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Record W3207840318 · doi:10.1002/fee.2420

Tropical forests are home to over half of the world’s vertebrate species

2021· review· en· W3207840318 on OpenAlexaff
Rajeev Pillay, Michelle Venter, José Aragón-Osejo, Pamela González‐del‐Pliego, Andrew J. Hansen, James Watson, Oscar Venter

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Northern British Columbia
FundersNational Aeronautics and Space Administration
KeywordsBiomeRainforestBiodiversityTropical rainforestDeforestation (computer science)EcologyTropicsGeographyEcosystemHabitatGlobal biodiversityTropical climateVertebrateAmazon rainforestRange (aeronautics)Tropical and subtropical dry broadleaf forestsAgroforestryBiology

Abstract

fetched live from OpenAlex

Tropical forests are renowned for their astonishing diversity of life, but the fundamental question of how many species occur in tropical forests remains unanswered. Using geographic range maps and data on species habitat associations, we determined that tropical forests harbor 62% of global terrestrial vertebrate species, more than twice the number found in any other terrestrial biome on Earth. Up to 29% of global vertebrate species are endemic to tropical forests, with more than 20% of these species at risk of extinction. Humid tropical forests (also known as tropical rainforests) and the Neotropics dominate as centers of species diversity, harboring more than 90% and nearly half of all tropical forest vertebrates, respectively. To maintain the biodiversity that underpins the ecosystem functions and services essential for human well-being, we emphasize the critical importance of environmental policies aimed at reducing tropical deforestation and mitigating deleterious anthropogenic pressures on these imperiled ecosystems.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.009
GPT teacher head0.225
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations249
Published2021
Admission routes1
Has abstractyes

Explore more

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