Recent progress in the study of the deep-penetrating geochemical migration mechanisms and methods.
Bibliographic record
Abstract
This paper systematically introduces the recent progress in the study of deep-penetrating geochemical migration methods and mechanisms in the international exploration community, with the focus on introducing the new models and theories of vertical migration of elements, including the reduced chimney model, electrically charged storm cell model and pump pressure effect mechanism. It summaries the main results of the deep-penetrating geochemical research, both domestically and internationally, mainly including the achievements obtained by the international collaborative research project funded by the Canadian Mining Industry Research Organization (CAMIRO), the research projects funded by the Research Centre for Landscape Environments and Mineral Exploration (CRCLEME) in Australia and research by other countries. On the basis of the outcomes of these research projects, the authors discuss the key factors affecting the selective leaching techniques. They think that the strategic choice of selective leaching techniques and the selective leaching of single target minerals are rather complex and affected by the process of soil formation and its physical-chemical factors such as pH and Eh to some extent, and additionally, they are also related to the controls on the concentration of the leachates, sampling depths and leaching conditions and process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".