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Record W2317635695 · doi:10.5558/tfc2014-097

Effects of aerial strip spraying on mixedwood stand structure and tree growth

2014· article· en· W2317635695 on OpenAlexafffundvenue
Philip G. Comeau

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of Alberta
FundersForest Resource Improvement Association of Alberta
KeywordsSTRIPSBuffer stripUnderstorySilvicultureYield (engineering)TriclopyrGrowing seasonForest healthEnvironmental scienceHorticultureAgronomyForestryMathematicsBotanyGlyphosateBiologyAgroforestryGeographySurface runoffEcologyMaterials science

Abstract

fetched live from OpenAlex

Herbicide application in strips offers a cost-effective way to accelerate spruce growth in young mixedwood stands, while also retaining the mixedwood character of the stand. In June of 2006 data were collected to evaluate the effects of aerial strip spraying on subsequent growth of treated stands. These blocks had been planted in 1991 and treated in 2000 with Triclopyr ester herbicide applied in strips. Treated and untreated strips averaged 5.8 m and 8.4 m in width, respectively, with 38% of the block area being effectively treated. In 2006 aspen density, DBH, and height were significantly lower in the treated strips compared to either untreated strips or completely untreated blocks. Spruce growth was significantly larger in treated strips with diameter growth responses beginning in the first and height growth responses in the second growing season following treatment. Yield estimates from the Mixedwood Growth Model (MGM) indicate that the strip spray treatments result in the highest spruce yields. However, when understory protection (harvesting aspen at age 70 and spruce at 120) is applied, the untreated blocks provided higher total yield and net present value.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.268

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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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

Citations11
Published2014
Admission routes3
Has abstractyes

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