The Kelp Forest Challenge: A collaborative global movement to protect and restore 4 million hectares of kelp forests
Bibliographic record
Abstract
Abstract Marine kelp forests cover 1/3 of our world's coastlines, are heralded as a nature-based solution to address socio-environmental issues, connect hundreds of millions of people with the ocean, and support a rich web of biodiversity throughout our oceans. But they are increasingly threatened with some areas reporting over 90% declines in kelp forest cover in living memory. Despite their importance and the threats they face, kelp forests are entirely absent from the international conservation dialogue. No international laws, policies, or targets focus on kelp forests and very few countries consider them in their national policy. The Kelp Forest Challenge addresses that gap. Together with 252 kelp experts, professionals, and citizens from 25 countries, the Kelp Forest Challenge was developed as a grassroots vision of what the world can achieve for kelp forest conservation. It is a global call to restore 1 million and protect 3 million hectares of kelp forests by 2040. This is a monumental challenge, that will require coordination across multiple levels of society and the mobilization of immense resources. Pledges may therefore include area for protection or restoration, enabling pledges which assist in conservation (funding, equipment, professional expertise, capacity building), or awareness-based pledges which increase awareness or education about kelp forests. Correspondingly, participants may be from government, scientific institutions, private sector, NGOs, community groups, or individuals. This challenge is the beginning of a 17-year mission to save our kelp forests and anyone and any organisation is invited to participate.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".