Deep‐Sea Mining: Integrating Geology, Oceanography, and Engineering: Shaping the Future: Deep‐Sea Minerals and Mining Congress; Aachen, Germany, 9–13 March 2008
Bibliographic record
Abstract
A strong increase in the global demand for metallic raw materials, coupled with rising market prices, has heightened interest in marine seabed mineral deposits and the feasibility of their extraction for many marine scientists, engineers, and mining companies. This interest focuses not only on base and precious metals but also on strategically important elements needed for high‐technology applications, such as cobalt, nickel, molybdenum, titanium, gallium, selenium, telurium, indium, and the rare earth elements. A symposium and workshop on deep‐sea minerals and mining was convened at the Rheinisch‐Westfälische Technische Hochschule (RWTH) Aachen, in Germany. More than 100 scientists from 16 countries assembled to discuss the opportunities and challenges of interdisciplinary research concerning deep‐ocean mineral resources. Two days of plenary talks presented by invited experts representing diverse disciplines were followed by a 1‐day workshop to discuss the current knowledge; to develop research strategies that address emerging exploration and recovery techniques; and to assess economic, legal, and ecological issues inherent in deep‐sea mining.
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 | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".