An investigation of infrared spectral features related to the presence of lithium in micas, East Kemptville tin deposit, Nova Scotia
Bibliographic record
Abstract
Lithium is an important commodity in the rapidly growing rechargeable battery industry and it is unclear whether future Li-demand will outgrow current supply. As such, it is important that the groundwork for exploration be ready in the event of a Li shortage. One possible source is within Li-micas in Sn-greisen deposits. The use of infrared spectroscopy, a cheap and portable technique, for Li-quantification within greisen-style mineralogy was explored. To do this, recreation of the 915 cm[superscript -1] signature, which was found to decrease in intensity as a function of octahedral Li occupancy after treatment of muscovite with molten LiNO[subscript 3] (White et al., 1961) was explored. Recreation of this feature was unsuccessful, which is likely due to a lack of empty octahedral positions to accommodate Li[superscript +] cations pre-treatment. The orientation of samples during analyses, both parallel and perpendicular to mica cleavage was found to have no effect on the wave number position of absorption features. Intensities o individual absorption features were found to vary as a function of sample thickness. In addition to this, the absorption features of 6 mica samples with varying Li concentration were compared. Absorption near 1411 and 2200 cm[superscript -1] were found to be shifted to higher wave numbers in samples with the highest Li, reflecting the replacement of octahedral Al by Li.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".