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Record W2492162719 · doi:10.14288/1.0096325

The effects of slashburning on the growth and nutrition of young Douglas-fir plantations in some dry, salal-dominated ecosystems

2010· article· en· W2492162719 on OpenAlexaboutno aff
Robert E. Vihnanek

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemDouglas firAgroforestryEnvironmental scienceForestryGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Twenty Douglas-fir plantations, ranging from 5 to 15 years old, were examined on the east side of Vancouver Island. In all areas studied, salal was the dominant ground cover, and was suspected of being a major competitor with trees for water and nutrients. In each plantation, part of the area has been burned and part was unburned. Stocking of planted Douglas-firs was found to be greater on the burned than on the unburned areas of 16 sites and height growth of planted Douglas-firs was greater on the burned than on the unburned areas of 18 sites. Some degree of nitrogen deficiency was inferred for 17 sites, but was not attributed to burning. Height and percent cover of salal was greater on unburned areas. Differences in height growth and percent cover of salal between burned and unburned areas were seen to be greatest where inferred burn severity was high. Browsing of Douglas-fir was more prevalent on burned areas but did not result in height growth being less than on adjacent unburned areas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.951
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.146
Teacher spread0.144 · 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 source (direct Gemma or distilled Codex), 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
Published2010
Admission routes1
Has abstractyes

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