Bibliographic record
Abstract
Because of the high costs and difficult logistics of geochemical surveys in remote regions, sediments collected from large streams must often suffice to determine the presence or absence of mineralization. Here we study this problem in Slesse Creek, a gold-rich river that drains north from the Mt. Baker District, Washington, USA, to enter the Chilliwack River, British Columbia, c . 90 km east of Vancouver. Anomalous Au values are derived from the abandoned Red Mountain Mine just south of the USA–Canada border. Sediments were collected from Slesse Creek and its tributaries, and from other large streams in the Chilliwack River basin. Concentrations of Au in tributaries from the Red Mountain Mine range from 250 to 2330 ppb and are much greater than in other tributaries. Sediments from Slesse Creek also have anomalous, but extremely erratic, Au values. This erratic distribution of Au is generally similar to that of magnetic grains and associated elements (e.g. V) but is negatively correlated with elements, such as Na, associated with light minerals. Results are interpreted as an evolution in sediment geochemistry from the tributaries to Slesse Creek: composition of the former appears to be controlled by source (i.e. geology) whereas geochemistry of sediments in Slesse Creek has been strongly modified by fluvial processes that concentrate Au and other heavy minerals. This shift from source- to process-related geochemistry invalidates the traditional geochemical dilution model and gives long anomalous dispersion trains for Au. These provide suitable targets for low-density regional surveys, but are difficult to interpret because of their erratic character. The contribution of fluvial processes to the concentration of Au can, however, be evaluated by comparison with elements such as V, that are associated with ubiquitous heavy minerals, or by plotting data on x – y – z source–process plots.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".