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Record W2153553984 · doi:10.1139/cjfr-2013-0201

Wood density of Norway spruce in uneven-aged stands

2014· article· en· W2153553984 on OpenAlexvenueno aff
Riikka Piispanen, Jaakko Heinonen, Sauli Valkonen, Harri Mäkinen, Sven‐Olof Lundqvist, Pekka Saranpää

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsPithPicea abiesBark (sound)CanopyDendrochronologyForestryBotanyHorticultureKarstBiologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

This study focused on wood density and annual ring width in Norway spruce (Picea abies (L.) Karst.) grown in uneven-aged stands (UAS). In total, 96 trees were harvested from five UAS that had been managed by single-tree selection for decades. A data set of 27 trees from even-aged stands (EAS) was used for comparison. In the UAS trees, high density and narrow annual rings were found in the juvenile wood near the pith. Thereafter, wood density rapidly decreased until the 20th annual ring, followed by an increase toward the bark. In the outermost rings, wood density again slightly decreased. The trends in wood density in the UAS trees correspond with those reported for naturally regenerated, even-aged Norway spruce stands, with the exception of the decrease in the outermost rings. A mixed linear model with ring width, cambial age, and canopy position as fixed parameters accounted for 53% of the variation in wood density of the UAS trees. In contrast to UAS trees, EAS trees showed increased wood density in the outer rings as a result of decreasing growth rate. The abrupt change in wood density of UAS trees may affect the properties of timber sawn close to the pith.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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.

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

Citations40
Published2014
Admission routes1
Has abstractyes

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