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Record W2111540607 · doi:10.1139/x05-214

Nutrient cycling in a mixed-species plantation of<i>Eucalyptus globulus</i>and<i>Acacia mearnsii</i>

2005· article· en· W2111540607 on OpenAlexvenueno aff
David I. Forrester, Jürgen Bauhus, Annette Cowie

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsAcacia mearnsiiEucalyptus globulusMonoculturePlant litterEucalyptusNutrientBiologyBotanyAgronomyBiomass (ecology)LitterEcology

Abstract

fetched live from OpenAlex

A doubling of aboveground biomass production has been observed in mixtures of Eucalyptus globulus Labill. and Acacia mearnsii de Wildeman when compared with monocultures after 11 years of growth. This study examined to what extent increased nitrogen (N) availability and accelerated rates of nutrient cycling may contribute to increased growth in mixtures. Monocultures of E. globulus (E) and A. mearnsii (A) and mixtures of these species were planted in a species replacement series: 100% E, 75% E + 25% A, 50% E + 50% A, 25% E + 75% A, and 100% A. Litterfall mass increased with aboveground biomass production and was highest in 50:50 mixtures and lowest in monocultures. Owing to higher N concentrations of A. mearnsii litter, N contents of annual litterfall were at least twice as high in stands containing A. mearnsii (32-49 kg·ha –1 ·year –1 ) as in E. globulus monocultures (14 kg·ha –1 ·year –1 ). Stands with A. mearnsii also cycled higher quantities of phosphorus (P) in annual litterfall than E. globulus monocultures. This study demonstrated that mixing A. mearnsii with E. globulus increased the quantity and rates of N and P cycled through aboveground litterfall when compared with E. globulus monocultures. Thus, mixed-species plantations appear to be a useful silvicultural system to improve nutrition of eucalypts without fertilization.

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)0.002
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 score0.579
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.025
GPT teacher head0.275
Teacher spread0.250 · 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

Citations90
Published2005
Admission routes1
Has abstractyes

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