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Record W2132702121 · doi:10.1139/x06-042

Comparing productivity of pure and mixed Douglas-fir and western hemlock plantations in the Pacific Northwest

2006· article· en· W2132702121 on OpenAlexvenueno aff
Mariano M. Amoroso, Eric C. Turnblom

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsWestern HemlockMonocultureTsugaInterspecific competitionIntraspecific competitionDouglas firHectareProductivityForestryCompetition (biology)BiologyBotanyEcologyGeography

Abstract

fetched live from OpenAlex

We studied pure and 50/50 mixtures of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western hemlock (Tsuga heterophylla (Raf.) Sarg.) plantations to compare attained total yields between mixed-species stands as opposed to monocultures of equal densities. Whether overall stand density influences this outcome has not been adequately investigated, and to address this we included three density levels (494, 1111, and 1729 trees/ha) in the analysis. At age 12, as components of the mixed stands, Douglas-fir exhibited greater height, diameter, and individual-tree volume than western hemlock at all densities. At 494 and 1111 trees/ha the monocultures had a higher volume per hectare than the mixed stand, but at 1729 trees/ha the mixed stand appeared to be just as productive as the pure stands. The increase in productivity by the mixture at high densities seems to have resulted from the partial stratification observed and most likely also from better use of the site resources. Because of this, less interspecific competition was probably experienced in the mixed stand than intraspecific competition in the pure stands. This study shows the important role density plays in the productivity of mixed stands and thus in comparing mixed and pure stands.

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 score0.912
Threshold uncertainty score0.965

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 studies0.0000.001
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 head0.028
GPT teacher head0.264
Teacher spread0.235 · 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

Citations111
Published2006
Admission routes1
Has abstractyes

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