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Record W4413801116 · doi:10.1016/j.wmb.2025.100243

The urgency of a Circular healthcare economy amidst geopolitical turbulence; strategies in the fight against raw materials and energy scarcity

2025· article· en· W4413801116 on OpenAlexaboutno aff
Bart van Straten

Bibliographic record

VenueWaste Management Bulletin · 2025
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsScarcityGeopoliticsHealth careCircular economyBusinessNatural resource economicsEnergy (signal processing)EconomyEconomicsPolitical scienceEconomic growthMarket economyPhysicsBiologyEcology

Abstract

fetched live from OpenAlex

• Healthcare waste contains highly valuable materials. • Technology is available to reprocess medical waste streams into high quality raw materials. • Global instability is increasingly driven by raw material scarcity. • Territorial claims and geopolitical tensions are rising in regions like Ukraine, Congo, Greenland, and Canada due to their strategic resource reserves. • There is a paradox: we incinerate waste while the global demand for critical raw materials intensifies. • Technology and science offer proven methods to recycle and recover valuable materials from surgical waste and hospital disposables.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.020
Scholarly communication0.0170.014
Open science0.0010.009
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0080.002

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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