The Improvement and Application of the Electrogeochemical Exploration Method
Bibliographic record
Abstract
The anionic and cationic species of elements from deeply buried deposits migrate to the near surface driven by various geological forces. The applied electric field is used to collect these active ions to the designed electrode, and ions with a negative charge go to the anode and positive to the cathode, which is called electrogeochemical exploration method, also called CHIM. Prospecting effect has been investigated by researchers since the coming up of CHIM. However, the cumbersome technical equipment, complex techniques and low production efficiency have restricted its potential application in field geological survey. This paper presents the newly developed CHIM alternatives that are electrified by a low voltage dipole. The improved technique allows both anionic and cationic species to be extracted simultaneously in an anode and in a cathode. Compared with the conventional CHIM method, the innovative techniques called dipole electrogeochemical method are characterized by simple instrumentation, low cost and easy operation in field, and in particular enables simultaneous extraction of anionic and cationic species of elements, from which more information can be derived with higher extraction efficiency. The “dipole” electrogeochemical extraction method was proposed and applied in the experiments of the Yingezhuang gold ore in Zhaoyuan, Shandong Province, the 210 gold ore in Jinwozi, Xinjiang Province, and the Daiyinzhang gold polymetallic deposit in Wutaishan, Shanxi Province. There are clearly anomalies above the gold ore body, indicating the effectiveness and feasibility of the improved dipole electrogeochemical method in both scientific research and mineral exploration. The results of anode extraction in several mining areas have shown good results, indicating that gold may be mainly negatively charged. In fact, many metal nanoparticles, clay minerals, or complexes of metal ions are negatively charged, so they migrate to the anode electrode and enrich.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".