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Record W2036052693 · doi:10.5558/tfc82819-6

Linking juvenile growth of white spruce with site index

2006· article· en· W2036052693 on OpenAlexaffvenueabout
Zhili Feng, Kenneth J. Stadt, Victor J. Lieffers, Shongming Huang

Bibliographic record

VenueThe Forestry Chronicle · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsAlberta Environment and Protected AreasUniversity of Alberta
Fundersnot available
KeywordsSite indexJuvenileDominance (genetics)Diameter at breast heightForestryGeographyMathematicsEcologyBiology

Abstract

fetched live from OpenAlex

The goal of this study was to link the growth of juvenile white spruce stands with an estimate of their site index. We applied a previously developed technique for correcting the height bias created by dominance switching among juvenile trees before trees reached the site index base age (50 years at breast height), using stem analysis data of white spruce from five mixedwood stands in central Alberta. For white spruce of approximately 15 years total age, we found the height of the current top height trees was approximately 14% greater than the height of the top height trees that would be selected close to breast height age 50. This height correction is essential to avoid an overestimate of site index. Secondly, to avoid the difficulties of determining breast height age or growth increment required for juvenile site index determination, we developed a linkage to site index using data from 168 white spruce trees that were longitudinally sectioned along the pith, selected from juvenile (age 6–15) spruce stands. Using this new equation, coupled with the correction for bias, we found that site index for white spruce from 49 test stands in Alberta, ranged from 9.0 to 31.8 m at base age 50, with a mean of 17.8 m; these values are in the range for spruce in mixedwood stands in Alberta. Key words: juvenile stand, height age curve, height bias adjustment, site index

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 score0.026
Threshold uncertainty score0.872

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)0.0010.000

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 head0.004
GPT teacher head0.187
Teacher spread0.182 · 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

Citations13
Published2006
Admission routes3
Has abstractyes

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