Bibliographic record
Abstract
As the global population increases, so does the demand for minerals and energy resources. Demand for some of the major global commodities is currently growing at rates of: copper - 1.6% p.a.(1); iron ore: 1.4% p.a.(2); aluminium - 5% p.a.(3); rare earth elements - 7% p.a.(4), driven not only by population growth in China, India, and Africa, but also by increasing urbanisation and industrialisation globally. Technological advances in renewable energy production and storage, construction materials, transport, and computing could see demand for some of these resources spike by 2600% over the next 25 years under the most extreme demand scenarios(5). Coupled with declining ore grades, this demand means that the global extent of mining environments is set to increase dramatically. Land disturbance attributed to mining was estimated to be 400 000 km(2) in 2007(6), with projected rates of increase of 10 000 km(2) per year(7). This will increase the worldwide extent of mining environments from around 500 000 km(2) at present to 1 330 000 km(2) by 2100, larger than the combined land area of New South Wales and Victoria (1 050 000 km(2)), making them a globally important habitat for the hardiest of microbial life. The extreme geochemical and physical conditions prevalent in mining environments present great opportunities for discovery of novel microbial species and functions, as well as exciting challenges for microbiologists to apply their understanding to solve complex remediation problems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".