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Record W4409409988 · doi:10.1080/11956860.2025.2481678

Long-term monitoring reveals dynamic patterns of forest structure and species diversity in the Paraguayan Upper Paraná Atlantic Forest

2024· article· en· W4409409988 on OpenAlexvenueno aff
Victoria Rika Kubota, Shin Ugawa

Bibliographic record

VenueEcoscience · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAtlantic forestEcologyGeographyTerm (time)Forest structureDiversity (politics)Species diversityForestryAgroforestryBiology

Abstract

fetched live from OpenAlex

The Upper Paraná Atlantic Forest (UPAF) is a crucial ecosystem providing critical ecosystem services. To comprehend the heterogeneity in the provision of the carbon sequestration service, this study investigated the variation in forest dynamics within the UPAF’s most representative forest type, focusing on changes in forest structure and tree species diversity as per the initial species composition. Three forest groups (A–C) were identified based on their initial species composition using tree census data from monitoring that spanned approximately 25 years across seven plots. Group A was characterized by low tree density and high species diversity, resulting in stable forest growth with moderate competition. The forest decline in Group B was linked to a reduction in large trees, irrespective of the growth of pioneer species. Group C was characterized by high tree density and low diversity, displaying a decline in biomass with intense competition. These results indicated that only Group A forests functioned as carbon sinks, while those of Groups B and C acted as carbon sources. This study is one of the few that reveals the heterogeneity in carbon sequestration service in the UPAF and provides valuable insights into forest management and conservation strategies.

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 score0.010
Threshold uncertainty score0.545

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 head0.013
GPT teacher head0.246
Teacher spread0.232 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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