Mapping the Global Impact of Mining Activities on Freshwater Biodiversity to Inform Conservation Priorities
Bibliographic record
Abstract
ABSTRACT Natural resource mining is a vital global industry serving sectors such as construction, infrastructure and electronics. The negative impacts of mining, exacerbated by poor governance and lax legislation, have detrimental consequences on the environment, especially in freshwater systems. Mining is shown to disrupt hydrological regimes, sediment dynamics and vegetation structure, which affect water quality, species composition and overall ecosystem health. However, little is known about the global extent of mining impacts on freshwater biodiversity, ultimately hindering mitigation efforts and effective policy implementation. Here, we address this knowledge gap by developing an impact probability model to generate global threat maps based on the impact of mining for freshwater fish, macrophytes and odonatan. We show that the impact of mining differs significantly between taxonomic groups, with hotspots of risk coinciding with high‐biodiversity and wilderness areas. Using a random forest machine learning model, we show that the extent of mining impacts is driven primarily by environmental and anthropogenic variables, such as land surface runoff and the Human Development Index. This overview of the global distribution of mining's threat is urgently needed for conservation plans to mitigate the impact of mining on biodiversity.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".