ColumbiaCanadian Forest Service
FundersNatural Resources CanadaU.S. Forest ServiceCanadian Forest ServiceGovernment of Canada
KeywordsMountain pine beetleDendroctonusGeographyInfestationForestryOutbreakEcologyBark beetleBiology

Abstract

fetched live from OpenAlex

In western Canada, the current outbreak of mountain pine beetle (Dendroctonus ponderosae) is of unprecedented proportions. Annual aerial overview surveys (AOS) are the primary means of accounting for the area and severity of mountain pine beetle impacts. Typically, reports of impacted areas do not consider severity—the proportion of trees killed within a given area. A common misconception is that all impacted areas will experience 100% pine mortality.We examined a time series of AOS data collected in British Columbia from 1999 to 2005. The year-toyear trends indicated that the AOS data effectively captured the infestation's increasing area, severity, and spatial variability. The cumulative area impacted between 1999 and 2005 was estimated at 11 million ha; 39% of this area was attacked in only one year. The approximate year of death was estimated by assuming a 50% severity threshold. Approximately 6.5 million ha experienced mortality. The results of this study emphasize the importance of reporting severity, as well as considering the cumulative effects of the infestation over time.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.064
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations32
Published2009
Admission routes3
Has abstractyes

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