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Record W2775497650 · doi:10.1093/njaf/25.3.139

Validation of Spruce Budworm Outbreak History Developed from Aerial Sketch Mapping of Defoliation in New Brunswick

2008· article· en· W2775497650 on OpenAlexaboutno aff
Sarah L. Taylor, David A. MacLean

Bibliographic record

VenueNorthern Journal of Applied Forestry · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSpruce budwormAbies balsameaBalsamChoristoneura fumiferanaForestryDendrochronologyAerial surveyGeographyEnvironmental scienceBiologyEcologyCartographyLepidoptera genitaliaTortricidaeBotanyArchaeology

Abstract

fetched live from OpenAlex

Abstract Aerial sketch mapping (ASM) of annual defoliation provides a means to quantify spruce budworm (Choristoneura fumiferana Clem.) outbreak histories, but accuracy is affected by factors such as navigation and weather conditions. We used ground-based defoliation estimates from 123 permanent sample plots (PSP) in New Brunswick and increment core growth data from a subset of PSPs, to validate ASM estimates of defoliation. From 1985 to 1993, 85% of 332 cases were correctly classified by aerial estimates as nil–light (0–30%) or moderate–severe (31–100%), with the proportion correct varying by measurement year, defoliation severity, and host species. Growth indices generated from 81 visually cross-dated and verified balsam fir (Abies balsamea [L.] Mill.) tree-ring series in 23 PSPs were significantly negatively correlated with aerial-derived cumulative defoliation for 87% of the PSPs, and correlation increased when aerial estimates were combined with ground survey data. We conclude that aerial surveys provide a reasonable estimate of defoliation history to estimate growth reduction.

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)0.003
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 score0.651
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.198
Teacher spread0.183 · 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

Citations24
Published2008
Admission routes1
Has abstractyes

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Same venueNorthern Journal of Applied ForestrySame topicFire effects on ecosystemsFrench-language works237,207