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Record W2180861728 · doi:10.1139/cjfr-2015-0092

Influence of climate on the growth of quaking aspen (<i>Populus tremuloides</i>) in Colorado and southern Wyoming

2015· article· en· W2180861728 on OpenAlexvenueno aff
M.M. Dudley, José F. Negrón, Ned Tisserat, Wayne D. Shepperd, William R. Jacobi

Bibliographic record

VenueCanadian Journal of Forest Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersNatural Resources Conservation ServiceNational Oceanic and Atmospheric Administration
KeywordsPrecipitationCrown (dentistry)Bark beetleCanopyEnvironmental scienceCankerForestryWoody plantClimate changeThinningBark (sound)AgronomyGeographyBotanyBiologyEcology

Abstract

fetched live from OpenAlex

We analyzed a series of increment cores collected from 260 adult dominant or co-dominant quaking aspen (Populus tremuloides Michx.) trees from national forests across Colorado and southern Wyoming in 2009 and 2010. Half of the cores were collected from trees in stands with a high amount of crown dieback, and half were from lightly damaged stands. We define the level of stand damage based on stand survey data in which lightly damaged stands had average crown dieback of 16% and heavily damaged stands averaged 41%. Upon analysis, two-thirds of the cores collected did not exhibit radial growth correlated with region-wide patterns (e.g., climate) and were classified as having a low cohesive response. The site variable most predictive of whether a stand exhibited high cohesive response or low cohesive response was site elevation, followed by aspect, slope, and canopy closure. Sites with high cohesive response stands were more likely to have aspen bark beetle damage, white rot, and Cryptosphaeria canker. We did not detect relationships between tree growth and summer precipitation from 1900–2008, but there was a relationship between growth and annual precipitation. A growth model included maximum May and July temperatures, as well as the current and previous year’s annual precipitation.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.581
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.067
GPT teacher head0.297
Teacher spread0.231 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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