Introduction of Collaboration for Environmental Evidence and guidelines and standards for evidence synthesis in environmental management
Bibliographic record
Abstract
Collaboration for Environmental Evidence (CEE) is an open community of stakeholders working towards a sustainable global environment and the conservation of biodiversity. CEE seeks to promote and deliver evidence syntheses on issues of greatest concern to environmental policy and practice as a public service. At present there are six national CEE Centres, based in Australia, Canada, France, South Africa, Sweden and the UK, and one international CEE Centre (SEI). The CEE has a strong network with other seven collaborators from around the world to practice and promote evidence-based environmental management. Systematic review (SR) and evidence synthesis methodology is now in widespread use in sectors of society where science can inform decision making and has become a recognised standard for accessing, appraising and synthesising scientific information. This article will introduce the development and function of CEE and elaborate the guidelines for systematic review in environmental evidence.
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 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 | 0.389 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".