Bibliographic record
Abstract
Introduction, definitions, and classification Volcanic rocks host significant base and precious metal ore deposits. But what is an ore deposit? The term ore deposit is an economic, not a geological term, and refers to a naturally occurring material which can be extracted, processed, and delivered to the marketplace or technology at a reasonable profit. The term mineral deposit bears no profitability implications. Ore not only refers to metals, or metal-bearing minerals (metallic ores), but also to many non-metallic minerals valued for their own specific physical or chemical properties, such as fluorite and asbestos that are classified as industrial minerals. In this definition water can also be classified as an ore. Considering water as an ore may not be as outlandish as it first appears. With the world's supply of clean, fresh water ever decreasing, countries with an abundance of fresh water are in a position to “mine” this commodity and offer it to countries that require this resource for either agriculture or consumption. The question, in this case, is not whether it can be done but, because of environmental concerns and the potential for natural habitat destruction, should it be done? The size of an ore deposit is measured or defined by its reserves. In the case of water, reserves are measured in liters and, when dealing with geothermal energy, temperature is also an important unit of measure.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".