A Discussion on Geochemical Characteristics and Genesis of Intrusions in Shizishan Orefield,Tongling area,Anhui Province
Bibliographic record
Abstract
Baimangshan,Qingshanjiao and Nanhongchong intrusions in the Shizishan orefield,Tongling area,Anhui province,are composed of pyroxene diorite,quartz diorite and granodiorite,respectively. They are predominantly calc-alkine,alkaline-rich,with higher Ba,Sr contents,and share the following similar characteristics with the high Ba-Sr granitoids: higher Ba,Sr and LREE and lower Y and HREE abundances,distinct Nb,Ta and Ti depletion,and absence of negative Sr and Eu anomalies. It is suggested that crystallization differentiation played an important role in evolution of magmas because they have covariant major elements. Fractionation of P- and Ti-bearing accessory minerals,such as apatite,ilmenite and titanite might result in a decrease of P and Ti abundances. The relative depletion in Y and HREE might be caused by hornblende and/or garnet retention,but not intermediate plagioclase in the source. Initial 87 Sr/ 86 Sr (0.7062～0.7101) and 143 Nd/ 144 Nd (0.5116～0.5121) ratios show EMⅠ-type signature,and are negatively correlated. The regional switching from compression to extension in the early Cretaceous in a post-collisional setting might readily trigger a rapid rise of hot Ba- and Sr-enriched upper asthenosphere material and result in widespread melting of lower crust to generate voluminous magmas in the deep-seated magma chamber.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".