International principles and standards for the practice of ecological restoration. Second edition
Bibliographic record
Abstract
International restoration standardsThe Society for Ecological Restoration (SER) is an international non-profit organization with members in 70 countries.SER advances the science, practice and policy of ecological restoration to sustain biodiversity, improve resilience in a changing climate, and re-establish an ecologically healthy relationship between nature and culture.SER is a dynamic global network, linking researchers, practitioners, land managers, community leaders and decision-makers to restore ecosystems and the human communities that depend on them.Via its members, publications, conferences, policy work, and outreach, SER defines and delivers excellence in the field of ecological restoration. Document development. International Principles and Standards for the Practice of Ecological Restoration (the Standards)was developed through consultation with professionals within the Society for Ecological Restoration and their peers in the global scientific and conservation communities.The first edition was launched in 2016 at the United Nations Biodiversity Conference in Cancún, Mexico.This event brought together key stakeholders from across the international policy arena, many of whom had been instrumental in driving the global initiatives to implement large-scale environmental restoration programs.Because the Standards were written as a living document to be modified and expanded through consultation and use by stakeholders, the launch included an open invitation for stakeholder input, to both improve the document and promote broad use.Subsequently, over a multi-year consultation period, SER invited input and review from a diverse spectrum of people and organizations contributing to ecological restoration.Key stakeholders contacted for comment included the secretariats of the Convention on Biological Diversity (CBD), United Nations Convention to Combat Desertification (UNCCD) including its Science-Policy Interface, Global Environment Facility, the World Bank, and members of the Global Partnership on Forest Landscape Restoration (GPFLR).In 2017, SER partnered with the IUCN Commission on Ecosystem Management to deliver an invited Forum on Biodiversity and Global Forest Restoration at which the SER Standards were reviewed (SER and IUCN-CEM 2018).SER also organized a symposium on the SER Standards and an open Knowledge Café at the 2017 SER World Conference on Ecological Restoration.Additional input was received at other events, including the 9 th Ecosystem Services Partnership World Conference in Shenzhen, China in 2017.To capture the perspectives of the SER community, SER invited online feedback via its website and sent an online survey to SER members, affiliates, and stakeholders.SER has also considered and responded to feedback from published critiques in its journal, Restoration Ecology.All comments received during the consultative review process were considered in the revision process.The second edition of the Standards was approved by the SER Science and Policy Committee, and the SER Board of Directors on 18 June 2019.As with the first edition, this version will be revised and improved as the discipline evolves through science, practice, and adaptive management.The Standards are compatible with and expand on the Open Standards for the Practice of Conservation (Conservation Measures Partnership 2013) and complement the REDD+ Social and Environmental Standards (REDD+ SES 2012), and other conservation standards and guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.040 | 0.032 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".