Improved method for hydrochemical exploration of mineral resources
Bibliographic record
Abstract
The article deals with a method for hydrochemical exploration and poorly studied areas based on the simulation and statistical modeling of the hydrochemical field. The peculiarity of the method is a prospecting area spotting under the following conditions: (1) the maximal ratio between river basin in the Riverhead without evident channel network and the total river basin; (2) the river network and tectonic deformations maximum; (3) presence of low-flow rate sections with relatively sharp breaks in grade of the water surface (outflow of rivers from mountainous areas onto the sub-mountain plain, extended sections of channel multi-branching). A sampling of 2-3 samples of surface water, 2-3 samples of river bed sediments, and 2-3 samples of ground water is taken at prospective sections and contiguous territories and the chemical composition determined. The geo-informational analysis and obtained data are used to determine the parameters of the model of the area under study, a predictive assessment of the hydrochemical indicators for prospective sections is carried out, and a detailed examination is planned and performed. The expected reduction in the cost of exploration compared to currently used methods is approximately 20%.References Alekseyenko V.A, 2005, Geochemical methods of ore deposits searches, Logos, Moscow. In Russian, 354p. Barsukov V.L, Grigoryan S.V, Ovchinnikov L. N, 1981. Geochemical methods of searches of ore deposits, Nauka, Moscow. In Russian, 318p. Benedini M., Tsakiris G, 2013. Water Quality Modelling for Rivers and Streams, Springer, Dordrecht, 287p. Chebotaryov N.P, 1962. Theory of stream runoff, Moscow State University, Moscow. In Russian, 464p. Dao Manh Tien, 1984. Methodology and features of geochemical specialization granitoide formations of Northern Vietnam, Azerbaijan State University, Baku. 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Symbols, standards and conventions, in: Modelling in aquatic chemistry. Nuclear energy agency, Paris, 35-68. Kolotov B.A, 1992. Hydrogeochemistry of ore deposits, Nedra, Moscow. In Russian, 192p. Kopylova Yu.G., Guseva N.V, 2014. Hydrogeochemical methods of searches of ore deposits, Tomsk Polytechnic University Publishing, Tomsk. In Russian, 179p. Kraynov S.R, Ryzhenko B.N, Shvets,V.M, 2004. Geochemistry of ground waters. Theoretical, Applied and Environmental Aspects, M: Science, Moscow. In Russian, 677p. Lasaga A.C, 1995. Fundamental approaches in describing mineral dissolution and precipitation rates, Reviews in Mineralogy. Chemical Weathering Rates of Silicate Minerals, Mineralogical Society of America, 31, 23-86. Lavyorov N.P. and Patyk-Kara N.G, 1997. Loosing ore deposits of Russia and countries of SNG, ed., Nauchny Mir, Moscow. In Russian, 453p. Lekhov A.V, 2010. Physical-geochemical hydrodynamic. KDU, Moscow. In Russian. 500p. Lerman A, 1979. 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Distribution of Inorganic Pollutants over the Depth of Upper Peat Deposit, Contemporary Problems of Ecology, 1, 118-124. Savichev O.G, Nguyen Van Luyen, 2015. Hydroecological condition between the Gam and Kau rivers (Northern Vietnam), Bulletin of Tomsk Polytechnic University, 7, 96-103. Savichev O.G, Nguyen Van Luyen, 2015. The technique of determining background and extreme values of hydrogeochemical parameters, Bulletin of Tomsk Polytechnic University, 9, 133-142. In Russian.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".