ION-MICROPROBE ANALYSIS OF FeTi OXIDES: OPTIMIZATION FORTHE DETERMINATION OF INVISIBLE GOLD
Bibliographic record
Abstract
Magnetite is a common mineral in a wide variety of hydrothermal Au-bearing ore-deposit types. FeTi oxides also occur as phenocrysts in some Au-bearing silicic volcanic rocks. In oxidized plutons, or reduced plutons that are undersaturated with respect to sulfide, FeTi oxides likely are an important host of Au. To evaluate the volumetric importance of magnetite and ilmenite as hosts for Au in igneous rocks and hydrothermal ore deposits, it is necessary to quantify Au in FeTi oxide minerals. We used secondary-ion mass spectrometry (SIMS) to analyze magnetite and ilmenite standards implanted with 197 Au. Samples were sputtered with a Cs + primary beam, and negative secondary ions were measured. Because of the small diameter of oxide phenocrysts in volcanic rocks, small raster size and beam-diameter are required. We used a 10- μ m diam beam rastered over an area with 50- μ m sides. To obtain sufficient background counts and optimum dynamic range, 197 Au – counts were measured for up to 5 s per cycle. We obtained measured detection-limits (MDL) of 10 ppb Au in magnetite and 240 ppb Au in ilmenite operating in high-mass-resolution (HMR) mode (M/ΔM = 4,000 to 5,000). HMR mode was required to eliminate an interference with 133 Cs 48 Ti 16 O – during analysis of ilmenite. The high MDL for ilmenite was due to the high inherent-Au content of the unimplanted standard. The siting of Au has important implications for both ore genesis and ore extraction. Quantification of Au in FeTi oxides may lead to more accurate mineralogical balances for hydrothermal Au deposits, as well as resolving questions regarding the partitioning of Au in silicic magmas and the behavior of Au in some magmatic-hydrothermal ore-forming systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".