Stream sediment geochemistry in mineral exploration: a review of fine-fraction, clay-fraction, bulk leach gold, heavy mineral concentrate and indicator mineral chemistry
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
Stream sediment surveys support early-stage reconnaissance mineral exploration and regional assessment programmes, enhanced by recent improvements in analytical method detection limits, continuously improving mineral chemistry, and new approaches to the interpretation of geochemical data. Sediment surveys may be used to predict catchment basin lithology, mineralization type based on pathfinder geochemistry, and geological features based on indicator mineral chemistry. Sediment surveys that target a finer-fraction sediment sample led to the discovery of the La Colosa gold deposit, Colombia. The Batu Hijau porphyry Cu–Au deposit in Indonesia was discovered based on an anomalous clay-sized fraction sample 12 km downstream. In an arid region with poorly developed drainages and minor topographic relief, the Ag-base-metal Navidad District in Argentina was discovered with clay-fraction sediment geochemistry. Heavy mineral concentrate (HMC) sediment surveys that include mineral chemistry determinations have led to global diamond discoveries. HMC surveys contributed to discovery of the Ring of Fire Ni–Cu–PGE and chromite district, Ontario, Canada. Discoveries and geochemical mapping can assist advancement of the application of stream sediment geochemistry in those global areas for which lithologies and deposits are exposed. Stream sediment surveys continue to be one of the most cost-effective geochemical methods for covering large areas for mineral exploration. Thematic collection: This article is part of the Reviews in Exploration Geochemistry collection available at: https://www.lyellcollection.org/topic/collections/reviews-in-exploration-geochemistry
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it