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A Comparison of the Soil Water, Nutrient Status, and Litterfall Characteristics of Tropical Heath and Mixed‐Dipterocarp Forest Sites in Brunei<sup>1</sup>

2000· article· en· W2177780321 on OpenAlexaff
Jonathan A. Moran, Martin G. Barker, Alison J. Moran, Peter A. Becker, Sheila M. Ross

Bibliographic record

VenueBiotropica · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNutrientEnvironmental sciencePhosphorusPlant litterCanopyLitterTropicsDipterocarpaceaeForestryTropical forestSecondary forestEcologyAgroforestryGeographyBiologyChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Two of the main hypotheses to explain the distribution and special characteristics of tropical heath forest are nutrient and water limitation. A study was undertaken to investigate both factors on two sites under tropical heath forest (Badas Forest Reserve) and mixed‐dipterocarp forest (Andulau Forest Reserve) in Brunei. Soil water potentials were monitored at depths of 20, 50, and 90 cm over wet and dry periods for five months at each site. The results showed the mixed‐dipterocarp forest site to be drier at 50 cm depth compared to the tropical heath forest site. There was no significant difference in water potentials between sites at 20 or 90 cm. Nutrient concentrations in the soil solution were monitored at the same depths over a seven‐month period at the same sites. A 12‐month litterfall study was also undertaken to monitor nutrient returns from the canopy at each site. The results of both studies suggest that the tropical heath forest site is poorer in nitrogen, but richer in calcium, than the mixed‐dipterocarp forest site. The results for phosphorus are less clear, but do not suggest that its limitation is a significant factor at the tropical heath forest site compared to the mixed‐dipterocarp forest site. Phosphorus and magnesium concentrations in the soil solution showed a strong positive correlation with sliding 30‐day rainfall totals at both sites.

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)0.000
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 score0.025
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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

Citations53
Published2000
Admission routes1
Has abstractyes

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