ECOLOGICAL RESTORATION OF EARTH'S ECOSYSTEM AND THE DECADE OF ECOSYSTEM RESTORATION
Bibliographic record
Abstract
Restoration ecology has demonstrated an astounding growth as a new discipline of applied science, since its emergence in the past decades. Future-aimed restoration should acknowledge the changing and unpredictable environment of the future, assume the dynamic nature of ecological communities with multiple trajectories, and connect landscape elements for improving ecosystem functions and structures. Ecosystem loss is depriving the world of carbon sinks, like forests and wetlands, at a time when humanity can least afford it. Ecosystem restoration aims to repair some damage done to the environment and regain ecological functionality. The United Nations (UN) recently declared 2021 to 2030 the Decade on Ecosystem Restoration- a global mission to revive billions of hectares, from forests to farmlands, from the top of mountains to the depth of the sea. The path to a more sustainable use of ecosystems must begin with the development of inclusive wealth measures which capture natural, social, human and manufactured capital and are thus more accurate ways to measure economic progress.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".