Thessally Resources view prospects in Australia's magnesite market
Bibliographic record
Abstract
Magnesite is the mineral name for MgCO[subscript]3 with a theoretical MgO content of 47.8% MgO and 52.2% CO[subscript]2. Deposits of magnesite are of two main types; (i) macrocrystalline or sparry rocks (Wilson, 2013) where Mg solutions have altered dolomite to magnesite, and (ii) cryptocrystalline magnesite replacing ultramafic rocks within the weathered profile. Other types of magnesite, such as sedimentary beds, crystalline magnesite replacement in ultramafic rocks and saline brines, do occur but are of secondary significance from an economic point of view. Also a pure form of magnesia (MgO) is produced from sea water. In 2012 USGS data shows world production capacity of CCM as 2.72m tonnes MgO and DBM as 7.64m tonnes MgO (Bray, 2012). Australia's share of this production capacity in 2012 was 8% CCM and 1.4% DBM. China dominates the supply of CCM with 53% of production capacity. Russia (3.4%), Spain (5.5%), and Brazil and Canada (3.5%) are other significant CCM producers, according to the USGS. As the main products from magnesite mines are in the calcined MgO form, this is how the product specifications are quoted. For CCM produced from natural magnesite the range of MgO is 85-95%, with 85-90% MgO being the typical range for animal feeds and fertilisers and 90-95% MgO for bulk industrial applications such as construction and paper processing.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.019 |
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".