THE CHARACTERIZATION AND QUANTITATIVE ANALYSIS OF CLAY MINERALS IN THE ATHABASCA BASIN, SASKATCHEWAN: APPLICATION OF SHORTWAVE INFRARED REFLECTANCE SPECTROSCOPY
Bibliographic record
Abstract
The application of shortwave infrared (SWIR) reflectance spectroscopy to the characterization of clay minerals in the Athabasca Basin, in Saskatchewan, has been evaluated by detailed examination of 70 mineral separates (20 of kaolinite, 10 of dickite, 19 of illite, 16 of chlorites and 5 of magnesiofoitite). Clay minerals in the Athabasca Basin are widespread in the sandstones and are particularly abundant in alteration haloes associated with unconformity-type U deposits. SEM, TEM, XRD, EMPA, EPR and SWIR analyses confirm that dickite is a major clay mineral in the sandstones. Kaolinite from different geological settings has distinct values of crystallinity (i.e., Hinckley index in the range 0.84 to 1.61 in alteration haloes, 0.45 to 0.7 in the bleached zones of the paleoregolith, and 0.12 to 0.31 in late fractures and cavities). The SWIR reflectance spectroscopy is capable of quantita-tively estimating the crystallinity of kaolinite by using a “14SP Index”. Attempts to use SWIR reflectance spectroscopy for structural and compositional analysis of Athabasca illite and sudoite were complicated by the common occurrence of impurities in these minerals. Binary and ternary mixtures using well-characterized mineral standards reveal that SWIR reflectance spectroscopy is capable of identifying clay minerals at abundances as low as 1 wt%. Also, numerical relationships between spectral features and the abundances of clay minerals have been established from the muscovite – kaolinite, muscovite – dickite and muscovite – sudoite mixtures, and have been used to improve an empirical SWIR method for quantitative analysis of clay minerals in the Athabasca Basin. The estimated precision and accuracy of the improved empirical method are between 5 to 10%,
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".