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Record W1983477683 · doi:10.3390/f3020230

Effects of Low Levels of Dispersed Retention on the Growth and Survival of Young, Planted Douglas-Fir

2012· article· en· W1983477683 on OpenAlexafffundabout
Nicholas Smith, William J. Beese

Bibliographic record

VenueForests · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsVancouver Island University
FundersMinistry of Forests, Lands and Natural Resource OperationsOregon State University
KeywordsBasal areaDouglas firAnimal scienceRetention rateSowingRetention timeHorticultureGrowth rateBiologyMathematicsEcologyBotanyChemistryGeometry

Abstract

fetched live from OpenAlex

Three large-scale, experimental, dispersed residual tree sites established in coastal British Columbia, Canada were measured for planted Douglas-fir tree growth and survival five to six years after planting. The dispersed trees were predominantly large diameter (60 cm+) Douglas-fir left with a range of 0% to 30% of the original forest stand basal area (0 m2 ha−1 to 14 m2 ha−1). Two sites had 0%, 5% and 15% retention, while one site had 0%, 5%, 10% and 30% retention. The trees were measured in sector plots established to randomly sample the range of microsites in each treatment. There was no detectable difference between height and basal diameter growth or mortality rates between the retention treatments over the measurement period, except for a reduction of basal diameter growth at the 30% retention level (p < 0.05). Thus a statistically significant impact on growth was demonstrated for the 30% retention compared to the 0% retention level. We expected intermediate growth rates between the 0% and the other lower retention levels but were unable to demonstrate this due to the low statistical power of the test (10 observations) and high site variability for these very young 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)0.000
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 score0.030
Threshold uncertainty score0.169

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 head0.009
GPT teacher head0.204
Teacher spread0.196 · 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

Citations6
Published2012
Admission routes3
Has abstractyes

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