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Record W2941039511 · doi:10.1139/cjfr-2018-0428

Spatial and genetic structure of the lodgepole × jack pine hybrid zone

2019· article· en· W2941039511 on OpenAlexafffundvenueabout
Ian G. Burns, Patrick M. A. James, David W. Coltman, Catherine I. Cullingham

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalUniversity of Alberta
FundersU.S. Forest ServiceMinistry of EnvironmentMinistry of Environment - SaskatchewanCanadian Forest ServiceOntario Ministry of Natural Resources and ForestryParks CanadafRI ResearchAlberta Agriculture and ForestryNatural Resources CanadaMinistry of Natural Resources
KeywordsMountain pine beetlePinus contortaIntrogressionDendroctonusHybrid zoneJack pineEcologyHybridBiologyAkaike information criterionGeographyGenetic variationPinus <genus>Bark beetleBotanyStatisticsGene flow

Abstract

fetched live from OpenAlex

In north-central Alberta, lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia) and jack pine (Pinus banksiana Lamb.) form a mosaic hybrid zone, the spatial extent of which remains poorly defined. We sought to refine the genetic and geographic distribution of this hybrid zone in western North America to provide information important in predicting future risk of mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreaks. We used 29 single nucleotide polymorphism (SNP) markers to discriminate lodgepole pine, jack pine, and their hybrids. We compared and contrasted spatial patterns of hybridization in northern and southern forest zones based on the colonization history of the two species. We found that patterns of introgression were more similar between the zones than expected by chance, but there were significant differences between these regions at specific loci. Using logistic regression, we created a robust predictive model to distinguish among lodgepole pine, jack pine, and their hybrids using a combination of geographic and environmental predictors. Using model selection based on Akaike information criterion, we found that location, elevation, and moisture are important predictors for species class. Quantification of the genetic differences between these two regions, combined with an accurate model for predicting the spatial distribution of lodgepole pine, jack pine, and their hybrids, provides essential information for continued effective management of forest resources.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.415
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.237
Teacher spread0.227 · 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

Citations23
Published2019
Admission routes4
Has abstractyes

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