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Record W7117467813 · doi:10.3390/su18010255

Global and Regional Gallium Recycling Potential and Opportunities: Based on Historical Material Flow Analysis

2025· article· en· W7117467813 on OpenAlex
Lyushui Zuo, Zhuo Huang, Huiling Song, Gopal Achari, Pengwei He

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Calgary
FundersNational Social Science Fund of ChinaNational Natural Science Foundation of China
KeywordsMaterial flow analysisGalliumMaterial flowScale (ratio)Flow (mathematics)Supply and demandScenario analysis

Abstract

fetched live from OpenAlex

Gallium plays a critical role in high-tech industries but faces supply risks. Improving the efficiency of recycling and utilization of secondary resources has become the most reasonable approach to addressing this. This study employs historical material flow analysis (2000–2019) to evaluate global and regional gallium recycling potential. The results indicate that gallium recycling remains underdeveloped, with three key opportunities identified: recycling gallium from primary production, new scraps, and old scraps. During 2000–2019, the cumulative amounts available from these sources were 239,760 tons, 3464 tons, and 955 tons, respectively, yet their recovery rates remain as low as 1.74%, 27.28%, and 0.84%, respectively. Regional analysis shows that the recycling potential and opportunities of gallium varies significantly across China, America, the EU, Japan, and the rest of the world. Current recycling technologies have shown potential for efficiently recovering gallium, but their economic viability relies on economies of scale and policy incentives.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.261
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it