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Record W4404635377 · doi:10.1101/2024.11.21.624504

Modeling Mountain Pine Beetle Abundance in Novel Hosts

2024· preprint· en· W4404635377 on OpenAlexaffabout
Xiaoqi Xie, Micah Brush, Evan C. Johnson, Catherine I. Cullingham, Mark A. Lewis

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCarleton UniversityUniversity of VictoriaUniversity of Alberta
Fundersnot available
KeywordsMountain pine beetleRange (aeronautics)TaigaAbundance (ecology)EcologyBorealPinus contortaPine barrensPine forestHost (biology)Jack pineBiologyEnvironmental scienceForestryPinus <genus>GeographyBotany

Abstract

fetched live from OpenAlex

Abstract The mountain pine beetle has recently expanded its range into northern and central Alberta, posing an immediate threat to the novel host, jack pine. To date, some experiments suggest that jack pine has limited defensive capabilities despite of the restrictions from the physical environment. In this work, we explore the susceptibility of jack pine compared to the primary host, lodgepole pine, evaluating the risk of potential range expansion into Canada’s boreal forest. We employ a hierarchical model, incorporating environmental and ecological covariates, to examine mountain pine beetle dynamics in a pine forest with lodgepole, hybrid and jack pines. Our results show that pine species significantly influence the probability of being killed, with jack pine being less likely to be infested than lodgepole pine, all else being equal. The hierarchical model demonstrates that beetles perform poorer in jack pine, characterized by a reproduction rate 0.16 times that of non-jack pine. Although jack pine is a suitable host, our results indicate that the number of infestations in jack pine could be lower than in lodgepole pine, with a reduced probability of emerging beetles.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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