GLOBAL AND REGIONAL GEOCHEMICAL INDEXES OF PRODUCTION OF CHEMICAL ELEMENTS
Bibliographic record
Abstract
This paper presents a geochemical assessment of the primary involvement of chemical elements in technogenesis in the world and individual countries. In order to compare the intensity of production of various chemical elements in different countries, the authors have introduced a number of new terms and parameters. The new term is “abstract rock” (AR) - an elemental equivalent, whose average composition corresponds to the average chemical composition of the upper continental crust. The new parameters are: “conditional technophility of an element” (TY), “specific technophility” (TYN) “regional conditional technophility” (TYR), “specific regional technophility” (TN), and “density of regional conditional technophility” (TS). TY equals to the tons of AR per year necessary for the production of the current level of the element. TY of different elements has been estimated for 2008-2010. The highest TY values are associated with C, S, N, Ra, and Au. TY of many micro- and ultramicroelements is of the order of n•1011t. TYN reflects the volume of AR per the world’s capita. TYN changes from the 1960s to 2010 indicates that the Earth’s population is growing much faster than its demand for many chemical elements. TYR, TN, and TS were used for the integrated assessment of technogenesis at the regional scale; they reflect the intensity of the technogenesis process at the level of individual countries and allow comparing countries with different levels of elements production, population, and areas. The TN and TS levels of the leaders in extraction of natural resources are below these values in other countries due to the large territories (Russia, USA, Canada, Australia, Saudi Arabia, Kazakhstan, Argentina, Bolivia, Venezuela, Colombia, Zambia, Mali, Libya, Mongolia, and Sudan), to the large population (Indonesia, Vietnam, the Philippines, Bangladesh, Nigeria), or to both high spatial and demographic dimensions (India, Brazil, France, Egypt, Thailand, Pakistan, Algeria, Tanzania, Congo (Kinshasa), Malaysia, and Morocco).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".