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Record W3201897249 · doi:10.1073/pnas.2022210118

Anthropogenic impacts on Late Holocene land-cover change and floristic biodiversity loss in tropical southeastern Asia

2021· article· en· W3201897249 on OpenAlexaff
Zhuo Zheng, Ting Ma, Patrick Roberts, Zhen Li, Yuanfu Yue, Huanhuan Peng, Kangyou Huang, Ziyun Han, Qiuchi Wan, Yaze Zhang, Xiao Zhang, Yanwei Zheng, Yoshiki Saito

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiodiversityGeographyDeforestation (computer science)EcologyVegetation (pathology)HoloceneSubtropicsWetlandAgroforestryAgricultureBiology

Abstract

fetched live from OpenAlex

Significance Palaeoecological analysis reveals that the expansion of rice agriculture in southern China and Southeast Asia around 2,000 y ago caused widespread deforestation and biodiversity changes in tropical and subtropical forests. Tropical forests, with the highest level of plant diversity and concentration of endemic species, suffered greater decline of arboreal richness than forests in subtropical and temperate areas. In subtropical ecosystems, total plant richness increased, despite arboreal decline, possibly thanks to the flourishing of herbs in an opening landscape. The disappearance of Glyptostrobus in southeastern Asia provides a case study as to how early rice agriculture endangered an endemic species by causing losses of its natural habitats, with prehistoric land-use changes leaving a clear legacy for today’s landscapes and species compositions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.292
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations121
Published2021
Admission routes1
Has abstractyes

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