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Record W4307939063 · doi:10.3390/f13111784

Can Spot Motor-Manual Brushing Treatments Be Effective for Controlling Aspen and Increasing Spruce Growth in Regenerating Mixedwood Stands?

2022· article· en· W4307939063 on OpenAlexaff
Philip G. Comeau

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTriclopyrBark (sound)HorticultureBasal areaEnvironmental scienceBiologyChemical controlEcology

Abstract

fetched live from OpenAlex

Broadcast motor-manual or manual brushing treatments applied to control aspen (Populus tremuloides Michx.) in young spruce (Picea glauca (Moench) Voss) plantations often result in increases in the number of aspen stems and the amount of aspen competition. In this study data collected at three locations is used to examine the potential effectiveness of spot brushing treatments, applied over radii ranging between 1.0 and 2.5 m around individual spruce, to aspen that are two, four or six years old. Results indicate that spot manual or motor-manual treatments result in reductions in the number and size of aspen stump sprouts compared to the untreated control. However, when aspen size and vigour are reduced due to site or other factors, as observed for blocks at one location, post-treatment aspen densities may still be high (e.g., above 5000 stems ha−1). Spot treatments applied when aspen regeneration was two years old were observed to be less effective than treatments applied at ages 4 or 6. At two study locations where control was effective, spot brushing treatments significantly increased spruce diameters after treatment compared to untreated. Broadcast or spot treatment using basal bark application of triclopyr ester (Release®) herbicide at one of the study sites resulted in similar increases in spruce diameter to those observed for motor-manual treatment.

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 score0.022
Threshold uncertainty score0.994

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 studies0.0010.000
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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