Stoichiometry-based estimates of ferric iron in calcic, sodic-calcic and sodic amphiboles: a comparison of various methods
Bibliographic record
Abstract
An important drawback of the electron microprobe is its inability to quantify Fe3+/Fe2+ ratios in routine work. Although these ratios can be calculated, there is no unique criterion that can be applied to all amphiboles. Using a large data set of calcic, sodic-calcic, and sodic amphibole analysis from A-type granites and syenites from southern Brazil, weassess the choices made by the method of Schumacher (1997, Canadian Mineralogist, 35: 238-246), which uses the average between selected maximum and minimum estimates. Maximum estimates selected most frequently are: 13 cations excluding Ca, Na, and K (13eCNK - 66%); sum of Si and Al equal to 8 (8SiAl - 17%); 15 cations excluding K (15eK - 8%). These selections are appropriate based on crystallochemical considerations. Minimum estimates are mostly all iron as Fe2+ (all Fe2 - 71%), and are clearly inadequate. Hence, maximum estimates should better approximate the actual values. To test this, complete analyses were selected from the literature, and calculated and measured values were compared. 13eCNK and maximum estimates are precise and accurate (concordance correlation coefficient- r c <FONT FACE=Symbol>»</FONT> 0.85). As expected, averages yield poor estimates (r c = 0.56). We recommend, thus, that maximum estimates be used for calcic, sodic-calcic, and sodic amphiboles.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".