MétaCan
Menu
Back to cohort
Record W2101605921 · doi:10.1139/x00-163

Early-age and later-age thinning affects growth, dominance, and intraspecific competition in <i>Eucalyptus nitens</i> plantations

2001· article· en· W2101605921 on OpenAlexvenueno aff
J. L. Medhurst, C. L. Beadle, W. A. Neilsen

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsThinningEucalyptus nitensBasal areaIntraspecific competitionDominance (genetics)PulpwoodBiologyCrown (dentistry)ForestrySilvicultureSowingStand developmentEucalyptusBotanyHorticultureMathematicsAnimal scienceEcologyGeography

Abstract

fetched live from OpenAlex

High-intensity thinning treatments were applied to young Eucalyptus nitens (Deane and Maiden) Maiden plantations aged 6 (early-age thinning), 8, and 9 years (later-age thinning). Thinning treatments were an unthinned control plus final density levels that ranged from 100 to 600 trees/ha, representing between 14 and 72% of pretreatment stand basal area. Initial planting densities were between 1143 and 1430 trees/ha. Cumulative basal area increment was significantly reduced after both early- and later-age thinning if more than 50% of the standing basal area was removed. When select groups of trees in the thinning treatments were compared with the equivalent groups of trees in the unthinned control, there was a significant response to early-age thinning in the best 100-400 trees/ha and to later-age thinning for the best 100-600 trees/ha. Height increment was not affected by thinning. However, mean live crown ratio increased with time in thinned treatments. Dominant and codominant trees showed the greatest growth response to thinning. From the data presented in this study, a final density in the range of 200-300 trees/ha is recommended. This density would improve the growth of individual trees during a rotation length of 20 to 25 years without seriously under utilizing site resources.

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 score0.405
Threshold uncertainty score0.999

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest ecology and managementFrench-language works237,207