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Record W3002813635 · doi:10.1111/csp2.169

Severe human pressures in the Sundaland biodiversity hotspot

2020· article· en· W3002813635 on OpenAlexaff
Megha Verma, William S. Symes, James Watson, Kendall R. Jones, James R. Allan, Oscar Venter, Frank E. Rheindt, David P. Edwards, L. Román Carrasco

Bibliographic record

VenueConservation Science and Practice · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Northern British Columbia
FundersMinistry of Education, India
KeywordsBiodiversityBiodiversity hotspotGeographyHotspot (geology)FootprintEcologyBiologyArchaeologyGeology

Abstract

fetched live from OpenAlex

Abstract We assess the magnitude and the extent of recent change of significant human footprint within protected areas, key biodiversity areas and the habitat range of 308 lowland forest specialist birds in Sundaland, a global hotspot of biodiversity in Southeast Asia. Using the most recent human footprint dataset, we find that 70% of Sundaland has been heavily modified by humans. This represents a 55% increase in areas under intense human pressure since 1993. Areas under intense human pressure covered on average 50% of the extent of key biodiversity areas, 78% of each protected area and 38% of the range of lowland forest specialist birds. The results imply that the actual level of protection by protected areas is only one‐third to half of that on paper once human footprint is accounted for. While all protected areas were impacted by human pressures, those managed strictly for biodiversity conservation presented the largest increases. These results highlight an exceptionally high human footprint across Sundaland and an impending further deepening of the biodiversity crisis across the region.

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)0.001
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.287
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 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

Citations42
Published2020
Admission routes1
Has abstractyes

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