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Record W2017909303 · doi:10.4236/oje.2013.35041

Assessing effects of seed source and transfer potential of white birch populations using transfer functions

2013· article· en· W2017909303 on OpenAlexafffund
Oluwatobi A. Oke, Jian R. Wang

Bibliographic record

VenueOpen Journal of Ecology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsLakehead UniversityUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReforestationGerminationSeedlingBiologyBetula pubescensAgronomyPopulationWhite (mutation)GreenhouseHorticultureBotanyEnvironmental scienceEcologyDemography

Abstract

fetched live from OpenAlex

Trees have adapted to their local climates, but with changes in the climate, they may currently or in the near future occupy climates that are sub-optimal for growth and survival. The goal of current reforestation is therefore to establish a new generation of trees with growth adapted to the future climate(s). Here, we present preliminary data of a study assessing the effects of seed source and transfer potential of white birch populations. Seeds from twenty-five white birch (Betula papyrifera Marsh.) populations collected acrossCanadawere grown in the greenhouse and observed for emergence time, germination and growth. The seedlings were later planted in a common garden. After one year, the seedlings were measured for height, root-collar diameter (RCD) and survival rate and average volume per seedling calculated. Transfer functions were used to estimate the climatic distance from which populations may be transferred to the test site. There was a significant effect of population on all growth variables. Initial height was positively correlated with 1-year height and survival. Germination rate negatively correlated with emergence time. Principal component analysis showed effects of seed origin on performances of the populations in the common garden. Summer temperature was the best predictor of the transfer distance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.473

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 studies0.0000.000
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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