MétaCan
Menu
Back to cohort
Record W2094591665 · doi:10.1139/x06-197

Twenty year responses of white spruce to mechanical site preparation and early chemical release in the boreal region of northeastern British Columbia

2006· article· en· W2094591665 on OpenAlexvenueaboutno aff
Jacob O. Boateng, Jean L. Heineman, John McClarnon, Lorne Bedford

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsScarificationPicea abiesBasal areaBorealTaigaForestrySeedlingBiologyAnimal scienceHorticultureEcologyGeography

Abstract

fetched live from OpenAlex

The effects of six mechanical site preparation treatments, two stock-type treatments, and early chemical release on survival and growth of planted white spruce (Picea glauca (Moench) Voss) were studied in the BWBSmw1 biogeoclimatic zone of northeastern British Columbia. After 20 years, spruce height and diameter were larger in all mounding treatments than in the control. Early results suggested better spruce performance on large than small mounds, but after 20 years, growth was equally good on small mounds as on mounds with 20 cm mineral capping. Spruce planted on hinge positions in the Bräcke patch and blade scarification treatments did not survive or grow well. Early chemical release improved spruce growth equally as well as the mounding treatments. Twenty year spruce survival averaged 71% in the 14 and 20 cm mound treatments, 60% in the early chemical release treatment, and ≤35% in the Bräcke patch and blade scarification treatments. A large stock type was also planted in untreated ground and, after 20 years, had similar survival and growth as the standard stock type. Differences in survival had a large effect on basal area at age 20 years. Trend analysis showed that treatments diverged into two distinct groups with regard to spruce size during the 20 year span of the study.

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)0.001
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 score0.570
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations47
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest ecology and managementFrench-language works237,207