Characteristics of Cell parameters of Pyrite and Quartz and Their Geological Significance at Shihu Gold Deposit in Western Hebei,North China
Bibliographic record
Abstract
Gold Mineralization of the Shihu deposit can be divided into 4 stages,pyrite-quartz stage,quartz-pyrite stage,polymetallic sulfide stage and quartz-carbonate stage.Cell parameters of pyrite and quartz from 4 stages were analyzed in this paper.The analytical results of pyrite indicated that the value of a0 and υ0 is minimum in Ⅲ stage;the values of a0 and υ0 decrease as mineralization stage proceeds from Ⅰto Ⅳ and as the depth of ore-body increases from 560 to 180 meters.The analytical results of quartz indicated that the values of c0 and the c0/a0 ratios decrease as mineralization stage changes;the values of a0 increase as the depth of ore-body increases;the values of c0 and the c0/a0 ratios change rhythmically as the depth of ore-body increases.Results of cell parameters suggested that pyrites have high contents of Co,Ni and As,and have not undergone S depletion.Moreover,Al and Fe are the major replacement impurities in isomorphism of quartz.Gold-bearing pyrites are characterized by small a0,while gold-bearing quartz are characterized by big a0,small c0 and c0/a0 ratio.The values of a0 of pyrite and the values of c0 and c0/a0 ratios of quartz are mostly larger than their ideal values.Therefore,denudation of the gold ore-body is slight,and potential value of this deposit is considerable.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".