Element concentrations in percentS:
Bibliographic record
Abstract
Objective and detailed data arc necessary:for app!ication of garnet-ratlo studies to till provenance. The garnets in common use differ sigrtificantly in cell dimensmn. Ratios of purple-colorless/orange-red garnets have been used empirically to establish prove-nance of tills in eastern North America. (See I)reimanis and others, 1957; Dreimanis, 1960; Connally, 1964.) Garnets are common in Pre-cambrian rocks, and, because they are chemically and mechanically resistant minerals, they are relatively abundant in heavy-mineral fractions of tills in Quebec, Ontario, and New York. This, plus the ease with which they are identi-fied, makes them attractive as potential indica-tor minerals. Some dangers inherent in broad regional con-elusions based on garnet colors in a population with relatively low sample density are: (1) ~ Manuscript received December 11, 1967. small local sources of garnet could mask any re-gional trend in the ratios; (2) glaciers could lo-cally incorporate garnets from older glacial sed-iments having different provenance; (3) com-monly in areas with a complex ice-flow history, only detailed local work can indicate which till unit happens to show in a given surface expo-sure; and (4) it is possible that several combi-nations in the complicated solid-solution garnet series could produce the same general color. In addition, color has been treated as a subjective criterion which makes difficult the comparison of results of different workers. For example, both color-classes of garnets are present in tills of the Appalachian region, Que-bec. (See Dreimanis, 1960, p. 112.) The present TABLE 1.--Pr0perties of orange-red and light purple garnets ~ ~ G a r n e t ~ color Orange-red Light purple
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.015 |
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".