A Preliminary Assessment of Climate Change Impacts – Implications for Mining Activity in Polish Coal Regions
Bibliographic record
Abstract
It is widely known and accepted that the global climate is changing with unprecedented speed. Climate models project increasing temperatures and changes in precipitation regimes which will alter the frequency, magnitude, and geographic distribution of climate-related hazards including flood, drought and heat waves. In the mining industry, climate change impacts are an area of research around the world, mostly in relation to the mining industry in Australia and Canada, where mining policies and mitigation actions based on the results of this research were adopted and applied. In Poland, there is still a lack of research on how climate change, and especially extreme weather events, impacts mining activity. This impact may be of particular importance in Poland, where the mining industry is in the process of intensive transition. The paper presents an overview of hazardous events in mining in Poland that were related to extreme weather phenomena. The needs and recommended actions in the scope of mitigating the impact of future climate change on mining in all stages of its functioning were also indicated. The presented analyses and conclusions are the results of the first activities in the TEXMIN project: The impact of extreme weather events on mining activities, identifying the most important factors resulting from climate change impact on mining.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".