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Record W2002351520 · doi:10.1134/s0013873810020053

Modern forest entomology: Directions of research, problems, and prospects

2010· article· en· W2002351520 on OpenAlexaboutno aff
А. В. Селиховкин, Elena Bondarenko, Б. Г. Поповичев

Bibliographic record

VenueEntomological Review · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEntomologyEcologyKairomonePopulationGovernment (linguistics)Forest ecologyPredationEcosystemSociology

Abstract

fetched live from OpenAlex

Based on analysis of publications, the principal directions of research carried out in the field of forest entomology in Russia and abroad are considered. Most of the foreign papers are published by entomologists from the USA (27%), Sweden (14%), Canada (12%), and Finland (8%), many studies are carried out by international research teams. In all, the publications considered cover over 90 species of forest insects (excluding predators and parasites) that may act as more or less important forest pests. Most of the papers are devoted to species of great economic significance. Most effort in entomological studies carried out in Russia is focused on the role of phytophagous insects in forest ecosystems, their population dynamics and the factors of its regulation, and ecology of selected pest species. In the studies carried out abroad, much more attention is paid to physiology of insects, the problems of communication, looking for pheromones, kairomones, and antifeedants to be used as new agents of plant protection and improvement of methods of their application, and also to interactions between phytophages and their host plants. The problems hindering development of forest entomology in Russia are considered; most of them result from lack of steady financial support of this field of research from the government.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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