Quantifying Hydrothermal Alteration: A Review of Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hydrothermal alteration is proximal to many base and precious metal deposits, and its products can provide insights into the characteristics of hydrothermal systems. To be useful to exploration geologists and researchers, however, alteration needs to be typified and quantified. Alteration type informs on mineralising style (e.g., have we found a porphyry or a volcanogenic massive sulphide deposit?), while quantification of its intensity helps position a sample within the system (e.g., how close are we to the main economic deposit?). Numerous methods—all having their specific advantages and disadvantages—are dedicated to the characterisation of alteration. As alteration is a process that induces chemical and mineralogical changes in rocks, it can be studied using petrological (e.g., mineral recognition in thin sections, mineral chemistry), mineralogical (e.g., alteration indices that use normative minerals), and chemical (e.g., mass balance calculations) approaches. This short review provides an overview of the methods useful to researchers and that are also applicable in an exploration context.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 it