Bibliographic record
Abstract
<abstract> <p>In this article, we focus on the next generation of green batteries that are closely related to semi-metallic tellurium and its deposits. We briefly summarize the chemical and geochemical characteristics of tellurium that ordinary readers are not familiar with or have never heard of, and its important role in many fields such as high-tech and medical care, current global resource distribution, major mineral extraction and purification technologies and market evolution, etc. The spatiotemporal distribution of the two main types of tellurium deposits, namely associated tellurium deposits and independent tellurium deposits, is introduced in detail. The geological and geochemical characteristics of the only independent tellurium deposit in the world are introduced in detail, so that relevant researchers can use this deposit as an example to discover more independent tellurium deposits around the world, meeting people's increasingly urgent demand for tellurium and realizing the sustainable development of human society. We believe that humans will discover more and more new energy metals in the near future to meet the dual goals of protecting the earth's environment and developing the economy, which are contradictory and mutually reinforcing. New generation of energy metal batteries must be small, compact, easy to carry, charge quickly, and have a long life.</p> </abstract>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".