American Mineralogisl, yolume 64, page I3II, IgTg Errors in chemical analyses of two titanian micas
Bibliographic record
Abstract
J. Jakob (1937) published analyses of three micas which he claimed contained trivalent titanium. His basis for this claim was that these micas contained more reducing power (Fe'z*) than total iron and he attributed the excess reducing power to trivalent tita-nium. Two of Jakob's micas were procured for this work from the Natural History Museums of Bern, Switzer-land and Vienna, Austria. The phlogopite from Bur-gess, Ontario (Jakob's #l) is the same specimen number (Vienna H.3752) as that used by Jakob. The biotite (Jakob's #3) from Sweden (Bern) is most likely the same specimen number. Ferrous iron was measured by a variation of Wil-son's procedure (Whipple, 1974), and total iron by Table l. Ferrous and total iron contents (Vo by weight) silver reductor followed by titration with ceric sul-fate. In all cases, great care was taken to redissolve magnesium fluoride produaed by attack of hydro-fluoric acid on the micas, because much iron can be occluded in the insoluble magnesium fluoride. The results (Table l) show that Jakob's analytical chemistry was in error. In neither case does the re-ducing power (Fe'*) exceed the total iron. A large er-ror occurs in Jakob's #1, from which Jakob recov-ered only 26 percent ofthe total iron content. Jakob's analysis of phlogopite #l appears in Deer, Howie and Zussman (1962) Rock-Forming Minerals,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.065 |
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".