Heavy Mineral Characteristics and Their Implication for Provenance of the Middle to Upper Triassic on the Northwest Margin of Junggar Basin, North China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heavy minerals are extremely sensitive indicators of provenance. Applying the methods of heavy mineral combination analysis, content distribution analysis, SPSS statistical component analysis, and correlation analysis, 18 heavy minerals are identified in the northwestern margin of the Junggar Basin (NW Junggar). According to the analysis of the heavy mineral characteristics and coefficients, 4 heavy mineral combinations are developed in the study area: Zircon-tourmaline-monazite-apatite-anatase, rutile-ilmenite-leucoxene-apatite-galenite, hematite-limonite-pyrite, and magnetite-epidote-hornblende. Previous results predicted that source rocks consist of intermediate-acidic magmatite, sedimentary rocks and metamorphic rocks, and intermediate-acidic magmatite is considered to be the main source rock. Furthermore, combined with the geological background of NW Junggar, Qier-Halaalat Mountain, which belongs to the Zaire Mountain front, is shown to be the provenance of the study area. The main sources of sediments are flesh-red granite, grey and greyish-green andesite, andesitic-porphyrite, grey and grayish-black tuff, siltstone, and sandstone of the Lower Carboniferous Tailegula Formation. Additionally, because of the high content of the angular-subangular and subangular-subrounded heavy mineral grains, these heavy minerals are both from near and distal provenance, with most being near-provenance deposits. During the process of provenance propulsion on the margin of the basin, clastics are mixed together and affected by regional dynamic metamorphism, which is probably the main reason for the existence of the metamorphic component.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it