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Record W2078417522 · doi:10.1139/x09-059

Current and future trends in juvenile wood density for coastal Douglas-fir

2009· article· en· W2078417522 on OpenAlexaffvenueabout
Michael Stoehr, Nicholas K. Ukrainetz, L.K. Hayton, Alvin D. Yanchuk

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsGovernment of British Columbia
Fundersnot available
KeywordsDouglas firRange (aeronautics)HadCM3Physical geographyEnvironmental sciencePrecipitationForestryClimate changeEcologyLinear regressionGeographyClimatologyBiologyMeteorologyStatisticsMathematicsGeologyGeneral Circulation Model

Abstract

fetched live from OpenAlex

Increment cores from 10 full-sib families in each of three planting series were collected on 22 test sites per series (a total of 7063 samples across 63 sites). Juvenile wood density for individual test sites ranged from 0.378 to 0.481. Stepwise multiple linear regression analysis with wood density as the dependent variable and a battery of annual and monthly climate variables as independent variables was used to model the current distribution of wood density across the landscape in coastal British Columbia. Differences in the average temperature between the coldest month and the warmest month, precipitation in July, and the mean annual precipitation were the only significant variables predicting wood density, accounting for 47% of the total variation across all sites. Using two future climate change models (CGCM2 A2x and HADCM3 A2x) to predict changes in the three climate variables, wood density was mapped. Wood density will be reduced generally in the present range of coastal Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii ), especially on southern Vancouver Island and along the coastlines of southern British Columbia. This may have implications for the future utility of Douglas-fir as a structural wood species, as well as for breeding and deployment.

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)0.000
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 score0.853
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.315
Teacher spread0.271 · 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

Citations15
Published2009
Admission routes3
Has abstractyes

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