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Record W2527784413 · doi:10.1017/cbo9781139177788.020

Policy drivers for peatland conservation

2016· book-chapter· en· W2527784413 on OpenAlexaff
R. E. Stoneman, C. G. Bain, David A. Locky, Nick Mawdsley, Michael McLaughlan, Shashi Kumaran-Prentice, Mark S. Reed, V. Swales

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMinistry of EnvironmentMacEwan University
FundersDepartment for Environment, Food and Rural Affairs, UK GovernmentGovernment of the United Kingdom
KeywordsPeatEcosystem servicesSustainabilityNatural resourceBogBusinessGeographyEnvironmental planningEnvironmental protectionEnvironmental resource managementEnvironmental scienceEcosystemPolitical scienceEcology

Abstract

fetched live from OpenAlex

Introduction Peatlands have long been recognised as a high priority for protection under international and national wildlife laws and agreements. Over the last half century this protection has essentially been reactionary in the face of more widespread land management policy and market forces, which have encouraged damage to peatlands. This damage has been mainly to support the delivery of provisioning services, such as food, timber and pulp, or the widespread extraction of peat and oil. Across the world, peatlands of different types face a variety of pressures from land use and land-use change as well as pollution (e.g. atmospheric pollution on British blanket bogs), making them more susceptible to impacts of climate change. Within the general framework of international agreements on peatland conservation, each country has developed its own approach to tackling the threats with varying degrees of success. While established wildlife conservation policy has helped limit the extent of damage to peatlands in some countries, there is a need and opportunity for a stronger and more urgent public policy response to address the significant ongoing losses of peatland biodiversity and ecosystem services. The recognition of the multiple benefits that peatlands provide has presented new avenues to support sustainably managed peatlands, in addition to reducing peatland loss through active restoration (e.g. Bain et al. 2011; Joosten, Tapio-Biström and Tol 2012). This chapter presents an overview of the principal international and national policy drivers, with examples from selected countries across the world to highlight how new resources could be directed at wise use and conservation of peatlands. Global overview of policy drivers for peatland conservation While peatlands have been regarded as wastelands, and areas to be ‘improved’ for agriculture and forestry since the late eighteenth century (Chapter 2), they are now recognised for their wildlife and increasingly for their ecosystem services. Peatlands, therefore, feature in some of the world's highest-level environmental policies. One of the earliest global agreements to recognise the importance of peatlands for protection was the Ramsar Convention (1971) that promoted the establishment and management of a network of protected wetlands. In 1996, it was reported that though peatlands represented 50% of the world's freshwater and terrestrial wetlands, less than 10% of the designated Ramsar sites had peatland as their dominant habitat (Chapter 15). Given continuing peatland loss and degradation, Contracting Parties set out guidelines to improve peatland protection (Ramsar 2003).

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)0.009
metaresearch head score (Gemma)0.010
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: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0090.009
Open science0.0010.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0200.001

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 head0.015
GPT teacher head0.200
Teacher spread0.184 · 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
GenreOther

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

Citations3
Published2016
Admission routes1
Has abstractyes

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