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Record W2156252211 · doi:10.1139/x04-193

Tree-size diversity between single- and mixed-species stands in three forest types in western Canada

2005· article· en· W2156252211 on OpenAlexfundvenueaboutno aff
Pàl Varga, Han Y. H. Chen, Karel Klinka

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsTsugaPinus contortaWestern HemlockLarchThujaDiversity indexShade toleranceSpecies diversityBiologyForestryEcologyBotanyGeographySpecies richnessCanopy

Abstract

fetched live from OpenAlex

Mixed-species stands are hypothesized to be more structurally diverse because of inherent differences in growth rates between tree species. We investigated three combinations of even-aged, single- and mixed-species coniferous stands in western Canada: (i) shade-tolerant western hemlock (Tsuga heterophylla (Raf.) Sarg.) and western redcedar (Thuja plicata (Donn ex D. Don) Spach), (ii) shade-intolerant lodgepole pine (Pinus contorta Dougl. ex Loud.) and western larch (Larix occidentalis Nutt.), and (iii) shade-intolerant lodgepole pine and shade-tolerant black spruce (Picea mariana (Mill.) BSP). We quantified stand structural diversity based on tree diameter and height by using Shannon's index and the coefficient of variation. Regression analysis was applied to examine the relationships between structural diversity, species composition, and stand age. Mixed-species stands of western hemlock and western redcedar had similar structural diversity to their respective single-species stands. Pine–larch and pine–spruce mixed-species stands had higher tree-size diversity than their respective single-species stands. Stand age was a significant factor for tree-size diversity, but its effect changed in different ways depending on the type of mixtures and index used to measure diversity.

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 score0.198
Threshold uncertainty score0.502

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 head0.039
GPT teacher head0.249
Teacher spread0.209 · 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

Citations71
Published2005
Admission routes3
Has abstractyes

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