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Record W4416415868 · doi:10.1093/biosci/biaf158

Pastoralism Can Mitigate Biodiversity Loss on Global Rangelands

2025· article· en· W4416415868 on OpenAlexaboutno aff
David D. Briske, Joris P. G. M. Cromsigt, María E. Fernández‐Giménez, Matthew W. Luizza, Pablo Manzano, Rashmi Singh

Bibliographic record

VenueBioScience · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityPastoralismRangelandEcosystem servicesMeasurement of biodiversitySustainabilityAsset (computer security)

Abstract

fetched live from OpenAlex

Sustainable pastoralism represents a primary strategy for supporting goals of the Kunming-Montreal Global Biodiversity Framework. Sixty-seven percent of biodiversity hotspots and 38% of key biodiversity areas globally include rangelands, but international conventions seldom recognize this vast biodiversity repository. We summarize four synergies between pastoralism and biodiversity conservation: working lands conservation, continuation of vital disturbance regimes, connectivity through transhumance corridors, and community-led governance. Actions that leverage these synergies offer critical opportunities to mitigate biodiversity loss through the creation of a vast conservation network that includes working lands and protected areas. This will require that the contemporary conservation paradigm envision pastoralists as an asset rather than a threat to biodiversity conservation and recognize grazing and fire as ecological disturbances vital to the maintenance of biodiversity. Greater inclusion of rangelands and sustainable pastoralism within global conservation frameworks has high potential to enhance attainment of global biodiversity targets.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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