Bibliographic record
Abstract
10 th International Symposium on Lead and Zinc Processing The Lead-Zinc conference series, initiated by the American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) in 1970, has evolved into a global collaboration of metallurgical societies. This conference series is held every three or five years. It is heartening to acknowledge the significant role played by international organizations including the Minerals, Metals & Materials Society (TMS), Metallurgy & Materials Society of the Canadian Institute of Mining, Metallurgy and Petroleum (MetSoc), the Mining and Materials Processing Institute of Japan (MMIJ), the Society of Metallurgists and Miners (GDMB, Germany), and the Nonferrous Metals Society of China (NFSoc) in fostering knowledge exchange and advancements in lead and zinc industries. The 10th International Symposium on Lead and Zinc Processing (PbZn 2023) was held in Changsha, China, organized by NFSoc and co-sponsored by Central South University (CSU), China ENFI Engineering Corporation (ENFI), and Hunan Science and Technology Association (HSTA). PbZn 2023 had more than 800 attendees, 230 presentations from over 20 countries. This proceedings volume is the result of over a year of collaborative effort, involving authors, editors, and volunteers. Comprising contributions from 20 countries, the volume reflects the global nature of lead and zinc industries, covering business trends, plant operations, fundamental developments, emerging technologies, and environmental considerations. We hope that this compilation will serve as a valuable record of the Lead-Zinc 2023 symposium and become a standard reference for the processing of lead and zinc. We extend our heartfelt gratitude to all the contributors, reviewers, and volunteers who have dedicated their time and expertise to make this proceedings volume a reality. List of International organization, International Organizing Committee, Editorial Committee are available in this Pdf.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".