Application of 55 multi-dimensional tectonomagmatic discrimination diagrams to Precambrian belts
Bibliographic record
Abstract
Fifty-five new multi-dimensional diagrams, mostly based on log-ratio transformations, were used to decipher tectonic settings for 17 cases of Precambrian belts in Canada, the USA, Poland, Finland, Jordan, Democratic Republic of Congo and Zambia, China, and India. The results of different sets of diagrams for basic-ultrabasic, intermediate, and acid magmas were generally internally consistent. Possible reasons for some inconsistencies may be related to the use of a sample group of mixed ages because of their uncertainties, extreme element mobility caused by metamorphism especially of high-grade type, analytical data quality, different petrogenetic processes for basic to acid magmas such as mantle versus crustal origin, and some deficiencies that still exist in the multi-dimensional diagrams. To partly overcome the last problem, one new multi-dimensional diagram is proposed and used to discriminate mid-ocean ridge and oceanic plateau settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".