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Record W2949030061 · doi:10.17516/1997-1370-0417

Economic Consequences of Insect Pests Outbreaks in Boreal Forests: A Literature Review

2019· review· en· W2949030061 on OpenAlexaboutno aff
E.D. Ivantsova, Anton I. Pyzhev, Evgeniya V. Zander

Bibliographic record

VenueJournal of Siberian Federal University Humanities & Social Sciences · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsTaigaOutbreakEcologyGeographyBorealDistribution (mathematics)Range (aeronautics)Natural resource economicsBiologyEconomics

Abstract

fetched live from OpenAlex

Outbreaks of pests are considered one of the most destructive types of natural forest disorders. In recent years the severity of such outbreaks has rapidly increased due to the process of global warming, which affects the rate of reproduction of pests and the range of their natural distribution. Economic consequences of these types of disturbances are of particular importance. Though many studies have been conducted in the field of tree phytophages ecology, the issues of estimating economic damage and the formation of mechanisms for its minimization have been poorly studied. This article presents a review of studies on the problem of the harmful effects of forest pests classified by their localization. The area of study includes mainly boreal forests located in a number of European countries, the USA, Canada and Russia. Our study reviews the range of instruments applied to prevent disturbances mentioned above and mitigate corresponding negative consequences. Despite the fact that there are theoretical approaches to the analysis of the economic consequences of forest damage by pests, they still cannot find practical application

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.042
GPT teacher head0.276
Teacher spread0.233 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2019
Admission routes1
Has abstractyes

Explore more

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