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Record W4200622161 · doi:10.1080/11956860.2021.2010332

Forest composition and structure after 200 years of succession following the eruption of Mount Tambora (Indonesia)

2021· article· en· W4200622161 on OpenAlexvenueno aff
Asep Sadili, Arief Hidayat, Supardi Jakalalana, Adi Kurniawan, Deni Sahroni, Solikin Solikin, Francis Q. Brearley

Bibliographic record

VenueEcoscience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsEcological successionSecondary successionFloristicsBiomass (ecology)Vegetation (pathology)EcologyBiologyStand developmentForestryGeographySpecies richness

Abstract

fetched live from OpenAlex

We examined the changes in tropical forest diversity, structure and trait composition during primary succession after volcanic disturbance. Whilst many studies have examined early stages of succession, fewer have looked at a precisely dated older location: 200 years old in this instance. To do this, we established a 0.5 ha plot on the lower slopes of Gunung (Mount) Tambora (Sumbawa, Indonesia) in which we enumerated and identified all trees ≥ 10 cm dbh, determined their key traits and calculated forest above-ground biomass. Saplings (1.5–3.0 cm dbh) were enumerated in one quarter of this area. We recorded 214 stems ≥ 10 cm dbh within 21 taxa contributing to an above-ground biomass of 135 Mg ha−1. Most trees had light wood and broad distributions suggestive of early successional traits, but leaves were generally small; most trees were insect pollinated and animal dispersed as expected at later stages of succession. Saplings were variable in their density and showed some floristic similarity with adult trees, but differences indicated the future trajectory of succession. Our floristic and structural data from the poorly studied drier forests of eastern Indonesia show that this 200-year-old forest is still undergoing succession at a slow rate.

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 score0.068
Threshold uncertainty score0.122

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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