Climate Change and Biodiversity Conservation
Bibliographic record
Abstract
The intricate connection between climate change and biodiversity conservation highlights the need for integrated approaches to address the challenges of ecological preservation and climate mitigation. Climate change exerts both direct and indirect impacts on biodiversity, affecting the physiology of species to life cycles, distribution, and the intricate web of ecological interactions. Existing pressures are exacerbated by the introduction of new challenges such as habitat destruction, invasive species, and disease outbreaks because of climate change. This combination of threats particularly impacts vulnerable groups like amphibians, demonstrating the urgent need for more dynamic conservation strategies. Traditional conservation methods, including the establishment of protected areas and genetic diversity preservation, must evolve to include 'climate-smart' conservation practices that prioritize actions beneficial for biodiversity in a changing climate. Habitat restoration, leveraging indigenous knowledge, and the integration of biodiversity considerations into climate-smart policies are crucial components of a comprehensive response to these environmental challenges. International agreements like the Kunming-Montreal Agreement and the Paris Agreement establish standards and targets, such as the 30x30 Target, that synergize biodiversity conservation alongside climate change mitigation. Urgently needed is a more effective and efficient global governance structure to manage Earth system tipping points, preventing ecological and climate crises. Immediate action is advocated to adhere to the 1.5°C temperature rise limit set by the Paris Agreement. Comprehensive climate action goes beyond carbon metrics, requiring a holistic strategy that acknowledges the intertwined nature of climate dynamics and biodiversity that can ensure the long-term protection of ecosystems.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".