Structurally Controlled Hydrothermal Dolomitization:A New Model in International Carbonates Field
Bibliographic record
Abstract
Dolomitization is very complex and there are about 10 traditional dolomitization models. A new model advocated by Lavoie: structurally controlled dolomitization is popular all over the world. The model is looked as an important proceeding in international carbonates exploration and research. The principle is that deep thermal fluid moves along faults extended to depth into shallow limestones and then moves laterally because of the obstruction of the dense clastics above and makes the limestones dolomitization. According to an example analysis of hydrothermal dolomitization in the Lower Silurian Sayabec Formation in northern Gaspe-Matapedia (Quebec), the authors present the principles of structurally controlled hydrothermal dolomitization, and summarize its characteristics and controlling factors. It is found that hydrothermal dolomites formed by structurally controlled hydrothermal dolomitization are the favorable reservoirs of oil and gas, and they are also associated with lead-zinc deposits. The authors assume that partial carbonates reservoirs and lead-zinc deposits in South China have the similar geological background to this new model by initial research. In short, the appearance of the model not only explains the diversity and complexity of dolomite genesis, but also is of referential significance for genesis analysis and exploration of the analogous deposits in China.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".