Key impacts of climate engineering on biodiversity and ecosystems, with priorities for future research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change has significant implications for biodiversity and ecosystems. With slow progress towards reducing greenhouse gas emissions, climate engineering (or ‘geoengineering’) is receiving increasing attention for its potential to limit anthropogenic climate change and its damaging effects. Proposed techniques, such as ocean fertilization for carbon dioxide removal or stratospheric sulfate injections to reduce incoming solar radiation, would significantly alter atmospheric, terrestrial and marine environments, yet potential side-effects of their implementation for ecosystems and biodiversity have received little attention. A literature review was carried out to identify details of the potential ecological effects of climate engineering techniques. A group of biodiversity and environmental change researchers then employed a modified Delphi expert consultation technique to evaluate this evidence and prioritize the effects based on the relative importance of, and scientific understanding about, their biodiversity and ecosystem consequences. The key issues and knowledge gaps are used to shape a discussion of the biodiversity and ecosystem implications of climate engineering, including novel climatic conditions, alterations to marine systems and substantial terrestrial habitat change. This review highlights several current research priorities in which the climate engineering context is crucial to consider, as well as identifying some novel topics for ecological investigation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it