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Record W2064289138 · doi:10.1139/x06-305

The influence of white pine blister rust on seed dispersal in whitebark pine

2007· article· en· W2064289138 on OpenAlexvenueno aff
Shawn T. McKinney, Diana F. Tomback

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersRocky Mountain Research StationUniversity of Colorado DenverNational Park Service
KeywordsBiologyBiological dispersalSeed dispersalRust (programming language)Seed predationSowingEcologyBotanyPopulation

Abstract

fetched live from OpenAlex

We tested the hypotheses that white pine blister rust ( Cronartium ribicola J.C. Fisch.) damage in whitebark pine ( Pinus albicaulis Engelm.) stands leads to reduced (1) seed cone density, (2) predispersal seed survival, and (3) likelihood of Clark’s Nutcracker ( Nucifraga columbiana (Wilson, 1811)) seed dispersal. We gathered data from two sets of paired forest sites in the Bitterroot Mountains of eastern Idaho and western Montana that were similar in topography, canopy structure, tree species composition, and successional stage, but differed in rust infection level, crown kill, and tree mortality. We counted initial (mid-July) and final (late August) seed cones, observed vertebrate seed predator activity, and documented nutcracker seed dispersal in study sites in 2001 and 2002. High-rust sites had higher rates of seed predation relative to cone abundance, lower predispersal seed survival, and fewer observations of nutcracker seed dispersal than paired low-rust sites. These findings suggest that as blister-rust-induced damage increases within stands in the Bitterroot Mountains, the likelihood of nutcracker seed dispersal decreases. We propose that whitebark pine in heavily rust-damaged forests may not self-regenerate and would therefore require planting of seeds or seedlings from genetically rust-resistant trees.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.783
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.271
Teacher spread0.235 · 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.

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

Citations72
Published2007
Admission routes1
Has abstractyes

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