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Record W2171123333 · doi:10.1139/x01-164

Impact of competitor species composition on predicting diameter growth and survival rates of Douglas-fir trees in southwestern Oregon

2001· article· en· W2171123333 on OpenAlexvenueno aff
Felipe Bravo, David W. Hann, Douglas A. Maguire

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersU.S. Geological SurveyOregon State University
KeywordsBasal areaCompetition (biology)Douglas firBiologyHardwoodCrown (dentistry)Range (aeronautics)EcologyForestryBotanyGeography

Abstract

fetched live from OpenAlex

Mixed conifer and hardwood stands in southwestern Oregon were studied to explore the hypothesis that competition effects on individual-tree growth and survival will differ according to the species comprising the competition measure. Likewise, it was hypothesized that competition measures should extrapolate best if crown-based surrogates are given preference over diameter-based (basal area based) surrogates. Diameter growth and probability of survival were modeled for individual Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees growing in pure stands. Alternative models expressing one-sided and two-sided competition as a function of either basal area or crown structure were then applied to other plots in which Douglas-fir was mixed with other conifers and (or) hardwood species. Crown-based variables outperformed basal area based variables as surrogates for one-sided competition in both diameter growth and survival probability, regardless of species composition. In contrast, two-sided competition was best represented by total basal area of competing trees. Surrogates reflecting differences in crown morphology among species relate more closely to the mechanics of competition for light and, hence, facilitate extrapolation to species combinations for which no observations are available.

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 score0.332
Threshold uncertainty score0.979

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 head0.034
GPT teacher head0.299
Teacher spread0.265 · 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

Citations45
Published2001
Admission routes1
Has abstractyes

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